MathWorks MATLAB and Simulink Release 2022b

MathWorks has introduced Release 2022b (R2022b) of the MATLAB and Simulink product families. Release 2022b introduces two new products and several enhanced features that simplify and automate model-based design for engineers and researchers tasked with delivering product innovations.

Simscape Battery, one of the innovations introduced in the R2022b release, provides design tools and parameterised models for businesses designing battery management systems. Engineers and researchers can 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 topology and includes cooling plate connections so electrical and thermal responses can be evaluated.

R2022b also features the new Medical Imaging Toolbox that 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.

Other updates to the MATLAB and Simulink tools include the AUTOSAR Blockset that develops services-oriented applications using client-server ARA methods and deploys them on embedded Linux platforms. The tool lets users define data types and interfaces in an architecture model.

The Fuzzy Logic Toolbox lets users design, analyse and simulate fuzzy inference systems (FIS) interactively using the updated Fuzzy Logic Designer app. The enhanced toolbox also allows engineers and researchers to design type-2 FIS using command-line functions or the Fuzzy Logic Designer app.

The HDL Coder allows users to generate optimised SystemC code from MATLAB for High-Level Synthesis (HLS) and use the frame-to-sample conversion for model and code optimisation.

The Model Predictive Control Toolbox uses neural networks as prediction models for nonlinear model predictive controllers. In addition, the toolbox lets users implement model predictive controllers that meet ISO 26262 and MISRA C standards.

The system Identification Toolbox allows users to 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-Weiner models.