Energy Management Strategy Based on Optimal System Operation Loss for a Fuel Cell Hybrid Electric Vehicle

汽车工程 营业成本 能源管理 燃料效率 能源消耗 电池(电) 荷电状态 计算机科学 功率(物理) 可靠性工程 控制理论(社会学) 工程类 能量(信号处理) 电气工程 控制(管理) 统计 物理 数学 量子力学 人工智能 废物管理
作者
Tianhong Wang,Yibin Qiu,Shuqi Xie,Qi Li,Weirong Chen,Elena Breaz,Alexandre Ravey,Fei Gao
出处
期刊:IEEE Transactions on Industrial Electronics [Institute of Electrical and Electronics Engineers]
卷期号:71 (3): 2650-2661 被引量:3
标识
DOI:10.1109/tie.2023.3269477
摘要

High operating cost and deficient longevity of stacks are the two primary factors that hinder the widespread commercial usage of fuel cell (FC) technology. In addition, most existing strategies concentrate solely on ameliorating system operating efficiency or fuel consumption, without fully considering the impact of other factors, such as the degradation of power source performance. Thereby, based on the above research background, this study presents an energy management strategy based on optimal system operation loss (OSOL-EMS), which considers various parameters, such as power sources' durability, to minimize the operating cost of electric vehicles. To accomplish this objective, this study formulates a life-cycle operating loss evaluation function related to the lifetime loss of the power sources and the hydrogen consumption cost of the FC. Additionally, the voltage loss is also utilized to evaluate the operating performance of the FC to restrict its output power fluctuation rate. In addition, this study also considers limiting the variation of the battery's state of charge (SOC) in order to decrease the equivalent hydrogen consumption of the system. Moreover, the high-efficiency operation zone for the stack is also divided. Additionally, given that the performance of FC is related to the working condition, an extended Kalman filter algorithm is used to update the operation parameters of the FC in real-time. The experiment results show that the proposed strategy has approximate global optimization ability and compared with equivalent hydrogen consumption minimization strategy and state machine control strategy, it can reduce the operating cost by 19.69% and 28.18%, respectively.
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