A Fusion Method Based on EEMD, Pearson Correlation Analysis, Improved LSTM, and Gaussian Function-Trust Region Algorithm for State of Health Prediction of Lithium-Ion Batteries

摘要

Aiming at the phenomenon of battery capacity regeneration, which leads to inaccurate prediction of lithium-ion battery state of health (SOH), a new fusion method based on ensemble empirical mode decomposition (EEMD), Pearson correlation analysis (PCA), and improved long short-term memory (LSTM) network and Gaussian function-trust region (GS-TR) algorithm is introduced to predict battery SOH. First, the EEMD method is adopted to process the battery SOH data to eliminate the impact of capacity recovery. Second, the decomposed data signals are classified by the PCA method, and the signals classified as high frequency and low frequency are respectively predicted by the improved LSTM algorithm and the GS-TR algorithm. Finally, the prediction results of the improved LSTM and GS-TR algorithms are integrated. The proposed fusion method avoids the complexity of the hybrid neural network model and improves the prediction efficiency. Based on the average results of the three data sets from NASA, the RMSE result of the proposed algorithm is reduced by 9.56% compared with the improved LSTM with the EEMD algorithm and 37.57% compared with the improved LSTM without the EEMD algorithm. The results show that the proposed method has higher adaptability and prediction accuracy.