Modern electric drives operate under highly transient and uneven torque–speed conditions defined by standardized drive cycles, where efficiency is typically estimated using steady-state efficiency maps whose predictive reliability under dynamic operation is not fully quantified. This thesis investigates the accuracy of steady-state efficiency map–based methods versus analytic time-stepping approaches for induction motor drive cycle analysis using a laboratory-scale machine and experimentally reproduced down-scaled drive cycles. Analytical, finite-element, and experimental modeling approaches are evaluated under identical drive cycles, including electro-thermal effects, and validated against measurements. The study systematically quantifies the trade-off between computational effort and prediction accuracy while analyzing the influence of grid resolution, grid placement, mesh density, and temperature. Results show that analytic time-stepping achieves the highest agreement with experiments, whereas steady-state efficiency maps offer a computationally efficient alternative but are sensitive to discretization and modeling choices. Finally, the work explores accelerated evaluation strategies using optimal sampling and data-driven surrogate models to enable efficient and confidence-based drive cycle performance prediction.
Kourosh Heidarikani (Fri,) studied this question.