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This paper picsenls a reconfigurable dual-band 4.86 and 8.98 GHa dipole anlenna on silicon using scrics MEMS switches. The effccts of series MEMS switches on the antenna performance are studicd. Thc design is performed using thc 3D electromagnetic simulator HFSS'. Using the dcsigned series MEMS switches, thc obtained antenna m u m loss is -lO.ZdB and -21.6dB, at the lower and uppcr frequencies, respectively. However, comparable to the case of ideal switches, a frequency shiA of0.4% and of 2.8% at the lower and uppcr frcqucncics, respecliveiy, is observed. The antenna, including the MEMS switches, has bandwidth of 1.9% and 13.6% at the lowcr and upper frequencies, respectively. The antenna directivity is 2 dB at the lower frequency, and is 3 dB at the uppcr frequency.

This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process.The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively.

Fast charging of the electric-vehicles is one of the paramount challenges in solar smart cities. This paper investigates intelligent optimization methodology to improvise the existing approaches in order to speed up the charging process whilst reducing the energy consumption without degradation in the light of the outrageous demand for lithium-ion battery in the electric vehicles (EVs). Two fitness functions are combined as the targeted objective function: energy losses (EL) and charging interval time (CIT). An intelligent optimization methodology based on Cuckoo Optimization Algorithm (COA) is implemented to the objective function for improving the charging performance of the lithium-ion battery. COA is applied through two main techniques: The Hierarchical technique (HT) and the Conditional random technique (CRT). The experimental results show that the proposed techniques permit a full charging capacity of the polymer lithium-ion battery (0 to 100% SOC) within 91 mins. Compared with the constant current-constant voltage (CCCV) technique, an improvement in the efficiency of 8% and 14.1% was obtained by the Hierarchical technique (HT) and the Conditional random technique (CRT) respectively, in addition to a reduction in energy losses of 7.783% and 10.408% respectively and a reduction in charging interval time of 18.1% and 22.45% respectively. Experimental and theoretical analyses are performed and are in good agreement on the polymer lithium-ion battery fast charging method.INDEX TERMS Constant current-constant voltage (CCCV), cuckoo optimization algorithm (COA), electric vehicles (EV), electric vehicle fast charging, lithium-ion battery, RC second-order transient.