Energy Management Strategy of Fuel Cell Commercial Vehicles Based on Adaptive Rules

Shiyou Tao, Zhaohui Peng () and Weiguang Zheng
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Shiyou Tao: School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
Zhaohui Peng: School of Automotive Engineering, Guangxi Technological College of Machinery and Electricity, Nanning 530007, China
Weiguang Zheng: School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China

Sustainability, 2024, vol. 16, issue 17, 1-22

Abstract: Fuel cell vehicles have been widely used in the commercial vehicle field due to their advantages of high efficiency, non-pollution and long range. In order to further improve the fuel economy of fuel cell commercial vehicles under complex working conditions, this paper proposes an adaptive rule-based energy management strategy for fuel cell commercial vehicles. First, the nine typical working conditions of commercial vehicles are classified into three categories of low speed, medium speed and high speed by principal component analysis and the K-means algorithm. Then, the crawfish optimization algorithm is used to optimize the back propagation neural network recognizer to improve the recognition accuracy and optimize the rule-based energy management strategy under the three working conditions to obtain the optimal threshold. Finally, under WTVC and combined conditions, the optimized recognizer is used to identify the conditions in real time and call the optimal rule threshold, and the sliding average filter is used to filter the fuel cell output power in real time, which finally realizes the adaptive control. The simulation results show that compared with the conventional rule-based energy management strategy, the number of fuel cell start–stops is reduced. The equivalent hydrogen consumption is reduced by 7.04% and 4.76%, respectively.

Keywords: fuel cell commercial vehicle; energy management strategy; operating condition identification; adaptive control (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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