The progression in Electric Vehicles (EVs) requires effective, reliable and maintainable power conversion system which is incorporated with Renewable Energy Sources (RESs) for eco-friendly charging. This research presents a hybrid energy system, with PV as primary source for energizing the Brushless DC motor (BLDC) of EV. The intermittent nature results with lower and fluctuated DC output boosted using a novel Extreme gain X-Boost converter. The converter shows higher power conversion with reduced stress and current ripples. The regulation of converter is performed using Proportional Integral (PI) controller with its parameters tuned using Chaotic Parrot Optimization Algorithm (CPOA), providing minimized overshoot, improved steady state error, faster convergences and enhanced settling time. In addition, battery and grid system is incorporated to power the BLDC motor at times of energy scarcity. The developed system is examined through MATLAB simulation under varying operating conditions of PV. The outcomes demonstrates improved converter efficiency of 96.16%, with higher voltage. Moreover, the controller offers reduced settling time of 0.3s with minimized overshoot. In addition, the Total Harmonic Distortion (THD) of the system lies below 5% indicating effective power quality based on IEEE standard limits. This shows that the proposed system offers suitability for EV charging with effective energy management.
J. Deepthi, S. N. Saxena· ITEGAM- Journal of Engineeri...· 0 citations
In this paper, an integrated real-time framework for coordinated Electric Vehicle (EV) charging is proposed based on load forecasting and multi-objective optimization techniques. The framework integrates load forecasting using Long Short-Term Memory (LSTM) and multi-objective optimization to minimize peak load, charging cost, and grid stress while maintaining high user satisfaction under dynamic smart-grid conditions. The proposed framework combines multi-step LSTM forecasting with a convex optimization scheduler operating on a 15-min rolling horizon that incorporates feeder constraints, electricity tariffs, charger limits, and departure state-of-charge requirements. Unlike conventional charging strategies, the proposed method enables adaptive and grid-aware charging decision-making in real time. The framework is evaluated under various EV penetration scenarios ranging from 30 to 100 EVs and is compared with uncontrolled charging, off-peak charging, and load-balancing strategies. The results demonstrate an average 25% reduction in peak load, a 15–20% reduction in charging costs, smoother feeder operation, and a user satisfaction rate exceeding 95% in meeting charging requirements. Furthermore, the proposed framework improves operational stability, reduces computational burden, and enhances charging coordination compared with conventional forecasting and heuristic scheduling approaches. These findings demonstrate the feasibility and scalability of integrating Machine Learning (ML)-based forecasting with real-time optimization for future smart-grid and EV energy management systems.
D. Janyavula, V. G. Kumar, S. N. Saxena· Engineering, Technology &...· 0 citations
