TY - JOUR
T1 - Imitation Learning-based Efficient Energy Management for Fuel Cell Electric Vehicles Using Data Aggregation
AU - Niu, Zegong
AU - Huang, Ruchen
AU - Zhou, Zheng
AU - He, Hongwen
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2025
Y1 - 2025
N2 - Emerging artificial intelligence (AI) learning technologies provide new insights into energy management strategies (EMSs) for fuel cell hybrid electric vehicles (FCHEVs). However, existing learning-based EMSs are obtained through inefficient trial-and-error learning, leading to low training efficiency. To address this issue, this paper proposes an EMS based on imitation learning (IL). Specifically, the EMS is trained using the behavior cloning (BC) algorithm to mimic the behaviors of dynamic programming (DP), avoiding the time-consuming trial-and-error and improving training efficiency. Furthermore, to further improve the adaptability and fuel economy of the strategy, a data aggregation mechanism is introduced to the BC-based EMS. This mechanism aggregates historical expert data with real-time expert data collected during driving to promptly update the parameters of the EMS. Simulation results show that the proposed BC-based EMS saves 70.48% of the training time cost compared with popular reinforcement learning-based EMS and its fuel economy reaches 96.15% of the DP-based EMS. Additionally, the proposed data aggregation mechanism enhances the adaptability of the BC-based EMS, resulting in a 2.96% improvement in fuel economy under unfamiliar driving conditions constructed from real-world velocity data. This paper provides a new solution to the energy management problem by adopting a novel AI learning approach.
AB - Emerging artificial intelligence (AI) learning technologies provide new insights into energy management strategies (EMSs) for fuel cell hybrid electric vehicles (FCHEVs). However, existing learning-based EMSs are obtained through inefficient trial-and-error learning, leading to low training efficiency. To address this issue, this paper proposes an EMS based on imitation learning (IL). Specifically, the EMS is trained using the behavior cloning (BC) algorithm to mimic the behaviors of dynamic programming (DP), avoiding the time-consuming trial-and-error and improving training efficiency. Furthermore, to further improve the adaptability and fuel economy of the strategy, a data aggregation mechanism is introduced to the BC-based EMS. This mechanism aggregates historical expert data with real-time expert data collected during driving to promptly update the parameters of the EMS. Simulation results show that the proposed BC-based EMS saves 70.48% of the training time cost compared with popular reinforcement learning-based EMS and its fuel economy reaches 96.15% of the DP-based EMS. Additionally, the proposed data aggregation mechanism enhances the adaptability of the BC-based EMS, resulting in a 2.96% improvement in fuel economy under unfamiliar driving conditions constructed from real-world velocity data. This paper provides a new solution to the energy management problem by adopting a novel AI learning approach.
KW - data aggregation
KW - Energy management strategy
KW - fuel cell hybrid electric vehicle
KW - imitation learning
UR - http://www.scopus.com/inward/record.url?scp=105004286547&partnerID=8YFLogxK
U2 - 10.1109/TTE.2025.3566075
DO - 10.1109/TTE.2025.3566075
M3 - Article
AN - SCOPUS:105004286547
SN - 2332-7782
JO - IEEE Transactions on Transportation Electrification
JF - IEEE Transactions on Transportation Electrification
ER -