时变非平稳厚尾量测噪声下的锂电池荷电状态强跟踪估计方法

doi:10.19595/j.cnki.1000-6753.tces.250256

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Abstract Under the background of large-scale construction of new power energy storage system, lithium-ion battery (LIBs) has been widely used as the main electrochemical energy storage component of energy storage power station because of its excellent energy characteristics. However, in the complex environment of the new energy storage system, the data measurement noise of lithium batteries is no longer white Gaussian noise, and its amplitude shows two characteristics: Unknown statistical characteristics of distribution and ourliers. To estimate battery parameter and state of charge (SOC) with high precision and fast dynamic response under this condition is one of the main difficulties in the research field of lithium-ion battery measurement.
Among the existing SOC estimation algorithms, ampere hour (AH) method and open circuit voltage (OCV) method are affected by the external environment and battery loss, which gravely effected the estimation accuracy. The method based on artificial neural network fitting is difficult to obtain reliable training data set under complex noise conditions, and the operation cost and space occupation are high. Methods based on conventional nonlinear filters are often unable to adapt to SOC estimation in non-Gaussian noise environments and cannot resist state model bias. To solve the problems above, a strong SOC tracking estimation method based on gereralized variable forgetting factor recursive least squares (GVFFRLS) online parameter identification and Gauss-G-Gamma mixed prior variational H-density robust cubature filter is proposed.
Firstly, an online parameter identification method combining Sage-Husa adaptive weighting and generalized Gauss kernel forgetting factor was proposed to improve the stability and dynamic accuracy of battery online parameter identification.
Then, a hierarchical Gaussian cubature Kalman filter was constructed based on the Gauss-G-Gamma mixed prior distribution noise modeling and variational parameter inference, achieving the adaptive update of variational parameters with the change of real noise distribution. Based on the QS-density function constructed by H-density theory, a new L2-1/2 norm-weighted robust loss function and status-measurement combination innovation was designed. The variational iterative calculation process was closely combined with the robust state posterior estimation constructed by H-density loss criterion, and a strong tracking filtering algorithm for SOC estimation was proposed.
Finally, based on the INR 18650-20R lithium battery test data with non-stationary thick tail measurement noise under different temperatures and dynamic conditions, the proposed algorithm is simulated and compared with the common SOC estimation filtering algorithm.
The results show that the voltage estimation accuracy is improved by 96.32% compared GVFFRLS with forgetting factor recursive least squares (FFRLS) method, and the average accuracy of SOC estimated by proposed algorithm under non-stationary thick tail measurement noise is improved by 78.94%, 80.80% and 75.05% compared with traditional Kalman filters including extended Kalman filter (EKF), unscented Kalman filter(UKF) and Sage-Husa unscented Kalman filter (SHUKF), respectively. The SOC initial value tracking convergence performance and measurement noise distribution registration performance are much stronger than the existing SOC estimation algorithms.
The theoretical derivation process of the proposed algorithm is relatively complex, but the iterative calculation process is simple, and its operational efficiency is similar to that of the existing algorithms. Therefore, this method is easy to be applied to the engineering implementation of battery management system (BMS). This algorithm features adaptability to non-stationary measurement noise and strong adaptive correction performance for SOC state deviation tracking. It can be applied not only in complex noise environments but also in SOC estimation under normal conditions, thereby optimizing and enhancing the performance of the BMS algorithm, improving the utilization efficiency and safety of lithium battery modules. The safety and stability monitoring and maintenance measures for new energy storage power stations have been further enhanced.

Key words： Battery SOC estimation cubature Kalman filter variable forgetting factor recursive least squares variational Bayes method H-density loss criterion

Received: 18 February 2025

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TM912

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	Shi Lin
	Wang Tianjing
	Huang Haidong
	Xiong Hao
	Zhang Qibing

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Shi Lin,Wang Tianjing,Huang Haidong等. Strong Tracking Method for State-of-Charge Estimation of Lithium Battery under Time Varying Non-Stationary Heavy-Tailed Measurement Noise[J]. Transactions of China Electrotechnical Society, 2026, 41(3): 1040-1061.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.250256 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I3/1040

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