|Table of Contents|

[1] Fan Jiayu, Xia Jing, Chen Nan, Yan Yongjun, et al. Online SOC estimation based on modified covariance extendedKalman filter for lithium batteries of electric vehicles [J]. Journal of Southeast University (English Edition), 2020, 36 (2): 128-137. [doi:10.3969/j.issn.1003-7985.2020.02.002]
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Online SOC estimation based on modified covariance extendedKalman filter for lithium batteries of electric vehicles()
基于修正协方差扩展卡尔曼滤波法的 电动汽车锂电池SOC在线估计
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
36
Issue:
2020 2
Page:
128-137
Research Field:
Traffic and Transportation Engineering
Publishing date:
2020-06-20

Info

Title:
Online SOC estimation based on modified covariance extendedKalman filter for lithium batteries of electric vehicles
基于修正协方差扩展卡尔曼滤波法的 电动汽车锂电池SOC在线估计
Author(s):
Fan Jiayu1;  Xia Jing2;  Chen Nan1;  Yan Yongjun1
1School of Mechanical Engineering, Southeast University, Nanjing 211189, China
2School of Foreign Languages, University of Shanghai for Science and Technology, Shanghai 200093, China
范家钰1;  夏菁2;  陈南1;  严永俊1
1东南大学机械工程学院, 南京 211189; 2上海理工大学外国语学院, 上海 200093
Keywords:
electric vehicle;  battery management system(BMS);  lithium battery;  parameter identification;  state of charge(SOC)
电动汽车;  电池管理系统;  锂电池;  参数辨识;  荷电状态
PACS:
U463.6
DOI:
10.3969/j.issn.1003-7985.2020.02.002
Abstract:
To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries, an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm. Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods, the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter. Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm, the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5% at most under the complex charging and discharging conditions of batteries. In the charging process under the DST condition, the EKF produces a larger deviation and lacks stability, while the MVEKF algorithm can estimate SOC stably and has a strong robustness. Therefore, the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.
为弥补传统电动汽车锂电池SOC估计算法估计误差大的缺陷, 考虑电动汽车动力电池复杂的工作条件, 将参数在线辨识方法和修正协方差扩展卡尔曼滤波(MVEKF)算法结合, 提出了一种锂电池SOC在线估计算法.新算法使用变遗忘因子递归最小二乘法实现模型参数在线辨识, 利用修正后的状态估计值重新计算迭代过程中的协方差, 并将新的过程增益值用于下一状态估计以减少滤波误差.恒脉冲放电和动态应力测试(DST)等实验表明:在电池复杂的充放电条件下, 与EKF算法对比, MVEKF滤波算法估计误差更小, 最多可减少5%的误差;在DST条件下的充电过程中, EKF会有较大的偏差且不稳定, 而MVEKF算法可稳定地估计SOC, 且鲁棒性强, 适用于电动汽车电池复杂多变的工作条件.

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Memo

Memo:
Biographies: Fan Jiayu(1994—), male, graduate; Chen Nan(corresponding author), male, doctor, professor, nchen@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.51375086).
Citation: Fan Jiayu, Xia Jing, Chen Nan, et al. Online SOC estimation based on modified covariance extended Kalman filter for lithium batteries of electric vehicles[J].Journal of Southeast University(English Edition), 2020, 36(2):128-137.DOI:10.3969/j.issn.1003-7985.2020.02.002.
Last Update: 2020-06-20