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

Yiming Ye, Hanchen Wang, Bin Xu and Jiangfeng Zhang

Energy, 2023, vol. 283, issue C

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.

Keywords: Energy management; Reinforcement learning; Battery; Supercapacitor (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:283:y:2023:i:c:s036054422301931x

DOI: 10.1016/j.energy.2023.128537

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