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State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies

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  • Tian, Yong
  • Huang, Zhijia
  • Tian, Jindong
  • Li, Xiaoyu

Abstract

State of charge (SOC) is one key status to indicate the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Cubature Kalman filter (CKF) is an intensively considered model-based approach for SOC estimation because of its merits in accuracy, convergence rate, and robustness compared with other Kalman filter methods. Nevertheless, CKF suffers from a non-positive definite error covariance matrix because of abnormal perturbations, inaccurate initial values and limited computer word length, causing the divergence of the CKF and the failure of SOC estimation. To address this issue, this paper introduces three typical matrix decomposition strategies, namely, singular value decomposition (SVD), UR decomposition and LU decomposition, to replace the Cholesky decomposition in the traditional CKF. The second-order RC equivalent circuit model is utilized to simulate the dynamics of a lithium-ion battery. The theoretical errors of the methods are formulated by F-norms. The results indicate that three matrix decomposition strategies can overcome the problem of a non-positive definite error covariance matrix and improve the convergence rate of the CKF. In particular, the UR decomposition exhibits the best comprehensive performance because it has a moderate convergence rate and computational cost, and it is robust against the initial error covariance matrix.

Suggested Citation

  • Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
  • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221021654
    DOI: 10.1016/j.energy.2021.121917
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    References listed on IDEAS

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    2. Takyi-Aninakwa, Paul & Wang, Shunli & Liu, Guangchen & Bage, Alhamdu Nuhu & Bobobee, Etse Dablu & Appiah, Emmanuel & Huang, Qi, 2024. "Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data," Applied Energy, Elsevier, vol. 363(C).
    3. Li, Kangqun & Zhou, Fei & Chen, Xing & Yang, Wen & Shen, Junjie & Song, Zebin, 2023. "State-of-charge estimation combination algorithm for lithium-ion batteries with Frobenius-norm-based QR decomposition modified adaptive cubature Kalman filter and H-infinity filter based on electro-th," Energy, Elsevier, vol. 263(PC).
    4. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    5. Wu, Chunling & Hu, Wenbo & Meng, Jinhao & Xu, Xianfeng & Huang, Xinrong & Cai, Lei, 2023. "State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment," Energy, Elsevier, vol. 274(C).

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