To ensure performance, safety, and efficiency, thermal management is key to the operation of electric vehicles (EVs) as they continue to scale varying climates, charging behaviors, and duty cycles. This paper describes a path to thermal intelligence which leverages publicly available datasets. Some of these datasets include drive profiles from NREL Fleet DNA, climate data from NOAA GHCN, battery aging data from NASA and MIT, and workplace charging behaviors from ACN-Data. The paper also draws upon open-source simulator or learning tools such as PyBaMM, FASTSim, and pythermalcomfort. Using a combination of physics and machine learning, we obtain a 54% reduction in root mean square error (RMSE) for peak battery temperature predictions based on a physics-only baseline. The smart system utilizes physical and uses machine learning to predict cabin HVAC energy use, given different comfort constraints (PMV/PPD). During experimentations in urban commutes and last-mile delivery, we find that cabin HVAC range reductions can exceed 10% in extreme climates; as a countermeasure, we piloted comfort-aware setpoint relaxations as well as charging-aware pre-conditioning the night before. In the case of charging-aware pre-conditioning, by using real-world timestamps for the charging events, we reduced the starting battery temperature by 6.8°C while simultaneously increasing passenger comfort by 85%. All of this was done without an increase in onboard energy consumption. We believe this work provides for the construction of open thermal intelligence pipelines to maintain safety, efficiency, and comfort for future software-defined Electric vehicle and fleet platforms.

Electric vehicles, Thermal management, Big Data, Machine Learning, HVAC Energy Optimization, PyBaMM, Battery Temperature prediction, PMV/PPD comfort modeling, FASTSim, Simulation, Cabin Comfort, Thermal Intelligence

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