Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle. (15th April 2023)

• Record Type: Journal Article
• Authors: Zhang, Zhen; Zhang, Tiezhu; Hong, Jichao; Zhang, Hongxin; Yang, Jian; Jia, Qingxiao
• Is Part Of: Energy. Volume 269(2023)
• Publication Date: 2023-04-15
• Subjects: Hybrid vehicle -- Energy management -- Deep reinforcement learning -- Electric-hydraulic -- Simulation experiment; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2023.126858
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The configuration and development of energy management strategies (EMSs) are generating considerable interest regarding vehicles due to the rapid blossoming momentum of electric vehicles. Battery state of charge is one of the main characterization parameters for evaluating EMSs, so the practical advancement of the range is critical to the evolution of electric vehicles. A novel electric-hydraulic hybrid electric vehicle (EHHEV) is investigated in this paper, which has the characteristics of various working modes and multi-energy sources. According to the hybrid system's energy flow, a rule-based control strategy is established, and the superiority of EHHEV in energy management is verified by steady-state simulation. Further, this paper combines Q-learning with deep neural networks to construct a double deep Q-network (DDQN)-guided EMS to solve traditional control strategy and reinforcement learning issues. After appropriate hyperparameters setting and batch training, the EMS can make EHHEV realize the optimal switching among working modes. Experimental results showcase that the EMS can significantly enhance vehicle economy. This is the first of its kind to apply the DDQN to developing EMS for EHHEV. Highlights: Novel electric-hydraulic hybrid electric vehicles can improve energy management performance. Double deep Q-network algorithm has a positive effect on vehicle working mode switching. DDQN-guided energy management strategy is proposed to optimize the energy consumption rate. Off-line training and adaptability verification are applied to prove the applicability of the EMS. EHHEV realizes the optimal working mode switching under the interference of DDQN-guided EMS.