This paper introduces an energy management and control method for DC microgrid supplying electric vehicles (EV) charging station. An Energy Management System (EMS) is developed to manage and control power flow from renewable energy sources to EVs through DC microgrid. An integrated approach for controlling DC microgrid based charging station powered by intermittent renewable energies. A wind turbine (WT) and solar photovoltaic (PV) arrays are integrated into the studied DC microgrid to replace energy from fossil fuel and decrease pollution from carbon emissions. Due to the intermittency of solar and wind generation, the output powers of PV and WT are not guaranteed. For this reason, the capacities of WT, solar PV panels, and the battery system are considered decision parameters to be optimized. The optimized design of the renewable energy system is done to ensure sufficient electricity supply to the EV charging station. Moreover, various renewable energy technologies for supplying EV charging stations to improve their performance are investigated. To evaluate the performance of the used control strategies, simulation is carried out in MATLAB/SIMULINK.

This paper comprehensively reviews the current status of multidisciplinary technologies in electric vehicles. Because the electric vehicle market will expand dramatically in the coming few years, research accomplishments in power electronics technology for electric vehicles will be highly attractive. Challenges in power electronics technology for driving electric vehicles, charging batteries, and circuit topologies are being explored. This paper aims primarily to address the practical issues of the future electric vehicles and help researchers obtain an overview of the latest techniques in electric vehicles, focusing on power electronics-based solutions for both current and future electric vehicle technologies. In this work, different medium-and high-voltage DC-AC inverter topologies are investigated and compared in terms of power losses and component requirements. Recent research on electric vehicle power converters is also discussed, with highlighting on soft-switching and multilevel inverters for electric vehicle motor drives. In this paper, a methodical overview and general classification of DC-AC power converters are presented. In specific topologies, drawbacks such as voltage stresses on active switches and control complications may occur, which can make them difficult for immediate commercialization. However, various modified circuit topologies have been recommended to overcome these drawbacks and enhance the system performance.

This paper presents the control and simulation of an electric vehicle (EV) charging station using a three-level converter on the grid-side as well as on the EV-side. The charging station control schemes with three-level AC/DC power conversion and a bidirectional DC/DC charging regulator are described. The integration of EVs to the power grid provides an improvement of the grid reliability and stability. EVs are considered an asset to the smart grid to optimize effective performance economically and environmentally under various operation conditions, and more significantly to sustain the resiliency of the grid in the case of emergency conditions and disturbance events. The three-level grid side converter (GSC) can participate in the reactive power support or grid voltage control at the grid interfacing point or the common coupling point (PCC). A fuzzy logic proportional integral (FL-PI) controller is proposed to control the GSC converter. The controllers used are verified and tested by simulation to evaluate their performance using MATLAB/SIMULINK. The comparison of a PI-controller and a PI-Fuzzy controller for the EV charging station shows the effectiveness of the proposed FL-PI controller over conventional PI controller for same circuit operating conditions. A good performance for PI-Fuzzy in terms of settling time and peak overshoot can observed from the simulation results.

In this paper, a new multi-port DC-DC power converter used to deal with the intermittent nature and slow response in renewable energy applications is proposed. The proposed converter integrates a DC-DC converter and a DC-AC inverter, and the proposed circuit integrates various renewable energy sources in addition to the energy storage unit. By combining renewable energy sources with a statistical trend to offset each other, the impact of the intermittency can be considerably minimized. This combination increases the overall system reliability and usability. Moreover, integrating such systems with energy storage systems can overcome the slow response issue of renewable sources. It can provide the additional energy required by the load or absorb the extra energy provided by the power sources, which greatly improves the dynamics of the overall system. The proposed converter can reduce the system cost and size and improve the efficiency and reliability. The operation principle is studied in detail, and the design considerations are provided. The proposed architecture and its control strategy were analyzed and studied using the Simulink/MATLAB environment. Finally, the feasibility of the proper operation of the studied converter was experimentally verified based on the results of experimental studies conducted on a 300 W prototype implemented in a laboratory.

The disadvantage of photovoltaic (PV) power generation is that output power decreases due to the presence of clouds or shade. Moreover, it can only be used when the sun is shining. Consequently, there is a need for further active research into the maximum power point tracking (MPPT) technique, which can maximize the power of solar cells. When the solar cell array is partially shaded due to the influence of clouds or buildings, the solar cell characteristic has a number of local maximum power points (LMPPs). Conventional MPPT techniques do not follow the actual maximum power point, namely, the global maximum power point (GMPP), but stay in the LMPP. Therefore, an analysis of the occurrence of multiple LMPPs due to partial shading, as well as a study on the MPPT technique that can trace GMPP, is needed. In order to overcome this obstacle, the grey wolf optimization (GWO) method is proposed in order to track the global maximum power point and to maximize the energy extraction of the PV system. In addition, opposition-based learning is integrated with the GWO to accelerate the MPPT search process and to reduce convergence time. Simultaneously, the DC link voltage is controlled to reduce sudden variations in voltage in the event of transients of solar radiation and/or temperature. Experimental tests are presented to validate the effectiveness of the proposed MPPT method during uniform irradiance and partial shading conditions. The proposed method is compared with the perturbation and observation method.