This paper presents two different approaches to improve the electromagnetic torque and output power of the hybrid Externally Excited Synchronous Motor (EESM) applied to Electric Vehicles (EVs). An analytical approach is first considered to define the main parameters of the proposed machine. Based on the obtained results from the analytical model, the hybrid EESM is designed with different rotor shapes and step-skewing magnet segments to reduce the total losses and improve torque ripple. Then, Finite Element Analysis (FEA) is applied to compute and simulate electromagnetic parameters, such as the magnetic flux density, mean torque, and output power. The development of these two approaches is validated on an actual EESM machine and the agreement with the theory is shown.

Recently, power companies apply optimal algorithms for short-term load forecasting, especially the daily load. However, in Vietnam, the load forecasting of the power system has not focused on this solution. Optimal algorithms and can help experts improve forecasting results including accuracy and the time required for forecasting. To achieve both goals, the combinations of different algorithms are still being studied. This article describes research using a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This combination limits the weakness of the convergence speed of GA as well as the weakness of PSO that it easily falls into local optima (thereby reducing accuracy). This new hybrid algorithm was applied to the Southern Power Corporation’s (SPC—a large Power company in Vietnam) daily load forecasting. The results show the algorithm’s potential to provide a solution. The most accurate result was for the forecasting of a normal working day with an average error of 1.15% while the largest error was 3.74% and the smallest was 0.02%. For holidays and weekends, the average error always approximated the allowable limit of 3%. On the other hand, some poor results also provide an opportunity to re-check the real data provided by SPC.

Interior Permanent Magnet Synchronous Motors (IPMSM) are increasingly utilized across various fields as a result of their superior performance and high power density. To ensure efficient operation in electric vehicles and industrial applications, these motors have to meet the stringent performance objectives of low vibration. This study proposes an optimization method based on Genetic Algorithm (GA) techniques to minimize losses and material costs. First, an analytical model is developed to determine the required parameters for an IPM motor with five pole pairs, 15 slots, and a power rating of 7.5 kW. Subsequently, the GA is implemented using the analytical model to optimize the electromagnetic parameters of the IPM motor. Finally, the Finite Element Method (FEM) is applied to simulate the parameters obtained from the analytical model and GA. The results before and after optimization, including material cost functions, total losses, and motor outputs, were compared to validate the proposed method.