A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries

In this work, accelerated aging tests at different temperatures are conducted for 228Ah mining lithium-ion batteries, and an SOH prediction method with health features (HFs) optimization and ensemble learning method is proposed. Firstly, six health features are extracted from cyclic charge/discharge data. Simultaneously, to solve the problem of a single feature not fully reflecting the SOH at multiple temperatures, canonical correlation analysis is introduced to construct the feature fusion vector to obtain the comprehensive health feature (C–HF). Secondly, the complementary ensemble empirical mode decomposition method is used to smooth the features and the SOH of the battery to extract the raw data of the battery to be tested in the stable frequency range. Then, four different datasets are used to comprehensively evaluate the performance of C–HF in the ensemble learning method. Compared with other HFs, the optimized feature C–HF has the best SOH prediction in all datasets, with high prediction accuracy and strong robustness. Finally, we compare SVM, LSTM, and LSTM-SVM with the proposed method in this paper for SOH prediction. Whether 70 % or 30 % of training datasets are used, the proposed method's estimation results are closer to the actual in SOH.