Electrified vehicle development requires one to address many challenges in parallel such as Total vehicle efficiency, New engineering capability – power electronics, high-voltage battery, motors, embedded software quality and transition from prototyping work style to production mind-set and processes. MathWorks helps engineers tackle these challenges through two things:
MATLAB/Simulink which is a unified design environment for system engineering, software engineering, and data science, and Model-Based Design which is a development method. Model-Based Design helps engineers meet the demand for electric vehicles by facilitating the move from concept car to production-ready, fuel-efficient vehicle. Engineers quickly build conceptual system models, make design trade-offs in simulation, and frontload algorithm development before prototype components or vehicles are available.
Our Interaction with R Vijayalayan, Manager, Automotive industry and control design vertical application engineering teams, MathWorks India. R Vijayalayan manages the automotive industry and control design vertical application engineering teams at MathWorks India. He specializes in the field of Electrification, Virtual Vehicle and Model-Based Design.
How MathWorks is revolutionising the EV sector?
In addition to the technical gaps mentioned above, automotive companies in India also face an experience gap compared to their counterparts in other countries where development of EV products have been under way for a decade or more. Closing this experience gap is crucial for the success in developing and optimizing EVs for both the local market and global market. One way to address this skill gap is to lower the barrier of critical engineering tasks. For example, simulation is essential for EV engineering from design optimization and virtual validation. Yet, developing EV models from scratch requires considerable experience. MathWorks provides vehicle model templates to lower this barrier. Engineers can use these models to jump start their vehicle models, which would enable design trade-off analysis and component sizing, control parameter optimization, and hardware-in-the-loop simulations. The simulation, coupled with data analytics, enables engineers to carry out virtual development using key asset they have, such as test data and India drive patterns, to accomplish the key task they must accomplish such as system testing and calibration. With the increase in software content in today’s electric vehicles, companies are also
turning toward virtual vehicles to test their software as soon as possible.
In addition to the above MathWorks is deeply invested in developing skills amongst the professionals and students. There are quite a few live sessions and webinars which are offered complimentary to professionals and students. We partner with industry organizations to amplify the adoption of these skills. The recently concluded Electrification series where we partnered with ICAT is an example. MathWorks also sponsors student competitions like BAJA, REEV where students get the opportunity to apply their theoretical knowledge in practical scenarios. Through our sponsorship we give complimentary access to our tools in addition to technical mentoring. The education focused team at MathWorks works closely with academicians to help them design courses that incorporate industry skills into the curricula. Through our Startup and Accelerator program we offer benefits including discounted licenses and technical mentoring to upcoming corporates.
MathWorks’ association with Ather Energy and how this is a great benchmarking solution for EV segment?
MathWorks has been partnering with Ather Energy, India’s first intelligent electric scooter manufacturer, to develop electric scooters. The EV segment is nascent, and Ather Energy had to face multiple challenges, including recruiting the right people and developing the technology to be able to build such a product. The team also had to develop several prototypes to figure out what would work before launching pathbreaking electric scooters like the 450+ and the 450X.
The MathWorks tools MATLAB and Simulink were used to scale up the speed of testing, for the 450 Plus and the 450X while model-based design enabled virtual prototyping. Ather Energy was able to identify and validate the best ideas through simulation using Model-Based Design, allowing them to deliver a full-featured scooter in less time.
Vehicle dynamics and mechanical components were explicitly modelled using first principles on the MathWorks tool Simulink. To evaluate design trade-offs, Ather Energy conducted extensive simulations of the plant model. Ather Energy refined the design until they identified a motor and battery configuration that met the target acceleration and range requirements while staying within budget, size, and temperature constraints. Next, in Simulink, Ather Energy developed algorithms for battery charging, power control, and temperature control. Ather Energy’s engineering team has engaged with MathWorks Consulting Services to receive training in production code generation, model review, and process review.
What are your thoughts on model-based system engineering? How are MathWorks’ solutions enabling enhanced testing of software architectures?
Engineers want to use model-based systems engineering (MBSE) to manage system complexity through requirement analysis, improving communication, and early performance optimization. However, there is often a disconnect between MBSE tools and the tools used for design and implementation. This disconnect makes it challenging to maintain traceability throughout the development process, as well as requiring engineers to perform manual translation between MBSE tools and design/implementation environments. MathWorks systems engineering tools combine with MATLAB and Simulink to create a unified modeling environment, enabling the use of a single platform throughout systems engineering, design, implementation, and verification processes tools.
What are some of MathWorks’ efforts in leveraging simulation for system development and what does it involve?
Engineers and scientists use Simulink to perform multidomain modeling and simulation, because you can reuse models across environments to simulate how all parts of the system work together.
With Simulink, engineers can:
Leverage pre-built and fully assembled reference application models of automotive powertrains, including gasoline, diesel, hybrid, and electric systems.
Customize the system architecture by using library components, configure and parameterize the electrified powertrain components, including motors, generators, and energy storage.
Develop large-scale models through componentization with reusable system components and libraries.
Combine the models into one system-level simulation even if they weren’t built in Simulink.
Run massive simulations in parallel on your multicore desktop, computer cluster, or the cloud, without writing lots of code.
Share simulations as standalone executables, web apps, and Functional Mock-up Units (FMUs)
How do you think range analysis, component sizing and building digital twins for system development are dependent on simulation?
Full vehicle simulation models can be effectively used for range analysis, component sizing and in general for design trade-off studies. Systematic reuse of these models is a basic principle of Model-Based Design, where models form a digital thread connecting development, design optimization, code generation, and verification and validation. This digital thread need not be limited to the development process; it can be extended to deployed systems in operation when design models are reused as digital twins.
A digital twin—an up-to-date representation of a system or subsystem as it operates—can be used to assess the current condition of the asset, and more importantly, optimize the asset’s performance or perform predictive maintenance.
Digital twin can be built for model of a component, a system of components or a system of systems.
Examples include pumps, engines, batteries, manufacturing lines, and a fleet of vehicles. Digital twins are used in a variety of applications like anomaly detection, asset management, and fleet management. For e.g., in fleet management, the ability to monitor the whole fleet using digital twins brings additional advantages in terms of planning operational events and improving maintenance strategies.
What are the advantage of simulating plant and controller in one tool and its benefits to engineers?
An accurate plant model is a vital part of control system development. After creating an accurate plant model, you can verify the functionality of the control system, conduct closed-loop model-in-the-loop tests, tune the controller gains using simulation, and optimize the algorithm before you deploy the model in the actual plant.
How are control design tools and optimization algorithms contributing to improved overall design?
Control design software ideally supports each stage of the control system development process, from plant modeling to compensator design to deployment, through automatic code generation. Design optimization is the process of finding the best design parameters that satisfy project requirements.
Common tasks for teams looking to develop, implement, and test a control system in one control design software environment include:
Creating accurate plant models using physical modeling, system identification, and parameter estimation
Designing feedback compensators using techniques ranging from classical design such as Bode and root locus, to automated tuning of multivariable decentralized control systems, using H-infinity algorithms
Verifying a control design through simulation, formal verification methods, and real-time simulation
Automatically generating C code, IEC 61131-3 structured text, and Verilog and VHDL for targeting microcontrollers, programmable logic controllers (PLCs) and FPGAs MATLAB, Simulink, a long with various interactive control system design and optimization tools provide interactive environment and APP based automation for completing various steps in the control system design process, and help making the system models more accurate and realistic.
