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A Method for State of Charge and State of Health Estimation of LithiumBatteries Based on an Adaptive Weighting Unscented Kalman Filter

Author

Listed:
  • Fengyuan Fang

    (College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Caiqing Ma

    (College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Yan Ji

    (College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

Abstract

This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective function to optimize the traditional gradient descent algorithm is used. For SOC estimation, an adaptive weighting unscented Kalman filter algorithm is proposed to deal with the nonlinear update problem of the mean and covariance, which can substantially improve the estimation accuracy of the internal state of the lithium battery. Compared with fixed weights in the traditional unscented Kalman filtering algorithm, this algorithm adaptively adjusts the weights according to the state and measured values to improve the state estimation update accuracy. Finally, according to simulations, the errors of this algorithm are all lower than 1.63 %, which confirms the effectiveness of this algorithm.

Suggested Citation

  • Fengyuan Fang & Caiqing Ma & Yan Ji, 2024. "A Method for State of Charge and State of Health Estimation of LithiumBatteries Based on an Adaptive Weighting Unscented Kalman Filter," Energies, MDPI, vol. 17(9), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2145-:d:1386731
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    References listed on IDEAS

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    1. Qinyao Liu & Feiyan Chen, 2023. "Model transformation based distributed stochastic gradient algorithm for multivariate output-error systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 54(7), pages 1484-1502, May.
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