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Your search keyword '"Ramachandaramurthy, Vigna K."' showing total 332 results
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332 results on '"Ramachandaramurthy, Vigna K."'

26. Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications

Author

Shanmugam, Yuvaraja, Narayanamoorthi, R., Ramachandaramurthy, Vigna K., Bernat, Petr, Shrestha, Niranjan, Son, Jeonggi, and Williamson, Sheldon S.

Abstract

The rapid advancement of electric vehicle (EV) technology toward environmentally friendly transportation emphasizes the necessity of dynamic wireless charging. However, challenges, such as the initial charging infrastructure cost, power transfer efficiency, and output power pulsation, pose significant limitations to dynamic wireless charging. Overcoming these challenges requires optimizing the design of various functional elements in dynamic charging, including the magnetic coupler, spacing between couplers, high-frequency inverter, and compensators. Despite the nonlinear relationships among these elements, obtaining mathematical relations proves cumbersome. This article proposes an effective machine learning (ML) approach to achieve the optimal design of the charging track, considering the cross-coupling effect. The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. The ML approach, which predicts optimal design parameters with a trained dataset, is more efficient with reduced duration than conventional finite element analysis (FEA) tools and stochastic methods. The learning algorithms consider variables such as core structure, cross-coupling effect, and coil flux pipe length. Simulation and experimental prototype validation for a 3.3 kW system demonstrated an impressive efficiency of 93.21%.

Published
2024
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36. Coordinated electric vehicle scheduling using time-of-use scheme and priority charging.

Author

Hashim, Mohd Syahmi, Yong, Jia Ying, Ramachandaramurthy, Vigna K., Mansor, Muhamad, Tan, Kang Miao, and Yahaya, Jabbar Al Fattah

Subjects
ELECTRIC vehicles, ELECTRIC vehicle batteries, ELECTRIC charge, PEAK load, ELECTRIC automobiles, ELECTRIC power consumption
Abstract

This article discuss electric vehicle charging scheduling algorithms to minimize the peak load demand. The charging schedule makes use of a time-of-use system and an optimum method that adjusts the amount of electricity used to charge an electric car depending on its battery's state of charge. This study was implemented in a commercial-residential area and considered various constraints to ensure the study practicality. Three scenarios of electric vehicle charging were tested and analyzed. The outcome of the first scenario demonstrated that uncoordinated charging of electric vehicles would cause a new peak in the grid load. For the second scenario, time-of-use scheme was implemented for electric vehicle charging, which was able to reduce peak loads compared to uncoordinated electric vehicle charging. The final scenario demonstrated that the suggested coordinated electric vehicle charging scheduling algorithm successfully reduced uncoordinated charging load by 33% and increased off-peak load by 87.5% while maximizing the charging power of each electric vehicle battery. [ABSTRACT FROM AUTHOR]

Published
2024
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