Experimental demonstration of time-efficient auto-Calibration of a vehicle thermal management system using safe reinforcement learning
article
Future automotive powertrains become increasingly complex to achieve optimal and robust performance in realworld conditions. This is accompanied by rapidly escalating time and cost demands for calibrating powertrain control systems using existing map- and model-based methods. This challenge highlights the urgent need for self learning control strategies, which autonomously learn optimal control settings on the road. This work demonstrates the potential of Reinforcement Learning-based self learning control for a battery electric vehicle thermal system with safety constraints. To realize Reinforcement Learning on the tested vehicle, a novel exploration method is implemented, which explicitly deals with system safety and minimizes experiment time. By combining an online-learned Gaussian Progress Regression model and a reciprocal Control Barrier Function, the optimal direction and step size for information-rich actions is determined during exploration. Validated on a vehicle test-bench, the proposed method calibrates the reference generator to optimize steady-state operation of the heat pump system. Safe and robust performance is demonstrated for varying ambient temperature and humidity, achieving a relative error in heat pump efficiency within 2% of the true optimum across all validation points. Compared to conventional map-based control, the Reinforcement Learning-based approach reduces calibration time by 69%.
Topics
TNO Identifier
1028525
ISSN
0967-0661
Source
Control Engineering Practice, 172
Publisher
Elsevier
Article nr.
106944