Prediction state of charge of Ni-MH battery pack using support vector machines for Hybrid Electric Vehicles

This paper investigates the use of a support vector machine (SVM) to predict the state of charge (SOC) of a large-scale Ni-MH battery pack in hybrid electric vehicles (HEV). Estimate the state of charge (SOC) is very essential for HEVspsila energy monitoring and management systems. The nonlinear SOC dynamics is represented by a nonlinear autoregressive moving average with exogenous variables (NARMAX) model that is implemented using SVM regression model. Accuracy of the presented SVM method has been verified by UDDS and US06, which a composite aggressive driving cycle provided by U.S. Department of Energypsilas Hybrid Electrical Vehicle test program. The results showed that SVM is able to estimate the SOC with high accuracy and high noise tolerating ability.