This study introduces an efficient methodology for evaluating the performance of electric motors in electric vehicles, focusing on the trade-off between simulation accuracy and computational cost. Three standardized driving cycles—Worldwide Harmonized Light Vehicles Test Procedure (WLTP), New European Driving Cycle (NEDC), and Federal Test Procedure (FTP)—are employed to represent realistic operating conditions. The performance of an Interior Permanent Magnet Synchronous Motor (IPMSM) is analyzed through high-fidelity electromagnetic simulations using Ansys Maxwell. For each cycle, full-scale Finite Element Analysis (FEA) is performed across all torque-speed operating points to obtain detailed efficiency and performance insights. Although this exhaustive approach ensures high accuracy, it is computationally demanding and unsuitable for iterative design or optimization. To address this, the Energy Center of Gravity (ECG) clustering method is introduced to reduce the simulation workload. By analyzing the energy distribution across each driving cycle, ECG identifies a small set of energy-representative operating points that effectively capture the motor’s typical load behavior. These selected points are then simulated, and the resulting efficiency estimates are compared with those from the full-cycle FEA. The comparison shows that the ECG method maintains high accuracy—with efficiency deviations under 2%—while reducing simulation time from hours to just minutes. This demonstrates the method’s effectiveness in achieving a balance between precision and efficiency. The proposed approach offers a scalable and practical solution for performance evaluation and early-stage optimization of electric vehicle powertrains, enabling faster design iterations without sacrificing result fidelity.

The purpose of this paper is to investigate the challenges that arise while doing an analysis of electric machines throughout the course of a complete driving cycle. Because of the complicated nature of this process, which is caused by the large number of operational points, the use of specialist equipment and the expenditure of a considerable amount of time are both requirements. This study's overarching goal is to improve the efficacy of electric machine design by investigating clustering strategie. The purpose of this study is to investigate various methods of clustering in order to create an electric machine design that is more effective. The proposed approach employs clusters of operating points to identify chosen Representative points or RPs to construct electric machines with the highest possible efficiency within a given operating range. The study suggests the automated k-Means method for cluster analysis and RPs detection. To test the effectiveness of the proposed method, the study conducted an electromagnetic design study and analysis of the internal permanent magnet machine (IPM) for the WLTP driving cycle. As a result, the operating point set was reduced to only eight points, allowing for an assessment of the k-Means technique's efficiency. This study has implications for researchers and practitioners seeking to improve electric machine design and efficiency in the automotive industry.

Future electric car issues that need to be addressed (EV) are mainly the power/energy densities of the energy storage systems and the electric motor performances. Interior Permanent-Magnet Synchronous Machines (IPMSM) are commonly utilized in today's electrical automobiles because they have a wider constant power speed range and a better power/torque density, a compact structure, a higher efficiency than induction machines. Magnetic steel sheets are frequently utilized in IPMSM, and the usage of low-iron-loss materials in IPMSM to increase performance has recently been investigated. This paper deals with a study on the influence of Different magnetic steel sheets technologies on the design of IPMSM for automotive applications. The designed IPMsynchronous motors results of the simulation In terms of efficiency, torque and output power, various magnet material components are compared to one another.