State of health (SOH) is essential for battery management, timely maintenance, and safety incident avoidance. For specific applications, a variety of SOH estimation methods have been proposed. However, it is often difficult to apply these methods to other applications. In this article, a novel feature extraction method is proposed to extract health indicators (HIs) from general discharging conditions. A voltage partition strategy is used to obtain the discharge capacity differences of two cycles [△ Q ( V )] from nonmonotonic or pulse discharge voltage curve, and a filtering strategy is employed to obtain smooth voltage curves under dynamic discharging conditions. The standard deviations of the discharge capacity curve and △ Q ( V ) are selected as HIs and are verified to have strong correlations to battery capacity under different datasets for three types of batteries. By using these HIs as input features, typical data-driven methods, including linear regression, support vector machine, relevance vector machine, and Gaussian process regression (GPR), are constructed to predict battery SOH. The estimation results of these methods are compared under different operating conditions for the three types of batteries. Good estimation accuracy is achieved for all these methods. Among them, the GPR has the best performance, and its maximum absolute error and root-mean-square error are lower than 1% and 1.3%, respectively.

中文翻译：

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通用放电电压信息支持锂离子电池的健康评估

健康状态 (SOH) 对电池管理、及时维护和安全事故的避免至关重要。对于特定应用，已经提出了多种 SOH 估计方法。但是，通常很难将这些方法应用于其他应用程序。在本文中，提出了一种新的特征提取方法来从一般放电条件中提取健康指标（HI）。使用电压划分策略获得两个循环的放电容量差异[△ 问 ( V )] 来自非单调或脉冲放电电压曲线，并采用滤波策略在动态放电条件下获得平滑的电压曲线。放电容量曲线与△的标准差 问 ( V ) 被选为 HI，并被验证与三种类型电池在不同数据集下的电池容量具有很强的相关性。通过使用这些 HI 作为输入特征，构建了典型的数据驱动方法，包括线性回归、支持向量机、相关向量机和高斯过程回归 (GPR)，以预测电池 SOH。这些方法的估计结果在三种电池的不同运行条件下进行了比较。所有这些方法都实现了良好的估计精度。其中探地雷达性能最好，其最大绝对误差和均方根误差分别低于1%和1.3%。