State of Charge Estimation Method for Lithium-Ion Battery Based on Lithium Dendrite Influence
Zhao Jingying1, Cui Yujie1, Liu Jianmeng2
1. School of Electrical Engineering Hebei University of Technology Tianjin 300401 China; 2. State Grid Beijing Fang Shan Electric Power Company Beijing 102401 China
Abstract：With lithium dendrite growth, the diaphragm may bepiercedforming a microshort circuit that increases the Ohmic internal resistance (R0). At the same time, lithium dendrite accumulation can exacerbate non-uniformity in the electrode surface reaction, leading to dynamic changes in the polarization resistance and polarization capacitance.However, without accounting for dynamic mutations caused by lithium dendrites, traditional identification methods based on fixed or slowly varying parameters degrade SOC estimation accuracy.Additionally, the distinct characteristics of lithium dendrites under varying charge and discharge rates affect the actual capacity change, thereby influencingthe accuracy of SOC estimation. Method: A new strategy was proposed based on the ant lion optimization (ALO) algorithm and an adaptive interpolation improved strong tracking extended Kalman filter (AISTAEKF). First, the growth characteristics of lithium dendrites induced by the pulse-current concentration were analyzed, clarifying differences in lithium dendrite growth rates and their impacts on battery parameters across high, medium, and low SOC stages. Recognizing the distinction between internal resistance and polarization capacitance, the ALO algorithm was introduced to characterize the nonlinear relationship between the battery's terminal voltage and its resistance- capacitance parameters. The SOC three-stage Huber loss function was designed by setting thresholds δ1, δ2, and δ3 to measure the deviation between the model-predicted terminal voltage and the estimated value. A global parameter identification method of the equivalent circuit model was obtained. Then, an adaptive interpolation algorithm based on capacity fluctuations was introduced, using nonlinear indices nz and nl to determine interpolation factors that perform linear pseudo-interpolation on the sampled parameters to compensate for dynamic response characteristics. A fading factor derived from a robust tracking algorithm was used to update the SOC error covariance matrix. The orthogonality of the estimation residual vector was ensured, and the tracking capability for SOC sudden changes was improved. Additionally, a noise-adaptive mechanism using forgetting factors α and β dynamically adjusts the noise covariances of the process and measurement parameters, thereby enhancing the algorithm's robustness. Hybrid power pulse characteristic (HPPC), dynamic stress test (DST), urban dynamometer driving schedule (UDDS), and the united states government’s light vehicle drive cycle (US06) experiments were designed to verify the effectiveness of the SOC estimation model. Results: (1) Under different temperature gradients and complex conditions, the comparative analysis of the equivalent circuit model based on FFRLS, PSO-KF, and ALO algorithms verifies that the ALO algorithm can effectively capture the changes of terminal voltage and resistance capacitance parameters caused by lithium dendrite growth in parameter identification. Its root mean square error (RMSE) was reduced by 13.93%~53.59% relative to PSO-KF. (2) According to the SOC estimation of six algorithms, EKF, AEKF, STAEKF, AISTAEKF, LSTM, and CNN-LSTM, the AISTAEKF algorithm has good SOC estimation accuracy in a wide temperature range (0℃~45℃), different aging degrees, and a wide range of operating conditions. The maximum error (MAX) was within 0.936% under HPPC conditions, and the tracking delay was shortened to 84 ms under UDDS conditions.
赵靖英, 崔宇杰, 刘建猛. 考虑锂枝晶影响的锂电池荷电状态估计方法[J]. 电工技术学报, 2026, 41(6): 2101-2118.
Zhao Jingying, Cui Yujie, Liu Jianmeng. State of Charge Estimation Method for Lithium-Ion Battery Based on Lithium Dendrite Influence. Transactions of China Electrotechnical Society, 2026, 41(6): 2101-2118.
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2026, Vol. 41 
