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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68553
Volume:
10 |
10 |
Oct 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Elecrical and Electronics Engineering
Holy Mary Institute of Technology and Science
Elecrical and Electronics Engineering
Holy Mary Institute of Technology and Science
Elecrical and Electronics Engineering
Holy Mary Institute of Technology and Science
Elecrical and Electronics Engineering
Holy Mary Institute of Technology and Science
The proposed study proposes a state-of-the-art solar PV electric vehicle (EV) charging system based on intelligent control for improving grid stability, power quality and renewable energy utilisation. The system is designed to efficiently perform Grid to Vehicle (G2V) and Vehicle to Grid (V2G) activities for a wide range of load and climatic conditions. It consists of a solar PV array, a grid interface, batteries and a bi-directional DC-DC converter. A Second Order Sliding Mode Cascaded Control (SOSMCC) method is adopted to keep the DC-link voltage steady and the charging and discharging of electric car batteries smooth when the solar radiation is subject to unexpected fluctuations or variations. Moreover, a Linear Extended State Disturbance Observer (LESDO) is integrated to improve the dynamic performance by lowering the settling time, overshoot and undershoot under transient operating conditions. Furthermore, the harmonic suppression is achieved via the Sparse-Aware Bias-Compensated Adaptive Filtering (SABCAF) method to eliminate the harmonics and accurately extract the fundamental current component as per the IEEE 519 standards, which enhances the power quality. Complete system analysis for evaluation of system stability, energy management and charging efficiency is performed using MATLAB/Simulink models. The simulation results show that the proposed technique has higher performance than the old method in convergence speed, mean square error, harmonic rejection and disturbance rejection ability. The suggested technology is dependable, efficient and ecologically benign and can be useful for a future smart electric vehicle charging infrastructure incorporating renewable energy sources.
KEYWORDS
Finally, a reliable, efficient, and eco-friendly solar photovoltaic (PV) electric vehicle (EV) charging system will fuel the future of transportation and energy management. Charging EVs with renewable solar power is one way to minimise our dependence on polluting fossil fuels and other traditional energy sources. The proposed system includes a solar array, a bidirectional DC-DC converter, a battery energy storage system, a Voltage Source Converter (VSC) and improved charging control algorithms, designed to work consistently and efficiently under varied load and weather circumstances. Maximum Power Point Tracking (MPPT) is used to extract the maximum energy from PV systems. In contrast, SOSMCC is good at managing the DC-link voltage and making the system more stable. Furthermore, the Linear Extended State Disturbance Observer (LESDO) enhances the disturbance rejection capacity and transitory oscillation minimisation under dynamic operational settings. The SABCAF method further enhances the power quality by minimising the harmonic distortion and maintaining the Total Harmonic Distortion (THD) within the limits specified by the IEEE 519 standard. The efficiency of the charging process, the correctness of the voltage management, the elimination of harmonics, and the smoothness of G2V and V2G operations are simulated and verified by MATLAB/Simulink. To summarise, the proposed layout is an intelligent, practical and energy efficient electric vehicle charging network which can support renewable energy smart grid applications. This network has the potential to be an important constituent of sustainable development and environment friendly transportation.
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Publication Date
10/31/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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