Abstract：Lithium-ion batteries are booming in new energy generation and electric vehicles, but poor safety is a key constraint to their development.Severe electrical abuse can induce battery failure or thermal runaway, which will affect the lifetime and safety of the energy storage system. Batteries that are not equipped with protection devices usually experience thermal runaway under electrical abuse. Batteries equipped with protection devices (safety valve, current interrupt device (CID)) can provide protection in the electrical abuse, but the irrecoverability of the device will lead to battery failure. It is difficult to achieve fast and reliable failure warning based on voltage and temperature. Electrochemical impedance spectroscopy (EIS) is a non-invasive method for characterizing internal information, and shows obvious advantages in safety warning. In this paper, battery failures caused by severe overcharging or overdischarging are investigated, and the corresponding early warning strategy based on EIS is proposed.The experimental results show that the method has good rapidity and reliability.It can avoid battery failure caused by electrical abuse and guarantee the safe operation of the energy storage system. Firstly, EIS scanning experiments were carried out at different temperature and state of charge (SOC). The impedance data were analyzed from multiple perspectives, including real part, imaginary part and distribution of relaxation time (DRT).And the characteristic impedance suitable for electrical abuse warning was extracted. The special impedance can reflect abnormal SOC of the battery.Secondly, the evolution rules of voltage, temperature and impedance during abuse were analyzed, and suitable failure warning criteria was extracted. The slope of the characteristic impedance turned from negative to positive before the battery failure.In order to minimize the interference of noisy data and improve the diagnosis, impedance curvature was used for safety warning. Finally, a clustering-based battery failure warning method was proposed. The method can adaptively adjust the parameters according to the working conditions, which in turn is applicable to different charging and discharging currents. Its effectiveness for warning was verified by conducting failure experiments under different working conditions. The algorithm can give an early warning at least 313 s before overcharge failures and at least 114 s before overdischarge failures. The following conclusions can be drawn from the experimental analysis: (1)The impedance real part in the low and middle frequency bands is strongly correlated with the SOC. For the characteristic impedance (real part at 10 Hz),it will show a local minimum point before both overcharge failure and overdischarge failure. Based on this law, it is feasible to achieve safety warning for battery failure. (2) Compared to the raw data of characteristic impedance, the impedance curvature is a more appropriate warning parameter. The impedance curvature can filter out noise points and amplify the difference of the required minima, which reduces the diagnostic difficulty of the warning algorithm. (3) The density-based spatial clustering of applications with noise (DBSCAN) algorithm shows good performance in battery failure warning. Compared to the traditional threshold discrimination method, the proposed method adjusts the model parameters according to the current magnitude and EIS data, which enhances the model's ability to meet different operating conditions.
袁奥特, 蔡涛, 刘政辰, 罗航宇. 基于电化学阻抗谱的锂离子电池电滥用失效预警研究[J]. 电工技术学报, 2025, 40(7): 2306-2321.
Yuan Aote, Cai Tao, Liu Zhengchen, Luo Hangyu. Study of Electrical Abuse Failure Early Warning of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy. Transactions of China Electrotechnical Society, 2025, 40(7): 2306-2321.
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