Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle

Chunyang Qi, Yiwen Zhu, Chuanxue Song, Guangfu Yan, Feng Xiao, Da Wang,, Xu Zhang, Jingwei Cao and Shixin Song

Energy, 2022, vol. 238, issue PA

Abstract: As the core technology of hybrid electric vehicles (HEVs), energy management strategy directly affects the fuel consumption of vehicles. This research proposes a novel reinforcement learning (RL)-based algorithm for energy management strategy of HEVs. Hierarchical structure is used in deep Q-learning algorithm (DQL-H) to get the optimal solution of energy management. Through this new RL method, we not only solve the problem of sparse reward in training process, but also achieve the optimal power distribution. In addition, as a kind of hierarchical algorithm, DQL-H can change the way of exploration of the vehicle environment and make it more effective. The experimental results show that the proposed DQL-H method realizes better training efficiency and lower fuel consumption, compared to other RL-based ones.

Keywords: Deep reinforcement learning; Energy management; Hybrid electric vehicle; Hierarchical reinforcement learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221019514

DOI: 10.1016/j.energy.2021.121703

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