基于最小二乘支持向量机的电池剩余电量预测

A prediction model for remaiming capacity in batteries based on least square support vector machine

提出了一种最小二乘支持向量机的电池剩余电量预测新模型。以电池端电压和新旧程度为输入,电池的剩余电量为输出,通过电池充放电实验获得数据样本。以实验数据为基础,建立最小二乘支持向量机模型,利用训练好模型预测电池在静置状态下的剩余电量。该方法具有建模速度快、预测精度高、操作简便等优点。不仅克服了常规的BP预测模型的不足,而且性能优于标准支持向量机预测模型。

A novel prediction model for remaining capacity of batteries based on least square support vector machine （LS-SVM） was proposed. With battery ending voltage and degree of new as inputs, remaining capacity of resting batteries as output, test was taken to get data samples.Throghout data samples above, LS-SVM model was established, and remaining capacity of batteries can predict by the model. Experimental results show that the construction speed of this LS-SVM model is higher than that of the SVM model, while the prediction errors are less. Moreover, compared with BP model, the accuracy and speed of prediction are much higher than that of the former.