An improved adaptive estimator for state-of-charge estimation of lithium-ion batteries

被引:41
|
作者
Zhang, Wenjie [1 ,2 ,3 ]
Wang, Liye [1 ,4 ]
Wang, Lifang [1 ,4 ]
Liao, Chenglin [1 ,4 ]
机构
[1] Chinese Acad Sci, Inst Elect Engn, Key Lab Power Elect & Elect Drive, Beijing 100190, Peoples R China
[2] Chinese Acad Sci, Inst Elect Engn, Beijing 100190, Peoples R China
[3] Univ Chinese Acad Sci, Beijing 100149, Peoples R China
[4] Beijing Coinnovat Ctr Elect Vehicles, Beijing 100190, Peoples R China
关键词
Lithium-ion batteries; State-of-charge; Improved adaptive battery state estimator; Generality; Robustness; Dynamic characteristics; UNSCENTED KALMAN FILTER; SLIDING MODE OBSERVER; ELECTROCHEMICAL MODEL;
D O I
10.1016/j.jpowsour.2018.09.016
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
State-of-charge is an important indicator for guiding the charging-discharging operation of lithium-ion batteries. In this paper, an improved adaptive battery state estimator is proposed, which estimates the state-of-charge accurately under both the constant current constant voltage charging conditions and various dynamic operating conditions. Besides, the state-of-charge estimation error is less than 3% under various common perturbations and parameter uncertainties, including the uncertainty of battery capacity, the Gaussian white noise and the biases of the sensors, which proves the robustness of the estimator. Generally, the drivers prefer to charge the electric vehicles after every driving. In other words, the lithium-ion batteries are likely to be charged or discharged from any state-of-charge and polarization state. In order to simulate this usage habit, several data segments are picked out from original data for state-of-charge estimation. Experiment results show that the state-of-charge estimation error reaches and remains within 3% rapidly under both the constant current constant voltage charging conditions and the dynamic discharging conditions. In conclusion, the improved adaptive battery state estimator is a promising algorithm for engineering application.
引用
收藏
页码:422 / 433
页数:12
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