Prediction of battery charging process based on aggregation neural network

Abstract

An online sequence extreme learning machine neural network prediction model based on sample aggregation method (AOSELM) is proposed, which is used to solve the prediction of the charging process of lead-acid batteries. The model is based on the online sequence extreme learning machine (OSELM) neural network and finds the similar samples of the test samples based on the mean influence value (MIV) algorithm. Similar samples are used for temporary incremental learning of the neural network, then the updated neural network makes predictions on the test samples. Taking the terminal voltage prediction of the battery charging process as an example, the BP neural network, the OSELM neural network and the AOSELM neural network are used to compare the predictions, and the charging process of the battery is predicted based on the AOSELM neural network. The comparison results show that the AOSELM neural network has higher prediction accuracy and adaptive ability than the BP neural network, and reduces the maximum absolute percentage error and the maximum absolute error of the prediction result when compared with the OSELM neural network. The prediction shows that the charging process of lead-acid batteries can be accurately predicted by the AOSELM neural network.