Internal short circuit (ISC) is one of the main causes of thermal runaway (TR) accident in power battery systems, to effectively avoid the development of early stage ISC towards TR, this paper innovatively proposes an ISC fault diagnosis method based on the evolution of the cell charging voltage slope (CCVS) in variable voltage window (VVW). Firstly, the ISC characterisation parameter, i.e. CCVS, is extracted through battery cell aging and equivalent ISC experimental studies. This parameter has two advantages: on the one hand, the charging voltage slopes of different cycles of one cell in different voltage windows keep the same evolution law, so that the mechanism of VVW can be established, and the generalisation ability of the algorithm is improved. On the other hand, the charging voltage data of one cell as the research object effectively avoids the interference of inconsistent factors between battery cells. Then, the battery data are preprocessed using wavelet denoising, and a sliding window strategy is introduced to calculate the positional ranking of the CCVS in the historical data, which represents the capacity ranking level of the cell in this charging segment, defined as the capacity rating factor (CRF). Further, a double-layer diagnostic strategy based on the 3 σ criterion and distance factor assessment is used to localise the fault, which can effectively avoid the occurrence of false alarms. Finally, experimental and real-world vehicle data validate the effectiveness of the method, different types of battery data verify the applicability of the method, and the comparison results of the same type of model show that the proposed method is significantly superior in terms of robustness, accuracy and computational efficiency.
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