Design and Performance Optimization of Battery Pack with AI-Driven Thermal Runaway Prediction

Battery thermal management is a critical factor in ensuring the performance, safety, and longevity of electric vehicle (EV) battery packs. This study investigates the effectiveness of a forced air convection cooling system, optimized cell spacing and suitable configuration in maintaining optimal battery cell temperatures. A 3D computational model was developed to analyze the temperature distribution of a battery pack under varying airflow velocities, cell spacings and configurations. The numerical simulations were validated through experimental testing, demonstrating a strong correlation between simulated and measured results. The findings reveal that with a 2 m/s velocity of the fan, the battery’s maximum temperature is reduced by 7% compared to the case of natural convection, while the fan consumed only 4% of the battery pack available capacity. An AI algorithm was trained on the experimental data obtained to perform data-driven predictions of failures. The results provide valuable insights for optimizing air cooling systems in EV applications. Future work will explore the effect of non-uniform air flow distribution in reducing the risk of thermal runaway and avoiding hot spots in the battery pack for optimal performance.