A Threshold-Adaptive Framework for Quantitative Diagnosis of Internal Short Circuits in LiFePO4 Batteries Using Multi-Feature Incremental Capacity Curves

Although incremental capacity (IC) curve analysis is promising for early internal short circuit (ISC) detection, its diagnostic accuracy degrades significantly across a wide resistance range, especially for low-resistance events dominated by leakage currents. To overcome this limitation, we propose a threshold-adaptive ISC diagnostic framework that dynamically integrates quantitative resistance calculation (for high resistance, R ≥ 100 Ω) with Gaussian process regression (GPR)-based leakage current analysis (for low resistance, R < 100 Ω). Validated on 20 Ah LiFePO4 batteries, this approach achieves <6% error for 100–300 Ω and <8% error for <100 Ω (after GPR correction), demonstrating robust, implementation-ready solutions for real-world battery safety monitoring.