Electric vehicles (EVs) offer a sustainable choice to traditional vehicles by reducing greenhouse gas emissions and improving air quality but also contribute to grid flexibility and stability through smart charging and vehicle-to-grid technologies. This paper conducts a mini review on integrating EVs with renewable energy sources and their impacts on power systems, environment, health, economy, and social welfare. For an informed understanding of the structure and growth of the literature along with recent research topics, the review is enriched with a data-driven and visualized bibliometric analysis of publications retrieved from Scopus over the past five years using VOSviewer software. The study delves into the economic implications of EVs, revealing both consumer cost savings and broader economic benefits, including reduced oil demand and associated environmental impacts. Also, the review discusses the significant health benefits of transitioning to EVs, such as the reduction in transportation-related emissions and their contribution to public health improvement. The review's findings indicate a significant interest within the scientific community regarding the study and resolution of various issues surrounding the integration of EVs into contemporary power grids alongside renewable sources, which aligns with the recent surge in scholarly publications. Also, by analyzing the current trends, challenges, and future directions, the study sheds light on the need for continued interdisciplinary research collaboration and policy support for a more sustainable and resilient energy future.

Incorporating electric vehicles (EVs) on a large scale into the distribution grid is a challenging task. Feeder overloads, system power losses, and voltage violations could all arise because of the unregulated charging of EVs. A centralized and coordinated control approach can monitor and manage the large number of EVs, however, requires high bandwidth and reliable communication channel. Proportional based decentralized charging control reduces charging current with the decreased local voltage that provides significant disadvantages to the downstream EVs. Therefore, this work developed a modified proportional decentralized control approach for EV battery charging that can improve the local grid voltage under heavy loading without significant charging current reduction of downstream EVs. A SiC-MOSFET switch-based H-bridge converter is utilized to experimentally validate the grid connected EV system. The proposed system is initially developed in MATLAB Simulink, and a SiC-MOSFET switch-based H-bridge converter was developed for laboratory validation.

This study delves into the effects of integrating electric vehicles (EVs) into power networks, enriched with photovoltaic (PV) systems and wind turbines, alongside energy storage solutions. With the uptick in EV adoption aimed at reducing CO2 emissions and fossil fuel use for environmental benefits, this paper offers a comprehensive comparison of EV integration techniques in the context of PV and wind energy systems, and superconducting magnetic energy storage (SMES) systems. It addresses challenges such as power loss, voltage instability, load balancing, and reactive power compensation across varying scenarios; hence, unveiling the pivotal role of SMES in enhancing grid resilience. A coordinated control strategy based-fuzzy logic control is proposed for optimizing the charging/discharging activities of SMES units and EV batteries, aiming to enhance grid management and efficiency. The FLC crucial function hinges on the electricity price and the active power signals at the grid-interface with devices and residential loads. This proposed control system meticulously orchestrates power sharing among the PV and wind systems, EVs, and the SMES, promoting optimal grid performance. The effectiveness of this integrated control framework is validated through simulation analyses using Matlab/Simulink, showcasing improvements in power system dynamics, stability, and overall sustainability.

Image resolution, or in general, image quality, plays an essential role in the performance of today's face recognition systems. To address this problem, we propose a novel combination of the popular triplet loss to improve robustness against image resolution via fine-tuning of existing face recognition models. With octuplet loss, we leverage the relationship between high-resolution images and their synthetically down-sampled variants jointly with their identity labels. Fine-tuning several state-of-the-art approaches with our method proves that we can significantly boost performance for cross-resolution (high-to-low resolution) face verification on various datasets without meaningfully exacerbating the performance on high-to-high resolution images. Our method applied on the FaceTransformer network achieves 95.12% face verification accuracy on the challenging XQLFW dataset while reaching 99.73% on the LFW database. Moreover, the low-to-low face verification accuracy benefits from our method. We release our code11Code available on https://github.com/Martlgap/octuplet-loss to allow seamless integration of the octuplet loss into existing frameworks.