文件名称:Supportvectormachinebasedbatterymodelforelectricve
- 所属分类：
- 人工智能/神经网络/遗传算法
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- [PDF]
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- 2012-11-26
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- 175kb
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- al***
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The support vector machine (SVM) is a novel type of learning machine based on statistical learning theory
that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish
the relationship between the load voltage and the current under different temperatures and state of
charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed
on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the
SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate
the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61 .
that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish
the relationship between the load voltage and the current under different temperatures and state of
charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed
on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the
SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate
the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61 .
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Support vector machine based battery model for electric vehicles.pdf