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As battery temperature greatly affects performance, safety, and life of Li-ion batteries in plug-in and electric vehicles under various driving conditions, automakers and battery suppliers are paying increased attention to thermal management for Li-ion batteries in order to reduce the high temperature excursions that could decrease the life and reduce safety of Li-Ion batteries. Currently, the lack of fundamental understanding of the heat generation mechanism due to complex electrochemical phenomena prohibits accurate estimation of the heat generation within Li-ion cells under various operating conditions. Heat from Li-ion batteries can be generated from resistive dissipation, the entropy of the cell reaction, heat of mixing, and other side chemical reactions. Each of these can be a significant source of heat under a range of circumstances.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.