Battery Fault Diagnosis Scheme Based on Improved RBF Neural Network

Abstract

In this paper, the fault diagnosis scheme for battery is investigated by an improved radial basis function (RBF) neural network. First, the causes of battery faults and the difficulties of fault diagnosis are analyzed. Second, by using the characteristics of experimental data, the subtractive clustering method (SCM) is employed to determine the number of hidden layer neurons, center vector, and expansion coefficient in the RBF neural network. Then, a battery fault diagnosis scheme is designed based on the proposed improved RBF neural network. The simulation results show that the designed scheme can accurately diagnose the type of battery fault with a fast training speed.