Energy-efficiency optimization and control for electric vehicle platooning with regenerating braking

被引:0
|
作者
Li, Zhicheng [1 ]
Wang, Yang [1 ,2 ]
机构
[1] Shenzhen Polytech, IoT Res Inst, Shenzhen, Peoples R China
[2] Shenzhen Polytech, IoT Res Inst, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
autonomous driving; battery powered vehicles; control system synthesis; optimization and uncertainty; MANAGEMENT; STRATEGY;
D O I
10.1049/itr2.12445
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
It is a critical problem to improve energy efficiency for electric vehicle platooning systems. Moreover, different from internal combustion engine vehicles, the electric engine has higher efficiency, and further regenerating braking is widely used to recycle part of the energy in the electric vehicle when it is braking. What is more, if vehicles take a formation to drive, they can save more energy. Combining all the favorable factors, this paper presents a two-layer energy-efficiency optimization strategy for electric vehicle platooning. The upper layer presents an optimization method to find the optimal velocities and distances between vehicles under different road conditions during the cruise status of the electric vehicle platooning. Due to the nonconvex cost function and considering regenerative braking, the optimization problem is addressed by the dynamic programming method combined with the successive convex approximation method. Further, the lower layer presents a real-time Model Predictive Control (MPC) strategy, and it directly introduces the battery pack state of charge consumption as the input, which not only finishes the control mission but also consumes minimal energy. Finally, simulation results are provided to verify the effectiveness and advantages of the proposed methods. Based on the bidirectional energy flow in the engine of EVs, the problems of how to find the optimal velocity and distance between vehicles, and how to control EVs with the minimal energy cost for EV platooning are addressed by a two-layer strategy. The upper-layer optimization strategy solves the problem of how to find the optimal velocity and distance between vehicles in different road sections for EV platooning. The dynamic programming method combined with the successive convex approximation method is introduced to solve the non-convex optimization problem for energy saving. The lower-layer control strategy solves the problem of how to control EVs with minimal energy cost for the EV platooning in the transient state process. Aiming at costing minimal energy, the new MPC form is presented.image
引用
收藏
页码:203 / 217
页数:15
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