Next-Generation AI-Driven Transportation Electrification: Methods, Systems, and Applications

Dear Colleagues,

Transportation electrification is central to the global transition toward low-carbon, efficient and sustainable mobility. However, its large-scale deployment faces complex challenges across multiple stages and system layers, including charging infrastructure planning, grid integration, fleet operation, energy management, user behavior uncertainty, system resilience, battery degradation and battery life-cycle management. Recent advances in next-generation artificial intelligence (AI), such as foundation models, large language models, multimodal AI, deep reinforcement learning, federated learning, graph learning, physics-informed AI and edge intelligence, offer powerful new tools to address these challenges across vehicles, infrastructure, grids, batteries and mobility systems.

This Special Issue serves as a timely platform for academia and industry to share cutting-edge research on next-generation AI-enabled methods, systems and applications in transportation electrification. It welcomes interdisciplinary contributions that integrate transportation engineering, electrical engineering, computer science, energy systems, operations research, industrial engineering, supply chain management and urban planning. By emphasizing AI as an enabling force for intelligent planning, operation, control, life-cycle management and decision-making, this issue particularly encourages studies that demonstrate methodological innovation, system-level integration, real-world deployment or policy relevance. Topics of interest include, but are not limited to, the following:

• Next-generation AI foundations for transportation electrification
• AI-enabled transportation–energy network planning and operation
• Smart charging, vehicle-grid integration and intelligent energy management
• AI-driven intelligent EV fleet, vehicle and infrastructure management
• AI-enabled EV battery health, degradation and life-cycle management
• AI for EV battery circular economy, secondary use, recycling and disassembly
• Distributed, federated, edge and privacy-preserving AI for electric mobility
• Trustworthy, explainable, secure and resilient AI for transportation electrification
• Human-centered, equitable and policy-relevant AI for sustainable electrification

Collectively, these topics highlight the methodological, system-level and application-oriented advances enabled by next-generation AI across transportation electrification. This Special Issue particularly welcomes innovative, practical and interdisciplinary studies that address critical bottlenecks in electrified mobility systems. Contributions may include original research articles, systematic reviews, short communications and industrial case studies covering algorithmic innovation, system framework design, simulation-based evaluation, field deployment validation and decision-support applications. In this Special Issue, next-generation AI refers to emerging AI paradigms that go beyond conventional task-specific data-driven models by learning from large-scale heterogeneous and multimodal data, integrating physical mechanisms and domain knowledge, supporting adaptive and autonomous decision-making and enabling trustworthy, explainable, secure and deployable intelligence in complex transportation–energy systems.

Dr. Yongxing Wang
Dr. Chaoru Lu
Dr. Dongfan Xie
Dr. Senlei Wang
Guest Editors

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