Electric Vehicle Range Improvement by Utilizing Deep Learning to Optimize Occupant Thermal Comfort

Heating, ventilation and air-conditioning (HVAC) systems can have a significant impact on the driving range of battery electric vehicles (EV’s). Predicting thermal comfort in an automotive vehicle cabin’s highly asymmetric and dynamic thermal environment is critical for developing energy-efficient HVAC systems. In this study we have coupled high-fidelity Computational Fluid Dynamics (CFD) simulations and Artificial Neural Networks (ANN) to predict vehicle occupant thermal comfort for any combination of steady-state boundary conditions. A vehicle cabin CFD model, validated against climatic wind tunnel measurements, was used to systematically generate training and test data that spanned the entire range of boundary conditions which impact occupant thermal comfort in an electric vehicle. Artificial neural networks (ANN) were applied to the simulation data to predict the overall Equivalent Homogeneous Temperature (EHT) comfort index for each occupant. An ensemble of five neural network models was able to achieve a mean absolute error of 2 ºC or less in predicting the overall EHT for all occupants in the vehicle on unseen or test data, which is acceptable for rapid evaluation and optimization of thermal comfort energy demand. The deep learning model developed in this work enables predictions of thermal comfort for any combination of steady-state boundary conditions in real-time without being limited by time-consuming and expensive CFD simulations or climatic wind tunnel tests. This model has been deployed as an easy-to-use web application within the organization for HVAC engineers to optimize thermal comfort energy demand and, thereby, driving range of electric vehicle programs.