1 Table of Contents •Introduction •Overview •Context •Component Overview •Design and Implementation •Results •Common Pitfalls & Troubleshooting •Summary & Next Steps •References 2 Introduction A virtual vehicle can reproduce vehicle dynamics, driver inputs, road scenarios, sensor stimuli, and network communication long before the complete physical vehicle is available. However, a successful desktop simulation answers only one part of the engineering question: does the algorithm behave correctly as a model? Processor-in-the-Loop (PIL) adds the target processor to the validation loop. The plant, scenario, and test harness remain in MATLAB ® and Simulink ® , while selected generated algorithm code is cross-compiled, downloaded, and executed on the NXP processor. Inputs are sent from the host to the target, and the computed outputs are returned to the simulation for comparison and analysis. This makes PIL the bridge between a virtual vehicle that behaves correctly on the development computer and embedded software that must produce equivalent results on its intended processor. It also provides target-based execution-time measurements, helping engineers assess whether an algorithm is not only functionally correct, but also suitable for its timing budget. 3 Overview This article presents how Processor-in-the-Loop fits into a Model-Based Design workflow for virtual vehicle development. The goal is to show where PIL adds value between desktop simulation and deeper hardware integration, and how the same virtual vehicle and scenario can be reused to validate generated code on the target processor. In this workflow, the model remains the starting point. The virtual vehicle provides the plant behavior, the simulated environment provides repeatable driving conditions, and the selected algorithm is generated and executed on target hardware. PIL therefore supports a controlled transition from model behavior to target implementation behavior.   Figure 1. PIL connects virtual vehicle simulation with generated algorithm execution on target hardware. 4 Context The practical context for this article is the Hello World with the Model-Based Design Toolbox — Model. Generate. Drive. project. In that setup, driver inputs come from a physical steering wheel and pedals, the vehicle is driven through a RoadRunner simulated environment, and an S32N processor communicates with the host simulation while making vehicle-level decisions. The virtual vehicle is created with MathWorks tools and reused as the common integration point for driver inputs, vehicle behavior, RoadRunner scene interaction, Unreal Engine visualization, CAN communication, and closed-loop feedback from the physical setup. From that perspective, PIL is not used to move the entire virtual world to the processor. Instead, the virtual vehicle and simulated scenario remain on the host while selected generated algorithms are executed on the target. This keeps the environment flexible and repeatable while bringing processor behavior into the validation loop. 5 Component Overview A PIL-enabled virtual vehicle workflow combines the following elements: Virtual vehicle - represents vehicle dynamics, driver interaction, powertrain, steering, braking, CAN communication, and feedback paths. Virtual scene and scenario - provides roads, lanes, signs, intersections, actors, traffic movement, and repeatable test conditions using RoadRunner and Unreal Engine. PIL component - contains the selected generated algorithm code that is cross-compiled and executed on the target processor. S32N main node - acts as an aggregator and decision-maker, receiving information from sensing nodes and sending high-level commands to actuator nodes. S32N positioning: S32N is a suitable solution for running complex central-compute algorithms in PIL because it is positioned at the point where vehicle-level decisions, aggregated data, CAN communication, and actuator commands come together. 6 Design and Implementation A practical PIL workflow starts by selecting a bounded algorithm and keeping the plant, scene, and test harness on the host. This makes the test setup easier to control and keeps the comparison focused on the generated target implementation. 6.1 Select the algorithm boundary Relevant candidates include data aggregation, vehicle-level decision logic, Automated Emergency Braking logic, actuator command generation, CAN signal processing, and telemetry preparation. 6.2 Create repeatable virtual tests Use the simulated environment to define controlled driving conditions such as road geometry, actors, traffic movement, obstacle placement, and driver commands. The same scenario can be replayed for model and PIL execution. 6.3 Establish the model baseline Run the selected scenario with the original Simulink implementation and log the component inputs, outputs, and vehicle-level signals required for comparison. 6.4 Run the generated implementation in PIL Generate and build the selected component for the supported S32N5 target configuration. During the PIL run, Simulink sends test vectors to the target and receives the target results while the rest of the virtual vehicle continues to execute on the host. 6.5 Compare and profile Compare model and PIL outputs using the acceptance criteria defined for the algorithm. Where supported, collect target-side execution-time data to evaluate whether the generated component fits its timing budget. PIL Setup on S32N5. 7 Results The result of this workflow is a direct comparison between model behavior and generated code running on the target. A useful result set shows whether target outputs remain equivalent to model outputs, whether decision thresholds and state transitions occur under the same scenario conditions, and whether the target-side execution time fits the assigned budget. Because the virtual scenes are controlled and repeatable, failing cases can be preserved as regression scenarios and rerun after model, configuration, or implementation changes. 8 Common Pitfalls & Troubleshooting The PIL boundary is too large - keep the virtual world, visualization, and detailed plant on the host. Simulation time is confused with target execution time - use target-side profiling for algorithm timing conclusions. Model and target interfaces differ - keep signal definitions, data types, scaling, units, and sample times consistent. CAN definitions are inconsistent - reuse the same DBC definitions across the simulation and physical network. PIL is treated as complete system validation - PIL validates selected generated code on the processor; full distributed-system behavior still requires later integration stages. 9 Summary & Next Steps PIL connects the virtual vehicle, simulated scenarios, and S32N5 target execution into one validation flow. The host continues to simulate the driver, vehicle, road, actors, and environment, while selected generated main-node algorithms execute on the S32N5. A practical next step is to select one bounded S32N5 function, define its acceptance criteria, and replay a representative RoadRunner scenario first with the model and then in PIL. Suitable starting points include data aggregation, AEB decision logic, high-level actuator command generation, or CAN signal processing. 10 References Hello World with the Model-Based Design Toolbox — Model. Generate. Drive. Creating virtual vehicle with MathWorks - Overview Creating Virtual Scenes & Scenarios with MathWorks (RoadRunner & Unreal Engine) MathWorks: Processor-in-the-Loop Simulation

      1 Table of Contents • Introduction • Required Software • Required Hardware • References • Conclusion     2 Introduction This article is part of the Zone Node series and describes the software and hardware environment used throughout the project. The purpose of this article is to describe the software and hardware setup required to follow the series and reproduce the results. Before examining communication routing, control logic, or integration challenges, it is important to understand the tools and platforms that support the development and execution of the zonal node application. This article introduces the software components used to develop, configure, and deploy the application, as well as the hardware platforms used to demonstrate the zonal controller functionality. This information provides the foundation required for the remaining articles in the series. Overview of the development flow The zonal node application presented in this series is developed using a combination of Model-Based Design tools, NXP software components, and automotive-grade hardware platforms. At a high level: Application modeling starts in MATLAB® and Simulink®, where communication routing and control logic are implemented graphically. Code generation converts the model into production-ready embedded software using the code-generation tools provided by MathWorks and NXP. Deployment compiles the generated software and loads it onto the target hardware, where it is used to demonstrate communication between multiple vehicle networks. This environment was selected to support rapid development, easier validation, and improved traceability between model design and generated software. By using a Model-Based Design approach, algorithm development, communication integration, and application verification can be performed within a common framework. The software and hardware presented here are used consistently throughout the series and will be referenced when discussing communication routing, system behavior, and integration scenarios. Figure 1. Development flow diagram The workflow begins with application development in Simulink. Communication routing logic, control functions, and software configuration are implemented within the model. The NXP Model-Based Design Toolbox (MBDT) provides hardware-specific blocks that enable integration with S32K3 peripherals and communication interfaces. Following code generation, the application is compiled and deployed to the target hardware, where communication routing functionality can be validated. This article is intended for: Engineers interested in reproducing the zonal node demonstration Simulink users developing automotive communication applications Developers evaluating Model-Based Design workflows Engineers working with NXP automotive microcontrollers and evaluation boards By understanding the software and hardware environment early in the series, readers will be better prepared to follow the implementation details presented in subsequent articles.     3 Required Software The following software components are used throughout the project: MATLAB® and Simulink® – model development and simulation Embedded Coder® (required MATLAB toolbox) – automatic code generation from the model Simulink models – the zonal node routing application model referenced throughout the series NXP Model-Based Design Toolbox (MBDT) – S32K3 support and peripheral configuration NXP additional tools – FreeMASTER and S32 Design Studio for build, deployment, and debugging CAN analysis software – monitoring and validating CAN communication LIN analysis software – monitoring and validating LIN communication 3.1 MATLAB® and Simulink® MATLAB® and Simulink® form the foundation of the development environment. They are used to create the zonal node application, implement communication routing logic, configure software behavior, and perform model-based verification activities. The application described throughout this series is developed as a Simulink model and later translated into embedded software using automatic code-generation tools (Embedded Coder®). 3.2 NXP Model-Based Design Toolbox (MBDT) The NXP Model-Based Design Toolbox (MBDT) extends Simulink with hardware-specific support for NXP automotive microcontrollers. For this project, MBDT for S32K3 version 1.8.0 is used. The toolbox provides blocks and configuration interfaces for communication peripherals, timers, digital I/O resources, and other hardware modules available on the target device. It also integrates with the code-generation workflow, allowing Simulink models to be converted into software that can run directly on the S32K3 microcontroller. Note: Installation and configuration instructions are provided in the dedicated article series (How to install .MLTBX). Readers who have not yet installed the toolbox should complete that step before continuing with this series. 3.3 CAN Analysis Software CAN analysis tools are used during development and validation to observe CAN and CAN FD traffic exchanged between the zonal node and other network participants. Typical use cases include: Monitoring transmitted and received CAN frames Verifying CAN-to-CAN routing behavior Measuring message timing and bus utilization Troubleshooting communication issues Examples of commonly used software include PCAN-View, CANalyzer, and CANoe. 3.4 LIN Analysis Software LIN analysis tools are used to monitor communication between the zonal node and LIN-connected edge devices. Typical use cases include: Verifying LIN schedule execution Monitoring frame transmission and reception Validating signal timing and integrity Testing LIN-to-CAN routing scenarios Examples of commonly used software include PLIN-View and LINalyzer.     4 Required Hardware The following hardware components are used throughout the project: S32K344 automotive microcontroller – used to execute the zonal node application S32K344-WB Evaluation Board – used as the development and validation platform CAN analysis hardware – used to monitor and verify CAN/CAN FD communication LIN analysis hardware – used to monitor and verify LIN communication 4.1 S32K3 Microcontroller The S32K3 family provides: Arm® Cortex®-M7 processing cores CAN FD communication interfaces LIN communication support Safety-oriented automotive features Low-power operating modes Rich peripheral connectivity These capabilities make the device suitable for implementing communication aggregation and routing functions within the scope of this project. 4.2 Evaluation Hardware The zonal node application runs on the S32K344-WB Evaluation Board, a development platform based on the NXP S32K344 microcontroller. The board provides access to the communication interfaces and processing capabilities of the target device while offering an integrated platform for software development, debugging, and validation activities. Within the scope of this project, the board is used to execute the routing application and exchange messages with nodes connected through CAN and LIN networks. Its communication interfaces, debugging connectivity, and expansion capabilities make it suitable for evaluating zonal communication architectures and routing scenarios. Figure 2. S32K344-WB evaluation board 4.3 Communication Networks The examples presented throughout this series use CAN and LIN networks to demonstrate message forwarding, routing, and protocol translation scenarios. These networks provide the communication backbone between the zonal node, central controller, and edge nodes, and are referenced throughout the upcoming routing and integration articles. 4.4 Network Analysis Hardware Additional hardware tools are used during development and validation to observe network traffic and verify communication behavior. CAN analysis interfaces can be connected to the network to monitor transmitted and received CAN/CAN FD frames, validate routing functionality, and troubleshoot communication issues. LIN analysis interfaces can be used to monitor LIN schedules, frame exchanges, and LIN-to-CAN routing scenarios. These tools provide visibility into network activity and support verification of the communication flows presented in later articles of this series.     5 References Model-Based Design Toolbox (MBDT) Embedded Coder® Documentation MATLAB® and Simulink® Documentation S32K3 Microcontrollers S32K344-WB Evaluation Board     6 Conclusion This article introduced the software and hardware environment used throughout the zonal node project. It presented the development tools, code-generation workflow, and target hardware that support the implementation of the communication routing application. The next article will build on this foundation by examining the internal logic control mechanisms used within the zonal node and how they contribute to communication handling across multiple networks.

1 Table of Contents • Introduction • FMCW Radar Signal • The Radar Cube • Processing Chain Overview • Mapping the Processing Chain onto the S32R45 • Range FFT • Doppler FFT • Non-Coherent Combining • CFAR Detection • Clustering (DBSCAN) • Angle Estimation (MUSIC DoA) • NXP Radar SDK Integration • Tools and Ecosystem • Conclusion 2 Introduction In the previous articles of this series, we introduced the fundamentals of automotive radar in the Radar Overview and the hardware/software setup in Radar SW & HW Environment. Building on that foundation, this article follows the radar data on its journey through the complete processing chain — from digitized ADC samples to a final target list describing each object’s range, velocity, and angle. The chain is modeled and prototyped in MATLAB® using Radar Toolbox™ and deployed on the NXP S32R45 radar processor through the NXP Model-Based Design Toolbox for RADAR. The key processing stages are distributed across the S32R45 Cortex-A53 cores, the SPT accelerator, the BBE32 DSP accelerator, and the LAX accelerator, using optimized kernels from the NXP Radar SDK. 3 FMCW Radar Signal Frequency-Modulated Continuous Wave (FMCW) radar transmits a continuous chirp whose frequency increases linearly over time. The received echoes are mixed with the transmitted signal, producing a beat frequency (also called intermediate frequency, IF) proportional to the round-trip delay, which directly encodes target range. Figure 1: FMCW Radar Transmit and Receive Chirp Diagram The diagram plots frequency (vertical axis) against time (horizontal axis) and shows two piecewise-linear signals. The transmitted signal Tx (blue) rises linearly across the chirp, while the received signal Rx (orange) has the same shape but is delayed in time: Tx(t) = A Tx · cos( 2π f c t + 2π (f B / 2T) t² + φ 0 ) Rx(t) = A Rx · cos( 2π f c (t − t d ) + 2π (f B / 2T) (t − t d )² + φ 0 ) The relevant variables are summarized below: Symbol Meaning Tx(t) Transmitted signal (blue curve) Rx(t) Received signal (orange curve) t d Propagation delay between Tx and Rx IF Frequency difference between Tx and Rx during the chirp T Chirp (ramp) duration f c Chirp starting frequency f B Chirp bandwidth R 0 Range of detected target v Velocity of detected target The three key physical intuitions of FMCW radar are: Range comes from the beat frequency, since the propagation delay t d = 2(R 0 + v·t)/c produces a frequency offset IF = (2 f B )/(T·c) · R 0 during the linear ramp. Velocity comes from the phase evolution across successive chirps via the Doppler effect. A moving target introduces a Doppler frequency f v = (2 f c / c) · v. The chirps repeat at a fixed pulse repetition frequency (PRF), which must be high enough to capture this Doppler shift. Angle comes from the phase differences introduced across multiple receive antennas, enabling direction estimation and spatial separation of targets. Chirp parameters are chosen according to sensing requirements: the chirp duration must exceed the round-trip time to the farthest target plus the additional time needed for mixing and signal formation. 4 The Radar Cube After mixing and sampling, the acquired data is organized into a 3D structure called the radar cube, which is the input to the entire digital processing chain. For a single antenna, each chirp produces a sequence of time samples (fast time) arranged into a column — one column per chirp. Stacking chirps side by side forms a 2D matrix (samples × chirps), and repeating this for every receive antenna and stacking along a third dimension produces the cube: samples × chirps × antennas. Figure 2: The radar cube Every subsequent stage operates on this cube, progressively collapsing its dimensions and transforming raw echoes into higher-level target information. 5 Processing Chain Overview The FMCW processing chain converts the radar cube into a compact target list through a sequence of well-defined stages. Before looking at each block in detail, the table below provides a roadmap of the inputs and outputs at every step: Stage Input Output Range FFT Radar cube Range cube Doppler FFT Range cube Range–Doppler cube Non-Coherent Combining Range–Doppler cube Range–Doppler map CFAR Detection Range–Doppler map Detections DBSCAN Clustering Detections Target clusters MUSIC DoA Clusters + antenna data (range, velocity, angle) Figure 3: Processing Chain Overview The process begins at the ADC, where analog signals are digitized. Range and Doppler FFTs extract distance and velocity, forming a range–Doppler representation. Data from multiple antennas is then combined to improve robustness, CFAR detects potential targets using adaptive thresholding, DBSCAN groups detections into individual targets, and MUSIC DoA estimates each target’s angle — converting raw samples into structured outputs of range, velocity, and angle. 6 Mapping the Processing Chain onto the S32R45 A key advantage of the NXP platform is that each stage of the chain is mapped onto the most suitable compute resource of the S32R45. The FFT-based stages and Non-Coherent Combining run on the SPT accelerator, the CFAR detection runs on the BBE32 DSP, the clustering runs on the Cortex-A53 cores, and the linear-algebra-heavy MUSIC estimation is offloaded to the LAX accelerator: Figure 4: Radar processing chain hardware mapping This mapping is what allows developers to prototype the entire chain in MATLAB and then deploy each stage to dedicated radar hardware without leaving the Model-Based Design environment. 7 Range FFT The first stage operates on the radar cube by processing the fast-time samples within each chirp. For every antenna and chirp, the time-domain signal — containing superimposed beat frequencies from multiple targets — is transformed into the frequency domain using an FFT, separating the frequency components that each correspond to a distinct propagation delay, and therefore a specific range. In this application, the radar front end does not perform in-phase and quadrature (I/Q) demodulation, so the acquired signal is purely real-valued. The resulting FFT spectrum is therefore symmetric, carrying redundant positive and negative frequency components. Since only the positive frequencies correspond to physically meaningful beat frequencies here, the negative-frequency half of the spectrum is discarded. The radar cube is thus converted into a set of range profiles, where each sample index becomes a range bin. The output preserves the chirp and antenna dimensions but now contains complex values indexed by range — magnitudes indicating reflection strength, and phases retained for later processing. Figure 5: Range FFT output On the S32R45, this stage is executed on the SPT accelerator using the rangeFFT kernel provided by the NXP Radar SDK and exposed through the NXP Model-Based Design Toolbox for RADAR. 8 Doppler FFT Building on the range-transformed data, the second stage processes the slow-time dimension by examining how the complex samples evolve across consecutive chirps. For a given range bin, a moving target produces a small phase difference between the corresponding Range FFT outputs of consecutive chirps. This progressive phase shift is a manifestation of the Doppler effect and encodes the target’s radial velocity. By analyzing these phase variations over time using a second FFT, the processing chain extracts the Doppler frequency components associated with motion. This step converts the phase evolution observed across successive Range FFT outputs into velocity information, effectively mapping stationary and moving targets into different Doppler bins. The output is a set of range–Doppler maps, one per antenna, where each cell represents a specific combination of distance and radial velocity and holds a complex value describing the target echo. The Doppler FFT output is shifted (typically via an FFT-shift operation) so that the zero-Doppler component is centered and negative Doppler frequencies appear first, giving a more intuitive velocity axis: negative values for targets moving in one direction, positive for the other. Figure 6: Doppler FFT output As with the Range FFT, this stage is accelerated on the SPT accelerator through the Radar SDK dopplerFFT kernel. 9 Non-Coherent Combining At this point, each antenna provides its own range–Doppler map, differing mainly in phase due to the direction of arrival. These maps are combined across the antenna dimension, typically by computing magnitudes and aggregating them through averaging. The input is a set of complex-valued maps; the output is a single range–Doppler magnitude matrix in which the antenna dimension has been collapsed. This suppresses uncorrelated noise and reinforces consistent target reflections, producing a cleaner, more robust representation well suited for detection. Figure 7: Non-Coherent Combining output Note: the pre-combining per-antenna data is retained, because it is required later for MUSIC direction-of-arrival estimation. This stage corresponds to the Non-Coherent Combining kernel ( NonCohComb ) of the NXP Radar SDK, executed on the SPT accelerator. 10 CFAR Detection The combined range–Doppler magnitude matrix is then scanned to identify potential targets. Each cell is evaluated against a locally adaptive threshold derived from its surrounding neighborhood: nearby training cells estimate the noise level, while guard cells are excluded to avoid contaminating the estimate with the target’s own energy. Through this process, the continuous-valued matrix becomes a discrete set of detections — cells whose magnitude significantly exceeds the estimated noise background. These are effectively points in range–velocity space representing likely target reflections. CFAR runs on the BBE32 DSP accelerator. 11 Clustering (DBSCAN) CFAR detections often include several neighboring points from the same physical target, as well as isolated points caused by noise. To organize them, DBSCAN clustering is applied in the range–velocity domain, grouping points based on spatial density. Taking the detection coordinates as input, DBSCAN forms clusters where dense regions correspond to real targets, while sparse detections are discarded as noise. The output is a set of target clusters, each consolidating a single target’s range and velocity. This stage runs on the Cortex-A53 cores. 12 Angle Estimation (MUSIC DoA) For each cluster, the detections are traced back to the complex per-antenna data from the range–Doppler stage. These per-antenna samples form vectors encoding the phase differences related to the direction of arrival. Combining multiple detections within a cluster, a covariance matrix is estimated to capture the spatial characteristics of the received signals. An eigenvalue decomposition separates signal and noise subspaces, and criteria such as AIC determine the number of significant sources. The MUSIC algorithm then scans possible directions and identifies those that best match the signal subspace. MUSIC has important practical limitations: the number of detectable signals must be strictly smaller than the number of antennas, otherwise the covariance matrix cannot be properly decomposed. It is also sensitive to low signal-to-noise ratio, correlated reflections, and array calibration errors — constraining its use in scenarios with many closely spaced targets or too few antenna elements. On the S32R45, the MUSIC implementation is offloaded to the LAX accelerator through the NXP Model-Based Design Toolbox for RADAR, demonstrating how computationally intensive linear-algebra operations can be accelerated directly from MATLAB-generated code. The result is a direction-of-arrival estimate for each clustered target, completing its spatial characterization. 13 NXP Radar SDK Integration The NXP S32R45 Radar SDK (RSDK 1.2.0) provides optimized radar processing kernels designed for the S32R45 radar processor. Through the NXP Model-Based Design Toolbox for RADAR, these kernels are callable directly from MATLAB and are automatically integrated into the generated application, so developers work at the algorithm level while the toolbox handles deployment to the accelerators. In the processing chain presented in this article, the Radar SDK supplies optimized implementations for: Range FFT — SPT accelerator ( rangeFFT ) Doppler FFT — SPT accelerator ( dopplerFFT ) Non-Coherent Combining — SPT accelerator ( NonCohComb ) CFAR — BBE32 DSP accelerator This lets developers prototype and validate the full chain in MATLAB while leveraging the S32R45 SPT, BBE32 DSP, and LAX hardware accelerators in the final deployed application, and it supports both standalone and Processor-in-the-Loop (PIL) execution. 14 Tools and Ecosystem This processing chain brings together products from both MathWorks and NXP. MathWorks MATLAB® Radar Toolbox™ MATLAB Coder™ Embedded Coder® NXP S32R45 radar processor (Cortex-A53 + SPT + BBE32 DSP + LAX) NXP S32R45 Radar SDK 1.2.0 NXP Model-Based Design Toolbox for RADAR 1.0.0 NXP Model-Based Design Toolbox for SPT 1.9.0 S32 Design Studio 3.6.1 S32R45 – High-Performance Processor for Imaging Radar 15 Conclusion The FMCW radar processing chain transforms the raw radar cube into a compact set of target descriptors through a sequence of well-defined stages. Starting from time-domain samples, range and velocity are extracted with FFT operations, detections are found through adaptive thresholding and grouped into targets, and angle estimation leverages antenna diversity to determine direction. Each final target is characterized by the tuple (range, velocity, angle). By mapping these stages onto the S32R45 Cortex-A53, SPT, BBE32 DSP, and LAX resources through the NXP Radar SDK and the NXP Model-Based Design Toolbox for RADAR, the entire chain can be prototyped in MATLAB and deployed to dedicated radar hardware within a single Model-Based Design workflow.

1 Table of Contents • Introduction • Reference Architecture • Hardware Environment • Software Environment • Radar Signal Chain • From Simulation to Target • Installing the NXP Toolchain • Next Article in the Series • References 2 Introduction This article presents the NXP hardware platforms and the MathWorks and NXP software tools used to build an automotive radar application. The development workflow is anchored in the MathWorks example "Radar Signal Simulation and Processing for Automated Driving," which provides a reference architecture spanning driving-scenario simulation, radar modeling, and signal processing. The resulting signal-processing chain is then adapted and deployed onto NXP radar hardware using NXP-specific toolboxes and hardware accelerators. This article is part of the Radar Application Development Series, which describes the complete workflow for developing, deploying, and optimizing automotive radar applications on NXP radar platforms. The purpose of this article is to introduce the overall software and hardware environment and show how the different tools, hardware components, and processing engines fit together within a radar development workflow. The next article in this series, "Radar - Processing Chain," will explore in depth each processing block presented in the Radar Signal Processing section: Range FFT, Doppler FFT, Non-Coherent Combining, CFAR Detection, Clustering, and Direction-of-Arrival (DoA) estimation. 3 Reference Architecture The starting point for the radar application is the MathWorks reference example, which models a complete automotive radar system end to end. The workflow begins by defining a highway driving scenario using the Automated Driving Toolbox ( drivingScenario ), where vehicles and traffic participants are modeled. The ground-truth generated data then feeds the radar model.   Figure 1: MathWorks example bird's-eye plot with Radar detections A 77 GHz FMCW radar is parameterized from high-level system requirements such as: Maximum detection range, typically 250-300 m for long-range radar Range resolution, around 1 m Velocity resolution Maximum relative target velocity, up to around 230 km/h The example then builds a transceiver model with antenna arrays, transmitter/receiver components, and signal-propagation effects, generating synthetic detections that estimate the position and velocity of surrounding vehicles. For the NXP application, the reference architecture is divided into two domains: Environment Simulation Executes entirely within MATLAB, and is responsible for: Driving scenario generation Vehicle motion simulation Target ground-truth generation FMCW signal generation Radar channel and propagation modeling Radar Signal Processing Contains the processing chain deployed on the S32R45 platform: Range FFT processing Doppler FFT processing Non-Coherent Combining CFAR detection Clustering Direction-of-Arrival (DoA) estimation The Radar Signal Processing domain forms the basis of the embedded radar application deployed on the S32R45 Evaluation Board. 4 Hardware Environment 3.1 S32R45 Evaluation Board The primary processing platform is the NXP S32R45 Evaluation Board, a development platform for high-performance 77 GHz radar applications such as adaptive cruise control, autonomous emergency braking, and cascaded imaging radar. It integrates several specialized processing engines optimized for radar workloads. Processing engine Role 4x Arm® Cortex®-A53 cores Application-level processing, radar control, clustering, and object management SPT Accelerator Optimized FFTs and high-throughput radar signal-processing kernels BBE32 DSP Vectorized signal processing, detection algorithms, and custom radar kernels LAX Accelerator Matrix and linear-algebra operations for accelerated angle estimation   Figure 2: S32R45 block diagram 3.2 TEF82xx Customer Application Board The TEF82xx is a fully integrated 76-81 GHz RFCMOS automotive radar transceiver providing the RF front end for signal generation and capture. It integrates 3 transmit channels, 4 receive channels, ADCs, a low-phase-noise VCO, and a phase rotator, and is fully compatible with the S32R45. In the current application, the input signal is sourced from simulation rather than hardware, so the TEF82xx is not actively used. It is included as a placeholder for future hardware-in-the-loop and real-sensor integration. 5 Software Environment The radar application combines MathWorks toolboxes for algorithm development with NXP toolboxes and tools for deployment and accelerator integration. 4.1 MathWorks Tools Tool Role in the workflow Radar Toolbox FMCW waveform generation, propagation modeling, detection, and analysis Automated Driving Toolbox Scenario modeling, road/vehicle simulation, and ground-truth generation 4.2 NXP Tools Tool Version Role in the workflow S32 Design Studio for S32 Platform 3.5 IDE, compiler, debugger, and deployment environment for the S32R45, including its accelerators NXP Model-Based Design Toolbox for SPT 1.9.0 Bit-exact SPT simulator integration and rapid prototyping in MATLAB NXP Model-Based Design Toolbox for RADAR 1.0.0 MATLAB integration for S32R45; SPT/LAX kernel execution, code generation, and PIL workflows NXP Radar SDK (S32R45) 1.2.0 Optimized radar algorithms, accelerator libraries, SPT/LAX kernels, and embedded deployment infrastructure Together, these tools act as the gateway between the MathWorks and NXP ecosystems, enabling algorithm development, simulation, code generation, deployment, and SIL/PIL validation within a common workflow. 6 Radar Signal Chain Once the FMCW echoes are generated or captured, the signal-processing chain transforms the radar cube into a list of detected objects. The application currently implements the following stages. # Stage What it does Runs on 1 ADC Acquisition Digitizes the beat signal into a radar data cube, using samples x chirps x antennas TEF82xx ADCs → S32R45 2 Range FFT Fast-time FFT converts beat frequency into target range SPT accelerator 3 Doppler FFT Slow-time FFT resolves velocity, producing the processed radar cube SPT accelerator 4 Non-Coherent Combining Combines the magnitude of the range/Doppler-processed radar cube across channels, producing the range-Doppler magnitude matrix SPT accelerator 5 CFAR Detection Applies Constant False Alarm Rate thresholding on the range-Doppler magnitude matrix to detect possible targets BBE32 DSP 6 Clustering Groups neighboring detections, for example using DBSCAN, into physical objects Cortex-A53 cores 7 Angle / DoA Estimation Estimates azimuth/elevation across the antenna array, using methods such as beamforming or MUSIC LAX accelerator The output of the chain is a list of detected objects with range, relative velocity, and angle of arrival. Future Improvements The current application focuses on signal processing and object detection. Planned enhancements include: Multi-target tracking, including Kalman filtering and track-to-track association Hardware-in-the-loop testing using the TEF82xx front end 7 From Simulation to Target The MathWorks reference example executes entirely within MATLAB. During deployment, the example is partitioned into the Environment Simulation block and the Radar Signal Processing block, where the computationally intensive signal-processing functions are replaced with NXP-optimized implementations from the Radar SDK. This delivers faster execution, reduced CPU utilization, and accelerator offloading on the S32R45. 8 Installing the NXP Toolchain 7.1 Installation Order The development tools must be installed in the following order to ensure that all external dependencies required by the NXP MBDT for RADAR are available before it is configured: S32 Design Studio for S32 Platform 3.5 S32R45 Radar SDK 1.2.0 NXP Model-Based Design Toolbox for SPT 1.9.0 NXP Model-Based Design Toolbox for RADAR 1.0.0 7.2 Integrating the Development Environment After NXP MBDT for RADAR is installed, the integration of S32 Design Studio and S32R45 Radar SDK is performed using the MATLAB Live Script: mbd_lax_dependencies_path.mlx The script is located at the root of the NXP MBDT for RADAR installation. Running this script configures the required dependency paths and establishes the connection between MATLAB, the NXP Model-Based Design Toolbox for RADAR, S32 Design Studio, and the S32R45 Radar SDK. Once the script is completed successfully, the environment is ready for simulation, code generation, accelerator kernel execution, and Processor-in-the-Loop (PIL) validation. 7.3 Installation Methods The NXP toolboxes ship as MATLAB Toolbox packages (.mltbx) and can be installed in three ways: Manual install (.mltbx) - Double-click the .mltbx file, or right-click and select Install in MATLAB. The Add-On Manager installs and registers the toolbox automatically. Via NXP Support Package - Install NXP_Support_Package_RADAR from MATLAB Add-Ons, then follow the guided steps to download and install MBDT for RADAR and generate/activate the free license. Via the Automotive Software Package Manager - A bundle installer that walks through toolbox installation, dependency configuration, and license activation. 9 Next Article in the Series This article introduced the software environment, hardware environment, deployment workflow, and high-level radar signal-processing architecture. The next article, "Radar - Processing Chain - RSDK," will provide a detailed analysis of each processing block presented in Section 5: Range FFT Doppler FFT Non-Coherent Combining CFAR Detection Clustering Direction-of-Arrival (DoA) Estimation It will also explain how these algorithms are mapped onto the S32R45 processing resources and how the NXP Radar SDK accelerates the execution of each stage. 10 References MathWorks Radar Signal Simulation and Processing for Automated Driving Radar Toolbox Automated Driving Toolbox NXP S32R45 High-Performance Processor for Imaging Radar S32R45 Evaluation Board TEF82xx 77 GHz Radar Transceiver Model-Based Design Toolbox MBDT for RADAR - Knowledge Base NXP Support Package for RADAR How to install .MLTBX

  1 Introduction Turning a motor control concept into a running dual-motor traction real system requires more than a control algorithm. It requires a connected software and hardware environment that can take the design from simulation to generated code, from target deployment to real-time calibration, and finally to validation on physical motors. This article continues the Motor Control System series by moving from the system-level overview to the enablement layer behind the application. It highlights the MathWorks and NXP tools, software components, MCU resources, and power-stage building blocks that make the Dual-Motor EV traction system possible. At the core of the workflow is Model-Based Design. MathWorks tools are used to model the field-oriented control algorithms, define the CAN communication interfaces, and support validation across simulation stages. NXP tools then bring those models onto the S32K396 target platform, connecting the generated application to real-time peripherals, gate-driver hardware, and motor feedback signals. Together, these elements form the development backbone of the dual-motor application: a path that starts with definition of the control strategy and ends with validation on real hardware. 2 Table of Contents • Software • Hardware • References • Conclusion 3 Software The software environment provides the modeling, simulation, communication, code generation, and deployment capabilities required by the Motor Control System. Each tool contributes a specific part of the development flow. 3.1. Motor Control Blockset Motor Control Blockset is the control-algorithm engine behind the traction application. It provides a ready-to-use environment for designing, simulating, and deploying motor control algorithms, while also supporting optimized C code generation from Simulink. In this Motor Control System, the MCB models the Field-Oriented Control strategy for Permanent Magnet Synchronous Motors. It supports the main control-loop blocks. These include Clarke and Park transforms, current and speed regulation, Space Vector Modulation, and position or speed feedback processing. The same model can run across desktop simulation and real-time validation. This keeps the controller consistent from early algorithm work to target execution. It also aligns the design across Model-in-the-Loop, Software-in-the-Loop, Processor-in-the-Loop, and hardware deployment stages. For more information, see the Motor Control Blockset documentation in the References chapter. 3.2. Vehicle Network Toolbox Vehicle Network Toolbox brings CAN communication into the model-based workflow. It provides MATLAB functions and Simulink blocks for sending, receiving, encoding, and decoding CAN messages. This makes network behavior visible and testable before deployment. In the Motor Control System, CAN exchanges commands, feedback, and status information. It links the ECU with the surrounding vehicle architecture. The toolbox helps define the signal interface, pack and unpack CAN frames, simulate bus traffic, and validate communication behavior before target execution. Communication is not treated as a late integration step. CAN interaction can be simulated and verified together with the control model. This reduces integration risk and makes ECU behavior easier to validate end to end. For more information, see the Vehicle Network Toolbox documentation in the References chapter. 3.3. NXP Model-Based Design Toolbox for S32K3 NXP Model-Based Design Toolbox for S32K3 connects the Simulink model to the NXP S32K3 target environment. It provides the embedded target support required to generate, build, download, and run applications on NXP microcontrollers. The toolbox provides peripheral blocks for hardware access. These include interfaces for ADC, PWM, CAN, SPI, UART, timers, interrupts, and other target resources used by motor control applications. For the Motor Control System, the toolbox enables the generated application to run on top of the S32K3 software stack. It also supports configuration flows based on NXP tools, real-time data visualization with FreeMASTER, and integration with optimized libraries such as AMMCLib. NXP Model-Based Design Toolbox for S32K3 is used as part of the enablement environment for the S32K3 complex applications. It provides the bridge between the model and the production-oriented embedded implementation. 4 Hardware The hardware environment provides the real-time execution platform and the power stage interface required to control the motors. The key hardware components are the NXP S32K396 microcontroller and the NXP MC33937 three-phase FET pre-driver. 4.1. The NXP S32K396 Microcontroller The NXP S32K396 is the main processing device used by the Motor Control System. It belongs to the S32K39 family of electrification microcontrollers and is optimized for traction inverter, torque vectoring, and smart actuation applications. The device combines real-time compute, motor control acceleration, advanced analog acquisition, high-resolution actuation, safety mechanisms, security services, and automotive networking in a single MCU platform. At the compute level, the S32K396 provides Arm Cortex-M7 processing resources running up to 320 MHz. The architecture supports safety-oriented execution through lockstep and split-lock configurations. This enables separation between safety-critical motor control tasks and additional monitoring or communication functions. For motor control, the device includes a dedicated motor control coprocessor called eTPU (Enhanced Time Processing Unit) and a programmable CoolFlux DSP. These resources can offload timing-critical functions from the main CPU. They support fast current-loop execution, resolver processing, PWM generation, analog sensing, and other functions required by high-performance FOC applications. The smart timer and I/O subsystem is also important for traction control. The S32K396 includes eFlexPWM modules with NanoEdge capability, eMIOS channels, Logic Control Units, and Body Cross-Triggering Units. These blocks help synchronize PWM generation, ADC triggering, fault handling, and real-time control events. The analog subsystem supports the feedback path of the inverter. It includes multiple SAR ADCs, Sigma-Delta ADCs, analog comparators, and sine wave generators. These resources are used to acquire phase currents, DC bus voltage, phase voltages, temperature signals, and position-related feedback. The communication subsystem enables integration with the vehicle network and external devices. The S32K396 provides CAN FD, Ethernet with TSN support, LIN/UART, SPI, I2C, QSPI, FlexIO, and Zipwire interfaces. In this Motor Control System, CAN is used for vehicle-level command and status exchange. Figure 4-1. S32K396 Block Diagram In the Motor Control System, the S32K396 executes the real-time control loops, reads current and voltage feedback, processes rotor position or speed information, generates PWM signals, and exchanges data with the vehicle network over CAN. The same platform can support one six-phase motor or two three-phase motors. This makes it suitable for the dual rear-motor architecture used throughout this article series. 4.2. The NXP MC33937 Three-Phase FET Pre-Driver The NXP MC33937 is the three-phase Field Effect Transistor pre-driver used between the microcontroller and the inverter power switches. It is designed for three-phase motor control and similar automotive actuation applications. The device contains three high-side FET pre-drivers and three low-side FET pre-drivers. Together, these six gate-drive channels control the external MOSFET bridge used by the three-phase inverter. The MC33937 interfaces with the S32K396 through six direct input control signals. These signals provide the fast phase control path from the PWM outputs of the microcontroller to the gate-driver stage. The device also includes an SPI interface. SPI is used for device setup, configuration, diagnostics, and safe control features. Reset, enable, and interrupt pins provide additional control and fault signaling between the pre-driver and the MCU. The MC33937 supports an extended operating range from 6 V to 58 V and is fully specified from 8 V to 40 V. This makes it suitable for 12 V and 24 V automotive systems, as well as higher-voltage transient operating conditions. The MC33937 also provides protection and monitoring features needed in motor control applications. These include undervoltage detection, overcurrent comparison, desaturation comparison, temperature limitation, phase voltage comparison, and protection against reverse charge injection from the external FETs. The device accepts both 3.3 V and 5 V logic-level inputs and provides 5 V logic-level outputs. This simplifies the connection with automotive microcontrollers and allows the pre-driver to fit into different control board designs. Figure 4-2. MC33937 Block Diagram In the Motor Control System, the MC33937 forms the actuation bridge between the PWM signals generated by the S32K396 and the three-phase inverter that drives each PMSM. It converts logic-level control commands into the gate-drive signals required by the external power stage. 4.3. NXP Evaluation Boards The Motor Control System hardware is built from modular NXP evaluation boards. This allows the same S32K396 control platform to be connected to one or two low-voltage three-phase inverter stages. Figure 4-3. NXP S32K396-BGA-DC1 The S32K396-BGA-DC1 evaluation board is the main controller board. It contains the S32K396 microcontroller in MAPBGA 289 package, an onboard debugger, communication interfaces, and the connectors required to access the real-time control signals. It is optimized for electrification applications such as traction drive and torque vectoring. The S32X-MB board is used as an I/O extension board. It is not a standalone development board. It must be used together with a compatible S32K39/37 evaluation board. In this setup, it expands the number of accessible peripherals and provides an additional motor control connector. Figure 4-4. S32X-MB Board The MCSPTR2AK396 kit provides the low-voltage motor control power stage used in the demo. From this kit, the demo uses the three-phase low-voltage pre-driver board and the PMSM motor. The power stage is based on the MC33937A pre-driver and is designed for BLDC or PMSM control. Figure 4-5. 3-Phase Low Voltage Motor Control Kit The kit also provides useful motor control interfaces. These include the three-phase motor output, Hall or encoder interface, resolver interface, DC bus sensing, phase voltage sensing, and protection feedback. These signals are required to close the control loop on the target hardware. 4.4. Dual-Motor Hardware Connections For the dual-motor hardware set-up, the S32K396-BGA-DC1 board provides the main MCU resources. The first three-phase motor control channel is connected through the primary motor control connector. The second channel is routed through the S32X-MB extension board. Each motor channel uses one low-voltage three-phase pre-driver and one PMSM motor. The PWM signals generated by the S32K396 are routed to the MC33937A gate-driver stage. The pre-driver then controls the external MOSFET bridge of the inverter. The feedback path is routed back from each power stage to the MCU. This includes phase current feedback, DC bus voltage, phase voltage, and position or speed feedback from the selected sensor interface. These signals are sampled and synchronized with the PWM events. The S32K396 therefore controls two independent three-phase inverter stages. Each motor has its own PWM outputs, sensing path, position feedback, and protection signals. The control software coordinates both channels and exchanges the resulting status information over CAN. This hardware arrangement can also be viewed as a scalable topology. The same MCU platform can be used for two independent three-phase motors or for one six-phase motor, depending on how the PWM outputs, sensing resources, and power stages are mapped. By connecting the software workflow with the hardware execution path, this enablement layer shows how a model-based motor control concept can be taken from algorithm design to a running dual-motor traction demonstrator on NXP silicon.   5 References Motor Control Blockset Documentation Vehicle Network Toolbox Documentation NXP Model-Based Design Toolbox for S32K3 S32K39-37-36 Microcontrollers for Electrification Applications MC33937: 3-Phase Field Effect Transistor Pre-Driver S32K396-BGA-DC1 Evaluation Board MCSPTR2AK396 BLDC/PMSM Motor Control Development Kit S32X-MB I/O Extension Evaluation Board 6 Conclusion This article described the software and hardware enablement required for the Motor Control System. The software environment combines MathWorks motor control and vehicle network capabilities with NXP target support. The hardware environment combines the S32K396 microcontroller with the MC33937 pre-driver and the inverter stage. Together, these elements provide the foundation for modeling, simulation, communication, code generation, deployment, and validation of the dual-motor control application. The next article will focus on the architecture and model description of the Motor Control System, including the main control layers, signal interfaces, and application structure.

    1 Table of Contents • Introduction • Open the generated project in S32 Design Studio • Debug the generated application in S32 Design Studio • Debug the code generated from the Simulink model • Conclusion 2 Introduction This article explains how to take a project generated with the Model-Based Design Toolbox (MBDT) in Simulink and open, build, and debug it in S32 Design Studio. It focuses on the transition from model execution in Simulink to target-level debugging and validation on S32 hardware.     3 Open the generated project in S32 Design Studio MBDT generates code from Simulink models and exports it as an S32 Design Studio-compatible project. After a successful model build, the generated <modelName>_Config folder contains the files required by the IDE. The project can then be opened directly from Simulink or imported into S32 Design Studio for further configuration, building, and debugging on S32 hardware. Before opening or debugging the project in S32 Design Studio, build the Simulink model. The build process generates the code and project structure required for IDE integration. You can open the generated project either directly from Simulink or manually from within S32 Design Studio. Use the Simulink option when you want to launch the generated project immediately after configuration. Use the IDE import option when you want to manage the project manually from an S32 Design Studio workspace. Open the project from Simulink To open the project from Simulink, open the model Hardware Settings from the Hardware tab or press Ctrl + E. Then go to Hardware Implementation → Hardware board settings → Target hardware resources → S32 Design Studio Project and select Open. Figure 1. S32 Design Studio project settings in Simulink A dialog appears and prompts you to select the S32 Design Studio installation path. Figure 2. S32 Design Studio installation path selection To select the S32 Design Studio installation path later, or to change it during toolbox usage, click Browse in the S32 Design Studio location field under the Tools Paths group. Figure 3. S32 Design Studio path changing The generated project opens in S32 Design Studio and is ready to build, configure, or debug. Figure 4. Generated project opened in S32 Design Studio Open the project inside the IDE To import the project manually into S32 Design Studio, follow these steps: Inside the IDE, select File → Import → Existing Projects into Workspace. Figure 5. Importing an existing project into the workspace Browse for the <modelName>_Config folder in Select root directory. Before clicking Finish, make sure that Copy projects into workspace is disabled. If the project is copied into the S32 Design Studio workspace, the build process will fail. Figure 6. Directory selection for the generated project     4 Debug the generated application in S32 Design Studio To build and debug the project in S32 Design Studio, select the project and click Debug. S32 Design Studio builds the project and automatically switches to the Debug perspective. Note: Ensure that the target hardware board is connected before starting the debug session. Figure 7. Starting the debug session Figure 8. Debug perspective in S32 Design Studio After the debugger launches and the application is loaded on the target, you can use the following actions to control program execution and inspect the generated code: The Breakpoint action sets a breakpoint when you double-click in the left margin of a .c file:   Figure 9. Breakpoint set in the generated source file The Step Over (F6) action executes the current line while remaining in the same function: Figure 10. Step Over action in the Debug toolbar The Step Into (F5) action enters a called function: Figure 11. Step Into action in the Debug toolbar The Step Return (F7) action runs to the end of the current function: Figure 12. Step Return action in the Debug toolbar The Resume (F8) action runs until the next breakpoint: Figure 13. Resume action in the Debug toolbar Figure 14. Breakpoint reached after pressing Resume action The Suspend (F9) action pauses execution at the current instruction: Figure 15. Suspend action in the Debug toolbar Figure 16. Function paused after pressing Suspend action The Terminate (Ctrl + F2) action stops the debug session and disconnects from the target: Figure 17. Terminate action in the Debug toolbar The Disconnect action leaves the target running while detaching the debugger: Figure 18. Disconnect action in the Debug toolbar     5 Debug the code generated from the Simulink model The code generated by the Simulink model can be found in the <modelName>_step() function. To enter this function, set a breakpoint before the function call, run the application until the breakpoint is reached, and then select Step Into. Alternatively, Ctrl + Click the function name to open the function and place a breakpoint inside it. Figure 19. modelName_step function In this function, you will also find the generated code for the blocks placed inside the Simulink model. Figure 20. Generated step function in the source code To monitor variable values, hover over a variable to see its current value: Figure 21. Variable value displayed on hover Alternatively, add the variable to the Expressions view by selecting Add new expression, entering the variable name, and pressing Enter. Figure 22. Add new expression in Expressions view Figure 23. Variable added to Expressions view Upon running the code, if the value changes, it will be highlighted. Figure 24. Variable value highlighted during debug The names of the variables in the generated code are the same as the names they have in the Simulink model, making it easier to debug the generated code. Figure 25. Variable name in Simulink model and generated code   6 Conclusion After identifying the generated function and monitoring key variables, you can validate how the Simulink model behavior maps to the generated application running on the target hardware. For more tutorials on installing, activating, and using S32 Design Studio, see the S32 Design Studio tutorials on the community page: S32 Design Studio Knowledge Base.

1 Table of Contents • Introduction • Overview • Context • References • Conclusion 2 Introduction This article provides a high-level overview of the typical workflow for developing an application using the toolbox. It explains how the main development stages fit together, from preparing the environment and selecting the target hardware to configuring the project, generating code, building the application, programming the target, and validating the results. The purpose of this topic is to help users understand the overall process and to guide them toward the related articles that describe each stage in more detail. 3 Overview Workflow Scope The workflow described in this article covers the main steps typically followed when developing an application with the toolbox. After the toolbox and supporting environment are prepared, the user can create a new model or open an existing example, select the target hardware, configure the required software components, prepare the Simulink model, generate code, build the application, program the target device, and debug and validate the behavior on hardware. This article is intended as an overview topic and does not replace the more detailed setup, modeling, and debugging documentation. Target Audience This article is intended for users who want to understand the overall development flow supported by the toolbox. It is useful both for new users who start from supported examples and evaluation boards and for advanced users who need to adapt the workflow to a custom target or project configuration. 4 Context Prerequisites Before following the workflow described in this article, the development environment should already be prepared. The setup process, including toolbox installation and the basic steps required to run an application, is described in the previous article. Depending on the selected project and application requirements, additional tools such as S32 Configuration Tools or EB tresos may be needed, especially when the default project configuration must be modified or when a custom project is created. Toolbox Workflow The development flow typically starts with creating a new project or opening an existing example and then selecting the target hardware. Figure 1. Opening a Simulink project or toolbox example. The selected target determines the available peripherals, supported examples, software configuration options, and build settings. As part of this step, the user can start from the default project associated with the selected target. This default project provides a ready-to-use baseline configuration and is typically the recommended option for evaluation boards and quick start development. For more advanced use cases, the workflow can also use a custom project configuration adapted to the application requirements. Figure 2. Selecting a custom project configuration. If the user continues with the default project configuration, additional low-level software changes may be limited. However, when the default project needs to be modified or when a custom project is used, tools such as S32 Configuration Tools or EB tresos may be required. Figure 3. Low-level software configuration using EB tresos or S32 Configuration Tool.  Figure 4. S32 Configuration Tool Configuration Template. Once the software stack is prepared, the Simulink model must be configured. This includes adding and parameterizing the relevant toolbox blocks, defining the application behavior, setting the model parameters, and aligning the model with the selected target and software configuration. Figure 5. Embedded Coder. Figure 6. Build or Generate Code step. After the model configuration is complete, code can be generated from the Simulink model. This step transforms the model into source code suitable for the selected target platform. The generated output reflects both the model behavior and the configuration settings applied in the previous stages. The generated code is then built using a supported compiler toolchain. The build process compiles and links the generated code together with the required software components and libraries. Build settings may vary depending on the target, compiler version, and selected optimization or debug options. Figure 7. Generated code. After a successful build, the application can be programmed onto the target hardware and executed. At this stage, the user can debug the application using the supported debug tools, inspect signals and variables, and verify that the application behaves as expected on the real hardware platform. Figure 8. Programming and debugging the application on target hardware. The final step of the workflow is validation and iteration. If issues are found during testing or debugging, the user may need to update the model, adjust the low-level software configuration, or modify build settings. The workflow is therefore iterative, allowing repeated cycles of configuration, code generation, build, programming, and validation until the desired result is achieved. Related Topics Additional details for each workflow stage are available in related documentation topics. For environment preparation, toolbox setup, and the basic steps required to run an application, refer to the previous article. More detailed information about model creation and configuration is provided in the next article. Other related topics may include examples library, supported boards and derivatives, low-level software configuration, compiler versions and options, and debugger usage. 5 References For more detailed information, refer to the related toolbox documentation and associated setup, modeling, software configuration, compiler, and debugging articles. MathWorks Simulink MathWorks Embedded Coder Generate Code from Simulink Models 6 Conclusion The toolbox workflow provides a structured path from model-based development to execution on the target hardware. Users can start quickly from the default project associated with the selected target, while still having the flexibility to create and use a custom project configuration when required. By following this workflow and using the related detailed documentation, users can iteratively configure, build, program, debug, and validate their applications more efficiently.

    1 Table of Contents • Introduction • Overview • Target Audience • Context • References • Conclusion     2 Introduction A Battery Management System (BMS) is a system that monitors and manages a battery pack to ensure it operates safely, efficiently, and reliably, making it a critical component in electric vehicles. Its main functions include measuring voltages, currents, and temperatures and balancing the cells to maintain consistent performance. This overview introduces a series on the architecture, development and integration of a battery management system developed using NXP hardware and software. To accelerate this process, MathWorks ecosystem is used to streamline the development, maintain traceability from model to implementation and to validate complex embedded applications.     3 Overview Articles roadmap Developing a battery management system is a complex undertaking, and explaining it thoroughly requires a structured series of articles. Each article focuses on a key stage of the development process, offering detailed insight into how such a system is designed, implemented, tested, and validated from concept to deployment. The series includes the following articles: Software and Hardware Environment - An overview of the required software environment, including NXP software development kits (SDKs), real-time drivers (RTDs), and MathWorks toolboxes, together with the hardware platform used in the application. Architecture and Model Description - A detailed description of the system architecture, including the model structure, input and output signals, and the core algorithms used in the battery management system. Validate the BMS Algorithms (Model-in-the-Loop) - An explanation of how validated MathWorks battery management assets - such as state-of-charge (SoC) and state-of-health (SoH) estimation algorithms - can be adapted, integrated, and verified within the application model. Preparing BMS Algorithms for Code Generation (Software-in-the-Loop) - Guidance on generating production-oriented code from validated models and running software-in-the-loop (SiL) simulations to compare code behavior against the model-in-the-loop (MiL) baseline. Bringing the BMS Closer to Hardware (Processor-in-the-Loop) - Steps to prepare the model for execution on target hardware by deploying the generated software to an NXP evaluation board while emulating battery measurements on a host PC. Deployment and Validation on the High-Voltage BMS Reference Design Kit - Configuration of external devices to supply real data to the BMS algorithms, followed by system-level validation. Extending the Controller with CAN Communication - Integration of controller area network (CAN) communication by defining the CAN database, configuring the communication stack, and validating message exchange on the NXP hardware. Final Results - A summary and discussion of results, along with final validation of the complete battery management system. What is the Battery Management System? A Battery Management System (BMS) is a combined hardware and software system responsible for monitoring, controlling, and protecting an electric vehicle's battery pack. Technically, it acts as the central authority that has full visibility into the battery's operating conditions, such as cell voltages, pack current, and temperatures. Based on this information, the BMS makes real-time decisions to keep the battery within safe operating limits. It also enforces critical protections - such as preventing overcharge, over-discharge, over-temperature, or short-circuit conditions - which are essential for safety, reliability, and regulatory compliance. From a functional perspective, the BMS performs several key jobs that directly impact vehicle performance and longevity. These include estimating battery states such as State of Charge (SoC), State of Health (SoH), and available power, which higher-level vehicle systems rely on for range prediction and energy management. The BMS also manages cell balancing, ensuring that individual cells within the pack age uniformly and maintain similar voltage levels. This combination of accurate state estimation and active control helps maximize usable energy, protect the battery from accelerated degradation, and maintain consistent performance throughout the vehicle's life. On the hardware side, a BMS typically consists of sensing components (voltage, current, and temperature sensors), cell monitoring and balancing ICs, a microcontroller, isolation components, and communication interfaces. These elements work together to acquire high-precision measurement data from the battery pack and execute control actions such as enabling contactors or activating balancing circuits. In many architectures, the system is distributed, with multiple cell monitoring units communicating with a central BMS controller.   The software layer ties everything together and is often the most complex part of the system. BMS software includes low-level drivers for sensors and communication, real-time control logic, diagnostic and fault-handling mechanisms, and advanced algorithms for state of charge estimation. It must integrate seamlessly with the rest of the vehicle through networks such as CAN, allowing the BMS to exchange data with vehicle control units, chargers, thermal management systems, and the powertrain. Through this tight hardware-software integration, the BMS becomes a core enabler of safe operation, efficient energy use, and coordinated vehicle behavior.     4 Target Audience This article series is intended for engineers, technical specialists, and decision-makers involved in the development, integration, or evaluation of high-voltage battery management systems for electric vehicle applications. It is especially relevant for readers who want to understand how BMS algorithms, embedded software, hardware platforms, and validation workflows come together in a complete development process. The content is suitable for both engineers looking for practical implementation guidance and technical stakeholders interested in the benefits of using a Model-Based Design approach with MathWorks and NXP solutions. The main target audience includes: Embedded software engineers Control and algorithm engineers Battery system engineers Electric vehicle system architects Model-Based Design engineers Hardware and integration engineers Test and validation engineers Technical managers and project leads     5 Context In the electric vehicle architecture presented in this series, the Battery Management System is located in the rear zone of the vehicle. It is a safety-critical controller responsible for battery supervision, but it operates within a highly interconnected ecosystem. It bridges: Battery pack (physical layer) Vehicle Control Network (communication layer) Powertrain and Vehicle Behavior (functional layer) The HVBMS is implemented on the reference design bundle for 800 V high-voltage battery management systems. It provides a complete hardware solution including: RD-K358BMU - battery management Unit (BMU) RD33774CNT3EVB - cell monitoring unit (CMU) RD772BJBTPL8EV - battery junction box (BJB) 18 Cell Battery Pack Emulator       6 References Speed-Up BMS Application Development with NXP's HVBMS RD and Model-Based Design Toolbox (MBDT) Model-Based Design Toolbox NXP Community 800 V Battery Management System (BMS) Reference Designs Using ETPL Model-Based Design Toolbox (MBDT)     7 Conclusion This article introduced the Battery Management System within the context of an electric vehicle architecture and established the technical foundation for the rest of the series. It described the role of the Battery Management System and illustrated how a Model-Based Design workflow can be implemented by combining the MathWorks and NXP ecosystems. The next article will focus on the software and hardware environment needed to develop, simulate, and deploy a Battery Management System using MathWorks and NXP solutions.

    1 Table of Contents • Overview • Executive Summary - What is .MLTBX • Context - Where to obtain the .mltbx file • Method 1 - Manual Installation (.mltbx) • Method 2 - Install via NXP Support Package • Method 3 - Automotive Software Package Manager • Conclusion     2 Overview NXP provides a range of MATLAB ® Toolboxes distributed as .mltbx packages to support modeling, simulation, configuration, and code generation for NXP microcontrollers and processors. These toolboxes integrate directly with the MathWorks environment and enable faster development workflows by extending MATLAB/Simulink with NXP-specific blocks, drivers, and examples. The scope of this article is to guide users through the process of installing an NXP .mltbx toolbox obtained from the official NXP website. It explains the prerequisites, where to download the toolbox, and how to install and verify it within MATLAB. The instructions are intended for engineers and developers who have basic familiarity with MATLAB but may be new to installing third-party toolboxes distributed outside of MathWorks Add-Ons. By following this guide, readers will be able to correctly install the NXP toolbox, ensure it is recognized by MATLAB, and prepare their environment for subsequent development and evaluation tasks.     3 Executive Summary - What is .MLTBX An .mltbx file is a MATLAB Toolbox package used to distribute and install MATLAB or Simulink extensions. It is a self-contained archive created by MathWorks that can include functions, Simulink blocks, documentation, examples, and setup scripts. When opened in MATLAB, an .mltbx file is installed using the Add-On Manager, which automatically places the toolbox in the default add-ons folder, and registers the toolbox within the environment. This format allows third-party vendors - such as NXP - to safely deliver toolboxes outside of the MathWorks Add-On Explorer while preserving a standard installation experience. In short, a .mltbx file is the official and recommended way to package, install, update, and uninstall MATLAB toolboxes.     4 Context - Where to obtain the .mltbx file There are multiple ways to get the .mltbx file, as shown below: Manual download and install - from NXP site (.mltbx file) Installation via MATLAB - Add-Ons / toolbox flow (NXP Support Package) Installation via Automotive Software Package Manager - bundle installer All methods are valid and can be used depending on your setup and preferences. The Automotive Software Package Manager approach installs bundles and generates an installer that walks through the steps automatically. Prerequisites Before installing the toolbox, ensure the following: MATLAB is installed on your machine You have access to the toolbox download source Note: The .mltbx file cannot be used without MATLAB. The toolbox is only available for Windows and may require additional prerequisites such as: Embedded Coder MATLAB Coder Simulink Coder     5 Method 1 - Manual Installation (.mltbx) The manual installation flow is simple, once prerequisites are met. Manually download the .mltbx file from the NXP site and install it. Typical install behavior: Open MATLAB → run or double-click the .mltbx file → install → toolbox is added automatically. Installed toolboxes are placed under MATLAB Add-Ons directories and appear in the Add-On Explorer. Step 1 - Select the toolbox family As a first step, on the NXP site, select "Automotive SW - Model-Based Design Toolbox".     Step 2 - Select the target software In our example, we are selecting "Automotive SW - S32K3 Software".   Step 3 - Select the S32K3 Model-Based Design Toolbox Select "Automotive SW - S32K3 - Model-Based Design Toolbox".   Step 4 - Choose Product Information Select the Product Information: "Model-Based Design Toolbox S32K3 1.8.0".   Step 5 - Accept Software Terms and Conditions The Software Terms and Conditions will appear - select "I Agree".   Step 6 - Download the .mltbx file After the terms and conditions agreement, you can download the .mltbx file.   When downloading, save the file under the .zip extension, as shown below.   Step 7 - Reveal file extensions in Windows To see and change the file extension, follow the next steps: Press the three dots visible below:   Select "Options". Deselect "Hide extensions for known file types".   Press Apply and OK. After this update, the file will be visible with its extension.   Step 8 - Change the file extension to .mltbx Change the file extension from .zip to .mltbx :   A pop-up will appear - press "Yes":   View after changing the file from .zip to .mltbx:   Step 9 - Install the toolbox in MATLAB Double-click the .mltbx file and accept the License Agreement.   The installation process will start and it will take a few moments to be finalized.  Installation Finalized     Toolbox registered in MATLAB Add-On Manager        6 Method 2 - Install via NXP Support Package The NXP Support Package add-on is a guided installer that: Checks and validates all installation prerequisites Directs users to the page where the required .mltbx package can be downloaded Allows users to select the .mltbx package to install Provides the option to open relevant documentation resources Step 1 - Open MATLAB Launch MATLAB.   Step 2 - Navigate to Add-Ons Go to: Add-Ons → Get Add-Ons.     Step 3 - Install the toolbox Load the toolbox file or follow your internal download process. Note: Direct download via Add-On Explorer may not always be available, depending on licensing and setup.     7 Method 3 - Automotive Software Package Manager This method uses the Automotive Software Package Manager, which installs bundles and generates an installer that walks through the steps automatically. Step 1 - Access Package Manager Use the Automotive Software Package Manager.   Step 2 - Select required components Choose: Target platform - e.g. S32K3 Required tools - e.g. FreeMASTER, Model-Based Design Toolbox   Step 3 - Generate installer The tool generates a bundle installer.   Step 4 - Run installer Run the generated installer. Follow the step-by-step instructions.     8 Conclusion Installing an NXP .mltbx toolbox is straightforward once the MATLAB prerequisites are in place. Depending on your workflow, you can choose the manual .mltbx installation, the guided NXP Support Package, or the Automotive Software Package Manager bundle installer - all three methods produce a properly registered toolbox inside MATLAB. With the toolbox installed and verified, your environment is ready to start developing, simulating, and generating code for NXP microcontrollers and processors. Stay tuned for the next article, where we will dive into using the newly installed toolbox to build your first Model-Based Design project.

  1 Table of Contents •Introduction •Overview •Context •References •Conclusion 2 Introduction This article walks through the complete process of setting up the NXP Model-Based Design Toolbox (MBDT) and running a first application on NXP hardware. Before starting the installation, make sure that the prerequisite toolboxes are available in MATLAB. By the end of this guide, the reader will have a fully functional MBDT environment and will have successfully generated, compiled, and deployed embedded C code from a Simulink model to NXP hardware. 3 Overview This guide begins with the installation prerequisites and required toolboxes, then continues with the MATLAB Add-On Explorer flow for installing NXP_Support_Package_S32K3 . After the support package is installed, the guide explains how to launch the multistep installer, verify the required toolboxes and installation path, download the toolbox package from NXP, and complete the toolbox installation before running the first application. Installation Scope and Workflow This article focuses on practical installation flow required to start working with the NXP Model-Based Design Toolbox and run a first example application. It covers the software prerequisites, the toolbox setup sequence, and the validation steps needed before opening and deploying a model on the target board. The installation content in this guide should use the current multistep installer flow. Target Audience This article is intended for engineers and technical professionals who want to begin developing embedded applications for NXP hardware using a Model-Based Design workflow. The main target audience includes: Embedded software engineers MATLAB / Simulink developers evaluating NXP hardware Control and algorithm engineers Students and academic researchers using NXP evaluation boards Model-Based Design engineers Hardware integration engineers 4 Context 3.1 Prerequisites Before starting the installation, verify that the following prerequisite toolboxes and setup conditions are met: MATLAB installed - Required by the support package and multistep installer flow. Simulink installed - Required for model-based development and Simulink example execution. Embedded Coder installed - Required for embedded C code generation from Simulink models. MATLAB Coder installed - Required by the current S32K3 support package prerequisites. Simulink Coder installed - Required by the current S32K3 support package prerequisites. Embedded Coder Support Package for ARM Cortex-M Processors installed - Required by the installer verification step and target support flow. NXP account - Required to access the NXP download page and retrieve the toolbox package. Short local installation path - The installation path should be local, short, and should not contain whitespace to avoid setup issues. Figure 1 - MATLAB Add-On Manager confirming requirement are installed 3.2 Toolbox Setup NXP's Model-Based Design Toolbox is delivered as a MATLAB Toolbox Package that can be installed offline or online from MathWorks Add-ons. The recommended installation path uses the NXP Support Package, a graphical wizard that guides through download, installation, and license activation in a single workflow. Note: Throughout this guide, the placeholder {platform} refers to the NXP MCU family targeted by the toolbox (for example S32K3 , S32K1 , S32M2 , MPC57XX , etc.). Each family has its own dedicated Support Package and Toolbox in the MATLAB Add-On Explorer. When following the steps below, replace {platform} with the identifier matching the hardware family in use, for instance, for the S32K3 evaluation boards, the script name becomes NXP_Support_Package_s32k3.m and the path command becomes mbd_s32k3_path . Step 1 - Install NXP Support Package from MATLAB Add-On Explorer Install the current NXP support package directly from the MATLAB Add-On Explorer. This package provides the multistep installer flow used to verify prerequisites, download the toolbox, and guide the installation for S32K3. In MATLAB, navigate to Home → Add-Ons → Get Add-Ons. Figure 2 - Open the Add-On Explorer from the MATLAB Home tab Search for NXP_Support_Package_S32K3 in the Add-On Explorer. Figure 3 - Search results for NXP_Support_Package_S32K3 in the Add-On Explorer Open the package page and click Add to start the installation. Figure 4 - Open the NXP_Support_Package_S32K3 page and click Add Review the license agreement for NXP_Support_Package_S32K3 and click I Accept. Figure 5 - License agreement shown during installation of NXP_Support_Package_S32K3 Wait for the installation to complete. When finished, the Getting Started Guide opens automatically. Figure 6 - Support package installation completed successfully In the MATLAB Command Window, run sp_s32k3.nxp.setup(); to launch the multistep installer. sp_s32k3.nxp.setup(); Figure 7 - Run sp_s32k3.nxp.setup(); from the MATLAB Command Window Step 2 - Use the multistep installer to download and install the toolbox The multistep installer guides you through prerequisite verification, toolbox download, installation, activation, and access to the documentation for S32K3. Figure 8 - Welcome page of the S32K3 multistep installer In the installer, continue to the download step. On the NXP website, review the software terms and conditions and click I Agree before downloading the toolbox package. If the product download page does not open automatically, sign in to your NXP account and open the Product Download page for the required S32K3 toolbox release or click the link from Download page of the S32K3 multistep installer. Figure 9 - Download page of the S32K3 multistep installer Figure 10 - Accept the NXP software terms and conditions before downloading Download the toolbox package from the Product Download page. The installer accepts both .zip and .mltbx files. Figure 11 - Product Download page for the S32K3 MBDT package The setup verification step checks whether all required toolboxes are installed in MATLAB and whether the installation path is valid for the S32K3 toolbox setup. If any dependency is missing or an unsupported version is detected, resolve the issue before continuing to the download and installation steps. Figure 12 - Setup verification page showing required toolboxes and installation path checks Important: It is recommended to install MATLAB and the NXP Toolbox into a location that does not contain special characters, empty spaces, or mapped drives. Use a short local path whenever possible. After downloading the package, return to the installer and continue with the local file selection step. Browse to the downloaded archive or toolbox package and click Install to continue. The installer accepts both .zip and .mltbx files. Figure 13 - Browse to and download the S32K3 MBDT package from the Product Download page Figure 14 - Accept the license agreement for NXP_MBDToolbox_S32K3 Accept the toolbox license agreement to allow MATLAB to complete the MBDT installation. Figure 15 - Toolbox installation in progress After the installation is complete, use the Add-On Manager context menu to open the installed toolbox folder if you need to inspect the package contents or access installed files directly. Wait until the installation finishes. The process may take several minutes depending on the system configuration and package size. Figure 16 - Open the installed toolbox location from MATLAB Add-On Manager Step 4 - Set the Path for Toolchain Generation The MBDT uses Simulink's toolchain mechanism to enable automatic code generation with Embedded Coder. When installed as a MATLAB add-on, the toolbox path is configured automatically. If manual configuration is still required in your environment, run the platform path script from the installation directory. If manual setup is required, in MATLAB change the Current Directory to the toolbox installation folder: ..\MATLAB\Add-Ons\Toolboxes\NXP_MBDToolbox_{platform}\ Then run the configuration script: mbd_{platform}_path Figure 17 - Output of the mbd_{platform}_path script in the MATLAB Command Window 3.3 How to Run an Application With the toolbox installed and the compiler configured, the following steps demonstrate how to open, build, and deploy the LED blinky example - the embedded equivalent of Hello World to an NXP evaluation board. Open an Example Model Open MATLAB and start Simulink by typing simulink in the Command Window (or by clicking the Simulink button on the Home tab). In the Simulink Start Page, open the Simulink Library Browser (View → Library Browser, or press Ctrl+Shift+L). In the Library Browser tree, expand NXP Model-Based Design Toolbox for {platform} to confirm that the NXP blocks are available. This validates that the toolbox is properly registered with Simulink. Open the Example Projects tab from the Simulink Start Page, it lists every example shipped with the MBDT, grouped by peripheral (ADC, CAN, DIO, PWM, UART, etc.). Browse the list, select the example matching your hardware (for instance s32k3xx_dio_s32ct for the LED blinky on FRDM-A-S32K312 / FRDM-A-S32K344 ), and click Open to load the model. Figure 18 - MBDT Examples Library available from the Simulink Library Browser Open the example model ( .slx / .mdl file). Configure the Target Hardware Figure 19 - Model Settings  Figure 20 - Code Generation Tip: Example models that ship with the MBDT are pre-configured for a specific evaluation board. Always verify the hardware target matches your physical board before building. Build and Deploy Connect the NXP evaluation board to the PC via USB. In Simulink, open the Hardware tab and click Build, Deploy & Start (or use Ctrl+B). Monitor the MATLAB Diagnostic Viewer for build status messages. Verify on Hardware Confirm that the application runs on the target hardware as expected - for example, observe the LED blinking at the rate defined in the model. If the application produces serial output, open a terminal and verify the expected data on the communication port. Use debugging or monitoring tools to inspect variable values and system signals from the running application in real time. 5 References NXP Model-Based Design Toolbox - Product Page Automotive SW - S32K3 - Model-Based Design Toolbox Model-Based Design Toolbox S32K3xx Quick Start Guide (PDF) MathWorks Embedded Coder     6 Conclusion This article described the complete setup of the NXP Model-Based Design Toolbox: from installation and compiler configuration to building and deploying a first application to NXP hardware. The next article in the series focuses on the Toolbox Workflow, presenting in detail the end-to-end development flow with the MBDT, from configuring a Simulink model with NXP blocks, through code generation with Embedded Coder, to building, deploying and validating the resulting application on NXP hardware.

      1 Table of Contents • Introduction • Overview • Context • References • Conclusion     2 Introduction Virtual vehicles are becoming a common part of modern automotive development, helping teams validate vehicle behavior, driver interaction, and system integration in realistic digital environments before moving to broader physical testing. Figure 1. Virtual vehicle plant model The goal of this first article is to present the virtual vehicle system used in the Hello World demo at a high level and establish the context for the articles that follow. The focus here is on what the subsystem is, why it is relevant in the demo, and how Model-Based Design supports its development within the MathWorks and NXP ecosystem.     3 Overview The importance of this subsystem lies not only in its functional role of simulating the vehicle and linking it to a physical zonal architecture, but also in how it demonstrates an efficient model-based workflow. Rather than building separate assets for vehicle behavior, driver interaction, visualization, and hardware communication, the workflow starts from a configurable virtual vehicle model that can be tested, extended, and connected to other parts of the system. The Virtual Vehicle Composer is a MathWorks tool that enables you to create a Simulink vehicle model for system-level testing, software integration testing, and driver-in-the-loop workflows. The generated model can simulate key vehicle functions such as powertrain, steering, braking, and overall vehicle dynamics. Powertrain Blockset and Vehicle Dynamics Blockset provide reference applications and component models that help define and simulate vehicle behavior in more detail. Simulink 3D Animation supports visualization and interaction with 3D environments, helping connect the vehicle model to a more realistic driving experience. This accelerates development in several ways: The vehicle can be configured and built from a structured workflow rather than assembled manually from scratch. The same model can support simulation, software integration, and connection to external hardware. The built-in 3D interface with Unreal Engine helps connect the vehicle behavior to a realistic visual environment. RoadRunner scenes and scenarios can be incorporated into the simulation workflow to create interactive driving scenarios. CAN communication and feedback from the physical setup can be integrated into the Simulink-based system model. The same workflow can be extended to support additional sensing paths, such as radar data generation and off-board processing on NXP radar hardware. This series is intended for: Engineers learning Model-Based Design with MATLAB and Simulink Developers working with NXP automotive processors and microcontrollers Teams building virtual validation and hardware-connected automotive demonstrations Engineers interested in Driver-in-the-Loop workflows Students and researchers studying vehicle architectures, simulation, and embedded integration Anyone interested in a reproducible example of simulation-to-hardware integration using MathWorks tools and NXP platforms Readers will gain a clearer, step-by-step understanding of how a virtual vehicle can be created, integrated into a 3D driving scene, connected to a physical zonal platform, and used as part of a broader model-based development workflow.     4 Context Created with the Virtual Vehicle Composer, the Simulink hybrid electric vehicle (HEV) model is used not only for standalone simulation, but is reused as the common integration point for driver inputs, RoadRunner-based scene interaction, including actor scenarios implemented in RoadRunner, Unreal Engine visualization, CAN communication, and closed-loop feedback from the physical setup. Figure 2. Virtual vehicle system model In the implemented setup, a driver controls the virtual vehicle through an Xbox-compatible steering wheel and pedals. These inputs are processed by the Simulink model, which updates the vehicle behavior inside a RoadRunner scene rendered through Unreal Engine. At the same time, the virtual vehicle sends key signals such as speed, steering, braking, turn indicators, hazard lights, and beam light commands over CAN to a physical setup that represents an electric vehicle built from multiple NXP reference boards organized in a zonal architecture. The physical platform includes a main node, zonal nodes, and multiple end nodes. These elements receive the simulation-driven commands and reproduce the state of the virtual vehicle in hardware. Communication is bidirectional, so feedback generated by the physical setup can also influence the simulated vehicle. For example, if front or rear parking sensors detect an obstacle, that information can be returned to the virtual vehicle model and used to trigger braking behavior. All major functional aspects of this interaction, including driver input handling, vehicle behavior, signal exchange, and feedback response, are defined in the Simulink model. This supports rapid refinement and validation before deeper integration into the full system. An additional part of the setup extends the virtual vehicle interaction toward sensing and perception workflows. Actor poses from the virtual scene are used to generate a radar cube, which is sent to an NXP S32R45 board that runs a radar processing chain. This expands the role of the virtual vehicle beyond motion and body-domain interaction. It shows how the simulated environment can also stimulate external sensing functions and hardware processing paths as part of the same demo workflow. Figure 3. Virtual Vehicle highlighted within the demo The virtual vehicle component is highlighted in the architecture diagram from Figure 3 to show its position in the overall project setup and its connection to the driver interface, the 3D environment, the physical zonal platform, and the radar processing path. The next articles in the series will build on this system overview and examine the virtual vehicle in more detail, including the software and hardware environment, the model architecture, the vehicle creation workflow, the driver input options, RoadRunner and Unreal integration, CAN communication, and the final results and challenges observed during development.     5 References The following resources provide useful background for the technologies referenced in this article: MathWorks documentation for Virtual Vehicle Composer MathWorks virtual vehicle documentation and examples MathWorks RoadRunner documentation MathWorks documentation for Unreal Engine simulation with Simulink NXP Model-Based Design Toolbox overview     6 Conclusion The virtual vehicle subsystem provides the foundation for the Hello World demo by supplying a reusable vehicle model that supports simulation, validation, and integration within a model-based workflow. This article established its purpose and position in the overall architecture. In the next articles, we will move from this high-level overview to the practical details of how the subsystem is created, connected, and exercised in the complete demo.

  1 Introduction Radar (Radio Detection and Ranging) is a key sensing technology in modern vehicles, used to perceive the environment by transmitting radio waves and analyzing their reflections from surrounding objects. In automotive systems, radar enables reliable detection under a wide range of weather and lighting conditions. This article introduces the automotive radar node of our demo and explains how it can be integrated into a modern vehicle electronic architecture.   2 Table of Contents •Introduction •Overview •Context •References •Conclusion   3 Overview How radar supports automotive systems Within an automotive system, the radar node plays a central role in advanced driver assistance systems (ADAS) and automated driving functions, such as adaptive cruise control, collision avoidance, and blind-spot detection. It continuously measures object presence and motion in the vehicle’s surroundings, providing robust and real-time perception data. In this implementation, the radar application is developed using NXP’s Model-Based Design Toolbox for Radar, a MATLAB add-on developed by NXP. By using this toolbox, developers can design, simulate, and generate code while leveraging the hardware accelerators available on the target platform to achieve high performance and deterministic execution. The offloading of processing to the accelerators is achieved through the integration of the NXP Radar SDK within the MATLAB environment. Target Audience This series of articles serves a wide range of engineering and technical stakeholders involved in the design, development, and integration of radar systems. This chapter outlines the intended audience: Embedded Software Engineers Radar Engineers System Architects & Vehicle Architecture Engineers Hardware Engineers Model-Based Design and MATLAB Developers Academic and Research Communities   4 Context The radar application is targeted for the NXP S32R45 MCU, a high-performance processor designed specifically for automotive radar signal processing. In the vehicle electronic architecture, the radar node is connected directly to the Central Node, which is responsible for sensor fusion and higher-level decision-making. For each radar frame, the NXP S32R45 MCU detects and processes objects in the field of view and transmits, via CAN, the distance, speed, and direction of each detected object to the Central Node. This structured data exchange enables efficient integration of radar information into the overall vehicle perception and control system.   Figure 1. Example integration of the radar node into a vehicle electronic architecture.   5 References MathWorks Model-Based Design Toolbox for RADAR Community Accelerate the Discrete Fourier Transform with NXP SPT   6 Conclusion In conclusion, the radar node is a fundamental building block of the modern vehicle electronic architecture, providing accurate and reliable perception data that enables advanced safety and automation functions. This introductory chapter has outlined the role of the radar node within the vehicle system and its contribution to environment perception for advanced driving functions. The next chapters will build on this foundation by exploring the radar signal processing chain, the implementation approach, and the main software components that enable the application on the target platform.

    1 Table of Contents • Introduction • Overview • Context • References • Conclusion     2 Introduction Automotive lighting systems play an essential role in vehicle safety, visibility, and communication with other road users. In general, these systems can be grouped into two main categories: Front Lighting and Rear Lighting. Both help provide road illumination for the driver and signal the vehicle's actions and presence to surrounding traffic. Front Lights - General Role and Functions Front lighting improves the driver's visibility in different driving conditions, including low light, nighttime driving, and adverse weather. It includes several key functions commonly found in modern vehicles, such as: Daytime Running Lights (DRL) - increase vehicle visibility during daytime driving Turn Lights - indicate the driver's intention to change direction Head Lights - provide road illumination during nighttime or low-light conditions Fog Lights - improve visibility in fog, rain, snow, or other low-visibility situations Rear Lights - General Role and Functions Rear lighting is primarily used to communicate the vehicle's status and intentions to other road users. It includes important functions such as: Stop Lights - signal braking actions Head Lights - make the vehicle visible from behind Turn Lights - indicate the intended direction of travel Fog Lights - improve vehicle visibility in low-visibility conditions     3 Overview The lighting system presented in this article is developed using a Model-Based Design (MBD) approach. This methodology enables early validation of system behavior, systematic refinement of the control logic, and a direct path from simulation to embedded implementation. The control behavior is modeled in MATLAB/Simulink, where the functionality is structured into modular and reusable components. Stateflow is used to describe the control logic, providing a clear and formal representation of operating modes, state transitions, and event-driven behavior. The Simulink model runs on the NXP S32K3 platform and communicates with other vehicle nodes via CAN Bus. Message reception and signal handling are managed using the Vehicle Network Toolbox, which simplifies CAN communication by utilizing DBC files without introducing additional hand-written interface code. This integration supports a smooth transition from simulation to embedded deployment through automatic code generation, minimizing the risk of discrepancies between modeled behavior and deployed software. Target audience: Engineers interested in Model-Based Design for automotive applications Those learning or experimenting with simulation-based development and control logic Anyone using NXP automotive hardware platforms who wants to faster develop complex applications on real embedded systems Figure 2 - Front Hazard Lights Activated     4 Context In this project, separate models are implemented for front and rear lighting to showcase the physical layout of the car and keep the logic simple and easier to test. Each lighting area handles its own functions, while staying synchronized with overall vehicle behavior through standard vehicle communication.   Figure 1 - Front and Rear Lights System highlighted within the EV architecture All lighting commands are received via the CAN bus, ensuring consistent and predictable behavior for functions such as Daytime Running Lights (DRL), Head Lights, Fog Lights, Turn Indicators, and Stop Lights. Using CAN-based commands reflects standard vehicle communication practices and allows the lighting logic to be evaluated under conditions close to those in a production system. Incoming CAN messages are processed by the lighting module. Based on vehicle states and received commands, the module: interprets CAN signals and system status, prioritizes lighting functions and handles fault-related conditions, turns on the lights. This structure keeps responsibilities clear: the CAN layer provides high-level commands, while the lighting control logic handles decision-making and execution. The result is a deterministic and easy-to-follow path from vehicle-level inputs to visible lighting behavior. In our project, the system uses addressable LEDs, allowing individual control of multiple light segments within each lamp. This enables a realistic representation of modern automotive lighting systems, where lighting units are no longer simple on/off devices but consist of multiple independently controlled segments. Addressable LEDs rely on a dedicated communication protocol to transfer control data such as color, brightness, and activation timing to each individual LED element. To simplify the integration of this protocol and ensure deterministic behavior, the LED communication was configured and integrated using NXP's Model-Based Design workflow. This approach allows the LED control logic and communication timing to be defined, simulated, and validated directly at model level. The system behavior can be easily followed from input to output, since each step is clearly defined. CAN messages trigger specific actions, and the result is directly visible in the LEDs. This makes the logic straightforward to understand and verify.     5 References Model-Based Design Toolbox (MBDT) Community Model-Based Design Toolbox (MBDT) - S32K3 - How To     6 Conclusion This article provides a simple overview of how Model-Based Design can be applied to develop an automotive lighting system using NXP hardware, focusing on the general architecture and design approach. In the following articles, we will explain the configuration, implementation, and deployment of the lighting system on the NXP hardware.

1 Table of Contents • Introduction • Overview • Context • References • Conclusion 2 Introduction The steering system is an essential and safety-critical component of any vehicle, responsible for controlling the direction of wheel movement and guiding the vehicle along the intended path. In our Hello World with MBDT project, the Steering subsystem delivers this capability by driving a steering motor to a desired angle and direction, transmitting the resulting torque to the road wheels through the steering column and rack-and-pinion assembly. Figure 1. Hello World with MBDT Demo – Steering system This article series presents the Electric Power Steering (EPS) system in Electric Vehicle (EV) architecture and covers the hardware, software, code generation, and vehicle network integration needed to implement the system using a Model-Based Design (MBD) workflow with MathWorks tools and NXP hardware. 3 Overview 2.1. What will this series of articles cover? The articles in this series will present the Steering System within an EV architecture and cover the following topics: Software and Hardware Environment Overview of the MathWorks and NXP tools used to develop, test, and validate the EPS control system. Logic Control Description of the model architecture, signal interfaces, and core control algorithms implemented in the Steering System. Deployment on Real Hardware Integration with physical hardware, the stepper motor, and configuration of the NXP MCU peripherals required for motor control. CAN Integration Definition of the CAN communication interface, including database design and integration on the target NXP platform. System Validation Presentation of the final implementation results and validation of the complete system behavior. 2.2. What is the Electric Power Steering System? Electric Power Steering (EPS) eliminates the hydraulic pump found in conventional steering systems, instead relying on an electric motor driven by an Electronic Control Unit (ECU). Torque and position sensors mounted on the steering column feed real-time measurements to the ECU, which computes the required assist level and commands the motor accordingly. This on-demand assist approach improves energy efficiency, enables precise tuning of steering feel, and provides a programmable interface for Advanced Driver Assistance Systems (ADAS). Figure 2. Electric Steering Rack and Pinion EPS systems are classified based on where the electric motor is mounted on the steering mechanism. Column Assist Type (C-EPS) - The electric motor and control unit are mounted directly on the steering column inside the cabin. Pinion Assist Type (P-EPS) - The electric motor is attached to the pinion shaft within the steering gear box. Dual-Pinion Assist Type (DP-EPS) - This system separates the assist function from the steering mechanism. One pinion gear connects the steering wheel, while the electric motor applies assistance to a second, separate pinion gear directly on the steering rack. Rack Assist Type (R-EPS) - The electric motor is mounted directly onto the main steering rack, either via a concentric motor around the rack or a belt drive. Steer-by-Wire (SbW) - The mechanical connection (steering column and intermediate shaft) between the steering wheel and the wheels is entirely removed. Key Characteristics of Steer-by-Wire EPS: The wheel's movement is handled completely by electronic sensors, algorithms, and actuators It allows for completely customizable steering ratios Frees up interior cabin space Relies heavily on redundant electronics and fail-safes 2.3. Target Audience This series is intended for engineers and technical stakeholders involved in the development, integration, and evaluation of electric power steering systems, including the following audiences: Mechanical and Embedded Software Engineers Motor Control & Power Electronics Engineers System Architects & Vehicle Architecture Engineers Model-Based Design and Simulink Developers Academic and Research Communities 4 Context In the example vehicle architecture used throughout this series, the Steering System is located in the front zone of the vehicle. The Steering ECU is built around the NXP S32K312 microcontroller, which provides both CAN and LIN connectivity. Note: The NXP S32K312 microcontroller provides the processing performance, peripheral set, and communication interfaces (CAN, LIN) required for automotive steering control applications. The ECU drives the stepper motor to the commanded position and communicates desired angle and direction requests over CAN to the Zonal Controller, which coordinates these signals with the central vehicle control node. 5 References Steering column - Wikipedia Power steering - Wikipedia Electric Power Steering (EPS) System Parts Solutions | NXP Semiconductors Electric power steering system (EPS) Clemson Vehicular Electronics Laboratory: Electric Power-Assisted Steering Electric Steering Rack and Pinion 6 Conclusion This article introduced the Electric Power Steering system architecture, its core components, and its position within a modern EV platform. It outlined the Model-Based Design approach using MATLAB/Simulink and NXP hardware as the development foundation, from algorithm modeling through automatic code generation and hardware deployment. The next article will focus on the software and hardware environment required to develop, simulate, and deploy the EPS control system using MathWorks and NXP solutions.

      1 Table of Contents • Introduction • Overview • Context • References • Conclusion     2 Introduction This article series explains the role and behavior of a zonal controller communication component in a modern automotive electrical/electronic (E/E) architecture. This first article provides a short, high-level introduction to the zonal node and establishes a common understanding of its main responsibilities. The series gradually explains how this component enables message exchange between in-vehicle communication networks, with a particular focus on routed and broadcast communication over CAN and LIN. Later articles move from these concepts into more detailed design and implementation topics. As the entry point to the Zonal Communication and Control series, this article focuses on the zonal node from an architectural perspective. It does not cover system-level use cases or application-specific configurations, which are addressed in later articles.     3 Overview This article introduces an S32K3-based zonal node and explains how it connects to several in-vehicle networks. In practice, the zonal node sits between central vehicle controllers and local devices such as sensors, actuators, or small control modules, helping messages move between them. The zonal node receives messages from the central controller and forwards them to local nodes, while also sending status information and responses back to the central side. Depending on the system design, it can distribute the same message to multiple nodes or route specific messages only to the intended recipients. In addition to message forwarding, the zonal node may perform limited local processing, such as message filtering, signal aggregation, data validation, or basic decision-making related to communication handling. However, higher-level functional decisions are typically managed by central controllers, with the zonal node focusing primarily on efficient and reliable data exchange. This role becomes clearer in the context of evolving automotive E/E architectures. Traditional designs relied on many purpose-specific electronic control units (ECUs) connected through dedicated wiring. As system complexity increased, that approach added wiring weight, raised cost, and limited scalability. Figure 1. Zonal controller highlighted within the EV architecture Zonal architectures address these limitations by grouping nearby functions within the same physical area of the vehicle and moving more processing into central computing units. In this model, the zonal controller manages local communication and forwards relevant information to the central system. In this context, the S32K3 MCU family supports the required functionality by providing automotive communication interfaces such as CAN FD and LIN. On devices that include the necessary interfaces, the zonal node can connect different network types and handle message traffic between them. Within the scope of this project, the S32K3 platform is suitable for implementing the zonal node due to its available communication peripherals, processing capability, and automotive safety features, which are sufficient for the number of connected nodes and the complexity of the communication tasks considered. This article is intended for: System architects evaluating zonal or domain-based vehicle designs Embedded software engineers implementing communication routing logic Engineers evaluating MCU platforms for multi-network automotive applications By reading this series, you will understand why zonal communication components matter, how they fit into modern vehicle architectures, and how the S32K3 platform can support this role.     4 Context In a complete vehicle system, the zonal node sits between the central control system and local hardware. Its main job is to pass, route, or translate messages, not to make application-level decisions. Keeping these roles separate helps the system remain predictable, reliable, and easier to scale. The zonal node may receive messages from central controllers that manage vehicle-wide functions or from local devices such as sensors, actuators, and smaller control modules. It then exchanges this information across different networks in a controlled and time-aware way. Note: CAN and LIN remain important because they are widely used in automotive systems and are well suited to many control tasks. The S32K3 family supports these needs with integrated CAN FD and LIN interfaces and Arm® Cortex®-M7 CPU cores for routing and control tasks. It also includes automotive safety features aligned with ISO 26262 and low-power modes that are useful in some system designs. Together, these features allow the zonal node to handle several communication channels at the same time while keeping the network interfaces clearly separated. High-Level Architecture Diagram Figure 2. Diagram concept for S32K3 Zonal Node Figure 2 shows where the zonal node sits in the system: between the central control side and the local edge nodes, acting as the bridge between networks. Later articles will expand this context in a structured way. The series will first present the overall system, then describe the software and hardware environment that supports the zonal node. It will also cover internal control logic and key communication topics such as CAN-to-CAN routing, LIN-to-CAN routing, and Ethernet-to-CAN communication. Finally, it will discuss common challenges in multi-network routing and zonal integration.     5 References NXP Body Domain and Zonal Controller S32K3 for Zonal Aggregator     6 Conclusion This article provided a high-level introduction to the S32K3-based zonal node as a communication component in modern automotive architectures. It explained what the node does and where it fits in the system, creating a basis for the more detailed topics covered later in the series. Instead of focusing on implementation details, this introductory article explained why zonal nodes are needed and which problems they help address. The next articles in the series will build on this foundation by exploring system structure, configuration, communication routing strategies, and design challenges in greater detail.

    Table of Contents Why embedded development needs a better workflow What Model-Based Design is A simple mental model: from idea to executable model to hardware Why engineers use it: the core advantages Verification along the way: MIL, SIL, PIL, HIL How NXP enables this with Model-Based Design Toolbox (MBDT) What comes next in this article series     1 Why embedded development needs a better workflow Modern embedded systems are no longer isolated functions running on a single controller. In today's vehicles and intelligent machines, applications span sensing, communication, control, safety logic, diagnostics, and multiple processing nodes that must work together as one system. As this complexity grows, traditional workflows based mainly on handwritten code and late-stage hardware testing become difficult to scale, hard to validate early in the development cycle, and slow to iterate. Issues are often discovered late, when integration becomes more costly and harder to manage. Model-Based Design offers an alternative approach designed to address these challenges. It enables earlier validation and a more structured development flow, where verification is not an afterthought, but part of every stage of development.     2 What Model-Based Design is   Model-Based Design is a visual way of programming, where you build your functionality by drawing an engineering diagram, and that diagram can be executed—either as a simulation on your computer or as code running on real hardware. In this approach, models become the central engineering artifact used to design, simulate, verify, and deploy embedded systems. Instead of starting from low-level implementation details, engineers create an executable model of the application behavior, simulate, verify, refine it, and then generate code for the target system. This model-centric workflow makes designs easier to understand, easier to reuse, and less prone to errors. It also enables model-based testing, where test cases can be derived directly from system models and used to verify behavior early in development.     3 A simple mental model: from idea to executable model to hardware A simple way to think about Model-Based Design is this: you describe what the system should do in an executable model, validate that behavior in simulation, and then carry the same design through to the final implementation. In this approach, the model is not just documentation—it becomes an active engineering asset used for design, simulation, verification, and code generation. This creates a direct path from idea to application, where requirements, design, prototyping, testing, and deployment are connected in one continuous workflow.     4 Why engineers use it: the core advantages One of the biggest advantages of Model-Based Design is that it changes where engineering effort is spent. Instead of focusing primarily on how to implement functionality at a low level, engineers can focus on what the system should do—its behavior, control strategy, and response to real-world scenarios. This approach also enables early validation. System behavior can be simulated on a PC before the final hardware is available, allowing issues to be detected earlier and reducing costly rework late in the development cycle. In addition, Model-Based Design enables hardware-independent simulation, where algorithms can be developed and validated before being tied to a specific target platform. This allows teams to explore designs faster and reuse validated functionality across different hardware solutions. As a result, teams benefit from: faster iteration during development improved traceability between design and implementation reduced integration risk more consistent validation across development stages Ultimately, this contributes directly to faster time-to-market, as development cycles are shortened and fewer late-stage issues need to be addressed. Some concrete examples can be found in the following articles: From Virtual Vehicle to All-Electric Off-Road UTV in Less Than a Year Dyson Accelerates New Product Development with System-Level Simulation     5 Verification along the way: MIL, SIL, PIL, HIL A key strength of Model-Based Design is that validation happens continuously throughout development. This is typically organized into several stages: Model-in-the-Loop (MIL): the model is tested against a simulated environment Software-in-the-Loop (SIL): generated code is executed on the host PC and compared to model behavior Processor-in-the-Loop (PIL): code runs on the target MCU to verify functional correctness and performance Hardware-in-the-Loop (HIL): the controller is tested against a real-time or emulated system before final deployment These stages provide a structured validation path, ensuring that issues are detected early and confidence is built progressively before running on final hardware. Model-Based Design also supports reuse and scalability. A validated model can be adapted, parameterized, or reused across multiple systems, reducing development effort and improving consistency.     6 How NXP enables this with Model-Based Design Toolbox (MBDT) To make this workflow practical on real embedded hardware, NXP provides the Model-Based Design Toolbox (or MBDT). This acts as a bridge between the MathWorks' and NXP's software ecosystems, and allows the entire workflow to be done from one environment, as depicted in the diagram above. Concretely, this allows engineers to use MATLAB and Simulink to design, simulate, verify, and automatically generate code that can run directly on NXP microcontrollers and processors. MBDT provides: block libraries for hardware access integration with configuration tools for pins, clocks, and peripherals support for PIL workflows code generation and deployment capabilities profiling and runtime monitoring through tools like FreeMASTER This creates a complete end-to-end flow—from model to validated application running on target hardware. Engineers can explore functionality at a high level, validate behavior through simulation, and deploy with confidence onto real systems.     7 What comes next in this article series In the articles that follow, we will move from this general introduction to concrete, real application examples. We will show how Model-Based Design and NXP tools can be applied across a modern system architecture, covering applications such as battery management, motor control, radar, steering, lighting, and parking sensors. Each example will illustrate how functions can be designed, validated in simulation, and deployed onto the appropriate hardware nodes. The key idea is simple: Model-Based Design helps engineers focus on system behavior while reducing the gap between concept, implementation, and validation. With NXP's Model-Based Design Toolbox, this approach can be carried from the modeling environment all the way to a running application on hardware. MBDT  https://www.nxp.com/mbdt https://mathworks.com/nxp 

  1 Every great build starts with "Hello World" Every engineer remembers their first “Hello World” — that small, satisfying moment when an idea typed on a screen suddenly comes to life on a real machine. This series is a take on that same feeling, only this time the “machine” is a car. It’s a demonstrator that looks and behaves like a real vehicle, showcasing the combined use of tools from both the NXP and MathWorks ecosystems. This demo has been showcased at several events, including most recently at the MathWorks booth during Embedded World 2026 and MathWorks Automotive Conference, where the demo video that accompanies this series was filmed. Think of these articles as a guided tour through how the whole thing comes together, piece by piece. ▶ Watch the demo in action — presented at the MathWorks booth, Embedded World 2026 2 Table of Contents • Every great build starts with Hello World • From a model on a laptop to silicon on the bench • From the steering wheel to every node • And it grew up along the way • Built to be rebuilt — and learned from • A demonstrator, not a blueprint • The article series — one domain at a time 3 From a model on a laptop to silicon on the bench How does a car end up running on NXP silicon, starting from a model on a laptop? That’s where the NXP Model-Based Design Toolbox (MBDT) comes in. It acts as the bridge between the MathWorks ecosystem — Simulink and MATLAB — and NXP’s processors and embedded tools. An application is designed and modeled in Simulink, MBDT generates optimized code for the chosen NXP target, and that code is deployed straight onto the hardware. The main advantage of this approach is what it allows before any board is involved: an application can be validated and tuned in simulation first, and hardware that isn’t physically present can simply be simulated in its place. The results: early issue detection, shorter development cycles, and a faster time to market — backed by a toolchain that has been validated end to end. Figure 1. NXP Model-Based Design Toolbox One Pager 4 From the steering wheel to every node At the heart of the demo is a driver-in-the-loop setup: a physical steering wheel and a set of foot pedals feed signals directly into the simulation, where a virtual car is driven in simulation, into an environment developed through a RoadRunner simulated environment. From there, a clear hierarchy carries every input down to the hardware. The main node — an S32N processor — sits at the center: it communicates with the host PC running the simulation and makes the vehicle-level decisions. It then hands those decisions to a zonal node that acts as a gateway, fanning the signals out to the end nodes that handle each function — the front and rear lights, the front and rear parking sensors, the radar, and the steering rack, and, on the traction side, the battery management system and motor control. The effect is immediate and physical: steering and acceleration in the virtual world set the model on the table moving; shifting into reverse spins the motors up in the right direction; and when an obstacle appears behind the physical car, it stops on its own, with the rear lights turning red across every node — just like a production vehicle. Throughout, a live dashboard built with NXP’s FreeMASTER Lite shows the vehicle state as it happens, from the reverse camera to the parking sensors, blending signals from the virtual world with readings from the physical hardware. Figure 2. Demo architecture — main node (S32N), zonal gateway, and end nodes. 5 And it grew up along the way Behind all of these are the core functions of a real car — lighting, parking sensors, steering rack, motor control, and battery management — spread across roughly ten microcontrollers and processors and sixteen NXP evaluation boards and reference designs. There’s no need to unpack every component here, because each one earns its own dedicated article series later on. What’s worth knowing is how it all grew: this didn’t start as today’s car. It began as a battery management system (BMS), then gained cloud connectivity, then motor control — which evolved into a full traction inverter demo — and from there the remaining vehicle domains, from body and lighting to chassis and parking, were layered on one by one until it became a complete vehicle topology. In other words, existing MathWorks and model-based examples were assembled, domain by domain, into a car. 6 Built to be rebuilt — and learn from Why go to all this trouble? Mostly to document the work, share the thinking behind it, and show how to actually use MBDT. A big part of the appeal is that everything runs on NXP evaluation boards, which means the whole thing can be reproduced. There’s no need to redo a complex custom hardware design before starting; the same boards can be picked up to get going right away. That also makes the demo a hands-on learning platform: a place to explore the model-based workflow by doing one domain at a time. Note: A word on scope — this is a proof of concept that demonstrates the development workflow, not production firmware as it stands today. A great path forward is NXP’s CoreRide, which you can read more about on this page: Software-Defined Vehicle Development: NXP CoreRide Platform — but that part will not be covered in this series. Whether the field is automotive, electrification, industrial automation, or robotics — or simply an interest in model-based development — there should be something here worth taking away. 7 A demonstrator, not a blueprint One last note on how to read all of this. This car is a demonstrator, not a reference design. It was built with the hardware that happened to be on hand, so some of the boards and NXP solutions used aren’t necessarily the optimal fit for a given function — for a specific job, a different microcontroller might serve better. The point was never to say “use exactly these parts.” The point is the steps and the approach: the workflow itself, and how the pieces fit together. With that in mind, the articles below each take a part of this build and show how it’s done. Welcome to “Hello World” with the Model-Based Design Toolbox. 8 The article series — one domain at a time Each part of the demo car gets its own dedicated write-up, grouped into the twelve tracks below. As articles go live, the placeholders will be replaced with links. Bookmark this page — it will keep growing. NXP MBDT — How-To & Introduction What is Model-Based Design Toolbox? How to install Model-Based Design Toolbox? MBDT Setup and How-to run an application Develop an MBDT application workflow Create a new model and configure it for NXP Hardware Create a new configuration project using the S32CT How to MBDT Dio Port/Pins FreeMASTER & FreeMASTER Lite Introduction to FreeMASTER Using FreeMASTER block in Simulink Visualize and control variables in FreeMASTER Create web dashboard with FreeMASTER Lite Parking sensors Overview SW & HW Environment Logic Control (Main model overview) Lights Overview SW & HW Environment Logic Control (Main model overview) Motor Control Overview SW & HW Environment Logic Control (Main model overview) Battery Management Systems Overview SW & HW Environment Logic Control (Main model overview) Steering Overview SW & HW Environment Logic Control (Main model overview) Radar Overview SW & HW Environment Processing Chain - NXP Radar SDK Main Node Overview SW & HW Environment Logic Control (Main model overview) Zone Node Overview SW & HW Environment Logic Control (Main model overview) Software & Integration Creating virtual vehicle with MathWorks Overview SW & HW Environment Logic Control Creating Virtual Scenes & Scenarios with MathWorks (RoadRunner & Unreal Engine)  Processor-in-the-Loop (PIL) What is next? Export to & Debug generated code to S32 Design Studio IDE Others Getting Started with FRDM-A-S32K312 using Model-Based Design  Note: This index is updated as new articles are published.

  1 Table of Contents • Introduction • Overview • Context • References • Conclusion     2 Introduction This article presents an automotive system built around a central computer that processes high volumes of data to manage interactions and decisions across the vehicle. Implemented on an NXP S32N55 board, a main node orchestrates peripheral nodes — Lighting, Motor Control, Steering, Radar, and Parking Sensors — over CAN, demonstrated through real-time interactions and Driver-in-the-Loop (DiL) simulations. The same architecture also enables stimuli and scenarios to be injected directly from Simulink/MATLAB via the Model-Based Design Toolbox (MBDT), turning the setup into both a functional prototype and a flexible test bench that shortens the loop between design, validation, and refinement.     3 Overview The communication hub acts as a comprehensive aggregator and decision-maker, serving as the central intelligence of the entire automotive control network. This architectural choice follows industry's best practices by consolidating critical decision-making processes into a single, robust processing unit capable of efficiently managing multiple concurrent data streams and executing time-sensitive commands. Centralizing this logic also simplifies maintenance and traceability, since the rules governing vehicle behavior live in one well-defined place rather than being scattered across multiple ECUs. For a project of this nature, the NXP Model-Based Design Toolbox (MBDT) offers a practical development path: control logic and application behavior can be designed in Simulink/MATLAB and deployed directly onto the S32N55, without a separate hand-coding step. The graphical, model-based workflow makes the system's structure easier to follow and adjust, while built-in support for CAN communication and integration with tools like FreeMASTER for live telemetry simplify both stimulus injection and runtime observation. The result is a smoother path from initial concept to a working prototype that can be iterated on and validated in a controlled, repeatable way. In this specific implementation, the main node hosts an application that fulfills two complementary roles: data aggregator and decision-maker. As an aggregator, it collects, synchronizes, and interprets incoming signals from the sensing nodes; as a decision-maker, it translates that fused view of the environment into concrete commands for the actuators. Practically, our system receives data over CAN from the peripheral sensing nodes (Radar, Parking Sensors) and dispatches commands to the actuator nodes (Motor Control, Lights, Steering). The main node is also designed to make safety-critical decisions based on the incoming inputs — for example, triggering Automated Emergency Braking (AEB) when the Parking Node or the Radar Node detects a hazardous situation. Because these decisions are made centrally, the response logic can take the full context into account (vehicle speed, proximity of obstacles, current steering input) rather than reacting to a single sensor in isolation.     4 Context At its core, the main node receives a continuous stream of data over the CAN bus from peripheral nodes distributed throughout the vehicle. These peripheral nodes include: Radar sensors — provide long-range object detection and relative velocity measurements, making them ideal for highway-speed scenarios and forward collision awareness. Parking sensors — monitor the immediate vicinity of the vehicle for obstacles and potential collision risks, typically at very short range and at low speeds. Fault sensors — for actuator nodes, like the motor control, steering and lighting systems. The CAN bus protocol guarantees the reliable, deterministic communication required to meet the stringent timing demands of automotive safety systems. Its built-in arbitration, error detection, and message prioritization make it a natural fit for a distributed architecture in which safety-relevant signals must always reach the main node within a bounded time window. To streamline communication across components, a CAN Database ( DBC ) file has been created that contains all the signals and messages used throughout the system. The DBC file acts as a single source of truth for the entire network: every node — whether sensing or actuating — references the same definitions for message IDs, signal layouts, scaling factors, and value ranges. This drastically reduces the risk of integration mismatches when multiple boards are developed in parallel. Beyond its data aggregation role, the main node also serves as the command center for the vehicle's actuator systems. After receiving data from the simulation, it is being processed and then it transmits precisely timed control signals to critical subsystems, including the motor control unit, lighting system, and steering mechanism. This bidirectional architecture enables closed-loop control strategies, in which sensor feedback continuously informs actuator commands to achieve the desired vehicle behavior. Each actuator node remains responsible for the low-level handling of its hardware, while the main node provides the high-level command to the actuators. Since the main node is responsible for receiving, analyzing, processing and sending data, it also becomes the one responsible for sharing the telemetry information upstream, either to the cloud, or to real time monitoring tools like FreeMASTER. A particularly valuable aspect of this system is its seamless integration with the Simulink/MATLAB environment, which unlocks extensive possibilities for system validation and scenario testing. Engineers can inject stimuli into the simulation and analyze a wide range of driving conditions and edge cases without requiring a full-scale prototype. This is especially useful for reproducing rare or dangerous situations — such as sudden obstacles or sensor faults — in a fully controlled and repeatable environment. To achieve two-way communication between the main node and the simulation, the CAN bus itself is used to communicate with the Simulink model. This way, the physical prototype can feed stimuli into the simulation — and vice versa — on the same CAN bus that devices are using to communicate, significantly expanding the boundaries of the testing environment. The same DBC file that defines the on-vehicle communication is reused on the simulation side, ensuring that the messages exchanged between the real and virtual worlds remain perfectly consistent.   Note: Perhaps one of the most noteworthy features of the main node's active functions is its ability to make safety-critical decisions in real time based on aggregated sensor inputs. The system continuously monitors data from both the parking sensors and the radar node, detecting potentially dangerous situations that require immediate intervention: At low speeds — hazard detection is typically driven by the parking sensors mounted on the front and/or rear of the vehicle, where short-range, high-resolution distance measurements are most relevant. At driving speeds — the radar module takes over, collecting and analyzing data that is then forwarded to the main node for higher-level interpretation. In both scenarios, the main node remains the ultimate decision-maker, fusing all available data to determine the appropriate response. This clear separation between sensing, decision-making, and actuation keeps each component focused on a single responsibility and makes the overall system easier to reason about, extend, and validate.     5 References NXP Model-Based Design Toolbox (MBDT) Community Interacting with Digital Inputs/Outputs on MR-CANHUBK344 Communicating over the CAN Bus S32N Vehicle Super-Integration Processors     6 Conclusion This article has provided an overview of the communication hub's core functionality, offering a high-level perspective on how key systems interact within the overall architecture. The main node was presented both as a data aggregator and as a decision-maker, with a particular emphasis on its role in safety-critical scenarios and its integration with the Simulink/MATLAB environment. Future installments in this series will take a deeper dive into the communication hub — covering the specific board in use, detailed hardware and software requirements, and other technical considerations and implementation nuances. Subsequent articles will also explore individual peripheral nodes in more detail, building up a complete picture of the system one subsystem at a time.

  1 Table of Contents • Introduction • Overview • Context • References • Conclusion   2 Introduction Parking assistance systems are a familiar feature in modern vehicles, helping drivers detect nearby obstacles and maneuver the vehicle more safely. In our Hello World with MBDT project, the parking sensor subsystem provides this capability by measuring the distance to nearby objects and supplying that information to the rest of the system. Figure 1 - Physical concept This article introduces the parking sensor system and leads into the next articles in the series, where we will examine how this part of the project is developed. The Parking Sensors System (PSS) focus is set on how Model‑Based Design (MBD) enables the subsystem to be designed, simulated, tested, and deployed rapidly using MATLAB/Simulink and the NXP Model-Based Design Toolbox (MBDT).   3 Overview The role of this subsystem within the overall project describes the main elements that make up the parking sensor application and explains its purpose and behavior at a conceptual level. The article outlines how NXP's MBDT supports the development of this component and how a single model is reused for both front and rear parking modules. It also clarifies how this component fits into the larger project and how it connects to the rest of the components. The importance of this subsystem lies not only in its functional role of acquiring and processing distance information but also in how it demonstrates the efficiency of model‑based workflows. Rather than relying on traditional hand‑written embedded code, the entire application — logic, algorithms, peripheral drivers, timing behavior — can be designed graphically in Simulink. This accelerates development in several ways: Behavior can be simulated on the PC, without flashing hardware. The same model drives both simulation and embedded implementation. Peripheral interactions like Analog‑to‑Digital Converter (ADC) and Local Interconnect Network (LIN) are handled through dedicated blocks, not hand‑written code. Parameter tuning and validation are simplified through FreeMASTER, providing real-time visualization of the embedded system parameters. This accelerates development and ensures that the final embedded behavior matches the tested model. Developing an embedded sensor node application typically involves writing extensive low‑level code, configuring peripherals manually, and iterating slowly through hardware tests. This slows down development, limits experimentation, and creates fragmentation between design and implementation. The parking sensor subsystem demonstrates how Model-Based Design in Simulink solves this problem by enabling the entire feature to be built directly in Simulink. Engineers can model ADC acquisition, LIN communication, filtering logic, and threshold detection using graphical blocks rather than manual code. They can simulate the behavior instantly, refine algorithms quickly, and deploy the design to the microcontroller through automatic code generation. The MBD approach significantly improves the efficiency and reliability of developing, testing, and refining the complete parking sensor application. This series is intended for: Engineers learning Model‑Based Design with MATLAB/Simulink Developers working with NXP automotive microcontrollers Teams building rapid prototypes of embedded measurement and control features Students and researchers studying vehicle architectures Anyone interested in a full, reproducible example of embedded system development using MBDT Readers will gain a clear, step‑by‑step understanding of how a complete embedded feature is designed and implemented using a unified model‑based workflow.   4 Context A key aspect of the design is that the same PSS application developed in Simulink is used for both front and rear parking. Two separate S32K144 boards run the identical autogenerated code — one at the front of the vehicle and one at the rear. This showcases one of the major advantages of MBD: a single validated model can be scaled, cloned, and reused across multiple hardware nodes with minimal parametrization. Figure 2 - Parking System Architecture The purpose of the parking sensor subsystem is to provide a clean, consistent, and rapidly developed interface that delivers accurate distance information to the rest of the system. In the implemented setup, each ultrasonic sensor outputs an analog voltage proportional to distance. This signal is sampled by the ADC (Analog‑to‑Digital Converter) of the S32K144 microcontroller. The embedded application running on the S32K144 performs the acquisition sequence, processes the ADC values to compute distance measurements, and formats the results into a communication frame. The prepared data is then transmitted over the LIN bus to the zonal controller, where it can be further used by higher‑level vehicle functions. All functional aspects — ADC acquisition configuration, signal processing, communication formatting, and diagnostic handling — are defined directly in the Simulink model, enabling rapid refinement and immediate validation through simulation. During development, FreeMASTER is used to monitor live ADC samples from the ultrasonic sensors, observe processed distance values, and validate the behavior of the embedded application before integrating the component into the full system. The parking sensor component (front and rear) is highlighted to show its position in the project setup: Figure 3 - Parking System highlighted within the project Related articles in the series Note: Additional articles in the series, including topics such as Software & Hardware Environment, Architecture & Model Description, Deploy & Validate on Hardware, Final Results and Challenges, will be added here as they become available. Each will explore individual technical details such as ADC acquisition, model structure, filtering logic, and communication behavior introduced in this overview.   5 References Software & Hardware Environment for Parking Sensor System MathWorks Model-Based Design Toolbox for S32K Community Model-Based Design Toolbox for S32K How To NXP Support Package for S32K1xx NXP Model-Based Design Toolbox for S32K1 Toolbox Download These resources provide deeper insight into the tools and methods used to build the subsystem.   6 Conclusion The parking sensor subsystem demonstrates how Model-Based Design accelerates the development of embedded automotive features. By modeling the sensing logic in Simulink, validating behavior through simulation, downloading it automatically using MBDT and monitoring it on hardware with FreeMASTER, the entire application can be developed and deployed from within a single environment. Rather than duplicating the parking sensors logic, the application is implemented as a parameterized Simulink model. Using MBDT, the same model instance can be configured for the front or rear module by adjusting parameters such as communication identifiers. This approach enables consistent behavior across parking modules while minimizing duplication and simplifying maintenance. This article introduced the component's behavior, purpose, and development workflow. The next articles in the series will expand on specific technical aspects, building a complete understanding of the subsystem from model to deployment.

  1 Introduction This article series presents the Motor Control System (MCS) within an electric vehicle (EV) architecture. It introduces the end-to-end development flow, from controller and plant modeling to simulation, code generation, hardware deployment, and integration with the rest of the vehicle network. This opening article establishes the technical foundation for a series focused on the architecture, implementation, and integration of a dual-motor control system for EV traction applications. The series also shows how MathWorks tools can be used together with NXP software and hardware to support a Model-Based Design workflow. This approach helps engineers develop, verify, and deploy motor control applications more efficiently while maintaining traceability across the development cycle. Figure 1-1. Role of the Motor Control System within the EV traction domain     2 Table of Contents • Introduction • Overview • Context • References • Conclusion     3 Overview 3.1. What will this series of articles cover? The articles in this series define the development roadmap for the Motor Control System within a broader EV architecture. The series covers the following topics: Software and Hardware Environment - Overview of the MathWorks and NXP tools used to develop, test, and validate a dual-motor control system. Architecture and Model Description - Description of the model architecture, signal interfaces, and core control algorithms implemented in the Motor Control System. Model-in-the-Loop Development - Simulation of the controller and plant in Simulink to validate algorithms before code generation. Software-in-the-Loop Validation - Code generation for the validated controller and comparison of the generated software against the Model-in-the-Loop baseline. Processor-in-the-Loop Validation - Execution of the controller on NXP hardware while the plant remains simulated on the host system. Deployment and Validation on Real Hardware - Integration with physical hardware, scaling from single-motor to dual-motor operation, and configuration of the NXP MCU peripherals required for motor control. CAN Integration - Definition of the CAN communication interface, including database design and integration on the target NXP platform. Results and System Validation - Presentation of the final implementation results and validation of the complete system behavior. 3.2. What is the Motor Control System? Electric vehicles depend on traction systems that deliver efficient propulsion, accurate torque control, and safe operation. At the center of this functionality is the Motor Control System (MCS), which combines real-time control software, power electronics, sensing, actuation, and communication interfaces into a tightly coordinated embedded system. Figure 3-1. PMSM motor and controller as core elements of the traction system In modern EVs, the traction system delivers the torque and power needed to propel the vehicle. It is typically composed of the following elements: Electric motor - converts electrical energy from the battery into mechanical power at the wheels. Inverter system - converts DC energy from the battery into the controlled AC waveforms required by the motor. Transmission system - transfers the generated torque from the motor to the wheels. At its core, the Motor Control System regulates motor torque, speed, and position by controlling the voltage and current applied to the motor phases. A typical MCS includes the following functional layers: Control Algorithm - implements torque and current control strategies such as Field-Oriented Control (FOC). Sensing and Feedback - measures motor currents, voltages, rotor position, and temperature. Power Electronics - inverter circuitry that switches DC power into AC waveforms for motor drive. Embedded Processor - microcontroller executing real-time control loops. Communication Interfaces - CAN, LIN, or Ethernet for integration with other system modules. Together, these layers form a closed-loop control system that operates at high switching frequencies and under strict real-time constraints. Figure 3-2. Field-Oriented Control (FOC) architecture EV traction systems can be implemented using different architectures depending on the required balance of efficiency, performance, cost, and system complexity. A single-motor architecture uses one traction motor to drive either the front or rear axle. This approach reduces hardware complexity and cost, and it often improves vehicle range because of lower mass and lower overall energy consumption. A dual-motor architecture uses two independent traction machines that can be arranged in several drivetrain topologies. This configuration enables higher total power, better traction, improved vehicle dynamics, and stronger acceleration. The tradeoff is increased electrical and mechanical complexity, together with higher system cost. Figure 3-3. Example dual-motor traction architecture Advantages & Disadvantages of Dual Motor: Acceleration faster due to torque from both motors Superior traction and handling, especially in snow, rain or off-road conditions Slightly lower range due to increased weight and power consumption More expensive but can include AWD and performance benefits Advantages & Disadvantages of Single Motor: Slightly better range due to less energy consumption More affordable Moderate traction, suitable for most road conditions Slower acceleration Note: The example used throughout this series is based on a dual-motor rear-axle architecture, where each rear wheel is driven by its own motor. 3.3. Target Audience This series is intended for engineers and technical stakeholders involved in the development, integration, and evaluation of electric drive systems, including the following audiences: Embedded Software Engineers Motor Control & Power Electronics Engineers System Architects & Vehicle Architecture Engineers Hardware Engineers Model-Based Design and Simulink Developers Academic and Research Communities     4 Context In the electric vehicle architecture presented in this series, the Motor Control System is located in the rear zone of the vehicle. Each rear wheel is driven by an independent Permanent Magnet Synchronous Motor (PMSM). The Motor Control System ECU coordinates both motors and exchanges real-time data with the rest of the vehicle over the CAN network. Figure 4-1. Motor Control System highlighted within the EV architecture The traction ECU is built around NXP's S32K396 microcontroller, which supports both single 6-phase motor control and dual 3-phase motor configurations. The inverter stage is driven by the MC33937 pre-driver, which provides three high-side and three low-side FET pre-drivers for automotive motor control applications. Note: The inverter receives DC power from the vehicle battery, while battery operation and safety are supervised by the Battery Management System. The Motor Control System communicates over CAN with the Zone Node controller, which in turn exchanges commands and status information with the main vehicle control node responsible for speed and torque requests.     5 References PMSM Control Workshop BLDC Control Workshop A Model-Based Design (MBDT) Environment for Motor Control Algorithm Development Deploy Motor Control Algorithms on NXP S32K3 from Simulink Motor Control Rapid Prototyping on NXP S32M2 with MathWorks and Model-Based Design Toolbox Next Generation of NXP EV Traction Inverter with S32K39 MCU and FS26 SBC AN14326: 3-phase Motor Control Kit with S32K396 Application Note AN13884: 3-phase Sensorless PMSM Motor Control Kit with S32K344 using RTD AUTOSAR API Application Note Advancing Motor Control Performance with Digital Twins Extended Range Dual-Motor Electric Vehicle Model     6 Conclusion This article introduced the Motor Control System within an EV architecture and established the technical context for the rest of the series. It explained the role of the Motor Control System, compared single-motor and dual-motor traction topologies, and outlined how a Model-Based Design workflow can be applied using MathWorks tools together with NXP software and hardware. The next article will focus on the software and hardware environment required to develop, simulate, and deploy the Motor Control System using MathWorks and NXP solutions.