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SOC estimation of lithium battery with weighted multi-innovation adaptive Kalman filter algorithm
ID：111 View Protection：ATTENDEE Updated Time：2022-05-22 23:33:11 Hits：365 Poster Presentation

Start Time：Pending（Asia/Shanghai）

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Abstract
For the second-order equivalent circuit model of lithium batteries, in order to improve the accuracy of the extended Kalman filter (EKF) algorithm, a weighted multi-innovation adaptive Kalman filter (MI-AEKF) method is investigated for battery SOC estimation. Improve information utilization by expanding the single innovation from current time to multiple innovations containing current and historical time information Considering the influence of present and past data, a weighted multiple information theory is studied. Experiments are carried out under different working conditions. Considering noise variation, a recursive noise estimation based extended Kalman filter algorithm is adopted to realize adaptive correction of noise, and the results show that the method effectively reduces the divergence of the filtering and enhances the estimation accuracy and stability of the algorithm.
Keywords
lithium battery; state of charge(SOC); multi-information; extended Kalman filte
Speaker
XuJieyu
Student Qingdao University

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Important Date

  • Conference Date

    May 27

    2022

    to

    May 29

    2022

  • Feb 28 2022

    Draft paper submission deadline

  • May 29 2022

    Registration deadline

  • Jun 22 2022

    Contribution Submission Deadline

Sponsored By
IEEE Beijing Section
China Electrotechnical Society
Southeast University
Supported By
IEEE Industry Applications Society
IEEE Nanjing Section
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