Online diagnostic method for fault behavior of lithium batteries with secondary utilization

IF 4.1 3区化学 Q1 CHEMISTRY, ANALYTICAL

Journal of Electroanalytical Chemistry Pub Date : 2025-09-09 DOI:10.1016/j.jelechem.2025.119469

Junfu Li , Zhaowei Zhang , Tongxin Li , Runze Wang , Yaxuan Wang

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引用次数: 0

Abstract

With the continuous increase in battery deployment, the repurposing scale of retired batteries in energy storage and other secondary utilization scenarios has expanded significantly. However, performance degradation during cycling may elevate risks such as thermal runaway, posing critical safety concerns. This study addresses the safety concerns in retired lithium iron phosphate (LFP) battery echelon utilization by establishing a online failure diagnosis system based on mechanism model parameters. First, a battery model is established and an online collaborative identification method is constructed using the dual adaptive extended kalman filter (AEKF) algorithm. Subsequently, the evolution patterns of the model parameters are quantitatively analyzed, and a fault behavior diagnostic framework is proposed through a fault boundary construction method. Finally, the reliability of the failure boundary-based diagnostic method is validated through destructive analysis techniques encompassing scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). The results show that the voltage prediction errors of MAE less than 20 mV and RMSE less than 26 mV remain within the acceptable thresholds when voltage prediction is performed using the online identification parameters. The diagnostic accuracy of the failure behavior diagnostic method for the detected anomalies is 94.1%, and the leakage rate for all sample points is 8.4%, indicating that the failure behavior diagnosis method based on failure boundary has strong reliability and accuracy.

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二次利用锂电池故障行为在线诊断方法

随着电池部署的不断增加，退役电池在储能等二次利用场景中的再利用规模显著扩大。然而，循环过程中的性能下降可能会增加热失控等风险，从而引发严重的安全问题。本研究通过建立基于机理模型参数的在线故障诊断系统，解决退役磷酸铁锂（LFP）电池梯队利用中的安全问题。首先，建立了电池模型，利用双自适应扩展卡尔曼滤波（AEKF）算法构建了在线协同识别方法。在此基础上，定量分析了模型参数的演化规律，并通过构造故障边界的方法提出了故障行为诊断框架。最后，通过包括扫描电子显微镜（SEM）和x射线光电子能谱（XPS）在内的破坏性分析技术验证了基于故障边界的诊断方法的可靠性。结果表明，使用在线识别参数进行电压预测时，MAE的电压预测误差小于20 mV， RMSE小于26 mV，均保持在可接受的阈值范围内。故障行为诊断法对检测到的异常诊断准确率为94.1%，各样本点的泄漏率为8.4%，表明基于故障边界的故障行为诊断法具有较强的可靠性和准确性。

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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来源期刊

Journal of Electroanalytical Chemistry 化学-电化学

CiteScore

7.80

自引率

6.70%

发文量

912

审稿时长

2.4 months

期刊介绍： The Journal of Electroanalytical Chemistry is the foremost international journal devoted to the interdisciplinary subject of electrochemistry in all its aspects, theoretical as well as applied. Electrochemistry is a wide ranging area that is in a state of continuous evolution. Rather than compiling a long list of topics covered by the Journal, the editors would like to draw particular attention to the key issues of novelty, topicality and quality. Papers should present new and interesting electrochemical science in a way that is accessible to the reader. The presentation and discussion should be at a level that is consistent with the international status of the Journal. Reports describing the application of well-established techniques to problems that are essentially technical will not be accepted. Similarly, papers that report observations but fail to provide adequate interpretation will be rejected by the Editors. Papers dealing with technical electrochemistry should be submitted to other specialist journals unless the authors can show that their work provides substantially new insights into electrochemical processes.