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A rapid classification method of the retired LiCo x Ni y Mn 1 ? x?y O 2 batteries for electric vehicles

机译：Resired Lico X Ni Y Mn 1的快速分类方法？ X？Y O 2电池用于电动车辆

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With the aging of Lithium-ion batteries (LIBs) of electric vehicles in the near future, research on the second use of retired LIBs is becoming more and more critical. The classification method of the retired LIBs is challenging before the second use due to large cell variations. This paper proposes a rapid classification method based on battery capacity and internal resistance, because batteries with different capacities and internal resistances have different voltage curves during charge/discharge. First, the piecewise linear fitting method established by the specified tested batteries with capacities and their corresponding characteristic voltages is used to sort the batteries. Then combined with the nonlinear function approximation ability of the radial basis function neural network (RBFNN) model, battery capacity and internal resistance are predicted after the model training. 108 cells are used for the simulation classification with experimental classification performed on 12 cells. The results prove that the classification method is accurate.
机译：随着锂离子电池（LIBS）在不久的将来的锂离子电池（LIBS）的情况下，对退休客人的第二次使用的研究变得越来越重要。由于大的细胞变化，退休的Libs的分类方法在第二次使用之前具有挑战性。本文提出了一种基于电池容量和内阻的快速分类方法，因为具有不同容量和内部电阻的电池在充电/放电期间具有不同的电压曲线。首先，采用由特定测试电池建立的分段线性拟合方法及其相应的特性电压来对电池进行排序。然后结合了径向基函数神经网络（RBFNN）模型的非线性函数近似能力，在模型训练之后预测了电池容量和内阻。 108个细胞用于模拟分类，在12个细胞上进行实验分类。结果证明了分类方法是准确的。

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