基于概率密度分析法快速筛选退役电池模组

Fast Screening Method of Retired Battery Module Based on Probability Density Function

快速筛选出安全可靠有利用价值的电动汽车退役动力电池仍是急需解决的问题。通过随机概率密度函数(PDF)方法对电池容量标定过程中的充电电压数据进行处理,得到峰B顶点一定间隔内积分面积与SOH之间呈现较好二次函数关系,两者之间拟合优度在0.99以上。将测试获得的电池电压数据代入拟合公式得到估算SOH与实际SOH误差在3%以内。由此提出一种使用电池充电过程中部分电压数据基于PDF即可快速检测SOH以实现对电池模组快速筛选的方法。

It is still an urgent problem to quickly screen out safe,reliable and valuable retired power batteries for electric vehicles.The random probability density function(PDF)method was used to process the charging voltage data in the process of battery capacity calibration in this paper.It was found that there was a good quadratic function relationship between the integral area and SOH within a certain interval of peak B,and the fitting degree between them is more than 0.99.The error between the estimated SOH and the actual SOH was less than 3%.Therefore,a method was proposed to quickly detect SOH based on PDF using partial voltage data during battery charging to achieve rapid screening of battery modules.