Energy Optimal Control of Motor Drive System for Extending Ranges of Electric Vehicles

被引:29
|
作者
Zhang, Ying [1 ]
Zhang, Yingjie [1 ]
Ai, Zhaoyang [2 ]
Murphey, Yi Lu [3 ]
Zhang, Jing [4 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
[2] Hunan Univ, Coll Foreign Languages, Inst Cognit Control & Biophys Linguist, Changsha 410082, Peoples R China
[3] Univ Michigan, Coll Engn & Comp Sci, Dearborn, MI 48128 USA
[4] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
基金
中国国家自然科学基金;
关键词
Driving range extension; electric vehicles; energy efficiency; motor drive system; optimal control; MODEL-PREDICTIVE CONTROL; EFFICIENT CONTROL; DESIGN;
D O I
10.1109/TIE.2019.2947841
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The short driving range per charge of electric vehicle is a critical shortcoming hindering its popularization and application. This article proposes an in-vehicle energy optimal control (EOC) to improve the efficiency of the motor drive system. This article first builds a vehicle's longitudinal dynamics model and a motor drive system's efficiency model. Then, a Lagrange-Euler-based optimal control theory is formulated with detailed proof. Based on the vehicle dynamics model, a cost function is constructed by considering the energy consumption and speed demand. Combining the cost function and the Lagrange-Euler-based optimal control theory, an in-vehicle EOC is proposed. The idea of the in-vehicle EOC is mainly to make the motor drive system operate in the high efficiency region as much as possible, which results in longer driving ranges. The in-vehicle EOC is validated on a co-simulation platform and the results show it outperforms the model predictive control method.
引用
收藏
页码:1728 / 1738
页数:11
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