A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer

被引:96
|
作者
Xia, Bizhong [1 ]
Chen, Chaoren [1 ]
Tian, Yong [1 ]
Sun, Wei [2 ]
Xu, Zhihui [2 ]
Zheng, Weiwei [2 ]
机构
[1] Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 518055, Guangdong, Peoples R China
[2] Sunwoda Elect Co Ltd, Shenzhen 518108, Guangdong, Peoples R China
基金
中国博士后科学基金;
关键词
State of charge; Nonlinear observer; Lithium-ion battery; Electric vehicles; EXTENDED KALMAN FILTER; SLIDING MODE OBSERVER; OF-CHARGE; ELECTRIC VEHICLES; NEURAL-NETWORK; POLYMER BATTERY; SYSTEMS; HEALTH;
D O I
10.1016/j.jpowsour.2014.07.103
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The state of charge (SOC) is important for the safety and reliability of battery operation since it indicates the remaining capacity of a battery. However, as the internal state of each cell cannot be directly measured, the value of the SOC has to be estimated. In this paper, a novel method for SOC estimation in electric vehicles (EVs) using a nonlinear observer (NLO) is presented. One advantage of this method is that it does not need complicated matrix operations, so the computation cost can be reduced. As a key step in design of the nonlinear observer, the state space equations based on the equivalent circuit model are derived. The Lyapunov stability theory is employed to prove the convergence of the nonlinear observer. Four experiments are carried out to evaluate the performance of the presented method. The results show that the SOC estimation error converges to 3% within 130 s while the initial SOC error reaches 20%, and does not exceed 4.5% while the measurement suffers both 2.5% voltage noise and 5% current noise. Besides, the presented method has advantages over the extended Kalman filter (EKF) and sliding mode observer (SMO) algorithms in terms of computation cost, estimation accuracy and convergence rate. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:359 / 366
页数:8
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