Auto-Tuning Dynamics Parameters of Intelligent Electric Vehicles via Bayesian Optimization

Abstract

The vehicle dynamics model is a fundamental prerequisite for advanced software development for intelligent vehicles. This incites the need for accurate mathematical modeling to match the driving dynamics of real vehicles as closely as possible. However, an accurate vehicle model has a variety of parameters that often rely on massive real-vehicle testing to identify and calibrate. This process is a laborious and tedious task for automotive engineers. In this paper, we introduce APOVD, Automatic Parameter Optimization of Vehicle Dynamics, a Bayesian optimization framework that can search the abundant vehicle parameters automatically and efficiently. APOVD inherits the reliability and interpretability of physics-based vehicle models while enjoying the benefits of data-driven methods, that is, the ability to adapt and improve from the data. First, an 8-Degree-of-Freedom dynamics model is developed for a four-wheel independent drive (4WID) electric vehicle. Then, APOVD is used to tune the vehicle model parameters to close the gap between the real vehicle and the simulated vehicle model. Finally, the modeling accuracy of different parameters, various vehicle configurations, and different optimizers is compared in real driving data and CarSim-based simulation data. In the experiments, Bayesian optimization provided accurate vehicle parameters (more than 90% reduction in error) and effectively corrected for incorrect parameters.