A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier

IF 9.4 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI:10.1016/j.ress.2024.110517
Camilo Lopez-Salazar , Stephen Ekwaro-Osire , Shweta Dabetwar , Fisseha Alemayehu
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Abstract

The escalating applications of Lithium-ion (Li-ion) batteries in renewable energy and electric vehicles underscore the need for enhanced prognostics and health management systems to reduce the risk of sudden failures. Remaining useful life (RUL) determination is one of the most critical tasks in the field of battery prognostics nowadays. Even though statistical and machine learning (ML) methods have proven effective in research setups, many challenges prevent applying these prediction methods to real-life scenarios. These challenges include (1) scarcity of run-to-failure datasets with similar experimental conditions, (2) low data granularity when presented in capacity vs. discharge cycle pairs, and (3) lack of “temporal identifiers” in real-life scenarios. A temporal identifier is any label that provides knowledge about the current degradation state of a working battery. The research question developed for this study was, ‘Can the remaining useful life of a Li-ion battery having limited data without a temporal identifier be predicted?’ The specific aims were to estimate the temporal identifier of limited data and to predict the remaining useful life (RUL). An innovative framework incorporating reliability analysis and deep learning addresses these specific aims. Experimental data is used to test the framework's capabilities, limiting the training dataset to only three batteries and the testing dataset to a small sample (< 10 data points) of another battery. This new approach enabled the RUL prediction to achieve errors as low as ∼5 cycles and root mean square error of 6.24 cycles, outperforming other benchmark studies on Li-ion battery RUL prediction that use more battery degradation data without temporal identifier.
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在无时间标识符的有限数据条件下估算锂离子电池剩余使用寿命的综合框架
锂离子(Li-ion)电池在可再生能源和电动汽车中的应用日益增多,这凸显了加强预报和健康管理系统以降低突发故障风险的必要性。剩余使用寿命(RUL)的确定是当今电池预报领域最关键的任务之一。尽管统计和机器学习(ML)方法在研究设置中被证明是有效的,但将这些预测方法应用到实际生活场景中仍面临许多挑战。这些挑战包括:(1)具有相似实验条件的运行至故障数据集稀缺;(2)以容量与放电周期对表示的数据粒度低;(3)现实生活场景中缺乏 "时间标识符"。时间标识符是指能提供工作电池当前退化状态相关知识的任何标签。本研究的问题是:"在没有时间标识符的情况下,能否预测数据有限的锂离子电池的剩余使用寿命?具体目标是估算有限数据的时间标识符并预测剩余使用寿命(RUL)。一个结合了可靠性分析和深度学习的创新框架可以实现这些具体目标。实验数据用于测试该框架的能力,将训练数据集限制为三个电池,将测试数据集限制为另一个电池的小样本(10 个数据点)。这种新方法使 RUL 预测误差低至 5 个周期,均方根误差为 6.24 个周期,优于其他使用更多电池降解数据但没有时间标识符的锂离子电池 RUL 预测基准研究。
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来源期刊
Reliability Engineering & System Safety
Reliability Engineering & System Safety 管理科学-工程:工业
CiteScore
15.20
自引率
39.50%
发文量
621
审稿时长
67 days
期刊介绍: Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.
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