An imitation learning-based energy management strategy for electric vehicles considering battery aging

Abstract

—The benefits of an electrified powertrain system, including increased energy efficiency and decreased emission, have made electrification of powertrain systems a top priority for the automobile industry. There has been a significant advancement in studying state-of-the-art battery technology in electric vehicle applications. However, the performance and longevity of electric vehicles may suffer due to battery degradation during vehicle usage. Additionally, there is a need for additional research on energy saving and battery degradation in hybrid energy storage systems for electric vehicles equipped with batteries and supercapacitors. This paper proposes an imitation Q-learning-based energy management system designed to improve energy efficiency and reduce battery degradation for the battery and supercapacitor electric vehicle. A battery electric vehicle is also studied for comparison purposes. To test the efficacy of the proposed method, experiments are conducted using a motor-generator set and a dSPACE SCALEXIO system. The comparisons indicate that the battery degradation is reduced by 26.36% and energy efficiency is increased by 3.83% through the imitation Q-learning energy management strategy.