The expanding Electric vehicle (EV) market is fueled by the need for more efficient and dependable ways to recharge the battery. By eliminating the necessity for direct physical interaction between vehicles and charge equipment, the Wireless Power Transfer (WPT) methodology eliminates the drawbacks and risks associated with the conventional conductive system. The innovative WPT technique replaces the conductive charging system to keep a similar power rating and efficiency. Numerous strategies have been created to improve the effectiveness and dependability of the WPT model. As a result, this review article thoroughly analyses current major research articles that describe WPT technologies for EV charging. The papers are classified based on various coupling types along with magnetic couplers and shielding, compensation, misalignment tolerance and control methods in WPT systems. In addition, the possible research gaps and the challenges associated with the existing works of WPT systems are discussed. The reviewed results are analyzed based on performance metrics and implementation tools attained using above classifications and EMF exposure references employed in the WPT system. The comparative effectiveness is presented in the tables, diagrams, as well as interconnections for ease of presentation and conceptual understanding. The core asset of this article lies in the fact that the findings offer a good "one-stop" resource including both aspects of the system and with regard to the power stage. This review article also emphasizes the potential and obstacles of inductive wireless EV battery chargers. A developer can find this study will contribute substantially to selecting an optimum design for the enhancement of the WPT system.

This study introduces a type of solid‐state transformer (SST) for solar power station design and an energy management strategy (EMS) for the SST. The purpose of this study is to design a real efficient EMS for the photovoltaic‐assisted charging station in smart grid ancillary services and apply the optimal decision method. Also, the energy bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible sources. Also, taking into account the EBC results and the power order from the aggregator, a charge power allocation algorithm is designed to distribute the power of flexible electric vehicles (EVs). With the help of a case study and laboratory analysis, the proposed EMS strategy is effective in real‐time energy management and is suitable for practical applications. The obtained results show the stable performance in the calculation of the energy range and real‐time power allocation which improves the efficiency of the photovoltaic‐based charging station. Also, the SST improves the operation of charge stations for supplying the sustaining power.

One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users’ requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method.

Recently, electric vehicles (EVs) that use energy storage have attracted much attention due to their many advantages, such as environmental compatibility and lower operating costs compared to conventional vehicles (which use fossil fuels). In a microgrid, an EV that works through the energy stored in its battery can be used as a load or energy source; therefore, the optimal utilization of EV clusters in power systems has been intensively studied. This paper aims to present an application of an intelligent control method to a bidirectional DC fast charging station with a new control structure to solve the problems of voltage drops and rises. In this switching strategy, the power converter is modeled as a DC fast charging station, which controls the fast charging of vehicles with a new constant current or reduced constant current method and considers the microgrid voltage stability. The proposed method is not complicated because simple direct voltage control realizes the reactive power compensation, which can provide sufficient injected reactive power to the network. As a result, the test is presented on a fast charging system of electrical outlets with a proposed two-way reactive power compensation control strategy, in which AC/DC converters are used to exchange two-way reactive power to maintain the DC link voltage as well as the network bus voltage in the range of the basis. This charging strategy is carried out through the simulation of fast charge control, DC link voltage control, and reactive power compensation control to adjust the voltage and modify the power factor in the MATLAB software environment and is then verified. Finally, the results indicate that the proposed method can charge with high safety without increasing the battery’s maximum voltage. It can also significantly reduce the charging time compared to the common CV mode.