科研之友
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摘要
Model-based observers have been extensively studied for estimating battery healthy states. However, the pa-rameters of the battery model are sensitive to the state of charge (SOC). Therefore, a fast SOC estimation method is needed. In this paper, a current pulse of 90 s is used to obtain the SOC characteristic parameters of the battery. Then, 2500 sets of SOC characteristic parameters at different SOC are used to train four different algorithms, among which the cubic support vector machine (Cubic SVM) method has the highest accuracy in estimating the SOC. The trained Cubic SVM model can quickly estimate the approximate SOC for 9 different capacity batteries with less than 5 % SOC estimation error. Meanwhile, the SOC-related parameters proposed in this paper can provide a reference for the fast sorting of retired batteries.
