In the field of thermal protection, detailed three-dimensional computational fluid dynamics (3D-CFD) simulations are widely used to analyze the thermal behavior on a full vehicle level. One target is to identify potential violations of component temperature limits at an early stage of the development process. In battery electric vehicles (BEVs), transient load cases play an increasing role in evaluating components and vehicle systems close to real-world vehicle operation. The state-of-the-art 3D simulation methodologies require significant time and computational effort when running transient load scenarios. One main reason is the conjugate characteristic of the problem, meaning that conduction within the component and convection into the surrounding air occur simultaneously. This requires a detailed consideration of both the fluid and structural domains. Therefore, this article derives a time-efficient simulation methodology for transient component temperatures in electric vehicles. The approach is to extract heat transfer coefficients and reference temperatures from sample flow simulations and to construct convective meta-models. Solid component temperatures are then transiently computed whereby the low-dimensional meta-models provide the convective heat transfer. Dimensional analysis determines the smallest possible parameter space for the meta-modeling. Two different types of meta-models, a scalar regression model and a vector proper orthogonal decomposition (POD) approach, are tested and compared. The study examines at first the applicability of the heat transfer formulation under different flow and component temperature conditions using a generic flat plate test case. A low Biot number (Bi) is crucial to receive accurate temperature predictions as heat transfer coefficients are derived on uniform temperature walls. The methodology is subsequently applied to a sample component in the motor compartment. Measurements on a test rig and a transient load case comparison with a coupled simulation prove the validity of the numerical procedure. Scaling to full-vehicle applications is feasible. The new methodology delivers a highly accurate temperature prediction and increases computation efficiency, especially for sensitivity studies.