Charge Self-Baseline Drift Accumulation for Lithium-Ion Battery Micro-Short-Circuit Early Warning

Authors:
  • HCHaimei Cen
    Haimei Cen
  • LFLinfei Feng
    Linfei Feng
  • GSGuangwei Su
    Guangwei Su
  • CCChun Chang
    Chun Chang
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Abstract
Early-stage micro-short circuits (MSCs) can remain hidden within the normal pack-voltage range because their leakage currents are much smaller than normal charging and discharging currents. This study proposes a charge self-baseline drift accumulation (C-SBDA) method to extract directional and persistent fault evidence from incomplete charging records. The framework combines data-quality control, pack-median referencing, reference-voltage-domain alignment, cell-specific historical self-baseline correction, and three complementary drift features: mean downward-drift intensity A, significant-coverage ratio P, and end-region drift E. The features are robustly standardized, fused into a segment-level score, and accumulated using an exponentially weighted moving average (EWMA) to generate a cross-segment warning score.The method was validated using 21 controlled charging segments from a six-cell battery pack and eight charging segments from a 30-cell real-vehicle battery pack. In the controlled experiment, the experimentally faulted cell first exceeded the warning threshold at Segment 12 and was continuously confirmed at Segment 13, providing an eight-segment advance relative to Segment 21. The warning score showed a strong monotonic association with independently measured leakage severity, with a Spearman correlation coefficient of 0.9121. In the vehicle dataset, the real-vehicle abnormal cell first crossed the threshold in C6, remained continuously confirmed in C7, and returned to the top rank in C8, demonstrating persistent early risk evidence under practical charging conditions. Ablation studies and 67 one-factor sensitivity tests further confirmed the roles of self-baselining, complementary drift features, and EWMA accumulation. C-SBDA requires neither SOC estimation, OCV curves, equivalent-circuit parameters, nor fault-sample training, enabling low-complexity risk screening and abnormal-cell localization.

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