Energy Management Strategy Based on Reinforcement Learning and Frequency Decoupling for Fuel Cell Hybrid Powertrain

Hongzhe Li, Jinsong Kang () and Cheng Li
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Hongzhe Li: College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
Jinsong Kang: Institute of Rail Transit, Tongji University, Shanghai 201804, China
Cheng Li: CRRC Zhuzhou Electric Locomotive Research Institute Co., Ltd., Zhuzhou 412001, China

Energies, 2024, vol. 17, issue 8, 1-21

Abstract: This study presents a Two-Layer Deep Deterministic Policy Gradient (TL-DDPG) energy management strategy for Hydrogen fuel cell hybrid train, that aims to solve the problem that traditional reinforcement learning strategies require high initial values and are difficult to optimize global variables. Augmenting the optimization capabilities of the inner layer, a frequency decoupling algorithm integrates into the outer layer, furnishing a fitting initial value for strategy optimization. This addition aims to bolster the stability of fuel cell output, thereby enhancing the overall efficiency of the hybrid power system. In comparison with the traditional reinforcement learning algorithm, the proposed approach demonstrates notable improvements: a reduction in hydrogen consumption per 100 km by 16.3 kg, a 9.7% increase in the output power stability of the fuel cell, and a 1.8% enhancement in its efficiency.

Keywords: energy management strategy (EMS); hybrid electric train; reinforcement learning; Two-Layer Deep Deterministic Policy Gradient (TL-DDPG); frequency decoupling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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