MATLAB and Simulink release 2022b: simscape battery and updates that simplify and automate model-based design
The global battery management systems market is expected to reach $13.4bn by 2026. Bloomberg New Energy Finance indicates the growth can be attributed largely to electric vehicle (EV) market growth. The organisation’s latest report shows that 58% of global passenger vehicle sales will come from EVs by 2040.
Simscape Battery, one of the innovations introduced in the R2022b release, provides design tools and parameterised models for businesses designing these types of battery systems.
Engineers and researchers use Simscape Battery to create digital twins, run virtual tests of battery pack architectures, design battery management systems, and evaluate battery system behaviour across normal and fault conditions. The tool also automates the creation of simulation models that match desired pack topology and includes cooling plate connections so electrical and thermal responses can be evaluated.
“We’re excited to launch Simscape Battery as innovation in battery management systems is at an all-time high,” said Graham Dudgeon, Principal Product Manager, Electrical Systems Modelling, MathWorks. “The new product includes many design tools intended to simplify and automate Model-Based Design, including the Battery Pack Model Builder that lets engineers interactively create and evaluate different battery pack architectures.”
R2022b also features the Medical Imaging Toolbox. The toolbox provides tools for medical imaging applications to design, test, and deploy diagnostic and radiomics algorithms that use deep learning networks. Medical researchers, scientists, engineers, and device designers can use Medical Imaging Toolbox for multi-volume 3D visualisation, multimodal registration, segmentation, and automated ground truth labelling for training deep learning networks on medical images.
R2022b introduces updates to popular MATLAB and Simulink tools, including:
- AUTOSAR Blockset: Develop services-oriented applications using client-server ARA methods and deploy them on embedded Linux platforms. The tool lets users define data types and interfaces in an architecture model.
- Fuzzy Logic Toolbox: Design, analyse, and simulate fuzzy inference systems (FIS) interactively using the updated Fuzzy Logic Designer app. In addition, the enhanced toolbox allows engineers and researchers to design type-2 FIS using command-line functions or the Fuzzy Logic Designer app.
- HDL Coder: Generate optimised SystemC code from MATLAB for High-Level Synthesis (HLS) and use the frame-to-sample conversion for model and code optimisation.
- Model Predictive Control Toolbox: Use neural networks as prediction models for nonlinear model predictive controllers. In addition, the toolbox now lets users implement model predictive controllers that meet ISO 26262 and MISRA C standards.
- System Identification Toolbox: Create deep learning-based nonlinear state space models using neural ordinary differential equations (ODEs). Machine learning and deep learning techniques can also represent nonlinear dynamics in nonlinear ARX and Hammerstein-Wiener models.