Battery state of charge estimation basedon neural-network for electric vehicles

In this paper, the neural-network (NN) is proposed based approach to estimate the battery's state of charge (SOC) of the electric vehicles (EVs). In this approach, NNs are trained to extract important features from the battery's voltage, current, and temperature to estimate the battery's SOC. Such automated, noninvasive estimation will be critical in future EVs' energy monitoring and enhancement systems. Several NN-based estimation models including multilayer perceptron (MLP) network, radial basis function (RBF) network, linear support vector machines (SVM) network, and support vector machines network with MLP, and RBF kernels are developed for SOC estimation. The performance of these estimators is compared in terms of their accuracy and noise tolerance limits. The results showed that MLP and SVM are both able to estimate the SOC with high accuracy. SVM is found to be the best estimation method because of its high noise tolerating ability.