Abstract：By researching the correlation between the capacity degradation of lithium-ion batteries and the trends of current, voltage, temperature and other parameters during charging and discharging, a state of health (SOH) prediction method of lithium-ion batteries based on the extracting of indirect health features and improved Gaussian process regression (GPR) model under different temperature conditions is proposed. Considering the problem of difficulty in extracting IHF under variable temperature, the HF in the voltage and time curve is adaptively obtained by random number method. In addition, the GPR model was established with the rational quadratic covariance as the kernel function to solve the problem of capacity regeneration, among which the conjugate gradient algorithm was used to optimize the GPR model. Finally, two evaluation metrics, root mean square error (RMSE) and mean absolute percentage error (MAPE), were set up to validate the proposed health feature (HF) and degradation model with single-cell and multi-cell experiments on battery datasets at different temperatures, and the results show that the IGPR model of the CVD-ETS at room temperature and DTD-EVS at high (low) temperature are the most effective way to predict the nonlinear trend of capacity degradation for lithium-ion batteries, which has advantage in small sample size, high prediction accuracy and wide applicability.
韩乔妮, 姜帆, 程泽. 变温度下IHF-IGPR框架的锂离子电池健康状态预测方法[J]. 电工技术学报, 2021, 36(17): 3705-3720.
Han Qiaoni, Jiang Fan, Cheng Ze. State of Health Estimation for Lithium-Ion Batteries Based on the Framework of IHF-IGPR under Variable Temperature. Transactions of China Electrotechnical Society, 2021, 36(17): 3705-3720.
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2021, Vol. 36 
