TY - GEN
T1 - Research on Optimized Energy Management Strategy Based on Micro-trip Recognition
AU - Zhang, Zelong
AU - Huangfu, Yigeng
AU - Xu, Liangcai
AU - Zhao, Jun
AU - Shi, Wenzhuo
AU - Yu, Tianying
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The energy management strategy of fuel cell electric vehicle can greatly influence the performance of vehicle, so a lot of research in this field were done by researchers. In order to improve the adaptability to complex work conditions of traditional strategies, the work condition recognition methods based on intelligence algorithms were introduced to energy management strategies. However, there are a lot of disadvantages of present recognition methods, such as low recognition accuracy and low generality. Aiming at solving these problems, an optimized fuzzy energy management strategy based on micro-trip recognition is proposed in this paper. In this strategy, firstly the work conditions prepared for recognition are divided into several micro-trips to improve the accuracy of recognition. Then, the strategy is simulated on the simplified power system built in this paper and compared with the simulation result of a general rule-based strategy. The better performance of the strategy proposed proves the effectiveness and optimality of this method.
AB - The energy management strategy of fuel cell electric vehicle can greatly influence the performance of vehicle, so a lot of research in this field were done by researchers. In order to improve the adaptability to complex work conditions of traditional strategies, the work condition recognition methods based on intelligence algorithms were introduced to energy management strategies. However, there are a lot of disadvantages of present recognition methods, such as low recognition accuracy and low generality. Aiming at solving these problems, an optimized fuzzy energy management strategy based on micro-trip recognition is proposed in this paper. In this strategy, firstly the work conditions prepared for recognition are divided into several micro-trips to improve the accuracy of recognition. Then, the strategy is simulated on the simplified power system built in this paper and compared with the simulation result of a general rule-based strategy. The better performance of the strategy proposed proves the effectiveness and optimality of this method.
KW - Energy management strategy
KW - Micro-trip recognition
KW - Multi-objectional optimization
UR - http://www.scopus.com/inward/record.url?scp=85123776380&partnerID=8YFLogxK
U2 - 10.1109/PEAS53589.2021.9628812
DO - 10.1109/PEAS53589.2021.9628812
M3 - 会议稿件
AN - SCOPUS:85123776380
T3 - PEAS 2021 - 2021 IEEE 1st International Power Electronics and Application Symposium, Conference Proceedings
BT - PEAS 2021 - 2021 IEEE 1st International Power Electronics and Application Symposium, Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Power Electronics and Application Symposium, PEAS 2021
Y2 - 12 November 2021 through 15 November 2021
ER -