Due to their high efficiency and low cost of maintenance, brushless DC motors (BLDCMs) with trapezoidal electromotive forces (back-EMFs), have become widely used in various applications such as aerospace, electric vehicles, industrial uses, and robotics. However, they suffer from large current ripples and current spikes. In this paper, a new method for minimizing current spikes appearing during BLDCM start-up or sudden set point changes is proposed. The method is based on controlling the MOSFET gates of the motor driver using R-C filters. These filters are placed between the PWM control signal generator and the MOSFET gates to smooth these control signals. The analysis of the proposed method showed that the R-C filter usage affects the BLDCM steady-state performances. To overcome this limitation, the R-C filter circuit was activated only during current spikes detection. The effectiveness of the proposed method was analytically analyzed and then validated through simulation and experimental tests. The obtained results allowed a reduction of 13% in current spikes amplitude.

This paper presents an optimization model for the charging infrastructure of electric vehicles (EV) designed to minimize installation costs, maximize the utilization of photovoltaic energy, reduce dependency on the electrical grid, and optimize charging times. The model utilizes methodologies such as Linear Optimal Power Flow (LOPF) to align EV charging schedules with the availability of renewable energy sources. Key inputs for the model include Photovoltaic (PV) production profiles, EV charging demands, specifications of the chargers, and the availability of grid energy. The framework integrates installation costs, grid energy consumption, and charging duration into a weighted objective function, ensuring energy balance and operational efficiency while adhering to budgetary constraints. Five distinct optimization scenarios are analyzed to evaluate the trade-offs between cost, charging duration, and reliance on various energy sources. The simulation results obtained from Cadi Ayyad University validate the model’s effectiveness in balancing costs, enhancing charging performance, and increasing dependence on solar energy. This approach provides a comprehensive solution for the development of sustainable and cost-effective EV charging infrastructure.