Capacity prediction of Lithium-ion batteries based on adaptive sliding window pooling extreme learning machine algorithm

Abstract

Abstract Considering the safety and reliability, it is especially important to accurately predict the capacity decline trend of lithium-ion batteries. In this paper, a simple and easy-to-operate singular value decomposition technique is used to extract the health indicators (HIs) that are correlated with the capacity from the measurable parameters of battery, and then the HIs that have a high Pearson correlation coefficient with the capacity are selected for predicting the battery capacity. Aiming at the problems of low prediction accuracy and random dispersion of traditional extreme learning machine (ELM), this paper proposes an adaptive sliding window pooling extreme learning machine (ASW-PELM) algorithm. The algorithm first adaptively adjusts the window length according to the fluctuation of local data, and then dynamically traverses the data with the sliding window for data enhancement, and this adaptive sliding window mechanism provides effective data for the model prediction stage. Then it combines the pooling operation and the ELM to replace the random factor between the input layer and the hidden layer, which effectively solves the problem of random dispersion in the original learning model. The results of lithium battery capacity prediction under two sets of different experimental conditions show that the method has the highest prediction accuracy compared with other generalized algorithms.