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结合支持向量机的卡尔曼预测算法在VRLA蓄电池状态监测中的应用

, PP. 168-174

Keywords: 支持向量机,卡尔曼预测,SVM-KF,SOC,SOH

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Abstract:

阀控式铅酸蓄电池在性能劣化时的系统状态模型是难以准确获知的,针对这一问题,在建立了蓄电池等效电路模型及其线性系统状态空间描述基础上,导出了一种适用于蓄电池性能劣化时的非线性求解方法,进而提出了一种结合支持向量机的卡尔曼预测算法。利用支持向量机迭代修正卡尔曼预测过程中的新息误差,使得卡尔曼预测算法具备了对蓄电池劣化时的状态方程修正功能。实验结果表明,该算法能准确预测蓄电池的实时剩余容量,辨识出蓄电池健康状态的非线性劣化趋势。

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