基于改进容量增量分析法的锂电池可用容量估计

针对动力锂电池在使用过程中难以高效准确估计其衰退后可用容量的问题,提出一种不依赖滤波算法的容量增量分析法获取不同型号电池的容量衰退特征,并基于数据驱动的方法搭建可用容量估计模型。首先,分别分析低通滤波与小波滤波在获取容量增量曲线中存在的问题,并对比差分电压值在1、10、20、50 mV时容量增量曲线的形态。其次,采用移动方差算法对不同电压差分值下容量增量曲线的波动性做出评价,确定出峰值特性明显且平滑的容量增量曲线。提取曲线的峰值作为动力锂电池的老化特征,运用斯皮尔曼相关性系数验证老化特征与电池老化状态之间的相关性。然后,引入门控循环单元建立锂电池的可用容量估计模型。最后,将不同老化测试条件下的2类电池老化数据集用于模型验证。研究结果表明:所建立的估计模型能够有效估算锂电池全寿命循环内的可用容量值,2组数据集中测试结果的相对误差除个别值外,多数相对误差值在2%以内;数据组1中,分别选取电池1和电池3测试数据的前50%为训练数据,后50%为测试数据,训练结果绝对误差稳定在0.05 A·h左右,测试结果绝对误差在0.04 A·h左右;对电池2与电池3的全寿命循环可用容量做出估计,结果相对误差稳定在2%左右;数据组2中对电池5、电池6和电池7的全寿命循环可用容量估计结果的相对误差整体亦在2%以内;且模型能够对锂电池循环过程中出现容量再生现象的循环做出4%以内的准确估计,显示出良好的估算精度和泛化能力。

Estimation of Available Capacity for Lithium-ion Battery Based on Improved Increment Capacity Analysis

It is difficult to estimate the available capacity of lithium-ion battery efficiently and accurately after its decline.To address this problem,an increment capacity analysis method that does not rely on filtering algorithm is proposed to obtain the capacity decline characteristics of different types of batteries,and the estimation model of available capacity is built based on a data-driven approach.First,the shortcomings of low-pass filtering and wavelet filtering in obtaining increment capacity curves are analyzed respectively.Moreover,the patterns of increment capacity curves are compared for differential voltage values at 1,10,20,and 50 mV,respectively.Second,the moving variance algorithm is leveraged to evaluate the volatility of the increment capacity curve at different voltage differential values,and the volatility of the voltage value corresponding to the peak position of the curve is evaluated to determine the increment capacity curve with obvious and smooth peak characteristics.The peak value of the curve is extracted as the aging characteristic of the lithium-ion battery and the correlation between the aging characteristic and the aging state of the battery is examined using the Spearman correlation coefficient.Finally,two types of battery aging datasets prepared under different aging test conditions are employed for model validation.The results show that the established estimation model can effectively estimate the available capacity value within the full life cycle.Except for a few values,the relative error of the test results in the two data sets is within 2% for most of the relative error values.In dataset 1,the first 50% of batteries 1 and 3 are selected as training data and the second 50% are selected as test data,respectively,and the absolute error of training results is stable at approximately 0.05 A·h,and the absolute error of test results is approximately 0.04 A·h.These predictions are made for the full-life cycle discharge capacity values of batteries 2 and 3.The results show that the absolute relative error is restricted within at 2%.In dataset 2,the relative absolute error of the estimation results of the full-life cycle available capacity of batteries 5,6,and 7 is also less than 0.1 A·h (2%).The proposed model can make accurate estimations with less than 4% error when measuring the cycle of effective tracking of the capacity recovery phenomenon that occurs during the lithium-ion battery cycle.This result indicates satisfactory robustness and generalization ability.