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State of Charge Estimation for Electric Vehicle Batteries Based on LS-SVM

For the study of optimal control problems of battery power in electric vehicle, accurately estimating the state of charge (SOC) of the battery is a non-negligible part. This paper proposes a prediction model for state of charge of batteries Based on least squares support vector machine. It was with battery terminal voltage, temperature, electric current as inputs, state of charge as output. After gaining data samples through experiment platform, least squares support vector machine was established, and state of charge can be predicted by the model. The experimental results show that the prediction accuracy of the method Based on LS - SVM significantly better than BP neural network, so it can be used to predict battery SOC values.