摘要
针对锂离子电池健康状态评估和剩余寿命预测,提出一种结合单一和复合特征的混合模型方法。首先利用EEMD-LSTM-LightGBM模型,分析容量衰减曲线,有效预测电池容量再生现象,实现剩余寿命误差小于2个循环,电池健康状态(state of health,SOH)预测均方根误差低至1.2%。其次,开发基于复合特征的梯度提升决策树模型,通过相关性分析和主成分分析筛选特征,并优化超参数,实现均方根误差为0.014的预测精度。然后,基于这些算法开发锂离子电池SOH预测平台,集成单一和多特征模型,采用本地服务器部署,提供用户友好界面,直观展示容量退化曲线、预测误差等关键指标。实验结果表明,所提方法通过特征提取与算法集成,显著提升了锂离子电池健康状态评估和剩余寿命预测的准确性,可为相关平台的开发与应用提供参考。
A hybrid model combining single and composite features is proposed to address the problem of the state-of-health(SOH)assessment and remaining useful life(RUL)prediction of lithium-ion batteries.This method first uses the EEMD-LSTM-LightGBM model to effectively predict battery capacity regeneration by analyzing capacity degradation curves,and the goal that the error of RUL is less than 2 cycles and root mean square error(RMSE)SOH prediction is as low as 1.2%.Secondly,a gradient boosted desicion tree model based on composite features is developed,where feature selection is performed through correlation analysis and principal component analysis,and hyperparameters are optimized,resulting in an SOH prediction accuracy with an RMSE of 0.014.Next,based on these algorithms,a lithium-ion battery SOH prediction platform is developed on a local server,integrating both single and multi-feature models.It provides a user-friendly interface that visually displays key indicators such as capacity degradation curves and prediction errors.The experimental results show that through innovative feature extraction and algorithm integration the proposed method,significantly improves the accuracy of lithium-ion battery SOH assessment and RUL prediction,which can be used as a reference for the development and application of related platforms.
作者
周驰东
于婧
郁亚娟
常泽宇
陈来
苏岳锋
ZHOU Chidong;YU Jing;YU Yajuan;CHANG Zeyu;CHEN Lai;SU Yuefeng(School of Material Science&Engineering,Beijing Institute of Technology,Beijing 100081,China;State Grid Corporation of China,Beijing 100031,China)
出处
《湖南电力》
2025年第1期14-20,共7页
Hunan Electric Power
基金
国家电网有限公司科技项目(4000-202455375A-3-2-ZX)
关键词
锂离子电池
健康状态
剩余使用寿命
机器学习
长短期记忆网络
轻度向量提升机
lithium-ion battery
state of health
remaining useful life
machine learning
long short-term memory(LSTM)network
light gradient boosting machine
