TY  - EJOU
AU  - Wadekar, Supriya 
AU  - Mittal, Shailendra 
AU  - Wakte, Ganesh 
AU  - Kite, Mrunali 
AU  - Ghonmode, Aditya 
AU  - Devkate, Riya 

TI  - Uncertainty-Aware Distributed Optimization for IoEV Smart Charging and Battery Health Management in Cyber-Physical Smart Grids
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - 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.
KW  - Battery degradation; distributed optimization; electric vehicle charging; internet of electric vehicles (IoEV); smart grid; uncertainty-aware optimization

DO  - 10.32604/ee.2026.082685