Research on Speed Planning and Energy Management Strategy for Fuel Cell Hybrid Bus in Green Wave Scenarios at Traffic Light Intersections Based on Deep Reinforcement Learning

Fengyan Yi, Wei Guo, Hongtao Gong, Yang Shen, Jiaming Zhou (), Wenhao Yu, Dagang Lu, Chunchun Jia, Caizhi Zhang and Farui Gong
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Fengyan Yi: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Wei Guo: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Hongtao Gong: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
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
Wenhao Yu: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Dagang Lu: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Chunchun Jia: Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Caizhi Zhang: The State Key Laboratory of Mechanical Transmissions, School of Mechanical and Vehicle Engineering, Chongqing Automotive Collaborative Innovation Centre, Chongqing University, Chongqing 400044, China
Farui Gong: School of Intelligent Manufacturing, Weifang University of Science and Technology, Weifang 262700, China

Sustainability, 2024, vol. 16, issue 24, 1-15

Abstract: In the context of intelligent and connected transportation, obtaining the real-time vehicle status and comprehensive traffic data is crucial for addressing challenges related to speed optimization and energy regulation in intricate transportation situations. This paper introduces a control method for the speed optimization and energy management of a fuel cell hybrid bus (FCHB) based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The strategy framework is built on a dual-objective optimization deep reinforcement learning (D-DRL) architecture, which integrates traffic signal information into the energy management framework, in addition to conventional state spaces to guide control decisions. The aim is to achieve “green wave” traffic while minimizing hydrogen consumption. To validate the effectiveness of the proposed strategy, simulation tests were conducted using the SUMO platform. The results show that in terms of speed planning, the difference between the maximum and minimum speeds of the FCHB was reduced by 21.66% compared with the traditional Intelligent Driver Model (IDM), while the acceleration and its variation were reduced by 8.89% and 13.21%, respectively. In terms of the hydrogen fuel efficiency, the proposed strategy achieved 95.71% of the performance level of the dynamic programming (DP) algorithm. The solution proposed in this paper is of great significance for improving passenger comfort and FCHB economy.

Keywords: deep reinforcement learning; vehicle speed planning; vehicle–road coordination; fuel cell hybrid bus; 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: 2024
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