TY - GEN
T1 - An adaptive equivalent consumption minimization strategy for parallel hybrid electric vehicle based on Fuzzy PI
AU - Zhang, Fengqi
AU - Xi, Junqiang
AU - Langari, Reza
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/5
Y1 - 2016/8/5
N2 - This paper proposes a new energy management based on equivalent consumption minimization strategy (ECMS) for hybrid electric vehicles. The aim is to impose SoC charge-sustainability and enhance the fuel economy. First, the equivalent factor (EF) of ECMS is derived from Pontryagin's Minimum Principle. Second, a new adaptation law using Fuzzy Proportional plus Integral (PI) controller is developed to adjust EF in real-time. Finally, simulations for two driving cycles using ECMS are compared with rule-based (RB) control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results show that ECMS with Fuzzy PI adaptation of EF achieves significant improvement compared with RB in terms of fuel economy and is more robust than ECMS with constant EF.
AB - This paper proposes a new energy management based on equivalent consumption minimization strategy (ECMS) for hybrid electric vehicles. The aim is to impose SoC charge-sustainability and enhance the fuel economy. First, the equivalent factor (EF) of ECMS is derived from Pontryagin's Minimum Principle. Second, a new adaptation law using Fuzzy Proportional plus Integral (PI) controller is developed to adjust EF in real-time. Finally, simulations for two driving cycles using ECMS are compared with rule-based (RB) control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results show that ECMS with Fuzzy PI adaptation of EF achieves significant improvement compared with RB in terms of fuel economy and is more robust than ECMS with constant EF.
KW - Equivalent Consumption Minimization Strategy
KW - Hybrid Electric Vehicle
KW - equivalent factor
KW - fuzzy PI
UR - http://www.scopus.com/inward/record.url?scp=84983336003&partnerID=8YFLogxK
U2 - 10.1109/IVS.2016.7535426
DO - 10.1109/IVS.2016.7535426
M3 - Conference contribution
AN - SCOPUS:84983336003
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 460
EP - 465
BT - 2016 IEEE Intelligent Vehicles Symposium, IV 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Intelligent Vehicles Symposium, IV 2016
Y2 - 19 June 2016 through 22 June 2016
ER -