基于ICA的锂电池SOH估计曲线确定方法研究
Research on ICA-Based Method for Determining SOH Estimation Curve of Lithium Battery
王晗蕊 1陈则王 1徐肇凡1
作者信息
- 1. 南京航空航天大学 自动化学院,江苏南京 211106
- 折叠
摘要
针对如何提取容量增量(IC)曲线上更有效的特征参数进行锂电池健康状态(SOH)估计问题,提出了一种基于修正的洛伦兹电压容量(RL-VC)模型.首先使用传统滤波方法对锂电池进行容量增量分析(ICA).然后使用RL-VC模型进行对比,获得相应的特征参数并计算容量建模误差.在基于自主搭建的试验平台上获得的试验数据与开源数据集NASA中的动态数据集NCM中分别进行试验.VC容量建模的误差分别在0.23％和0.16％以内.RL-VC模型拟合的IC曲线提取的特征参数与锂电池容量高度线性相关,为后续SOH工作奠定了基础.基于RL-VC模型的IC分析方法相较于传统滤波方法,不仅在电池老化方面具有更高的鲁棒性,同时在特征参数提取方面避免了主观性和不确定性.
Abstract
Aiming at the problem of how to extract more effective characteristic parameters from the capacity increment(IC)curve for state of health(SOH)estimation of lithium batteries,a modified Lorentz voltage-capacity(RL-VC)based model is proposed.The capacity increment analysis(ICA)of lithium batteries is first performed using the traditional filtering method.Then the RL-VC model is used for comparison to obtain the corresponding feature parameters and calculate the capacity modeling error.The experimental data obtained based on the self-constructed experimental platform and the dynamic dataset NCM from the open-source dataset NASA are carried out separately.The errors of VC capacity modeling are within 0.23％and 0.16％,respectively.The feature parameters extracted from the IC curves fitted by the RL-VC model are highly linearly correlated with the capacity of Li-ion batteries,which lays the foundation for the subsequent SOH work.The IC analysis method based on the RL-VC model proposed in this paper not only has higher robustness in battery aging compared with the traditional filtering method,but also avoids subjectivity and uncertainty in feature parameter extraction.
关键词
锂电池/健康状态估计/IC曲线/容量增量分析Key words
lithium battery/estimates of state of health/IC curve/capacity increment analysis引用本文复制引用
基金项目
航空科学基金资助项目(20183352030)
航空科学基金资助项目(201933052001)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据