基于变窗口自适应无迹卡尔曼滤波的锂离子电池荷电状态预测

doi:10.19595/j.cnki.1000-6753.tces.240442

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Abstract In the lithium-battery charge state prediction, the Kalman filter algorithm is independent of a large number of dataset training. It can predict the state quantities with the observed data to obtain the optimal estimation of the system in the form of extended Kalman (extended Kalman filter (EKF)), untraceable Kalman filter (UKF), and other extended forms. However, the Kalman filtering algorithm and its extended forms for lithium battery nonlinear time-varying system obtain a fixed noise covariance, easily leading to the prediction error. Therefore, this paper proposes a variable-window adaptive untraceable Kalman (VAUKF) to determine the adaptive untraceable Kalman time window length, avoiding the prediction error caused by improper window length selection. The adaptive genetic algorithm (AGA) has been proven to achieve good parameter computation ability in avoiding local optimization and convergence speed problems. Thus, AGA calculates the optimal time window length, the overlapping grouped Allan ANOVA identifies the error sequence fluctuation, and the iterative process adjusts the window length appropriately. The VAUKF improves the SOC’s prediction accuracy and robustness compared to the AUKF.
First, based on the second-order RC lithium-ion battery equivalent circuit model, simulation modeling is carried out under the Federal Urban Driving Schedule (FUDS) and US06 high-speed cycling condition (US06) data. The noise level of the prediction process is obtained through the Allan variance, the variable window adjustment rule is determined, and the impact of noise fluctuations on prediction performance is analyzed. Then, the SOC prediction performance of VAUKF under different multiplicities is explored for the noise-matching window update rule, which provides more reasonable parameter conditions for VAUKF. Finally, the tracking ability and convergence speed of VAUKF and AUFK under different working conditions are analyzed, and the simulation results are discussed.
Compared with AUKF, the VAUKF decreases MAE by 25.3% and RMSE by 24.4% in RMSE under the FUDS condition. MAE and RMSE are decreased by 21.4% and 20.2% under the US06 condition. When the VAUKF takes different variance multiplicities, the FDUS condition still obtains better prediction results than AUKF, with the best performance when the multiplier is 10. The proposed VAUKF has better prediction performance than the AUKF with a fixed noise covariance matching time window. It can improve the anti-interference ability against time-varying noise the accuracy and robustness of SOC predictions.

Key words： State of charge (SOC) estimation adaptive unscented Kalman filter variable window adaptive unscented Kalman filter

Received: 20 March 2024

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TM912

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	Fan Xingming
	Wu Runwei
	Feng Hao
	Zhang Xin

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Fan Xingming,Wu Runwei,Feng Hao等. Lithium-Ion Battery State of Charge Estimation Based on Variable-Window Adaptive Untraceable Kalman Filtering Algorithm[J]. Transactions of China Electrotechnical Society, 2025, 40(6): 1974-1983.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240442 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I6/1974

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