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首页> 外文期刊>Advances in Radio Science >Impedance spectra classification for determining the state of charge on a lithium iron phosphate cell using a support vector machine
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Impedance spectra classification for determining the state of charge on a lithium iron phosphate cell using a support vector machine

机译:阻抗谱分类,使用支持向量机确定磷酸锂铁电池上的电荷状态

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摘要

An alternative method for determining the state of charge (SOC) on lithium iron phosphate cells by impedance spectra classification is given. Methods based on the electric equivalent circuit diagram (ECD), such as the Kalman Filter, the extended Kalman Filter and the state space observer, for instance, have reached their limits for this cell chemistry. The new method resigns on the open circuit voltage curve and the parameters for the electric ECD. Impedance spectra classification is implemented by a Support Vector Machine (SVM). The classes for the SVM-algorithm are represented by all the impedance spectra that correspond to the SOC (the SOC classes) for defined temperature and aging states. A divide and conquer based search algorithm on a binary search tree makes it possible to grade measured impedances using the SVM method. Statistical analysis is used to verify the concept by grading every single impedance from each impedance spectrum corresponding to the SOC by class with different magnitudes of charged error.
机译:给出了通过阻抗谱分类确定磷酸铁锂电池上电荷状态(SOC)的另一种方法。例如,基于等效电路图(ECD)的方法,例如卡尔曼滤波器,扩展卡尔曼滤波器和状态空间观察器,已经达到了针对这种电池化学的极限。新方法将开路电压曲线和电ECD的参数作为参考。阻抗谱分类通过支持向量机(SVM)实现。 SVM算法的类别由所有与定义的温度和老化状态的SOC对应的阻抗谱表示(SOC类别)。在二分搜索树上基于分治法的搜索算法使使用SVM方法对测量的阻抗分级成为可能。统计分析用于通过对每个阻抗谱中与SOC相对应的每个阻抗谱中的每个单个阻抗进行分级来对概念进行验证,其中每个类别的电荷误差大小不同。

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