Research on Energy Management of Hydrogen Fuel Cell Bus Based on Deep Reinforcement Learning Considering Velocity Control

Yang Shen, Jiaming Zhou, Jinming Zhang, Fengyan Yi (), Guofeng Wang (), Chaofeng Pan, Wei Guo and Xing Shu
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Yang Shen: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Jiaming Zhou: School of Intelligent Manufacturing, Weifang University of Science and Technology, Weifang 262700, China
Jinming Zhang: School of Intelligent Manufacturing, Weifang University of Science and Technology, Weifang 262700, China
Fengyan Yi: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Guofeng Wang: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Chaofeng Pan: School of Automotive Engineering, Jiangsu University, Zhenjiang 212013, China
Wei Guo: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Xing Shu: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China

Sustainability, 2023, vol. 15, issue 16, 1-19

Abstract: In the vehicle-to-everything scenario, the fuel cell bus can accurately obtain the surrounding traffic information, and quickly optimize the energy management problem while controlling its own safe and efficient driving. This paper proposes an energy management strategy (EMS) that considers speed control based on deep reinforcement learning (DRL) in complex traffic scenarios. Using SUMO simulation software (Version 1.15.0), a two-lane urban expressway is designed as a traffic scenario, and a hydrogen fuel cell bus speed control and energy management system is designed through the soft actor–critic (SAC) algorithm to effectively reduce the equivalent hydrogen consumption and fuel cell output power fluctuation while ensuring the safe, efficient and smooth driving of the vehicle. Compared with the SUMO–IDM car-following model, the average speed of vehicles is kept the same, and the average acceleration and acceleration change value decrease by 10.22% and 11.57% respectively. Compared with deep deterministic policy gradient (DDPG), the average speed is increased by 1.18%, and the average acceleration and acceleration change value are decreased by 4.82% and 5.31% respectively. In terms of energy management, the hydrogen consumption of SAC–OPT-based energy management strategy reaches 95.52% of that of the DP algorithm, and the fluctuation range is reduced by 32.65%. Compared with SAC strategy, the fluctuation amplitude is reduced by 15.29%, which effectively improves the durability of fuel cells.

Keywords: fuel cell bus; deep reinforcement learning; vehicle velocity control; energy management strategy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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