A system-level critical assessment of electric mobility integrating electric vehicle powertrains, advanced battery technologies, grid impacts, artificial intelligence, and sustainability

Electric mobility has emerged as a cornerstone of global decarbonization strategies, with its successful deployment critically dependent on the coordinated integration of vehicle powertrain engineering, advanced battery technologies, charging infrastructure, power grid interaction, and intelligent control systems. This paper presents a comprehensive system-level critical assessment of electric mobility, providing an integrated analytical framework that unifies electric vehicle (EV) powertrains, electrochemical energy storage, grid impacts, artificial intelligence (AI), and sustainability considerations. The study systematically examines EV propulsion architectures, charging technologies, and the operational characteristics of contemporary and emerging battery chemistries, including lithium-ion variants ( Nickel–Manganese–Cobalt, Nickel–Cobalt–Aluminum, and Lithium Iron Phosphate), solid-state batteries, and sodium-ion batteries, with particular emphasis on degradation mechanisms, thermal safety, second-life utilization, and recycling pathways. The impacts of large-scale EV charging on power distribution networks are rigorously analyzed through power quality and voltage stability modeling, highlighting harmonic distortion, feeder loading, and voltage deviation challenges associated with high-power fast-charging infrastructure. Advanced mitigation strategies, including active filtering and AI-based grid impact prediction, are discussed to enhance grid resilience. AI is positioned as a core enabling technology throughout the EV ecosystem, with detailed coverage of data-driven and physics-informed approaches for battery health estimation, remaining useful life prediction, range estimation, smart charging control, traffic-aware routing, and charging queue optimization. Furthermore, emerging quantum-inspired optimization and quantum machine learning paradigms are identified as promising tools for addressing high-dimensional uncertainty in routing, charging scheduling, and battery diagnostics. A life-cycle sustainability perspective is incorporated to evaluate the environmental performance of EVs, emphasizing the influence of electricity generation mix, battery manufacturing emissions, material criticality, and recycling efficiency on overall greenhouse gas reduction potential. By synergizing engineering models, AI-driven intelligence, grid interaction analysis, and life-cycle assessment, this work delivers a unified blueprint for accelerating the transition toward sustainable electric mobility. The presented framework offers clear technical guidance for researchers, policymakers, and industry stakeholders seeking to design resilient, intelligent, and environmentally responsible electric transportation systems.