Overview of machine learning applications to battery thermal management systems in electric vehicles
Natthida Sukkam, Thossaporn Onsree, Nakorn Tippayawong
Abstract
Electric vehicle (EV) is increasingly becoming an alternative vehicle of choice to replace an internal combustion engine-powered car. EV concept is clearly linked to sustainable development. Generally, there are four types of EVs: hybrid, plug-in hybrid, battery, and fuel cell EVs. The form of energy source and storage plays a key role for all EVs. Mostly, a lithium-ion battery (high-voltage battery) is used as energy storage due to its high energy density and long-life cycle. But, high rates of charging and discharging bring about high temperatures of the lithium-ion battery, reducing its useful lifetime. A battery thermal management system (BTMS) is crucial in improving EV performance. Here, in this work, we presented an overview of BTMS employed in the EV development, as well as applications of machine learning techniques to predict and optimize BTMS performance based on fast-charging protocols. Additionally, BTMS based on tropical environmental conditions like in Thailand was also discussed.