## 2 Related Work  
The integration of fully electric vehicles (FEVs) into smart cities is a multifaceted challenge that intersects various domains, including energy management [13; 14], transportation [15; 16] and data analysis [17]. This section reviews the current state of research in these areas, emphasizing recent advancements and highlighting gaps that our study aims to address. The transition to smart grids and renewable energy sources is fundamental to integrating FEVs into smart cities [18]. Various studies have explored energy management strategies to enhance the efficiency and sustainability of FEVs [19; 20]. For instance, the use of machine learning algorithms to predict energy consumption patterns and optimize charging schedules has shown promise in managing the energy demands of FEVs [21]. Additionally, the integration of solar energy systems with FEVs has been suggested to reduce reliance on grid electricity, thus lowering operational costs and environmental impact [22]. Energy storage systems (ESS) play a significant role in stabilizing the grid while accommodating the fluctuating demands of FEVs. Research highlights the need for robust and scalable infrastructure to support these advancements [23]. To address infrastructure challenges, a decentralized energy management system leveraging blockchain technology has been proposed, ensuring transparency and security in energy transactions and making the integration process more resilient to cyber threats [24]. The potential of integrating vehicle-to-grid (V2G) technology with FEVs to balance the energy supply and demand in smart grids has also been examined. Research suggests that V2G can enhance grid stability and provide additional revenue streams for FEV owners, encouraging wider adoption of electric vehicles [25; 26]. Furthermore, using FEVs as mobile energy storage units dispatched to areas with high energy demand has been explored, offering a flexible solution to energy distribution challenges in urban environments [27]. The transportation aspect of integrating FEVs into smart cities involves optimizing traffic flow, reducing congestion, and improving overall mobility. Connected vehicle technologies have been researched to enhance traffic management, demonstrating that real-time data from FEVs can be used to dynamically adjust traffic signals, thereby reducing congestion and improving travel times [28]. The impact of autonomous FEVs on urban mobility has also been highlighted through simulation models, suggesting that these vehicles could significantly reduce the number of vehicles on the road, leading to decreased congestion and lower emissions [29]. Studies have investigated the implications of FEV adoption on public transportation systems, indicating that integrating FEVs with public transit can create a more cohesive and efficient transportation network, ultimately enhancing overall mobility within smart cities [30]. The environmental benefits of such integration have been examined, concluding that a well-coordinated system could lead to significant reductions in urban air pollution and greenhouse gas emissions [31]. In addition to transportation, research has focused on the development of intelligent transportation systems (ITS) that incorporate FEVs to optimize route planning and reduce travel time. AI-driven algorithms have been used to enhance the efficiency of ITS, demonstrating substantial improvements in traffic flow and energy consumption [32]. Data analysis is fundamental in understanding and optimizing the integration of FEVs into smart cities. Big data analytics have been employed to predict maintenance needs and improve the reliability of FEVs, showing that predictive maintenance can significantly reduce downtime and maintenance costs [33]. Additionally, the importance of data privacy and security in the context of FEVs has been discussed, with proposals for secure data-sharing frameworks to ensure the confidentiality of sensitive information while enabling data-driven decision-making processes [34].