MathWorks India’s R Vijayalayan: ‘OEMs now prefer virtual vehicles to speedily test their software.’ 

A number of automakers are using MathWorks’ MATLAB and Simulink in their R&D and product development programmes to benefit from cutting-edge tech and accelerate time to market. R Vijayalayan, Manager, Automotive Industry Field Application Engineering Team, MathWorks India says India Auto Inc is now using far more simulation software than it did before.

New model development under strict cost control is the need of the hour for OEMs who are battling lockdowns and an economic slowdown. How can software and simulation enable cost savings?
The commonly reported cost control is typically caused by production stoppages, which are in turn driven by economic, manufacturing or supply concerns. In the meantime, R&D organisations are tackling their cost challenges which are due to rising design complexity and software content in vehicles.

In our interaction with R&D managers, we see an increased interest in software tools and simulation. One reason is that advanced development methods such as Model-Based Design have established a track record of addressing complex system design. Model-Based Design extends agile principles to the development of automotive systems that include physical components as well as software. From requirements capture, system architecture and component design, to implementation, verification, test and deployment, Model-Based Design shortens the time required to execute the development cycle through systematic use of models and data plus automation.

The model-based approach enables engineers to frontload development and rely more on use of models as virtual prototypes for performing trade-off studies, optimal calibrations, designing and testing controllers thereby reducing the time and resources spent vehicle/track/lab testing.

Can virtual product testing in itself meet the growing challenge of saving costs, or do these solutions end up increasing the expense of companies?  
With the need for efficiency improvement and meeting shrinking timelines, there’s a big need for virtualisation of the system development and testing using the simulation-based approach. This approach coupled with data analytics enables engineers to use the simulation models of vehicles or automotive systems as virtual dynos/vehicles. This helps in performing virtual testing or calibration that results in savings of time and cost.

The need of the hour is to translate human expertise into a systematic and repeatable framework for testing and calibration that can produce consistent results and be reusable across multiple programs by enabling engineers to perform most of the tasks at their computer.

With the increase in software content in today’s vehicles, companies are turning toward virtual vehicles to test their software as soon as possible. Using simulation, they can assemble all the key software components that have to work together for a specific application and study how well they meet their requirements. Virtual vehicles also enables engineers to assess functional behaviour and gain insight while reducing time spent in the vehicle, especially when the test cases have any hazardous scenarios.

Which are the areas where virtual / digital twins can help accelerate development while keeping a check on developmental costs?
The systematic reuse of models is a basic principle of Model-Based Design, where models form a digital thread connecting development, design optimisation, code generation, and verification and validation. This digital thread does not need to 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, optimise the asset’s performance or perform predictive maintenance. A digital twin can model 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 instance, 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 is the RoI equation that OEMs must think through to decide and adopt such design and development solutions?
From our experience, OEMs should be looking at the following key factors while determining the right solution.
– Extent of physical prototype usage and maximise simulation potentials
– Ability to translate human expertise into systematic and repeatable framework for virtual simulation and calibration.
– Improve design and development efficiency through systematic reuse of models.
– Availability of experienced users of the said solution, both in the industry and academia, the latter helping to ensure a continued supply of new engineers and to facilitate joint industry/academia R&D.
– Use of models as graphical and executable specification instead of textual specification while working with suppliers to avoid ambiguity and number of iterations between the OEM and its suppliers.
– Develop the competency of the engineers on tools and technologies through training to reduce the learning curve and maximise RoI of tool investment.

Which are the different aspects of simulation in automotive product development that could be solved by your solutions?
MathWorks solutions primarily address three areas: model-based system engineering, simulation for system development, simulation for software development. Model-based system engineering is about designing, analyzing and testing system and software architectures.

Simulation for system development looks at system performance, architecture trade-offs, range analysis, component sizing, validation of systems and building digital twins. Simulation for software development encompasses planning, control algorithms, development and test perceptions, automatic production code generation, compliance with standards like AUTOSAR, ISO 26262 and verification and validation of embedded systems.

With the EV industry seeing new traction, what is MathWorks’ roadmap with regard to simulation deployment?
Model-Based Design helps engineers meet the demand for EVs by facilitating the move from concept cars to production-ready, fuel-efficient vehicles. Engineers quickly build conceptual system models, make design trade-offs, and verify algorithms before prototype components or vehicles are available. Frontloading of system development using simulations to perform electric powertrain architecture trade-off studies and to size key components such as the battery pack and traction motor, before building prototype vehicles.

What growth outlook do you have for software and simulation platforms in the auto industry in the coming decade?
We are seeing increasing use of our simulation software among our clients –- from large OEMs to start-ups. While OEMs are challenged by the current situation and need to comply with multiple standards, start-ups are always looking at ways to work with less resources. When companies are faced with these challenges, they look toward simulation software. Numerous independent studies about the simulation software market support our growth outlook.

Which specific software / simulation platform of yours is in demand for the automotive industry in India and why?
Our tools MATLAB and Simulink enable automotive engineering organisations to accelerate vehicle development processes and to deliver vehicles that meet market requirements for safety, comfort, fuel economy, and performance. In addition, the following products extend the Simulink environment for automotive applications:

– Powertrain Blockset provides fully assembled reference application
– Models of automotive powertrains, including gasoline, diesel, hybrid, and electric systems.
– Vehicle Dynamics Blockset provides fully assembled reference application models that simulate driving maneuvers in a 3D environment.
– Automated Driving Toolbox provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems.
– Model-Based Calibration Toolbox provides apps and design tools for optimally calibrating complex engines and powertrain subsystems.
– RoadRunner is an interactive editor that lets you design 3D scenes for simulating and testing automated driving systems.
– System Composer helps to allocate requirements while refining an architecture model that can then be designed and simulated in Simulink.