AI-Driven Energy Management Systems for Electric Vehicles

Dear Colleagues,

The rapid electrification of transportation has placed unprecedented demands on the efficiency, reliability, and intelligence of energy management systems (EMSs) in electric vehicles (EVs). Traditional rule-based and model-dependent control strategies struggle to cope with the nonlinear, stochastic, and highly coupled dynamics of modern EV powertrains, battery systems, and hybrid energy-storage architectures. Recent advances in artificial intelligence (AI), particularly in machine learning (ML), deep learning (DL), and reinforcement learning (RL), have opened new pathways for developing adaptive, data-driven, and predictive EMS capable of operating under real-world uncertainty.

This Special Issue focuses on cutting-edge AI-driven methodologies for EV energy management, emphasizing learning-based control, state estimation, and optimization across batteries, power electronics, and vehicle-level systems. Topics of interest include, but are not limited to, RL-based power-split and torque allocation strategies, health-aware battery management incorporating SOC/SOH estimation, hybrid battery–supercapacitor coordination, and AI-enabled predictive control frameworks. Contributions addressing partially observable environments, belief-state estimation using DL-EKF or Bayesian approaches, and safety-critical learning under operational constraints are particularly encouraged.

The Special Issue also highlights the growing role of high-fidelity digital twins and simulation-based validation for training and evaluating AI controllers prior to deployment. Closed-loop learning frameworks that integrate physics-based models with data-driven adaptation are of special interest, as they enable robust performance, reduced battery degradation, and improve system lifespans without reliance on complete system models. Multi-agent and distributed learning architectures for large-scale or modular energy storage systems are also within scope.

By bringing together researchers and practitioners working at the intersection of AI, control, and electrified transportation, this Special Issue aims to advance the state of the art in intelligent EV energy management and accelerate the transition toward safer, more efficient, and more sustainable electric mobility.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Energy management systems for electric vehicles, hybrid electric vehicles, and electrified transportation.
• Battery management systems (BMS): modeling, estimation, diagnostics, and control.
• State estimation and health monitoring: SOC, SOH, SOP, RUL, and degradation modeling.
• Data-driven and AI-based energy management systems.
• Reinforcement learning, deep learning, and machine learning for energy systems.
• Adaptive, predictive, and optimal control strategies to manage energy.
• Hybrid energy storage systems: battery–supercapacitor and battery–fuel cell architectures.
• Modeling, optimization, and lifecycle management for energy storage systems.
• Digital twin frameworks for batteries, energy storage, and electric vehicles.
• AI-enabled predictive analytics and decision-making under uncertainty.
• Partially observable and stochastic energy management systems.
• Multi-agent and distributed control for large-scale energy systems.
• Management of energy in smart grids and microgrids.
• Integration of renewable energy sources with energy storage systems.
• AI-based power sharing, load balancing, and demand response.
• Energy management in vehicle-to-grid (V2G) and grid-to-vehicle (G2V) applications.
• Safety-critical learning and constraint-aware AI control for energy systems.
• Cyber–physical security and reliability of AI-driven energy management systems.
• Managing energy for autonomous and connected electric vehicles.
• Edge intelligence and IoT-enabled monitoring for energy and battery systems.
• Managing energy for smart cities and sustainable transportation systems.

I/We look forward to receiving your contributions.

Dr. Armin Lotfy
Dr. Mohamad Alzayed
Dr. Yashar Farajpour
Dr. Safi Bamati
Guest Editors

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