Predictive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

被引：1
|
作者：
Zhang, Fengqi [1 ]
Xu, Kanghui [1 ]
Zhou, Chunguo [1 ]
Han, Shaojie [2 ]
Pang, Hui [1 ]
Cui, Yahui [1 ]
机构：
[1] Xian Univ Technol, Dept Mech Engn, Xian, Shaanxi, Peoples R China
[2] Beijing Inst Technol, Sch Mech Engn, Beijing, Peoples R China
来源：
2019 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC) | 2019年
关键词：
Hybrid electric vehicle; equivalent consumption minimization strategy; equivalent factor; predicted velocity; POWER MANAGEMENT STRATEGY; ENERGY MANAGEMENT; ECMS;
D O I：
10.1109/vppc46532.2019.8952549
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The performance of equivalent consumption minimization strategy (ECMS) strongly relies on the value of equivalent factor (EF).The EF is related to the future driving cycles. To this end, this paper proposes a predictive ECMS for hybrid electric vehicle utilizing the predicted energy, which is calculated by the predicted velocity from BP neural network. The dynamic EF adaptation law is devised according to the future driving information. The aim is to improve the adaptability to the varying driving cycles and further optimize the fuel efficiency. Simulations for two typical driving cycles are conducted to illustrate the effectiveness of the proposed method. The results indicate that the performance of predictive ECMS is more robust than one without preview.
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页数：5
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