TY - GEN
T1 - A Rapidly Trainable Data-Driven Real-Time Energy Management Strategy for Fuel Cell Hybrid Electric Tractor
AU - Guo, Boyu
AU - Zhao, Jinghui
AU - Yan, Mei
AU - He, Hongwen
N1 - Publisher Copyright:
© Beijing Paike Culture Commu. Co., Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - This study proposes a real-time energy management strategy that can be swiftly trained and applied for a fuel cell hybrid electric tractor. The strategy learns from the optimal solution of power allocation control sequences in past real operating conditions with the same scene characteristics, enabling real-time power allocation control to be achieved in a single matrix computation. Due to its light computational load, it exhibits high computational efficiency in simulations while also demonstrating good fuel economy. Specifically, hydrogen consumption is only 2.18% higher than that of an energy management strategy based on dynamic programming, and it reduces equivalent hydrogen consumption by 4.74% compared to a traditional model predictive control strategy.
AB - This study proposes a real-time energy management strategy that can be swiftly trained and applied for a fuel cell hybrid electric tractor. The strategy learns from the optimal solution of power allocation control sequences in past real operating conditions with the same scene characteristics, enabling real-time power allocation control to be achieved in a single matrix computation. Due to its light computational load, it exhibits high computational efficiency in simulations while also demonstrating good fuel economy. Specifically, hydrogen consumption is only 2.18% higher than that of an energy management strategy based on dynamic programming, and it reduces equivalent hydrogen consumption by 4.74% compared to a traditional model predictive control strategy.
KW - Dynamic programming
KW - Energy management strategy
KW - Extreme learning machine
KW - Fuel cell hybrid electric tractor
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85212962115&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-8820-0_1
DO - 10.1007/978-981-97-8820-0_1
M3 - Conference contribution
AN - SCOPUS:85212962115
SN - 9789819788194
T3 - Lecture Notes in Electrical Engineering
SP - 1
EP - 9
BT - The Proceedings of the 11th Frontier Academic Forum of Electrical Engineering (FAFEE2024)
A2 - Yang, Qingxin
A2 - Li, Jian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024
Y2 - 20 June 2024 through 22 June 2024
ER -