SOC Estimation Method of Lithium Battery Based on Fuzzy Adaptive Extended Kalman Filter

被引：0
|
作者：
Gong M. [1 ]
Wu J. [1 ]
Jiao C. [2 ]
机构：
[1] School of Electrical Engineering, Xi'an Jiaotong University, Xi'an
[2] Nari Group Research Institute, Xi'an
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2020年 / 35卷 / 18期
关键词：
Adaptive extended Kalman filter (AEKF); Convergence speed; Fuzzy algorithm; State of charge (SOC);
D O I：
10.19595/j.cnki.1000-6753.tces.191810
中图分类号：
学科分类号：
摘要：
Accurate online estimation of the state of charge (SOC) of the lithium battery can effectively extend the battery life and improve the safety of the battery, which is very important for the battery management system (BMS) of the electric vehicle. Aiming at the problem of slow convergence in the initial running of the adaptive extended Kalman filter (AEKF) algorithm, this paper proposes a fuzzy AEKF (FAEKF) algorithm to improve the convergence speed of the AEKF algorithm. Taking the absolute value of the difference between the actual terminal voltage and the predicted terminal voltage of the NCR18650B ternary lithium battery and its change rate as the fuzzy input, using the noise measured R in the Kalman filter system as the fuzzy output, and adjusting the gain K of the algorithm by fuzzy controlling R in the iterative process, then realize the fuzzy adjustment of the convergence speed. Experimental results show that compared with the extended Kalman (EKF) and AEKF algorithms under the conditions of 0.5C rate constant current discharge condition and dynamic stress test condition (DST), the improved algorithm can improve the convergence speed, while not reduce the estimation accuracy, which is more practical in the online estimation of SOC. © 2020, Electrical Technology Press Co. Ltd. All right reserved.
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页码：3972 / 3978
页数：6
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