基于曲线压缩和极限梯度提升算法的锂离子电池健康状态估计

State of health estimation method for lithium-ion battery based on curve compression and extreme gradient boosting

为了精确地估计锂离子电池的健康状态(SOH),提出了一种基于道格拉斯-普克算法和极限梯度提升(XGBoost)算法的方法。首先对每组电压数据进行预处理,利用道格拉斯-普克算法对每次循环的恒流充电电压曲线进行矢量压缩;在此数据的基础上,运用XGBoost算法建立锂离子电池退化过程模型并估计SOH。对比实验结果表明,所提方法可有效压缩电池电压曲线、降低网络训练数据维度,同时具有较高的预测精度和较快的运行速度,可实现锂离子电池SOH的快速准确估计。

In order to accurately estimate the State of Health(SOH)of the lithium-ion battery,a method based on Douglas-Puck algorithm and Extreme Gradient Boosting(XGBoost)algorithm is proposed.Firstly,each set of voltage data is preprocessed,and the Douglas-Puck algorithm is used to vectorize the constant current charging voltage curve of each cycle.On the basis of this data,the XGBoost algorithm is applied to establish a lithium-ion battery degradation model and estimate the SOH.The results of comparative experiments show that the proposed method can effectively compress the battery voltage curve and reduce the dimension of network training data.At the same time,the developed method also has a higher prediction accuracy and faster running speed,and can realize the fast and accurate estimation of the lithium-ion battery SOH.