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- W2977774444 abstract "铅酸蓄电池作为变电站直流电源系统的核心设备，随着投运时间的增长，将会发生内阻增大、容量减小问题，从而导致铅酸蓄电池组的使用寿命减小。因此，研究铅酸蓄电池的寿命预测方法，对于保障变电站的安全稳定运行具有重要作用。在介绍支持向量机的基本原理的基础上，结合铅酸蓄电池的样本数据，通过交叉验证选取LIBSVM回归机最优参数，通过支持向量机回归预测模型建立铅酸蓄电池的寿命预测模型。实验结果表明，基于LIBSVM的铅酸蓄电池寿命预测模型具有较高的预测精度，该方法是切实可行的。 As the core of the DC power supply system, the performance of the lead-acid battery is the safe and stable operation of the whole substation. With the use of lead-acid battery pack time increases, the battery’s internal resistance will increase, the battery capacity will be reduced, resulting in lead-acid battery life. Therefore, it is of great significance to study the life prediction of lead-acid battery. Based on the basic principle of support vector machine (SVM) and the sample data of lead-acid battery, the optimal parameters of LIBSVM regression machine are selected by cross validation, and the life prediction model of lead-acid battery is established by support vector machine regression model. The experimental results show that the LIBSVM-based lead-acid battery life prediction model has high prediction accuracy, and the method is feasible." @default.
- W2977774444 created "2019-10-10" @default.
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- W2977774444 date "2017-01-01" @default.
- W2977774444 modified "2023-09-25" @default.
- W2977774444 title "Application of LIBSVM in Life Prediction of Lead-Acid Battery" @default.
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- W2977774444 doi "https://doi.org/10.12677/sg.2017.75045" @default.
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