Accurate estimation of state of health (SOH) is of great significance for the safety and reliability of lithium-ion batteries. In this paper, a novel method to estimate SOH online based on constant current charging curve is presented. In order to incorporate the factor of rates, a simple two-step data transformation process is carried out to make the method suitable for SOH estimation at different charging rates. Then polynomial is used to fit the transformed curve, and the coefficient sets of analytic expression obtained by fitting are taken as the battery aging feature variables. Finally, linear regression algorithm, the simplest machine learning algorithm, is employed to construct the mapping between feature variables and SOH, thus accomplishing the SOH estimation. When estimating SOH, only the charging curve of the whole constant current charging process is needed, regardless of the charging process at whatever rates. This method takes low computational cost, making it suitable for online estimation. The verification results on battery test data show that the method is of high accuracy and effectiveness.

中文翻译：

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基于恒流充电曲线的锂离子电池健康状态在线估计方法

准确估计健康状态（SOH）对于锂离子电池的安全性和可靠性具有重要意义。本文提出了一种基于恒流充电曲线在线估算SOH的新方法。为了结合速率因素，进行了简单的两步数据转换过程，使该方法适用于不同充电速率下的 SOH 估计。然后用多项式对变换后的曲线进行拟合，将拟合得到的解析表达式的系数集作为电池老化特征变量。最后，采用最简单的机器学习算法线性回归算法构建特征变量与SOH之间的映射，从而完成SOH估计。在估算 SOH 时，只需要整个恒流充电过程的充电曲线，不管充电过程是什么速率。该方法计算量小，适合在线估计。电池测试数据验证结果表明该方法具有较高的准确性和有效性。