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Author

S. Gomathi

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Conference Aug 2026

A Comprehensive Review of Electric Vehicle Charging Technologies, Architectures, Power Flow Mechanisms, and Standardization Frameworks

The fast pace of electric vehicle (EV) adoption has increased pressure on effective, consistent, and scalable charging infrastructure. In this paper, I provide a detailed overview of the EV charging system, including charging technologies, station designs, power flow designs, standards, and optimization methods. The most developed and implemented technology is known as conductive charging and wireless charging has better convenience to the user with continuous efficiency and cost problem. The different charging structures, such as the AC, DC, hybrid, and renewable-integrated architecture are contrasted in performance, scale, and interacting with the grids. The course of power transmission between Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) and voice-to-Everything (V2X) are mentioned and the use of EVs as active energy sources is noted. Artificial intelligence-based smart charging methods are demonstrated to increase grid stability and energy management. Moreover, such critical issues as grid overload, battery degradation, security concerns, and the absence of standardization is discussed. Lastly, the research directions towards an intelligent, sustainable, and decentralized solution to charging are described.

S. Gomathi · 0 citations
Conference Aug 2026

A Novel Two-Stage Residual-Corrected Stacking Framework for Photovoltaic Power Output Prediction

Proper prediction of photovoltaic (PV) power output is essential in ensuring the successful incorporation of solar energy in the smart grid systems and energy management systems. Current single-algorithm models are often ineffective to represent the compound non-linear interactions between meteorological variables, time variations and irradiance dynamics that cause solar generation variability. This paper presents the Solar-Adaptive Hybrid Ensemble (SAHE) which is a new two-stage stacking model that uses a new Random Forest (RF) base learner with an XG Boost residual-correction meta-learner, supplemented by solar-domain feature engineering such as clearness index, irradiance polynomial transforms, lagged target variables and cyclic temporal encodings. The SAHE framework has a Root Mean Squared Error (RMSE) equal to 23.8850 kWh, Mean Absolute Error (MAE) equal to 18.6632 kWh, coefficient of determination (R 2) equal to 0.8079, Mean Absolute Percentage Error (MAPE) equal to 8.7608% and Pearson Correlation Coefficient (PCC) equal to 0.9113 on the test set, compared to all comparison models, in all reported measures.

S. Gomathi · 0 citations

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