MathWorks Automotive Conference 2024

General Motors has a long partnership with MathWorks, utilizing MATLAB^® and Simulink^® to develop proprietary tools for vehicle efficiency and longitudinal performance analysis. Ever-increasing reliance on virtual methods in all phases of product development required improvements to simulation tool efficiency, speed, and fidelity. We also acknowledged the benefits of increased collaboration through modular tool design, democratization, and co-simulation. Recognition of these needs drove a complete redesign of core tool structure and interfaces. The newly introduced VERDE (Vehicle Energy and Range Development Environment) toolchain builds on MATLAB and Simulink with App Designer and Simscape™ to address these needs. In this presentation, hear about VERDE core capabilities, new interface, unique features, and integrated pre- and post-processing that enable General Motors to develop conventional, BEV, and hybrid architectures for leading-edge fuel economy, range, performance, and global CO2/GHG strategy.

The automotive industry is increasingly turning to computer-aided engineering (CAE) tools to streamline product development and reduce time and cost. However, the recent advancements in electric and hybrid electric vehicles have introduced complexities to the product development process. Electrified powertrains incorporate intricate electro-mechanical systems such as internal combustion engines, motors, and batteries, leading to a multitude of operating combinations that are controlled by a set of predetermined calibration variables. The calibration variables can be adjusted to optimize various attributes of the vehicles, including fuel economy and performance. With a vast calibration set available, it is impractical to manually determine the global optimum calibration based on empirical knowledge and experience. This project aims to address the challenge by reframing the calibration development as a mathematical optimization problem to identify the global optimum calibration for a defined objective function. Using Simulink^®, we have developed high-fidelity system-level vehicle models for Ford’s xEVs and utilized them to optimize powertrain calibrations. Leveraging genetic algorithms, a subset of evolutionary machine learning algorithms, we have optimized the calibration to meet specific objectives. Given the high dimensionality of the problem, many simulations (10,000+) must be executed. This necessitates the use of a computing cluster with 512 parallel simulations to complete the studies in a feasible time frame. The project utilized MATLAB^®, Simulink, Parallel Computing Toolbox™, and MDCS licenses, which facilitated the scalability of the problem, enabling us to complete the simulations in 100+ hours (which would otherwise take 30,000+ hours). Upon completion of the study, data analytics techniques are employed to analyze the large data set, and the best calibrations are provided to calibration engineers for further analysis and implementation in vehicles.

As automotive platforms advance, the use of cloud and other technologies is becoming central to enabling you to remain competitive. Learn about the 15-year journey of bringing MATLAB^® and Simulink^® to the cloud and the discoveries made along the way. See a high-level overview of the product capabilities and roadmap so that you can feel confident that these tools are ready for you when you start your cloud journey.