Optimization of thermal preconditioning in BEVs performing daily driving cycles

The electrification of the automotive sector is a pivotal strategy for global decarbonization; however, the widespread adoption of Battery Electric Vehicles (BEVs) faces a significant technical barrier: operational robustness in cold weather climates. This paper presents a computationally efficient optimization framework for the thermal preconditioning of BEVs performing daily driving cycles. The proposed approach utilizes a Control-Oriented Model (COM) embedded in an optimization algorithm to determine the pre-trip battery and cabin conditioning that minimizes the total grid energy of a predicted daily cycle with different segments, which can be classified into three main classes: parking, charging, and driving. The validation against a detailed dynamic model confirms the robustness of the approach, with energy prediction errors consistently below 0.4%. Meanwhile, the algorithm results in energy reductions of 1% to 9.6%, depending on the ambient temperature and route. The analysis highlights that the optimal strategy is highly sensitive to the interplay between ambient temperature and route characteristics including speed profile, topography and charger availability.