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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-thermal model

Author

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  • Li, Kangqun
  • Zhou, Fei
  • Chen, Xing
  • Yang, Wen
  • Shen, Junjie
  • Song, Zebin

Abstract

A novel algorithm containing an adaptive cubature Kalman filter (ACKF) modified by Frobenius-norm-based (fro-norm-based) QR decomposition (QR) and H-infinity(H∞) filter based on electro-thermal model is proposed to estimate the state of charge (SOC) of lithium-ion batteries (LIBS). First, an electro-thermal model with a second-order RC equivalent circuit model (ECM) and a lumped thermal model is employed to identify the internal parameters of LIBS at different temperatures. Then, to solve the non-positive definiteness of the error covariance matrix, an adaptive cubature Kalman filter is modified by fro-norm-based QR decomposition (ACKF-QR). Finally, to cope with uncertain noises especially non-Gaussian noises, the H∞ filter is combined with ACKF-QR to estimate the battery SOC (ACKF-QR-H∞). The ACKF-QR-H∞ algorithm is validated under different working conditions at different temperatures with incorrect initial values. The SOC estimation MAXE (Maximum absolute error) of the ACKF-QR-H∞ algorithm is less than 1% and its SOC estimation MAE (Mean absolute error) and RMSE (Root mean square error) are less than 0.32%. As compared with the same algorithm without considering temperature variations, the SOC estimation error of ACKF-QR-H∞ algorithm can almost reduce by half in most cases. When various noises are added manually, the ACKF-QR-H∞ algorithm can remain robust.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222026494
    DOI: 10.1016/j.energy.2022.125763
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