Model-Based Design Toolbox supporting Kinetis-V Series. System modeling, simulations, automatic code generation, validation, and verification MATLAB & Simulink workflows are now available on the Kinetis V microcontrollers by reusing MCUXpresso ecosystem: MCUXpresso SDK MCUXpresso Configuration Tool MCUXpresso IDE,GCC

      Product Release Announcement Automotive Processing   NXP Model-Based Design Toolbox   for S12ZVMx – version 1.4.0     Austin, Texas, USA September 9, 2020 The Automotive Processing, Model-Based Design Tools Team at NXP Semiconductors, is pleased to announce the release of the Model-Based Design Toolbox for S12ZVMx version 1.4.0. This release supports automatic code generation for S12ZVM peripherals and applications prototyping from MATLAB/Simulink for NXP S12ZVMx Automotive Microprocessors. This new release adds extended MATLAB version support (R2015a-R2020a), integrates with AMMCLib v1.1.21, is compatible with MathWorks Automotive Advisory Board checks, adds over 50 new examples and more.   FlexNet Location: https://www.nxp.com/webapp/swlicensing/sso/downloadSoftware.sp?catid=MCTB-EX   Activation link: https://www.nxp.com/webapp/swlicensing/sso/downloadSoftware.sp?catid=MCTB-EX   Technical Support: NXP Model-Based Design Toolbox for S12ZVMx issues are tracked through NXP Model-Based Design Tools Community space. https://community.nxp.com/community/mbdt   Release Content Automatic C code generation from MATLAB® for NXP S12ZVMx derivatives: S12ZVM 32/L31/16: MC9S12ZVM16 MC9S12ZVML31 MC9S12ZVM32 S12ZVML/C 128/64/32: MC9S12ZVML32 MC9S12ZVML64 MC9S12ZVMC64 MC9S12ZVML128 MC9S12ZVMC128 S12ZVMC256: MC9S12ZVMC256   Integrates the Automotive Math and Motor Control Library release 1.1.21: All functions in the Automotive Math and Motor Control Functions Library v1.1.21 are supported as blocks for simulation and embedded target code generation for: Bit Accurate Model for 16-bit fixed-point implementation Bit Accurate Model for 32-bit fixed-point implementation Bit Accurate Model for floating-point single precision implementation             Extended support for MATLAB versions We extended support for our toolbox to cover a wider range of MATLAB releases – starting from R2015a and going up to R2020a. This way we want to avoid locking out users that have constraints regarding MATLAB versions. Motor control examples We have added new motor control examples – BLDC (closed loop) and PMSM (closed loop, sensorless):   MAAB Checks (MathWorks Automotive Advisory Board) The toolbox is compatible with MathWorks Automotive Advisory Board checks – reports can be generated from Model Advisor:   Updated examples: We have added over 50 new examples, including: Motor control (both BLDC and PMSM) AMMCLib GDU (Gate Drive Unit) Profiler For more details, features and how to use the new functionalities, please refer to the Release Notes document attached.   MATLAB® Integration The NXP Model-Based Design Toolbox extends the MATLAB® and Simulink® experience by allowing customers to evaluate and use NXP’s S12ZVMx MCUs and evaluation boards solutions out-of-the-box with: NXP Support Package for S12ZVMx  Online Installer Guide Add-on allows users to install NXP solution directly from the Mathwork’s website or directly from MATLAB IDE. The Support Package provide a step-by-step guide for installation and verification. NXP Model-Based Design Toolbox for S12ZVM version 1.4.0 is fully integrated with MATLAB® environment in terms of installation: Target Audience This release (1.4.0) is intended for technology demonstration, evaluation purposes and prototyping S12ZVMx MCUs and Evaluation Boards.   Useful Resources Examples, Trainings and Support: https://community.nxp.com/community/mbdt                                                    

This article details the TPL communication setup between S32K1xx boards and MC3377xBTPL Battery Cell Controllers.  It covers both hardware and software setup for the Battery Management System models designed using Model-Based Design Toolbox for battery packs of more than 14 cells in series.  At the end of this article, the user will be able to setup the Battery Cell Controller hardware and to design a Simulink model that reads the cell and pack voltages, current, temperatures and faults status. The measured values will be displayed on the host PC using FreeMaster. 

This article details the SPI communication setup between S32K1xx boards and MC3377xBSPI Battery Cell Controllers. It covers both hardware and software setup for the Battery Management System models designed using Model-Based Design Toolbox for battery packs up to 14 cells in series.  At the end of this article, the user will be able to setup the Battery Cell Controller hardware and to design a Simulink model that reads the cell and pack voltages, current, temperatures and faults status. The measured values will be displayed on the host PC using FreeMaster. 

In the following articles, we are going to detail the capabilities of our BMS blocks and how to use them on the NXP battery cell controller DevKits.

This video shows the overall motor control application developed with Model Based Design Toolbox. We are going to assemble all the blocks developed throughout this course and we will have the motor running under Speed Controller supervision. We also discuss about the FreeMASTER and you can easily create nice control panels for the applications and how you can validate the Speed Controller and overall Motor Control application. We discuss about: - Speed Controller implementation in Simulink for real time systems; - Motor and Inverter protection for over-current, over- and under-voltage; - FreeMASTER control panel using HTML and Java Script; - Various tests on the MPC5744P DevKit and MotorGD DevKit;   NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

In this video we show how to build, simulate and test a speed estimator based on Hall Sensors for a BLDC motor. The estimator is first tested under Matlab Simulink environment and then ported into the Open Loop Control Simulink model and tested on the MPC5744P Development Kit with FreeMASTER over CAN interface.   We discuss about: - How to compute the speed; - How to build a speed estimator based on Hall sensors; - Step-by-step model building an enhancements; NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

In this video we discuss about control system theory and the mathematics behind the speed controller designing process. We are going to analyse the control system stability based on poles and zeros location and then we will compute the PI speed controller gains using the Root Locus allocation method. For the cases where the system transfer function is unknown we are discussing Ziegler Nichols method for finding the controller gains and we are going to verify the control system designing process by simulating a BLDC motor behavior and building a PI speed controller to handle the system response. We discuss about: - How to choose the controller type based on system transfer function; - How to analyze system stability starting from the characteristic polynomial; - What are the gain, zeros and poles of closed loop transfer function; - Root Locus allocation method based on second order ideal model with dumping factor and natural frequency; - Ziegler Nichols tuning methods; - Simulink models for BLDC motor and PI Speed Controller NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

In this video we discuss about Open Loop Control strategy and for the first time in this course we will put together all the models we have created so far and build the Simulink model that will allow us to rotate the BLDC motor. We discuss about: - Open Loop Control diagram; - Step-by-step model building an enhancements; - Test the open loop control system on the BLDC motor;   NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

Short unedited video - showing the Model Based Design at work on our custom demo platform created with the scope of supporting various scenarios testing.

Check this short video to see the cool stuff you can do with an S32K144 DevKit. NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

In this video we discuss about power stage (DevKit MotorGD) configuration that involves the configuration and initialization of the MC34GD3000 FET pre-driver via SPI commands and the setup of the FlexPWM peripheral to generate PWM commands to the inverter MOSFETs via pre-driver MC34GD3000.   We discuss about: - Pre-driver MC34GD3000 initialization sequence; - Pre-driver MC34GD3000 programming via SPI; - How to configure and test the FlexPWM peripheral; - How to test and validate the SPI communication between MPC5744P and MC34GD3000; NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

In this video we discuss about practical implementation of the motor phase commutation algorithm and how to validate and test such algorithm using different approaches in Model Based Design.    We discuss about: - How to build the commutation table starting from the hall sensor measurement experiment; - How to implement the Software Look Up Tables for rotating the motor in clockwise (CW) or counter clockwise (CCW) directions; - Simulink model that implement the commutation algorithm;   NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

In this video we discuss about how to use Processor-in-the-Loop (PIL) approach to generate the C-code and to validate the algorithm on the real hardware.  PIL simulation main goals are: - to generate and execute the C-code on the real target/microprocessor; - to help with specific algorithm and control designs by offering the means to optimize your software; - to establish a testing framework for the production code; PIL simulation can also use some of peripherals from the real target for inputs or outputs, making the simulation environment more realistic and closed to the final SW design specifications.   We discuss about: - What is PIL, When to use it and What is recommended for;  - How to convert any Simulink generic algorithm to run with PIL support using the Model Based Design Toolbox; - PIL Reference models;  NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

In this video we show the hall pattern identification procedure that can be applied to any motor in case you have no datasheet available. We will read the hall sensors outputs via the microprocessor and save the information for later use.   We show: - How to prepare the Hardware setup - How to go over each identification table - row by row - to apply DC voltage and rotate the rotor in different sectors 360 degrees. NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

This video is part of the https://community.nxp.com/thread/467938  Workshop module and shows how to implement a simple V/F (V/Hz) scalar control to spin the PMSM in open loop using Space Vector Modulation and trapezoidal speed profile.

In this video we discuss about how the motor phase commutation works. This is an essential topic to understand how the motor rotates based on 6-step commutation technique.   We discuss about: - How to build the commutation table based on hall pattern identification  - How to control the PWM sequence to implement a 6-step/trapezoidal commutation NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

This video is part of the Module 7: Torque Control  Workshop module and shows how to implement a FOC and control the PMSM torque and flux using standard PI controllers. This method is used to spin the PMSM in open loop using Space Vector Modulation. The video shows how to implement a control system with two control loops: FAST and SLOW

In this video we implement Simulink models for reading the hall sensors directly via GPIO or based on Hall transitions interrupts via eTimer Capture.   We discuss about: - How to build simple hall reading application using the available MBD Toolbox GPIO blocks  - How to enhance this simple model by adding interrupt service routines capabilities to read the hall sensors only when there is a transitions - How to count the number of hall transitions - How to validate the applications with FreeMASTER NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频

In this video we enhance a Simulink model to allow the reading of hall sensors after processor reset to get the initial position of the rotor.   We discuss about: - How to build a special initialization routine to read the halls once in the beginning - How to use StateFlow programming - How to mix the direct read of GPIOs with ISR based on hall transition readings NOTE: Chinese viewers can watch the video on YOUKU using this link 注意：中国观众可以使用此链接观看YOUKU上的视频