Due to environmental issues and depletion of resources, global attentiveness in electric vehicles (EVs) is growing. In particular, research on high specification motors for driving large EVs has attracted a lot of attention. In this study, an analysis and design process for a motor that can be universally applied in heavy vehicles to reduce environmental pollution was introduced. Motors for driving heavy vehicles require high specifications. Thus, an interior permanent magnet synchronous motor (IPMSM) with a delta-shaped magnet array was used to improve the torque and power characteristics. A step skew rotor structure was also used to dampen vibration and noise by minimizing the high-order harmonics in the cogging torque. The proposed analysis and design approach also reduces the total harmonic distortion (THD) of the back electromotive force (EMF). The effectiveness of the proposed analysis and design process and the usefulness of the resulting high-performance traction motor for heavy-duty vehicles were verified via the finite element method (FEM) and by experiment.

Countries are looking for a pathway toward a sustainable transition from fossil-based to less or zero-carbon sources in energy sector in order to reduce its impact from climate change. Among different renewable resources, photovoltaic power system (PV) is considered as one of the most promising technology, which has the biggest potential for increasing renewable energy. However, its profit can be varied depending on operation and management, which possibly causes performance degradation and safety issues due to faults. Therefore, we have collected actual operation data from the PV monitoring system which located in Changwon, Gyeong-nam province during one year. While most of the PV system has security function which is limited to its inverter protection, in this study, based on fault data software program is developed for fault diagnosis in order to increase robustness of the PV system and minimize operation cost. Also, the program comprises machine learning algorithm based on fault data to classify its types of faults. It also presents economic loss of each PV module considering mean time to repair (MTTR) occurred from the event of faults in the PV system. As a result, the program helps faster fault diagnosis of the PV system and decreasing overall operation cost for the system operator.