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It is noteworthy today that the creation and popularization of new energy has piqued the world's interest. As a result, new energy electric cars are liked and acknowledged by most customers as a representation of the development and use of new energy. The advancement of electric vehicles (EVs) has important implications for the sustainable use of energy resources. As the number of new energy EVs grows, so does the need for charging stations for these vehicles. Maximum simplification of charging station distribution may successfully satisfy the charging demands of EVs. As a result, determining the appropriate arrangement of EV charging stations has become an essential study issue. This paper proposed a novel algorithm for EV charging station optimization based on a neural network. The main idea is to optimize the cost of charging cost and the user's budget. Then, considering the target planning region of the charging station, the historical data is deployed to predict the time distribution of EVs based on the backpropagation neural network algorithm. Finally, the performance of swarm optimization is improved through the dynamic probability mutation method. Simulation results show that the proposed algorithm has better performance than existing algorithms in terms of global economic cost and low-power and high-power charging station's spatial location. [ABSTRACT FROM AUTHOR]

An adaptable infrastructure for dynamic power control (AIDPC) of battery chargers for electric vehicles has been proposed in this work. The battery power is dynamically adjusted by utilizing flexible active load management when the vehicle is plugged in. The battery charging and discharging prototype model is developed for storing the surplus power during the off-peak period and delivering the power during the shortfall of the grid. The development and adoption of two charging strategies, namely the constant current (CC) charging and the proposed dynamic constant current (DCC) charging, has been elaborated. When the Grid to Vehicle (G2V) operation mode is active in the CC charging method, the batteries are charged from the power grid with a constant amplitude sinusoidal current and unity power factor. In the proposed method of DCC, the batteries are charged from the grid with a decreased amplitude in sinusoidal current and unity power factor, and the battery charges from the dynamic dc link voltage. The battery charging and discharging depends upon the rate of change of dc link voltage according to the load variation of the grid. The simulation and experimental results support the rapid time response of the (AIDPC) load profile of the electrical system experiences a new peak due to the CC charging method. On the other hand, the vehicle scheduling algorithm successfully draws less power from the grid when the DCC charging method is used. [ABSTRACT FROM AUTHOR]

Widespread recharging of electric vehicle (EV) batteries could lead to frequent overloads, excessive power loss, and severe voltage fluctuations, especially at the distribution-system level. These challenges can be mitigated with smart charging initiatives, in which the system operator regulates EV charging with certain technical or economic objectives, provided that the EV owners are prepared to relinquish the charging control of their vehicles. Amidst concerns regarding the potential hike in electricity bills due to domestic EV charging, cost-minimizing objectives have been identified as compelling motivation for EV owners to participate in centralized charging programs. This paper presents a dynamic strategy for smart charging that can account for the uncertainties associated with vehicle mobility. The charging scheme aims to minimize energy costs with respect to a real-time pricing tariff while fulfilling the charge requirement of all EV users. The benefits of smart charging under both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes are analyzed. Furthermore, the impact of smart charging on the distribution system is assessed in terms of system demand, distribution efficiency, and load voltage. Results indicate that the proposed technique can reduce the consumers' energy bill by roughly one third. Although the smart V2G method leads to maximum saving, the inclusion of battery degradation cost tips the balance in smart G2V's favor. Moreover, the pair of smart charging solutions improves distribution-system operation, with all the monitored metrics of power distribution showing significant improvement. [ABSTRACT FROM AUTHOR]

This article introduces a unique dynamic balance strategy (DBS) that can overcome many disadvantages such as voltage and frequency deviations, overloading, high infrastructure costs that may occur in the network integration of electric vehicles (EVs). The proposed method is also aimed at keeping the comfort and satisfaction of the EV users at a high level without the obligation of the EV owners to inform the operator of the entry and charge planning. DBS introduces a real-time energy-sharing method that uses both the grid-to-vehicle (G2V) and vehicle-to-vehicle (V2V) topologies simultaneously without knowing the power demand profile in advance. However, from the perspective of smart grid, it is also possible to activate the vehicle-to-grid (V2G) topology in case of frequency or voltage fluctuations in the network with the proposed DBS. The proposed DBS was simulated in real time and phasor analysis for different penetration levels in MATLAB/Simulink program. Considering that EV driver behavior is highly variable and will vary regionally, it is shown that the proposed DBS gives dynamic results against all behavioral situations, varying penetration levels, and all the negativities that may occur in the penetration of EVs are overcome by keeping the satisfaction of EV drivers at a high levels. [ABSTRACT FROM AUTHOR]