Proactive Fault Detection in EV Batteries Via Hybrid Method: Cell-to-Cell Variation Pattern and Physics-Based Model Insights

This study presents a distinct methodology for the early detection of faulty cells in electric vehicle (EV) battery systems, leveraging temporal voltage deviation patterns under real-world charging scenarios alongside outputs from a physics-based model. A comparative longitudinal analysis was conducted on a fleet of twelve EVs—six exhibiting stable performance and the other six demonstrating early-stage anomalies characterized by intermittent transitions from drive to neutral mode. These behavioral cues were investigated as precursors to deeper battery degradation. The analysis focused on cell-level voltage dispersion in battery pack during the mid-to-high state-of-charge (SoC) range (approx. 20–30% to full charge). Vehicles in healthy condition consistently displayed minimal voltage deviation between BMS-measured cell voltages and physics-based model predictions, whereas those with latent faults showed markedly higher variance, particularly between the highest battery and model-expected cell voltages. Notably, this voltage divergence was often accompanied by a modest yet recurrent thermal rise of 2–3°C, suggesting early-stage thermal non-uniformity. All vehicles were monitored over extended distances under diverse, real-world driving and environmental conditions, enhancing the robustness and generalizability of the findings. The proposed approach underscores the diagnostic value of tracking voltage deviation trajectories as a non-intrusive, scalable means of forecasting cell-level degradation. This framework could significantly advance predictive maintenance strategies, improving both the reliability and operational lifespan of EV battery packs.