The state of health is an indicator of battery performance evaluation and service lifetime prediction, which is essential to ensure the reliability and safety of electric vehicles. Although a large number of capacity studies have emerged, there are few simple and effective methods suitable for engineering practice. Hence, a least square support vector regression model with polynomial kernel function is presented for battery capacity estimation. By the battery charging curve, the feature samples of battery health state are extracted. The grey relational analysis is employed for the feature selection, and the K-fold cross-validation is adopted to obtain hyper-parameters of the support vector regression estimation model. To validate this method, the support vector regression estimation model was trained and tested on the battery data sets provided by NASA Prognostics Center of Excellence. The experimental results show that the proposed method only needs some battery feature data, and can achieve high-precision capacity estimation, which indicates that the proposed method shows great efficiency and robustness.

中文翻译：

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基于恒流-恒压充电曲线的锂离子电池健康状态估计

健康状态是电池性能评估和使用寿命预测的指标，这对于确保电动汽车的可靠性和安全性至关重要。尽管已经进行了大量的容量研究，但很少有适合工程实践的简单有效的方法。因此，提出了具有多项式核函数的最小二乘支持向量回归模型，用于电池容量估计。通过电池充电曲线，提取电池健康状态的特征样本。灰色关联分析用于特征选择，并采用K折交叉验证获得支持向量回归估计模型的超参数。要验证此方法，支持向量回归估计模型在NASA Prognostics Center of Excellence提供的电池数据集上进行了训练和测试。实验结果表明，该方法只需要一些电池特征数据，就可以实现高精度的容量估计，表明该方法具有较高的效率和鲁棒性。