Prediction of state-of-health of lithium-ion battery based on CEEMDAN-SG-LSTM combined model

IF 7.1 3区材料科学 Q1 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY

Materials Today Sustainability Pub Date : 2024-09-29 DOI:10.1016/j.mtsust.2024.100999

Xu Li, Huilin Yu, Jianchun Wang, Yuhang Xia, Haotian Zheng, Hongzheng Song

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Abstract

State-of-health (SOH) is an important indicator for the maintenance and safe operation of batteries, and it is crucial for accurately predicting SOH. To address problems that the noise present in the original data lead to inaccurate prediction results. An Long-Short-Term-Memory (LSTM) method for SOH prediction is proposed based on the joint noise reduction model of complete ensemble empirical mode decomposition adaptive noise (CEEDMAN) and Savitzky-Golay (SG) filtering. Firstly, seven health indicators (HIs) were extracted by analyzing the voltage and current curves, and HIs with higher correlation with SOH were selected using Pearson correlation coefficient. Then, Intrinsic Mode Functions (IMF) components generated from SOH by CEEMDAN are divided into noise-component, noise-dominant-component, useful-signal-dominant-component, filtered noise-dominant-component and useful-signal-dominant-component are reconstructed into filtered SOH. Finally, the LSTM model is used for SOH prediction. Experiments show that proposed model captures the capacity regeneration phenomenon well with high prediction accuracy, and errors are all below 1.9%.

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基于 CEEMDAN-SG-LSTM 组合模型的锂离子电池健康状况预测

电池健康状态（SOH）是电池维护和安全运行的重要指标，准确预测电池健康状态至关重要。为了解决原始数据中存在的噪声导致预测结果不准确的问题。基于完整集合经验模式分解自适应噪声（CEEDMAN）和萨维茨基-戈莱（SG）滤波的联合降噪模型，提出了一种用于 SOH 预测的长短期记忆（LSTM）方法。首先，通过分析电压和电流曲线提取了七个健康指标（HIs），并利用皮尔逊相关系数选出了与 SOH 相关性较高的 HIs。然后，将 CEEMDAN 从 SOH 中生成的本征模式函数（IMF）分量分为噪声分量、噪声主导分量、有用信号主导分量、滤波噪声主导分量和有用信号主导分量，并将其重建为滤波 SOH。最后，利用 LSTM 模型进行 SOH 预测。实验表明，所提出的模型能很好地捕捉容量再生现象，预测精度高，误差均低于 1.9%。

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来源期刊

Materials Today Sustainability Multiple-

CiteScore

5.80

自引率

6.40%

发文量

174

审稿时长

32 days

期刊介绍： Materials Today Sustainability is a multi-disciplinary journal covering all aspects of sustainability through materials science. With a rapidly increasing population with growing demands, materials science has emerged as a critical discipline toward protecting of the environment and ensuring the long term survival of future generations.