An Online Data-Driven Fault Diagnosis and Thermal Runaway Early Warning for Electric Vehicle Batteries

Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods failure detection reducing safety risks from failing high energy lithium-ion batteries has become a major challenge industry. In this article, real-time scheme proposed. By applying both discrete Fréchet distance local outlier factor to voltage temperature data battery cell/module that measured in real time, cell will have detected before happens. Compared with widely used single parameter based approach, proposed one considerably improve reliability reduce false rate. The effectiveness method validated operational vehicles with/without daily use.