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In contemporary days, the research and development enterprises have been focusing to design intelligently the battery swap station (BSS) architecture having the prospects of providing a consistent platform for the successful installation of the large-scale fleet of hybrid and electric vehicles (i.e. xEVs). The BSS may calibrate its subsystem for the electric vehicle (EV) deployment by accomplishing similar idea as in existing gasoline refuelling stations, in which the discharged batteries are being replaced or swapped by partially or fully charged ones by spending a few minutes. The BSS approach has arisen as a promising technology to the traditional EV recharging station approach as it provides a broader experience of business prospects for the specific stakeholders. This work deals with the introduction to BSS including infrastructure, techniques, benefits over charging station and key challenges associated with BSS. Furthermore, an S34X-smart swapping station for xEVs is proposed and finally, the key thrust is research for BSS is discussed. To the authors' knowledge, this is the first kind of review work on BSS.

In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.

While addressing the issue of improving the performance of Photovoltaic (PV) systems, the simulation results are highly influenced by the PV model accuracy. Building the PV module mathematical model is based on its I-V characteristic, which is a highly nonlinear relationship. All the PV cells’ data sheets do not provide full information about their parameters. This leads to a nonlinear mathematical model with several unknown parameters. This paper proposes a new application of the Grasshopper Optimization Algorithm (GOA) for parameter extraction of the three-diode PV model of a PV module. Two commercial PV modules, Kyocera KC200GT and Solarex MSX-60 PV cells are utilized in examining the GOA-based PV model. The simulation results are executed under various temperatures and irradiations. The proposed PV model is evaluated by comparing its results with the experimental results of these commercial PV modules. The efficiency of the GOA-based PV model is tested by making a fair comparison among its numerical results and other optimization method-based PV models. With the GOA, a precise three-diode PV model shall be established.

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