Evaluating electric vehicle (EV) motor performance over dynamic drive cycles is essential for accurate energy efficiency prediction and system-level optimization. While conventional steady-state models enable rapid generation of efficiency maps, they can introduce significant errors due to grid interpolation and the omission of transient behavior. Limited understanding exists regarding how grid coarseness and modeling approach affect the discrepancy between steady-state and time-stepping solutions. This study quantifies these differences for a laboratory-scale induction motor (IM) operating under down-scaled drive cycles, using experimental time-stepping measurements as a reference. Efficiency maps are developed using three methods—analytic modeling, finite element analysis (FEA), and experimental testing—while time-stepping simulations are conducted using an analytic model. The study evaluates both total drive cycle energy efficiency errors and pointwise deviations across the torque-speed envelope for various grid resolutions. Results are compared against laboratory-based time-stepping measurements to identify trade-offs between computational efficiency and accuracy. Additionally, a sensitivity analysis is performed to assess the impact of operating point (OP) placement within the grid and temperature variation on the accuracy of efficiency mapping.
Heidarikani et al. (Tue,) studied this question.