TY - GEN
T1 - Intelligent Energy Management for Fuel Cell Bus Based on Enhanced Soft Actor-Critic Algorithm
AU - Huang, Ruchen
AU - Niu, Zegong
AU - Su, Qicong
AU - He, Hongwen
AU - Zhou, Zheng
AU - Zhou, Zhiqiang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Energy management strategies (EMSs) directly affect the fuel economy of hybrid electric vehicles, and deep reinforcement learning (DRL) has become the mainstream method for energy management in recent years. This paper proposes an intelligent DRL-based EMS for an urban fuel cell bus (FCB) powered by both fuel cell and battery to improve the energy efficiency of the FCB. To begin, an enhanced soft actor-critic (SAC) algorithm integrating the standard SAC algorithm with the prioritized experience replay (PER) mechanism is innovatively formulated. Then, an intelligent EMS based on the enhanced SAC algorithm is developed. After that, the superiority of the proposed EMS is evaluated in detail by comprehensive comparisons. Simulation results indicate that the proposed EMS accelerates the convergence speed by 62.79% while improving the fuel economy by 5.18% in comparison with the EMS based on standard SAC.
AB - Energy management strategies (EMSs) directly affect the fuel economy of hybrid electric vehicles, and deep reinforcement learning (DRL) has become the mainstream method for energy management in recent years. This paper proposes an intelligent DRL-based EMS for an urban fuel cell bus (FCB) powered by both fuel cell and battery to improve the energy efficiency of the FCB. To begin, an enhanced soft actor-critic (SAC) algorithm integrating the standard SAC algorithm with the prioritized experience replay (PER) mechanism is innovatively formulated. Then, an intelligent EMS based on the enhanced SAC algorithm is developed. After that, the superiority of the proposed EMS is evaluated in detail by comprehensive comparisons. Simulation results indicate that the proposed EMS accelerates the convergence speed by 62.79% while improving the fuel economy by 5.18% in comparison with the EMS based on standard SAC.
KW - deep reinforcement learning
KW - energy management strategy
KW - enhanced soft actor-critic
KW - fuel cell bus
KW - prioritized experience replay
UR - http://www.scopus.com/inward/record.url?scp=85185345921&partnerID=8YFLogxK
U2 - 10.1109/VPPC60535.2023.10403274
DO - 10.1109/VPPC60535.2023.10403274
M3 - Conference contribution
AN - SCOPUS:85185345921
T3 - 2023 IEEE Vehicle Power and Propulsion Conference, VPPC 2023 - Proceedings
BT - 2023 IEEE Vehicle Power and Propulsion Conference, VPPC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE Vehicle Power and Propulsion Conference, VPPC 2023
Y2 - 24 October 2023 through 27 October 2023
ER -
