基于经验模态分解的锂离子电池健康状态预测
State of Health Prediction for Lithium-ion Batteries Based on Empirical Mode Decomposition
刘征宇 1张政 2郭乐凯 2孟辉 2刘项2
作者信息
- 1. 合肥工业大学机械工程学院 合肥 230009;合肥工业大学智能制造技术研究院 合肥 230009
- 2. 合肥工业大学机械工程学院 合肥 230009
- 折叠
摘要
电池健康状态(State of health,SOH)预测是确保电子系统运行可靠性和安全性的关键因素.为了准确地预测锂离子电池SOH的整体退化趋势和局部容量再生现象,提出一种将经验模态分解(Empirical mode decomposition,EMD)与门控循环单元(Gated recurrent unit,GRU)和差分自回归移动平均模型(Autoregressive integrated moving average model,ARIMA)相融合的锂离子电池SOH预测方法.首先,利用EMD将电池原始SOH序列进行多尺度分解,并通过计算分解子序列的连续均方误差找到高低频分界点;然后,GRU用于预测具有强烈数据波动的高频子序列,ARIMA用于预测剩余的低频子序列和残差;最后,将每个子序列的预测结果进行叠加以获得最终预测结果.试验结果表明,与其他文献中预测方法相比,基于经验模态分解的融合模型具有更高的预测精度,可以更好地捕捉电池SOH整体退化趋势和局部容量再生特性.
Abstract
The prediction of battery state of health(SOH)is a key factor to ensure the reliability and safety of electronic system operation.In order to accurately predict the overall degradation trend and local capacity regeneration of lithium-ion battery SOH,a lithium-ion battery SOH prediction method combining empirical mode decomposition(EMD),gated recurrent unit(GRU)and differential autoregressive integrated moving average model(ARIMA)is proposed.First,the original SOH sequence of the battery is decomposed at multiple scales using EMD,and the high and low frequency demarcation points are found by calculating the continuous mean square error of the decomposed subsequences;then,GRU is used to predict high-frequency subsequences with strong data fluctuations,and ARIMA is used to predict the remaining low-frequency subsequences and residuals;finally,the prediction results of each subsequence are superimposed to obtain the final prediction result.The experimental results show that,compared with the prediction methods in other literatures,the fusion model based on empirical mode decomposition has higher prediction accuracy and can better capture the overall degradation trend and local capacity regeneration characteristics of battery SOH.
关键词
锂离子电池/经验模态分解/健康状态预测/容量再生/融合模型Key words
lithium-ion battery/empirical mode decomposition/state of health prediction/capacity regeneration/fusion model引用本文复制引用
基金项目
安徽省自然科学基金(1808085MF200)
工业和信息化部民用飞机专用专项科研项目(MJ-2017-D-26)
出版年
2024
NETL
NSTL
万方数据