Proceedings

R Vijayalayan, MathWorks India

Change is underway in the automotive industry with trends in vehicle electrification, autonomous driving, and wireless connectivity. Our reliance on software is set to increase, thereby leading us to an era of software-defined vehicles. See how technologies are shaping the industry and how Model-Based Design is evolving to manage the complexity and scale of future mobility systems. Explore developments in the Model-Based Engineering platform in the areas of systems engineering, artificial intelligence, simulation, software development, and the collaborative environment that is accelerating these trends.

Prasanna Deshpande, MathWorks India
Nukul Sehgal, MathWorks India

Our driving experience will soon be defined by the software running in the car and in the cloud to realize the megatrends of CASE (Connected, Autonomous, Shared, and Electrification). With this transformation, vehicle software is expected to be easily updateable, highly reusable, and abstracted.

In this session, explore workflows for agile product development and reduced dependency on hardware prototypes while meeting automotive industry standards.

Highlights:

• Building software for service-oriented architectures such as Adaptive AUTOSAR and DDS
• Augmenting advanced control algorithms with machine learning and deep learning
• Integrating Model-Based Design with continuous integration for the software factory
• Transitioning engineering workflows to the cloud

   

Tracy Austina Zacreas, Tata Technologies
Sandhya Anilkumar, Varroc Engineering (Tech Center)

MathWorks invites women leaders in the automotive industry to describe their journeys using MATLAB^®. Join this session to learn and be inspired by their experiences.

Dr. Vivek Venkobarao, Vitesco Technologies
Konstantin Alexeev, Vitesco Technologies

The virtual engine control unit vECU (virtual ECU) is a software replacement for a hardware ECU, which allows it to execute ECU software as it does on the target ECU hardware. The virtual ECU based on MATLAB^® and Simulink^® provides an effective simulation platform to integrate all the components and perform effective co-simulation to test and calibrate the ECU functions.

Using a vECU based on MATLAB and Simulink in the development process has many advantages:

• Desktop environment/emulator
• Easy setup
• Application software with virtual hardware for testing and calibration
• Additional degrees of freedom during testing
• Improved reproducibility of tests without actual hardware impact
• Non-destructive testing of various hardware components
• Cost optimization

Aurobbindo Lingegowda, Bosch Global Software Technologies

An electric vehicle system is comprised of various subsystems, components, and elements and interacts with road and traffic environments. The vehicle driving behavior influences how the load is transferred from wheels to powertrain components that deliver energy. In today’s electrified, connected, and automated vehicle systems, cross-domain interactions are tightly coupled, and their detailed analysis is tedious. To predict component performance/degradation over the life cycle, it is necessary to estimate the real-load conditions, and virtual environment is a key enabler. Multi-domain virtual vehicle simulation serves this purpose by comprising multiphysics models, which can represent complex vehicle system architectures under normal and critical test conditions. In this example, an electric vehicle model with all the components relevant for energy flow has been built and validated with real-vehicle behavior for baselining the vehicle model. Once the base vehicle model is built, it can be used for various use cases ranging from component competitive assessment, deriving loads for HV battery over 24-hours duration, and more.

Abhisek Roy, MathWorks India

Developing battery systems for modern electric vehicle applications is complex and requires a sophisticated control system. Design challenges arise at all stages of the V-cycle and a range of simulations are necessary at each stage.

In this master class, learn how to verify that a battery design meets the system requirements with the help of an EV system-level model. See the design of BMS algorithms models and an evaluation of the battery performance over a range of test cases including thermal behavior and range. Once these desktop simulation models meet the requirements, explore workflows for digital twin and predictive maintenance applications.

You will also discover how to:

• Determine battery pack size to meet system-level targets
• Design and analyze thermal management systems
• Develop control systems
• Realize digital twin and predictive maintenance applications

Deepika CP, KPIT Technologies
Bhagayashree Mukkawar, KPIT Technologies
Chinmayi Jamadagni, KPIT Technologies
Sanket S Shinde, KPIT Technologies
Srinivas Boppidi, KPIT Technologies

Automated driving and its increasing demand have led to the surge in robustness validation of AD software in simulation. Validating multiple components of AD software in closed loop gives the opportunity to functionally validate the software in integrated setup at the simulation level.

An end-to-end closed-loop framework helps to design higher-quality products, reduce costs, and deliver innovations faster. The closed-loop validation framework helps designers to understand the cascaded impact of a module’s performance.

The major challenge in creating an end-to-end closed-loop setup is integration of multiple tools. The MathWorks product chain has helped to overcome this challenge. In this session, hear about initial development and setup for service-oriented architecture (SOA) and future work.

In this case study, an end-to-end closed-loop framework of AD systems involves these major steps:

1. Developing AD components with Simulink^® and C/C++.
2. Creating scenarios using RoadRunner and the Driving Scenario Designer app.
3. Configuring plant models and sensors.
4. Integrating AD components as Simulink modules and S-functions using Automated Driving Toolbox™.

Ninad Pachhapurkar, Automotive Research Association of India (ARAI)
Jyoti Kale, Automotive Research Association of India (ARAI)

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, so does the uncertainty for operational environments, increasing the needs for virtual validation. Automotive companies typically have an abundance of real-world data recorded from a vehicle that is suitable for open-loop simulations. However, recorded data is often not suitable for testing closed-loop control systems since it cannot react to changes in vehicle movement. Creating scenarios from recorded vehicle data is complicated and involves multiple steps, from sensor selection and mounting and calibration to data collection, visualization, labeling, and working with maps. In comparison with US or European traffic and road conditions, Indian traffic conditions have different scenarios on the road, e.g., different types of vehicle classes, pet and farm animals in addition to pedestrians, bullock carts, tractors, etc. This leads to imminent requirements for virtual validation with real-world scenarios and the need to recreate scenarios from the recorded vehicle data. See a methodology to recreate virtual driving scenarios from recorded vehicle data to enable closed-loop simulation for testing ADAS functionalities.  

A virtual driving scenario is created by recreating roads using GPS sensor data and map import from OpenStreetMap^®, and targets vehicles using the detections from automatic labeling of lidar point cloud data. To test an ADAS feature in closed loop, one must model the ego vehicle (sensor and dynamics) as well as the scenario (roads and target vehicles). In the scenario, the driver’s vehicle is referred to as the ego vehicle and other vehicles on the road are referred to as target vehicles. The created virtual driving scenario is integrated into a closed-loop simulation to assess and test the behavior of ADAS features.

Dr. Rishu Gupta, MathWorks India
Munish Raj, MathWorks India

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 critical, making modeling and simulation essential. However, complex scenes and scenarios are difficult to model, and integrating these scenes and scenarios with the rest of simulation infrastructure is even harder.

In this master class, see how RoadRunner and RoadRunner Scenario can help you design environments and scenarios for the validation of automated driving algorithms. Discover how to incorporate these environments in a virtual test bench for open-loop and closed-loop validation in Simulink^® .

You will also learn how to:

• Author scenes and scenarios interactively
• Recreate real world scenes and scenarios from recorded data
• Create scenario variants from seed scenario
• Perform open-loop and closed-loop testing for automated driving features and subcomponents
• Set up test automation framework for virtual simulation

Sandhya Anilkumar, Varroc Engineering (Tech Center)

MathWorks invites women leaders in the automotive industry to describe their journeys using MATLAB. Join this session to learn and be inspired by their experiences.

Rashmi Gopala Rao, MathWorks India
Asif Tamboli, Tata Consultancy Services
Neha Mishra, Cummins India
Anand Bhange, FEV India
Mike Sasena, MathWorks

Join this panel discussion to discover how engineers are using virtualization to frontload developments of system, software, and data, and explore the challenges and solutions for increased virtualization while accelerating the future of mobility.