Reinforcement Learning-Based Adaptive Hierarchical Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Engineering Vehicles

Huiying Liu (), Hai Xu (), Haofa Li, Binggao He and Yanmin Lei
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Huiying Liu: College of Electronic Information Engineering, Changchun University, Changchun 130022, China
Hai Xu: Shenyang Aircraft Airworthiness Certification Center of CAAC, Shenyang 110043, China
Haofa Li: Weichai Power Co., Ltd., Weifang 261061, China
Binggao He: College of Electronic Information Engineering, Changchun University, Changchun 130022, China
Yanmin Lei: College of Electronic Information Engineering, Changchun University, Changchun 130022, China

Sustainability, 2025, vol. 17, issue 22, 1-21

Abstract: To enhance the operational efficiency of fuel cell engineering vehicles in transportation, reliable energy management strategies (EMSs) are essential for optimizing fuel consumption and power distribution. In this paper, we propose a novel energy management framework that utilizes a reinforcement learning-based adaptive hierarchical equivalent consumption minimization strategy (ECMS) to regulate fuel cell/battery hybrid system. The structure integrates deep Q-network (DQN), fuzzy logic, and ECMS algorithms and employs a long short-term memory neural network for working condition prediction. By combining DQN with the equivalence factor obtained using the battery state of charge penalty function and adjusting it using a fuzzy logic controller, the stability of the subsequent ECMS is enhanced. In a simulation environment, the proposed EMS achieves a 97.44% fuel economy compared to the dynamic programming-based global optimized EMS. Experimental findings indicate that the hierarchical ECMS effectively decreases the equivalent hydrogen consumption by 3.38%, 9.12%, and 16.39% compared to the adaptive ECMS, DQN-based ECMS, and classic ECMS, respectively. Therefore, the proposed methodology offers superior economic benefits.

Keywords: reinforcement learning; energy management; equivalent consumption minimization strategy; adaptive hierarchical (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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