|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()
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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
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
Keywords:
electric vehicle;  battery management system(BMS);  lithium battery;  parameter identification;  state of charge(SOC)
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.

References:

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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