This comprehensive systematic review explores the multifaceted impacts of electric vehicle (EV) adoption across technological, environmental, organizational, and policy dimensions. Drawing from 88 peer-reviewed articles, the study addresses a critical gap in the existing literature, which often isolates the impact of EV adoption without considering holistic effects. Technological advancements include innovations in the battery technology and energy storage systems, enhancing EV performance and mitigating range anxiety. The environmental analysis reveals substantial reductions in greenhouse gas emissions, with lifecycle assessments showing significant reductions for EVs compared to internal combustion engine vehicles, particularly when charged with renewable energy sources. Key comparisons include lifecycle emissions between mid-size battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs), and global average lifecycle emissions by powertrain under various policy scenarios. The organizational implications are evident, as businesses adopt new models for fleet management and logistics, leveraging EVs for operational efficiency and sustainability. Policy analysis underscores the crucial role of government incentives, regulatory measures, and infrastructure investments in accelerating EV adoption. The review identifies future research areas such as efficient battery recycling methods, the potential impact of EVs on grid stability, and long-term economic implications. This study offers insights for stakeholders aiming to foster sustainable transportation and achieve global climate goals.

Efficiently powering the billions of mobile devices currently in use is a growing challenge, as most of these devices are battery-operated and require substantial energy for reliable signal transmission. To address this, harvesting energy from radio frequency (RF) signals has emerged as a promising solution. This approach, known as Simultaneous Wireless Information and Power Transfer (SWIPT), has garnered increasing attention from both researchers and industry due to its potential to extend battery life. Among its various applications, relaying networks represent a key area in which SWIPT can make a significant impact. These networks offer improved coverage, higher data rates, lower latency, and better energy and spectral efficiency, which are critical attributes for meeting the demands of next-generation ultra-dense wireless networks. In this paper, we provide a comprehensive review of SWIPT in relaying networks, exploring its principles, structures, protocols, and technical aspects. We also examine the achievable rates in SWIPT-enabled relaying networks and discuss how machine learning could enhance these systems, presenting it as a promising direction for future research. Finally, we highlight several key challenges that remain to be addressed, emphasizing the need for continued exploration in the rapidly evolving landscape of wireless technology.

This paper investigates the achievable rate regions of wirelessly powered two-way communication employing wireless power transfer (WPT) between two users, facilitated by either a fixed or a mobile relay. The analysis encompasses several factors, beginning with an examination of the impact of enabling energy harvesting on achievable rates with a fixed relay, considering diverse transmission distances and varying the number of relay antennas. The study also explores the unique scenario of direct communication between the two users. Additionally, the paper investigates the influence of mobile relay trajectories and velocity on the achievable rate region. Results demonstrate significant variations in achievable rates based on these factors. The findings offer valuable insights, paving the way for future research endeavors.

Vehicle-to-Everything (V2X) technologies have become a key enabling technology in modern Intelligent Transportation Systems (ITS), supporting cooperative awareness, real-time data exchange, and coordinated decision-making among vehicles, infrastructure, and vulnerable road users. This paper presents a systematic survey of recent advancements in V2X-enabled ITS, with emphasis on safety enhancement, collision avoidance, and traffic efficiency optimization. Relevant studies were retrieved from the Scopus and Google Scholar databases, covering the period from 2020 to 2026, in accordance with the PRISMA 2020 guidelines. To ensure the robustness of synthesized claims, a formal 5-point methodological quality appraisal was applied to the 176 fully accessible included studies, explicitly categorizing them by their evidence maturity (e.g., software simulation vs. real-world field test). To resolve the structural overlap inherent in modern vehicular networks, this survey proposes a novel four-layer cyber–physical taxonomy. The literature is systematically categorized into: (1) Communication and Security Infrastructure, (2) Perception and Human Interaction, (3) Algorithmic and Decision-Making, and (4) Macroscopic Applications. The findings indicate that while V2X-supported coordinated maneuvers and Cooperative Adaptive Cruise Control significantly improve road safety and traffic stability, their success is strictly bounded by underlying latency constraints and human behavioral compliance. Furthermore, ML-driven approaches enhance predictive accuracy, communication efficiency, and security mechanisms within V2X systems. Despite notable progress, challenges remain in maintaining reliable communication under dense traffic conditions, strengthening cybersecurity defenses, ensuring system scalability, and fostering user acceptance. By synthesizing recent research advances and identifying open issues, this paper provides a comprehensive reference framework to guide future research and support the practical deployment of safe and reliable V2X-enabled transportation systems.

Electric vehicle (EV) battery-swapping is a timesaving alternative to conventional charging, but optimizing profitability and user satisfaction remains challenging. This paper presents a novel two-way EV battery-swapping model, where vehicles can act both as providers and customers. The model adjusts provider incentives and customer payments based on the State of Charge (SoC) and State of Health (SoH) of the battery. By utilizing EVs as providers, the model increases battery availability and reduces dependence on station-based charging. Key decision variables are optimized using an interiorpoint algorithm to maximize revenue while maintaining user satisfaction. The proposed model enhances the efficiency and scalability of EV battery-swapping stations, contributing to a green and economically viable transport network.

As electric vehicle (EV) adoption accelerates, battery swapping is emerging as a promising alternative to conventional charging, offering faster energy replenishment and potential integration with smart grid systems. However, existing batteryswapping models often overlook the diverse priorities of EV users, particularly in bidirectional systems where vehicles can function as both energy consumers and providers. This study captures and analyzes user preferences related to technical and behavioral satisfaction criteria, including State of Charge (SoC), State of Health (SoH), price and incentives, and fulfillment, through a structured survey of 57 EV users. Using the Fuzzy Analytic Hierarchy Process (FAHP), we derive empirically grounded weights for these criteria while accounting for uncertainty and subjectivity. Results reveal that EV customers prioritize battery quality, including State of Charge and State of Health, over pricing, while providers emphasize fair compensation and compatibility of State of Health. These findings offer actionable insights for designing user-centered battery swapping systems and contribute to the integration of multi criteria decision making (MCDM) in sustainable mobility planning.