This paper presents a comparative study between hairpin and traditional winding configurations in double-cage induction motors designed for electric vehicle (EV) propulsion. The analysis is carried out using finite element simulations to evaluate torque and efficiency across a wide frequency range, replicating real driving conditions. Unlike previous studies, this work focuses on how frequency variations affect the electromagnetic performance of double-cage motors, a key aspect for variable-speed EV applications. Results show that hairpin windings can reduce high-frequency losses and improve efficiency without compromising torque, although they may also introduce higher harmonic content and torque ripple. These results offer new insights for improving the dynamic performance and energy efficiency of EV motors under realistic operating conditions.

This paper presents a comparative design and performance analysis of an Interior Permanent Magnet Synchronous Machine (IPMSM) and an Electrically Excited Synchronous Machine (EESM) optimized for light electric vehicle (EV) applications. Both machines are evaluated under identical dimensional, thermal, and performance constraints using a multi-objective optimization framework based on semi-analytical modeling and 2D finite element analysis (FEA). The results show that, while the IPMSM delivers higher peak torque and slightly lower torque ripple, the EESM achieves competitive efficiency, especially in mid-to-high speed regions, without relying on rare-earth materials. Efficiency map analysis confirms that the EESM offers superior performance under medium- to high-speed conditions, making it well suited for urban EV operation.

The monitoring of vibration signals acquired in non-rotating parts represents a widespread methodology for induction machine health assessment. These signals provide direct insights about mechanical and electromagnetic signatures. However, detecting mechanical defects such as shaft misalignment remains challenging due to the complex interactions between mechanical and electromagnetic domains. The accurate measurement and acquisition of vibration signals plays a key role on elucidating possible fault indicators, which stem from different multi-physical mechanisms. The present paper presents an experimental study to analyze the effect of sensor location and placement within an induction machine housing for accurate mechanical fault identification. The comparison is performed for classical time and frequency-domain fault indicators. In addition, different time-frequency evolutions of start-up transient signals are introduced and compared.