锂离子电池健康状态预测和异常检测的数据驱动战略

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-08-09 DOI:10.1016/j.egyai.2024.100413
Slimane Arbaoui , Ahmed Samet , Ali Ayadi , Tedjani Mesbahi , Romuald Boné
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引用次数: 0

摘要

本研究探讨了监测锂离子电池(LIB)健康状况(SOH)的关键挑战,以应对对可再生能源系统日益增长的需求和减少二氧化碳排放的迫切要求。该研究引入了深度学习(DL)模型,即编码器-长短期记忆(E-LSTM)和卷积神经网络-LSTM(CNN-LSTM),这两种模型均用于预测电池的健康状况。E-LSTM 集成了用于降维的编码器和用于捕捉数据依赖性的 LSTM 模型。另一方面,CNN-LSTM 采用 CNN 层进行编码,然后采用 LSTM 层进行精确的 SOH 估算。值得注意的是,我们优先考虑模型的可解释性,采用了一种称为 "SHAPLEY Additive exPlanations(SHAP)"的博弈论方法来阐明模型的输出。此外,我们还开发了一种基于模式挖掘的方法,与模型协同识别导致 SOH 异常下降的模式。这些见解通过信息图呈现出来。所提出的方法依赖于麻省理工学院(MIT)的电池数据集,在准确估计 SOH 值方面取得了可喜的成果,其中 E-LSTM 模型的平均绝对误差(MAE)小于 1%,优于 CNN-LSTM 模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries

This study addresses the crucial challenge of monitoring the State of Health (SOH) of Lithium-Ion Batteries (LIBs) in response to the escalating demand for renewable energy systems and the imperative to reduce CO2 emissions. The research introduces deep learning (DL) models, namely Encoder-Long Short-Term Memory (E-LSTM) and Convolutional Neural Network-LSTM (CNN–LSTM), each designed to forecast battery SOH. E-LSTM integrates an encoder for dimensionality reduction and an LSTM model to capture data dependencies. CNN–LSTM, on the other hand, employs CNN layers for encoding followed by LSTM layers for precise SOH estimation. Significantly, we prioritize model explainability by employing a game-theoretic approach known as SHapley Additive exPlanations (SHAP) to elucidate the output of our models. Furthermore, a method based on pattern mining was developed, synergizing with the model, to identify patterns contributing to abnormal SOH decrease. These insights are presented through informative plots. The proposed approach relies on the battery dataset from the Massachusetts Institute of Technology (MIT) and showcases promising results in accurately estimating SOH values, in which the E-LSTM model outperformed the CNN–LSTM model with a Mean Absolute Error (MAE) of less than 1%.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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