Sliding Window Pooling Extreme Learning Machine Based Capacity Prediction of Lithium-ion Batteries

Abstract

Lithium batteries have been widely used in various fields. Accurately predicting the remaining useful life (RUL) of batteries is greatly significant for the safety, reliability and health management of batteries. Based on the measured parameters in the process of charge and discharge of battery, the extraction method of singular value decomposition (SVD) is used to get the health indicators (HIs) related to capacity. And the traditional extreme learning machine (ELM) method has been used to predict the RUL of lithium battery, but the prediction results are divergent, random and low precision. Therefore, this paper proposes sliding window pooling ELM (SW-PELM) method to solve these problems by adding a sliding window and pooling connections. The simulation experiment was carried out through the data of lithium-ion battery from three different research institutions. Experimental results show that the proposed algorithm can well improve the divergence and random ELM prediction results, and has higher prediction accuracy compared with other improved ELM algorithms.