New topologies of electric car use high power switching devices which must be integrated with a growing range of electronic systems in small places. A good performance of the car relies on the electromagnetic compatibility among these systems, which requires the characterization of the noise and interferences present in the system. To characterize the noise, both emission and susceptibility issues as well as coupling mechanisms must be studied. This paper presents the work performed for the electromagnetic mapping of a four-wheel fully electric car, which involves both simulation and experimental measurements. It has been carried out within the E-VECTOORC project (FP7-INFSO-284708), in collaboration with car manufacturers such as Jaguar Land Rover and Škoda.

WIRIS quizzes are an online mathematics tool for educational purposes that upgrade Moodle quizzes and allow the development of personalized quizzes using random data and conditional instructions. WIRIS quizzes can be used in any mathematics or science degree; their complex operators allow it to be used to solve electrical circuits. This tool promotes autonomous student learning and enables teachers to monitor that learning and make adjustments if necessary. Therefore, this tool improves teaching quality. It can also be used as an assessment tool by both the teacher and the student. This paper shows how WIRIS quizzes have been integrated into the virtual campus of the Polytechnic University of Catalonia (UPC), Spain, to teach electrical circuits. Lessons learned in performing and using WIRIS quizzes with second-year students at the UPC are shown.

This paper focuses on the experimental investigation for incipient fault detection and fault detection methods suitably adapted for use in permanent-magnet motors for direct-drive elevator systems. The proposed system diagnoses permanent-magnet motors having two types of faults such as short circuit of stator windings and bearing fault. After processing current data the classical fast Fourier transform is applied to detect characteristics under the healthy and various faulted conditions with MCSA.

At the moment renewable generation systems are increasing its presence. The paper is about how machine current signature analysis (MCSA) can reliably diagnose faults in squirrel cage generators. This paper focuses on the experimental investigation for incipient fault detection and fault detection methods, suitably adapted for use in wind generator systems using squirrel cage. The proposed system diagnoses asynchronous generators having three types of faults such as broken rotor bars, short circuit of stator windings and bearing fault. After processing current data the classical fast Fourier transform is applied to detect characteristics under the healthy and various faulted conditions with MCSA.