Optimal online energy management strategy of a fuel cell hybrid bus via reinforcement learning

被引：0

作者：

Deng, Pengyi ^{[1
,2
]}

Wu, Xiaohua ^{[1
,2
]}

Yang, Jialuo ^{[1
,2
]}

Yang, Gang ^{[1
,2
]}

Jiang, Ping ^{[3
]}

Yang, Jibin ^{[1
,2
]}

Bian, Xiaolei ^{[4
]}

机构：

[1] Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province, School of Automobile and Transportation, Xihua University, Chengdu,610039, China

[2] Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Xihua University, Chengdu,610039, China

[3] Chengdu Bus Co., Ltd., Chengdu,611730, China

[4] Department of Electrical Engineering, Chalmers University of Technology, Gothenburg,41296, Sweden

来源：

Energy Conversion and Management | 2024年 / 300卷

关键词：

Battery management systems - Buses - Charging (batteries) - Fuel cells - Fuel economy - Hybrid vehicles - Learning algorithms - Learning systems - Multiobjective optimization - Secondary batteries;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

An energy management strategy (EMS) based on reinforcement learning is proposed in this study to enhance the fuel economy and durability of a fuel cell hybrid bus (FCHB). Firstly, a comprehensive powertrain system model for the FCHB is established, mainly including the FCHB's power balance, fuel cell system (FCS) efficiency, and aging models. Secondly, the state–action space, state transition probability matrix (TPM), and multi-objective reward function of Q-learning algorithm are designed to improve the fuel economy and the durability of power sources. The FCHB's demand power and battery state of charge (SOC) serve as the state variables and the FCS output power is used as the action variable. Using the demonstration FCHB data, a state TPM is created to represent the overall operation. Finally, an EMS employing Q-learning is formulated to optimize the fuel economy of FCHB, maintain battery SOC, suppress FCS power fluctuations, and enhance FCS lifetime. The proposed EMS is tested and verified through hardware-in-the-loop (HIL) tests. The simulation results demonstrate the effectiveness of the proposed strategy. Compared to a rule-based EMS, the Q-learning-based EMS can improve the energy economy by 7.8%. Furthermore, it is only a 3.7% difference to the best energy economy under dynamic optimization, while effectively reducing the decline and enhancing the durability of the FCS. © 2023 Elsevier Ltd

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