Identification and Location of Micro-Overcharge and Micro-Overdischarge Abnormal Cells in Lithium-Ion Battery Modules Based on Impedance Spectroscopy
Wu Xu1, Yang Lijun1, Zeng Xisong1, Xiao Yanlin1, Li Siquan2
1. State Key Laboratory of Power Transmission and Transformation Equipment Technology Chongqing University Chongqing 400044 China; 2. State Grid Chongqing Electric Power Company Chongqing 400020 China
Abstract：Addressing the challenge of early identification and localization of abnormal cells subjected to minor overcharge/overdischarge (slightly exceeding the safe voltage window without triggering protection thresholds) in lithium-ion battery modules, and overcoming the limitation of conventional battery management systems (BMS) that rely on macro-parameter threshold alarms and fail to detect such subtle anomalies, this paper aims to develop a rapid, non-destructive diagnostic method based on electrochemical impedance spectroscopy (EIS) for precise early-stage detection and localization of faulty cells at the module level. A hierarchical “module-to-cell” detection framework based on EIS is proposed. Firstly, controlled cycling tests with graded minor overcharge (3.8~3.95 V) and overdischarge (1.8~1.65 V) were designed and performed on A123 26650 lithium iron phosphate (LFP) cells. The impact of such electrical abuse on impedance spectra was systematically analyzed. A fractional-order equivalent circuit model was established to fit the EIS data, from which highly sensitive characteristic parameters were extracted and screened: the charge transfer resistance (Rct) and the real part (Z’) and magnitude (|Z|) of impedance at a characteristic frequency of 10 Hz. Secondly, simulation studies were conducted to analyze the EIS characteristics at the module level and to investigate the influence mechanism of individual cell parameter abnormalities (R0, Rct, Y0, n) on the overall impedance of 2 to 5 series-connected modules, elucidating the evolution of abnormal features under the “averaging effect”. Finally, a two-step diagnostic procedure—“module-level preliminary screening followed by cell-level precise localization”—was constructed and experimentally validated on a 6-series LFP battery module. The experimental results demonstrate that: (1) In the early stage of minor electrical abuse, the charge transfer resistance Rct shows the highest sensitivity, with an increase of over 18% compared to normal cells, serving as a key indicator of interface degradation. The impedance real part and magnitude at 10 Hz also exhibit a significant increasing trend with the aggravation of abuse, with a maximum deviation of 26.1%, and can be measured rapidly. The combination of Rct and the 10 Hz impedance features constitutes the core diagnostic criterion. (2) Simulations reveal that although the module “averaging effect” dilutes the abnormal signal from a single cell, the relative increment of Rct maintains a fixed variation trend as the number of series connections increases and significantly affects the radius of the mid-frequency capacitive arc in the module EIS, proving the feasibility of module-level identification. Changes in the fractional-order capacitance parameter Y0 can lead to the appearance of a “double semicircle” feature in the EIS when abuse reaches a certain degree. (3) The proposed hierarchical localization method successfully achieved rapid, non-destructive identification and precise localization of abnormal cells with minor overcharge/overdischarge (≥200 mV) in the 6-series module experiment, with a total identification time within 3 minutes (including EIS measurement, model fitting, and feature extraction). (4) The method demonstrates good robustness across a temperature range of 10~30°C and a state of charge (SOC) range of 0%~60%, with optimal identification sensitivity observed at 20~30°C and medium-to-low SOC (0%~40%). This research confirms the effectiveness of the EIS-based module-cell two-layer diagnostic framework in overcoming the inability of conventional BMS to capture minor overcharge/overdischarge anomalies. By jointly utilizing Rct and 10 Hz impedance features, the method enables early identification and precise localization of abnormal cells without module disassembly within 3 minutes.
吴绪, 杨丽君, 曾锡松, 肖滟琳, 李思全. 基于阻抗谱的锂离子电池模组微过充微过放异常电芯识别定位[J]. 电工技术学报, 2026, 41(18): 6422-6436.
Wu Xu, Yang Lijun, Zeng Xisong, Xiao Yanlin, Li Siquan. Identification and Location of Micro-Overcharge and Micro-Overdischarge Abnormal Cells in Lithium-Ion Battery Modules Based on Impedance Spectroscopy. Transactions of China Electrotechnical Society, 2026, 41(18): 6422-6436.
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2026, Vol. 41 
