Vehicle Under-Hood Thermal Management: Development of a Reduced-Order Model for Accurate and Computationally Efficient Temperature Distribution Simulations

Electrified vehicle energy management plays a crucial role in the context of the European Green Deal by facilitating the transition toward sustainable mobility. The development of predictive and robust simulation tools is essential to implement and test different energy management strategies. This study aligns with this objective by presenting the development of an under-hood flows model designed for integration into a 1D vehicle simulator, which is used to perform vehicle simulations about longitudinal performances, energy consumption and range. Vehicle under-hood thermal management is inherently complex due to the interplay of internal flow dynamics and multiple heat transfer mechanisms. A purely 1D modeling approach lacks the spatial resolution required to capture detailed flow field characteristics, while a fully 3D CFD model is computationally prohibitive for scenarios requiring efficient simulations. To address this trade-off, a reduced-order model (ROM) approach is proposed. The methodology consists of three key stages. First, a CFD model is developed to generate flow field data based on a Design of Experiment (DoE), where inlet velocity and total heat rejection levels are systematically varied. Second, a clustering algorithm is employed to discretize the CFD flow field into a desired number of zones at different average temperatures. Third, a fluid dynamic network of 1D components is created, where heat transfer coefficients and thermal masses values are optimized by minimizing the discrepancy between transient temperature profiles obtained from CFD model and those predicted by the 1D representation. The results from the training and testing phases demonstrate that the proposed 1D model provides a reliable approximation of the under-hood temperature distribution. The model exhibits strong agreement with CFD predictions while maintaining computational efficiency, making it suitable for integration within a vehicle-level 1D simulator.