摘要
为了实现锂电池健康状态检测和电池故障诊断,在电池全生命周期退化数据基础上,分别使用容量增量分析和差分电压分析法进行特征提取,使用皮尔逊相关系数对健康因子进行相关性分析,并将其输入到人工神经网络用于电池健康状态(state of health,SOH)预测。针对电池容量非线性的退化特性以及局部重生现象,使用双指数函数对其进行建模。同时结合粒子滤波算法对模型参数进行估计,实现电池剩余使用寿命(remaining useful life,RUL)的概率密度预测。实验结果表明所提出的方法能够实现SOH的精准预测和RUL的不确定性估计。
To detect the health of lithium batteries and for battery fault diagnosis,and based on battery life cycle degradation data,increment capacity analysis and differential voltage analysis are used to extract features.The Pearson correlation coefficient is used to analyze the correlation of health factors.Health factors are input into an artificial neural network for battery state of health(SOH)prediction.From the battery capacity nonlinear degradation characteristics and its regeneration phenomenon,a dual exponential function is used to model it.A particle filter algorithm is used to estimate model parameters and achieve the probability density prediction of remaining useful life(RUL).The experimental results show that the proposed algorithm can achieve accurate prediction of SOH and uncertainty estimation of RUL.
作者
晋殿卫
顾则宇
张志宏
JIN Dianwei;GU Zeyu;ZHANG Zhihong(State Grid Shaanxi Information and Telecommunication Company,Xi’an 710005,China;School of Informatics,Xiamen University,Xiamen 361005,China)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2023年第1期122-130,共9页
Power System Protection and Control
基金
国家自然科学基金面上项目资助(62176227)
国家自然科学基金智能电网联合基金重点支持项目资助(U2066213)。
关键词
剩余使用寿命
电池状态检测
故障诊断
人工神经网络
粒子滤波
remaining useful life
battery status detection
fault diagnosis
artificial neural network
particle filter
