Comparative Analysis of Traditional And AI Based Fault Detection for Battery Thermal Management System

Electric vehicles (EVs) are crucial to reduce hazards in both rural and urban regions, providing environmental protection and convenience for public and private transportation. However, their growing dependence requires long-lasting systems of batteries with high voltage and efficiency. Optimized battery monitoring algorithms are essential for ensuring the battery pack's overall life cycle. Because of its great efficiency with energy, low self-discharge rate, power-to-weight ratio, and low maintenance requirements, lithium-ion batteries are desirable. The right battery technology and efficient utilization are crucial. Cell balancing serves as a fundamental feature of battery management systems (BMS) in electric vehicles (EV s), which assist in monitoring, controlling, and safeguarding Li-ion batteries against misuse. This research explores the performance of electric vehicles (EV s) in battery thermal management, sensor fault detection, and the use of AI techniques in EV technologies. A liquid-based battery thermal management solution designed for electric vehicles operating above 40°C. Fault detection time was essential for detecting and analysing faults in battery packs for accurate diagnosis and maintenance. Machine learning and deep learning are utilized to predict the performance of electric vehicle batteries. Battery management systems' safety and reliability may be improved by 98% and their mean relative error reduced to 0.055% using the IB-DRN-based false battery data recognition and categorization system. The experimental results show that IB-DRN shows the superior accuracy when applied to lithium-ion batteries for electric vehicles.