锂离子电池内短路（ISC）是引发热失控事故的主要原因，ISC故障的早期识别对于降低火灾或爆炸相关的风险至关重要，传统的基于滤波容量增量曲线的检测方法容易受到干扰。针对如何提取容量增量曲线上的特征参数进行早期内短路检测问题，提出了一种基于二阶RC等效电路的电压重构模型。首先，使用传统滤波方法对锂电池进行容量增量分析，然后使用电压重构模型进行对比，并对电压重构模型进行抗噪声干扰试验，电压重构的误差仅为0.000 8 V，为早期内短路检测工作奠定了基础。最后，在公开的试验数据上进行试验，研究了不同充电速率、不同电池类型对容量增量曲线的影响。在此基础上，得到了用于诊断短路故障的特征峰。试验结果表明，所提出的基于电压重构模型的方法能够有效对抗噪声干扰，为早期微内短路检测提供了参考。

Internal short circuit (ISC) in lithium-ion batteries is the main cause of thermal runaway accidents, and early identification of ISC faults is crucial to reduce the risk associated with fire or explosion, and traditional detection methods based on filtered capacity increment curves are susceptible to interference. Aiming at the problem of how to extract the characteristic parameters on the capacity increment curve for early internal short circuit detection, a voltage reconstruction model based on the second-order RC equivalent circuit is proposed. Firstly, the capacity increment analysis of lithium battery is carried out using the traditional filtering method, and then the voltage reconstruction model is used for comparison. And the voltage reconstruction model is subjected to anti-noise interference experiments, and the error of the voltage reconstruction is only 0.000 8 V, which lays a foundation for the early internal short circuit detection work. Finally, experiments are carried out on publicly available experimental data to investigate the effects of different charging rates and different battery types on the capacity increment curve. Based on thus, the characteristic peaks used to diagnose short-circuit faults are obtained. The experimental results show that the proposed method based on voltage reconstruction model can effectively counteract noise interference and provide reference for early micro-inner short circuit detection.

航空科学基金资助项目(20183352030, 201933052001)