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

Fengyuan Fang, Caiqing Ma () and Yan Ji
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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

Energies, 2024, vol. 17, issue 9, 1-18

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.

Keywords: multi-innovation Levenberg–Marquardt algorithm; adaptive weighting unscented Kalman filter; SOC estimation; SOH estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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