Open Access

ARTICLE

Uncertainty-Aware Distributed Optimization for IoEV Smart Charging and Battery Health Management in Cyber-Physical Smart Grids

Supriya Wadekar¹, Shailendra Mittal¹, Ganesh Wakte^2,*, Mrunali Kite², Aditya Ghonmode², Riya Devkate²

1 Department of Electrical Engineering, G H Raisoni University, Amravati, India
2 Department of Electrical Engineering, Tulsiramji Gaikwad Patil College of Engineering and Technolgy, Nagpur, India

* Corresponding Author: Ganesh Wakte. Email:

(This article belongs to the Special Issue: Renewable Energy Community (REC) Engineering towards Sustainable Development and Energy Poverty Reduction)

Energy Engineering 2026, 123(9), 8 https://doi.org/10.32604/ee.2026.082685

Received 20 March 2026; Accepted 18 May 2026; Issue published 06 August 2026

Abstract

The rapid expansion of electric vehicles (EVs) and the emergence of the Internet of Electric Vehicles (IoEV) have created considerable operational challenges for modern power systems. Large-scale EV charging can cause peak demand surges, voltage instability, and inefficient utilization of renewable energy resources when charging activities are not effectively coordinated. This study proposes an uncertainty-aware distributed optimization framework for smart EV charging in cyber-physical smart grids, in which charging schedules are coordinated while simultaneously considering grid capacity constraints, stochastic EV arrival patterns, renewable energy variability, and battery degradation effects. A multi-objective optimization model is formulated to minimize peak grid load, charging cost, and battery degradation. The optimization problem is solved using a distributed algorithm based on the Alternating Direction Method of Multipliers, enabling scalable coordination among multiple charging stations. Simulation studies were carried out in the MATLAB–Simulink environment with EV fleet sizes ranging from 100 to 500 vehicles integrated with solar photovoltaic generation. The results indicate a 39.2% reduction in peak feeder load, a 24% decrease in total charging cost, and an improvement in renewable energy utilization to 76.9%. In addition, the proposed framework reduces annual battery capacity loss from 6.7% to 3.8% compared with uncontrolled charging. The primary contribution of this work is the integration of uncertainty modeling, distributed optimization, and battery health prediction within a unified IoEV charging management framework.

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Keywords

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