Keynote: Architecting Software-Defined Vehicles Through Model-Based Design

9:45–10:15

The development of software-defined vehicles demands processes that can help decouple software from hardware, design self-contained apps for easy updates, and automate software build and testing, while still ensuring the consistency and traceability required for software quality, safety, and security.

Model-Based Design offers distinct advantages that can help you go from software architecture conceptualization to component design and back up to software integration and testing–enabling higher quality and reliability of the final product. In this presentation, you’ll see how Simulink^® is rapidly evolving to realize this vision. Recent investments have helped create an environment that accounts for major trends in service-oriented architectures, code-based component development, virtual testing, continuous integration, and much more.

Keynote: Technology Strategies for Next-Gen Vehicles

10:15–10:45

ChatGPT and Large Language Models with MATLAB

10:45–11:00

Learn how large language models (LLMs) work and how to build a transformer model in MATLAB^®. See a demo of an LLM-based model for MATLAB and how you can use it in your work, including which prompts to use.

Panel Discussion: The Transformative Journey of Software-Defined Vehicles with Model-Based Design

11:30–12:30

As software continues to drive significant innovations, the automotive industry is transforming itself to acquire the necessary skills and infrastructure to deliver vehicles that are connected, personalized, and updated over their lifecycle. This shift requires evolution in semiconductor technology, vehicle architecture, software development workflows, and collaboration among stakeholders to build an ecosystem. Join a well-curated panel discussion to explore aspects related to software-defined vehicles (SDVs) including:

• What are SDVs and what opportunities do they offer?
• How is vehicle architecture evolving for SDVs?
• How do AI, virtualization, and cloud-based workflows enable the development of SDVs?
• What is the role of Model-Based Design in accelerating the development of SDVs?
• How are organizations reorganizing and reskilling to achieve these goals?

Developing EV Components Using Virtualization and Scaling to the Cloud

14:45–15:15

The rising demand for advanced battery technologies requires frontloading of battery development. In this talk, discover how to virtualize battery development by integrating high-fidelity battery pack models, including thermal and cooling systems. Then learn how to integrate this model into a virtual vehicle and scale the simulation in the cloud. While this talk uses a battery as an example, the workflow is applicable to the development of any other EV component.

Highlights:

• Create comprehensive battery pack models with thermal and cooling considerations
• Ensure model accuracy and performance through rigorous unit testing
• Integrate battery pack models effortlessly into the virtual vehicle framework
• Leverage cloud workflows for faster, resource-intensive simulations and testing

Clean Technologies Power Electric 3-Wheelers: Last Mile Delivery of E-Vehicles Made in India for India

15:15–15:45

Over 5 lakh people lose their lives every year in India due to poor air quality from road transportation. In 2019, 21 of the 30 most polluted cities in the world were in India!

Founded in 2013 and based in Bengaluru, Altigreen designs, engineers, and produces 3-wheeled electric vehicles using proprietary and indigenously built technologies. Altigreen‘s Made in India/Made for India products are specifically designed for the environment, road conditions, and driving behaviors in India. Altigreen’s product offerings stand on four strong pillars: longest range, largest volumetric capacity, highest ground clearance, and greatest torque.

All components, including the motor, motor controller, power electronics converters, telematics/IoT, gearbox, battery, and BMS, are designed and manufactured in our 3 lakh square foot facility in Malur, Karnataka.

Altigreen uses MATLAB^® and Simulink^® products for its system level simulations and software design, and Embedded Coder^® for development. In this session, learn how using MATLAB and Simulink has helped us solve specific technical challenges related to component sizing, BMS design, and more—and reduce the time-to-market for our products.

EV Powertrain Design: Power Electronics, Control, and Reliability Evaluations

15:45–16:15

The Indian Institute of Technology Bombay (IITB) has established a center of excellence (CoE) for e-mobility to foster collaboration between industry and academia in addressing industry challenges. The CoE has been actively working with various companies to tackle issues concerning power electronics and e-mobility. This presentation highlights the success of the team’s simulation-driven approach to overcoming challenges related to power electronics control design and component reliability. This includes:

• A MATLAB^® simulation-based reliability study of power converters in EV applications.
• A method of anti-slip control for multi-motor single inverter EVs.
• Simulation studies on alternate capacitor technologies (electronic capacitors) for EV battery charging applications.

Master Class: Driving Efficiency and Performance Using Motor Control Workflows for Electric Vehicles

16:45–17:45

In this master class, explore the latest advancements in motor control algorithm design and deployment, specifically in the context of vehicle electrification. These algorithms play a crucial role in regulating performance characteristics such as speed and torque. The session will focus on highlighting the unique capabilities of these algorithms and providing an efficient process for their development and implementation.

Highlights:

• Motor modeling and generating characteristics curves as a function of motor parameters
• Using MTPA/field-weakening algorithms to meet the torque and speed requirements
• Validating motor requirements and redesigning the motor based on a standard drive-cycle test
• Using Field Oriented Control Autotuner block or PID Controller block for interactive control loop gain tuning
• Generating and verifying the code with static code analysis tools
• Deploying the algorithm to the hardware and verifying rate-monotonic/control loop execution time

Automated Driving Software Development for Commercial Vehicles

14:45–15:15

The emergence of autonomous commercial vehicles has become a significant trend driven by the need to address driver shortages and enhance safety. As these vehicles heavily rely on software for their operation, it is crucial to develop robust and efficient algorithms to enable autonomous driving. This talk highlights MATLAB^® and Simulink^® products, including Stateflow^®, in the software development process for autonomous commercial vehicles.

From requirements to testing, MATLAB and Simulink provide a comprehensive platform for control and path planning software development. The complexities inherent in designing algorithms for commercial vehicles necessitate powerful tools to tackle challenges effectively. MATLAB and Simulink offer a wide range of capabilities that aid in controls and planning algorithm development, allowing engineers to address complex scenarios.

They also facilitate code deployment for prototyping, enabling engineers to validate their algorithms on real-world hardware in the commercial vehicle. This prototyping capability provides invaluable insights and opportunities for refining and optimizing algorithms in terms of functionality and performance before full-scale implementation.

By leveraging the powerful capabilities of MATLAB, engineers can effectively navigate the intricate landscape of autonomous commercial vehicles, ensuring the development of robust, efficient, and safe autonomous driving systems.

Open Simulation Interface (ASAM OSI) Using RoadRunner for ADAS ECU Validation

15:15–15:45

ASAM OSI^® (Open Simulation Interface) is a generic interface based on Google’s protocol buffers for the environmental perception of automated driving functions in virtual scenarios. It is a specification for interfaces between models and components of a distributed simulation. ASAM OSI is strongly focused on the environmental perception of automated driving functions.

In this presentation, explore the capabilities of ASAM OSI in facilitating the simulation of advanced driver-assistance systems (ADAS) electronic control units (ECUs) within a virtual environment. Learn how constructing simulation models around ASAM OSI achieves a plug-and-play functionality that seamlessly integrates with any environmental simulator. This approach allows APTIV to develop a versatile platform capable of meeting the diverse requirements of customers while minimizing the need for extensive customization and enabling faster delivery. See how you can use RoadRunner as a tool that generates scenarios and creates ASAM OSI files, which are subsequently utilized for simulation and validation of the models and ECUs.

Scenario-Based Cosimulation of Autonomous Systems Using RoadRunner and CarMaker

15:45–16:15

Automated driving features are a combination of multiple complex systems. Testing and validation of these features requires millions of kilometers of scenarios; however, this is not feasible to test on on-road systems. In view of different levels of automation, there is a dire need of a modular and scalable platform that allows X-in-the-loop simulation.

Having a configurable vehicle model used for software-in-the-loop testing provides substantial advantages compared to the physical test drives. It enables automated, efficient, and extensive testing methods with minimal risk and costs for time and equipment.

The current simulation framework allows you to create test scenarios with predetermined traffic behavior. However, there is a need for systematic usage of scenarios for testing and validation of automated driving systems, needed particularly in modeling the driving environment and traffic dynamics. This allows a realistic, robust, and usable environment of the test scenarios.

In this talk, learn about a cosimulation framework between RoadRunner from MathWorks and CarMaker from IPG and how to use it with a third-party simulation framework for validation. See how to bring realistic traffic models into the driving environment to ensure all complex and corner case scenarios are covered during the validation. This cosimulation platform allows you to test millions of kilometers by combining vehicle simulation with virtual simulation software environments.

Master Class: Scenario-Based Virtual Validation for ADAS Features

16:45–17:45

Automated driving spans a wide range of automation levels, from advanced driver assistance systems (ADAS) to fully autonomous driving. As the level of automation increases, the need for testing these features on multiple scenarios becomes important and the testing requirements increase multifold, making the need for modeling and simulation more critical. Creating virtual environments in the form of scenes and scenarios along with a testbench is important to achieve effective simulation.

In this session, learn how RoadRunner and RoadRunner Scenario™ can help you design scenarios for simulating and testing automated driving systems. See how to incorporate scenarios in a closed loop with algorithms for testing automated driving systems.

Discover how to:

• Interactively author scenarios by placing vehicles and paths, defining logic, and parameterizing scenarios
• Export and import scenario and trajectories to ASAM OpenSCENARIO^®
• Programmatically create scenario variants from seed scenarios
• Set up scenario-based validation of ADAS features like highway lane change
• Set up a test automation framework for virtual simulation

Core Elements of an SDV Architecture for Cross-Domain Computing

14:45–15:15

As the automotive industry is going through transformation with new trends and technologies, the current vehicle architecture is reaching its limits and needs to have an innovative ground-zero approach for a future vehicle architecture.

The traditional method of automotive feature development involves designing hardware and software and then integrating them into the vehicle. As advancing features are becoming more interdependent, a software-driven approach to feature development can balance and limit complexity and integration efforts. Software-driven feature development is characterized by designing and laying out central computing units able to run not only features of different domains, but also any further features to the vehicle that might be added via pure software additions after vehicle shipment.

This session highlights the FEV demonstrator project, which lays out software-defined vehicle architecture based on cross-domain applications, and to prove its feasibility by a prototype. The project uses the existing Model-Based Development workflow while developing this software-driven architecture based on the Adaptive AUTOSAR standard.

SOME/IP-Based Classic AUTOSAR Simulink Model for Software-Defined Vehicle Application

15:15–15:45

In recent years, software in the automotive industry has become an important part of success. Vehicles are increasingly becoming part of the modern, digital, and fast paced world. To meet customer demands, it is important to develop application software in service-based algorithms. Service-oriented architecture (SOA) is the solution for software-defined vehicles to enable reliable large data transfer between ECUs and for efficient usage of communication backbone via FOTA (firmware over-the-air).

This talk highlights the following topics:

• Development of SOME/IP-based models (Methods and Events)
• Automatic code generation based on SoCs
• Usage of AUTOSAR Blockset new features
• Integration of application software with AUTOSAR stack

Predictive Maintenance as Vehicles Become More Software Defined

15:45–16:15

The automotive industry is witnessing its next phase of transformation. Vehicles with electric drivetrains and automated features are becoming advanced and sophisticated with continuous over-the-air software updates. For these complex software-defined vehicles, prognostics and predictive maintenance become ever more critical than before. This presentation proposes a machine learning based framework created and deployed through MATLAB^®. It utilizes minimally labeled vehicle data and identifies and flags anomalous behavior that went undetected or got introduced with the aging of components. This framework can be adopted for large real-time or time-series data for early identification of failures and can be deployed on the cloud or vehicle edge.

Highlights include:

• Handling and preprocessing the big data.
• Developing the AI models using the MATLAB toolchain to detect anomalies in the powertrain subsystem and proactively flag them to customers or OEMs.
• Deploying the predictive maintenance solution on the cloud, ensuring scalability and accessibility from anywhere.
• Designing app-based dashboards in MATLAB with intuitive visualizations, empowering users to make informed decisions about maintenance actions.

Master Class: Accelerating Development for Software-Defined Vehicles Using CI/CD

16:45–17:45

In this session, explore the application development and integration of reference workflows into a continuous integration (CI) and continuous delivery (CD) pipeline, and how to streamline the software development lifecycle with a Model-Based Design approach.

The key takeaways from this master class will be:

1. Using model-based development for service-oriented architectures
2. Setting up and automating Model-Based Design verification and validation using CI/CD
3. Deploying software-defined vehicle applications
4. Setting up a test automation framework for virtual simulation