Energy management for hybrid electric vehicles based on imitation reinforcement learning

Yonggang Liu, Yitao Wu, Xiangyu Wang, Liang Li, Yuanjian Zhang and Zheng Chen

Energy, 2023, vol. 263, issue PC

Abstract: An effective energy management strategy (EMS) in hybrid electric vehicles (HEVs) is indispensable to promote consumption efficiency due to time-varying load conditions. Currently, learning based algorithms have been widely applied in energy controlling performance of HEVs. However, the enormous computation intensity, massive data training and rigid requirement of prediction of future operation state hinder their substantial exploitation. To mitigate these concerns, an imitation reinforcement learning-based algorithm with optimal guidance is proposed in this paper for energy control of hybrid vehicles to accelerate the solving process and meanwhile achieve preferable control performance. Firstly, offline global optimization is firstly conducted considering various driving conditions to search power allocation trajectories. Then, the battery depletion boundaries with respect to driving distance are introduced to generate a narrowed state space, in which the optimal trajectory is fused into the training process of reinforcement learning to guide the high-efficiency strategy production. The simulation validations reveal that the proposed method provides preferable energy reduction for HEVs in arbitrary driving scenarios, and suggests an efficient solution instruction for similar problems in mechanical and electrical systems with constraints and optimal information.

Keywords: Energy management; Dynamic programming; Hybrid electric vehicle; Imitation reinforcement learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222027761

DOI: 10.1016/j.energy.2022.125890

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