Physics-Guided Sparse Identification of Nonlinear Dynamics for Prediction of Vehicle Cabin Occupant Thermal Comfort

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. The performance of the trained model was evaluated on drive cycle data from a Coldbox test for the same vehicle. Based on a physics-based transient lumped cabin comfort model three state variables (EHT, MRT and cabin air temperature) and four control inputs were chosen to develop the SINDy model. In addition to the control inputs, interaction terms between the different control inputs were also included. Despite training with limited data (two trajectories of different time lengths) the model learned the dynamics of the vehicle cabin and was able to predict EHT, MRT and cabin air temperature for a new set of control inputs reasonably well. The SINDy model can be used for predictions of thermal comfort under transient conditions with significantly lower turnaround times compared to physics-based simulations. It is an alternative to black box models by providing insights into the mechanisms driving a dynamical system.