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

Hui Bao, Y. Yu
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引用次数: 4

Abstract

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.
基于LS-SVM的电动汽车电池充电状态估计
在电动汽车电池功率最优控制问题的研究中,准确估计电池的荷电状态(SOC)是一个不可忽视的部分。提出了一种基于最小二乘支持向量机的电池电量状态预测模型。它以电池端电压、温度、电流为输入,电荷状态为输出。通过实验平台获取数据样本后,建立最小二乘支持向量机,并利用该模型对电荷状态进行预测。实验结果表明,基于LS - SVM的方法预测精度明显优于BP神经网络,可用于电池荷电状态的预测。
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