Designing electric vehicles for optimal range and performance requires a deep understanding of vehicle dynamics, powertrain systems, thermal management, and more. Virtual vehicle development is essential for creating robust designs and ensuring operational safety. By utilizing virtual vehicle simulations, engineers can gain valuable insights into real-world behavior, test a wide range of “what-if” scenarios, and validate embedded software functionality. This approach enables faster evaluation of different design variants, safe exploration of edge cases, and overall enhancement of product quality.

Highlights:

• Designing electric vehicles for performance and range evaluation 
• Utilizing vehicle simulation models to explore design trade-offs 
• Optimizing vehicle design through reduced-order models (ROM) 
• Integrating chassis control for advanced vehicle dynamics and scenario-based testing 

As the automotive industry accelerates towards software-defined vehicles (SDVs), the flexibility to generate both service-oriented architecture (SOA) and signal-based software is increasingly vital within modern E/E architectures. Leveraging high-performance computers (HPCs) and standards like AUTOSAR^® (Adaptive and Classic), Model-Based Design empowers engineers to seamlessly retarget software across heterogeneous platforms, minimizing manual code rewriting. This approach also supports systematic verification of SOA components and the integration of artificial intelligence (AI) functionalities. By automating Model-Based Design workflows within continuous integration (CI) pipelines, organizations can ensure consistent execution of process steps, utilize simulation for efficient issue reproduction and debugging, and accelerate the reliable deployment of advanced automotive software solutions.

The increasing complexity and performance demands of modern power electronic systems necessitate robust verification and rapid prototyping methodologies during the design phase.

See a comprehensive approach for hardware-in-the-loop (HIL) verification and prototyping of an OCDC controller and system on an FPGA using Simscape™ and the HDL Coder™ workflow.

The control algorithm is developed in Simulink^® and deployed to a TI C2000™ microcontroller using the C2000 Microcontroller Blockset. Simultaneously, the power stage is modeled using Simscape to accurately capture dynamic behavior, then synthesized to an FPGA using HDL Coder. This results in a complete closed-loop HIL setup combining control algorithm and real-time power stage emulation.

Key advantages of this methodology include:

• Elimination of complex physical hardware setups for power stage testing.
• Rapid prototyping of multiple converter topologies with near-accurate simulation results and high-fidelity validation of power stages without sacrificing the accuracy to greater extent.
• Seamless switching between designs, significantly reducing development cost and lead time.
• Reduced maintenance risks due to minimized dependency on physical test hardware.
• Greater potential for automation across the entire design and test workflow.

This integrated workflow accelerates development, enhances flexibility, and ensures reliable validation and rapid prototyping for power electronic control systems.

The next generation of automotive software architecture demands increased flexibility and scalability, which AUTOSAR^® Adaptive provides through service-oriented and dynamic execution models. This could be aided via a model-based development workflow for AUTOSAR Adaptive using MATLAB^® and Simulink^®. See how to design, simulate, and generate C++ code for POSIX-compliant ECUs, while incorporating features such as service discovery, communication management, and execution scheduling. This model-based development approach makes use of system-level modeling, software component configuration, and validation in a virtual simulation environment.

Through a real-world use case, learn how developers can iterate on their designs while ensuring compliance with AUTOSAR Adaptive specifications and successfully deploy from conceptual model to runnable code. Participants will gain insights into AUTOSAR Adaptive domain implementation, handling of repetitive tasks while enhancing traceability, and testing in automotive applications. 

See a comprehensive approach to accelerating the development and deployment of AI and machine learning algorithms in embedded control units using MATLAB^® and Simulink^®. By leveraging Model-Based Design principles, discover how end-to-end development pipelines can be derived and deployed efficiently to target microcontrollers in production programs. Integrating MATLAB and Simulink with cloud platforms, particularly AWS^®, enables scalable simulation, rapid calibration, and benchmarking of machine learning algorithms for highly nonlinear systems.

We showcase multiple real-world applications where AI integration was made practical with MATLAB and Simulink, ranging from control logic optimization to predictive diagnostics, and we highlight how these tools expedite the realization of concepts into deployable systems. Learn how these pipelines seamlessly integrate into Agile workflows, facilitating iterative development, validation, and deployment cycles. You’ll also explore the use of these pipelines in software-defined vehicles (SDVs), providing a foundation for robust machine learning lifecycle management in production environments.

Key benefits include simplified workflow setup, effective failure-mode testing, rapid deployment of high-fidelity models, and reduced dependency on physical testbenches through cloud-based simulation. There are also practical challenges such as limited workflow expertise, managing black-box recalibration, and the manual oversight required for training data validation. This work provides insights and best practices to system developers aiming to adopt AI and machine learning within embedded systems using MATLAB and Simulink and cloud infrastructure.

The increasing software complexity in modern vehicles pushes traditional validation methods to their limits. As software-defined vehicles (SDVs) evolve, software verification is an integral part of development from day one. However, current validation approaches, reliant on hardware-in-the-loop (HIL) and physical prototypes, face critical bottlenecks:

• Late-stage defect detection, increased rework costs, and prolonged development.
• Limited scalability, challenging efficient software variation testing.
• Fragmented validation environments, causing multi-domain integration inefficiencies.

To address these, the industry is shifting towards virtualization-driven validation. Virtual ECUs (VECUs) enable software testing before hardware availability, facilitating parallel validation, continuous testing, and faster iterations. This approach is bolstered by the Functional Mock-up Interface (FMI) 3.0 standard, which explicitly supports virtual ECU testing, enhancing interoperability. Meanwhile, OEMs adopt virtual validation, integrating VECUs with existing simulation infrastructure remains a challenge.

Learn about VECU execution integration with existing Simulink-based simulation infrastructure based in Simulink^®, enabling OEMs to leverage current environments for new VECU methods, benefiting from FMI 3.0. Transitioning from model-in-the-loop (MIL) and software-in-the-loop (SIL) to virtual testing, this solution facilitates FMU-based Level 2/Level 3 VECU simulation, calibration, measurement, and CAN simulation. Co-simulation of VECU (FMU) with Simulink enhances testing, reduces physical prototype needs, and accelerates development. This approach demonstrates improvements in efficiency, cost-effectiveness, and accuracy in automotive control system development. 

As modern vehicles become increasingly reliant on advanced chassis control systems, there is a growing need for scalable, realistic, and efficient validation workflows. In this master class, see a simulation-driven approach for testing chassis control algorithms using Vehicle Dynamics Blockset™ and RoadRunner Scenario.

Discover how engineers can virtually design, test, and validate control logic within high-fidelity, closed-loop simulations that replicate complex driving scenarios, such as urban intersections with traffic and signals. By combining configurable vehicle dynamics models with scenario-based testing, this workflow reduces the need for physical prototypes, accelerates development cycles, and enhances system robustness.

You will gain insights into integrating control algorithms with simulation environments, enabling earlier detection of issues, better design iterations, and comprehensive coverage of edge cases in a cost-effective manner.

Highlights

Explore, through reference examples and user stories, how to:

• Configure virtual vehicles with Simulink^®
• Integrate the chassis control algorithm 
• Author 3D scenes and scenarios with RoadRunner
• Integrate Simulink and RoadRunner 
• Simulate real-world scenes before in-vehicle testing

As the demands of the automotive industry grow more complex, adopting industrialized approaches to software development has become essential to ensure product quality and reliability. In this master class, explore how software factories—enabled by structured workflows and modern tools—can help organizations meet rigorous industry standards and deliver robust, certified automotive software. Participants will learn about organizing MATLAB^® projects for effective collaboration, implementing efficient version control with Git™, automating build and deployment with CI/CD pipelines, and managing development artifacts. You will also see comprehensive testing methodologies using MATLAB and Simulink^® to ensure software reliability.

Highlights:

• Practical strategies for structuring and managing automotive software projects.
• Techniques for improving collaboration and traceability across development teams.
• Automation of software build, test, and deployment processes.
• Approaches to rigorous testing and quality assurance with MATLAB and Simulink.