The accelerating adoption of electric vehicles has intensified the need for intelligent, efficient, and resilient mobility infrastructures. Integration of artificial intelligence and wireless communication technologies presents transformative opportunities for optimizing electric vehicle operations, enhancing energy management, and supporting sustainable urban transportation. Artificial intelligence enables predictive analytics for traffic flow, dynamic fleet management, and adaptive energy distribution, while wireless networks ensure low-latency, reliable communication among vehicles, charging stations, and grid components. The convergence of these technologies facilitates real-time decision-making, smart charging, congestion mitigation, and seamless interaction across multimodal transport systems. Advanced predictive models and edge computing frameworks enhance operational efficiency, grid stability, and user accessibility, creating scalable solutions for high-density urban environments. The chapter examines current trends, challenges, and future directions in AI-driven and wireless-enabled electric vehicle ecosystems, highlighting their potential to transform mobility frameworks and contribute to environmentally sustainable urban planning. This comprehensive study provides insights into the strategic deployment of intelligent mobility systems, emphasizing innovation, reliability, and the integration of renewable energy resources within smart transportation networks.
The rapid transformation of urban transportation systems toward electrification has created unprecedented demands on mobility infrastructure and energy networks [1]. Electric vehicles (EVs) are positioned as a pivotal solution for reducing greenhouse gas emissions, minimizing air pollution, and promoting sustainable urban development [2]. The widespread adoption of EVs requires more than just advancements in battery technology or vehicle design; it demands a comprehensive ecosystem encompassing charging stations, renewable energy integration, intelligent traffic management, and energy-efficient fleet operations [3]. Growing urban populations, rising vehicle density, and regulatory pressures have intensified the need for infrastructure that supports seamless, reliable, and high-capacity EV deployment [4]. In this context, the integration of advanced computational intelligence and wireless communication becomes central to designing systems capable of managing complex mobility patterns, real-time energy demands, and dynamic operational constraints [5].
Artificial intelligence plays a fundamental role in optimizing EV operations by enabling predictive analytics, adaptive decision-making, and autonomous control of mobility networks [6]. Machine learning algorithms facilitate battery health monitoring, load balancing, and route optimization, improving both energy efficiency and operational reliability [7]. Predictive models allow transport authorities and fleet operators to anticipate congestion, forecast peak demand, and implement proactive scheduling strategies [8]. Intelligent fleet management systems enhance vehicle utilization, reduce downtime, and provide accurate forecasting for charging infrastructure requirements [9]. By incorporating data-driven decision-making into urban mobility frameworks, AI contributes to a resilient and scalable transportation ecosystem capable of responding to both routine and emergency scenarios [10].
Wireless communication technologies complement AI-driven operations by establishing a low-latency, high-reliability network for real-time vehicle-to-infrastructure (V2I) and vehicle-to-everything (V2X) interactions [11]. High-speed networks, including 5G, Wi-Fi 6, and emerging 6G protocols, facilitate seamless data exchange between EVs, charging stations, and centralized traffic control systems [12]. This connectivity enables coordinated energy management, dynamic route adjustments, and rapid response to unforeseen traffic or energy disruptions [13]. Wireless sensor networks embedded in charging stations provide continuous monitoring of battery status, station occupancy, and power distribution, allowing for adaptive control and predictive maintenance [14]. Integration of these networks with AI systems ensures that decision-making occurs in near real time, enhancing both operational efficiency and user experience [15].
