International Journal of Science, Engineering and TechnologyAuthors: Associate Professor H R Patil, Associate Professor M M Ganganallimath, Manu G. Hunagund, Naveen Itagi, Prajwal P. Kajagar, Prashant Y. Ghorpade
Abstract: The necessity for effective Battery Thermal Management Systems (BTMS) to keep lithium-ion batteries within their ideal temperature range has grown due to the explosive growth of electric vehicles (EVs). Battery longevity, safety, charging efficiency, and overall vehicle performance are all strongly impacted by battery temperature. Although they offer efficient thermal management, conventional cooling techniques including air cooling, liquid cooling, phase change materials, and heat pipes are constrained in dynamic driving and fast-charging scenarios. Predictive battery temperature management employing machine learning, deep learning, reinforcement learning, and Model Predictive Control (MPC) has been made possible by recent developments in artificial intelligence (AI). These methods increase battery safety, minimize energy usage, optimize cooling procedures, and precisely anticipate battery temperature. Conventional cooling technologies, AI-based thermal prediction techniques, predictive control tactics, and current advancements in intelligent BTMS are all summarized in this paper. Additionally, it draws attention to present issues and potential paths, such as Edge AI, Physics-Informed AI, and Digital Twins. All things considered, predictive AI-based thermal management presents a viable way to improve battery safety, energy economy, and next-generation electric vehicle performance.
