Kurtosis-Based State of Health Prediction of Lithium-Ion Batteries Using Probability Density Function

抄録

Lithium-ion batteries are widely used as power sources for various devices, so rapid and accurate estimation of the health status of lithium-ion batteries is an important means to reduce battery failures. This article conducts charging and discharging experiments on battery modules using Li_xNi_0.9Co_0.05Al_0.05O_1.57 (NCA) and lithium iron phosphate (LFP) as a positive electrode. A probability density function based methods for predicting the health status of lithium-ion batteries has been proposed. The n at the peak of the probability density function (PDF) curve of the battery charging voltage was used as input for the model to achieve accurate prediction of battery SOH. The experimental results show that there is a good correlation between this health indicator and battery SOH, with Pearson correlation coefficients greater than 0.96. Therefore, it can be concluded that it can indirectly reflect the current situation of battery SOH and serve as input for the model to further predict SOH. Long short-term memory networks (LSTM) have become a popular deep learning network method for predicting the health status (SOH) of lithium-ion batteries. The LSTM method without optimizing hyperparameters can easily lead to low accuracy in battery SOH prediction models. A modified LSTM method based on Sparrow Search Algorithm (SSA) is proposed for the prediction of State of Health (SOH) in lithium-ion batteries. When the training set only accounts for 20 % of the total data, the root mean square error (RMSE) of LFP battery prediction results is within 0.85 %, and the maximum absolute error (AE) is less than 2.5 %, while the RMSE of NCA battery SOH prediction results is within 0.7 %, and the maximum AE is less than 2.0 %. SSA-LSTM can accurately predict battery SOH under limited training data and has good robustness.