Research progress on powertrain and energy management strategy of fuel cell vehicle

被引：0
|
作者：
Chen J. [1 ]
Gao W. [2 ]
Jia L. [3 ]
Yin Y. [2 ]
Wang C. [2 ]
Ouyang H. [1 ]
机构：
[1] College of Mechanical and Electrical Engineering, Central South University, Changsha
[2] Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing
[3] Zhejiang Fenergy Technology Co. Ltd., Jiaxing
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2024年 / 55卷 / 01期
基金：
中国国家自然科学基金;
关键词：
energy management strategy; fuel cell vehicles; hybrid powertrain system; reinforcement learning;
D O I：
10.11817/j.issn.1672-7207.2024.01.007
中图分类号：
学科分类号：
摘要：
The powertrain system is the core of fuel cell vehicle(FCV), which can be divided into two categories: the single power source system and hybrid powertrain system. Among them, the combination of fuel cell and auxiliary power source to form "electric-electric" hybrid system has become the mainstream. Three types of FCV hybrid systems were summarized according to different types of auxiliary power sources, namely fuel cell + battery, fuel cell + supercapacitor, and fuel cell + battery + supercapacitor, and the advantages and disadvantages of each type were compared. The representative energy management strategies for FCVs proposed in recent years were reviewed. Based on the differences in theoretical foundations and solution methods, the existing energy management strategies for fuel cell vehicles were divided into three categories: rule-based, optimization-based, and machine-learning-based strategies, and the pros and cons of each strategy in terms of optimality and real-time performance were summarized. Among them, the rule-based strategies are the easiest to implement and the most commonly used in engineering applications, but they cannot achieve optimal performance. The optimization-based strategies can approach or even reach the theoretical optimum, but there are problems such as excessive computation, long computation time, and poor real-time performance. The machine-learning based strategies, represented by reinforcement learning, are expected to achieve the ideal balance between optimality and real-time performance, but they still suffer from time-consuming model training and high trial-and-error costs. Therefore, there are still some challenges in practical vehicle applications. Based on literature research and analysis, this paper proposes the following perspectives. 1) The systems with high-power fuel cells as the core are the future development direction of FCV hybrid systems. 2) It is necessary to further improve the degree of intelligence and develop personalized energy management strategies based on actual usage scenarios. 3) There is an urgent need to develop a comprehensive evaluation system for FCV energy management strategies. © 2024 Central South University of Technology. All rights reserved.
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页码：80 / 92
页数：12
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