Journal of Electrical Engineering and Automation
IRO Journals
Volume 2 — Issue 3 — September 2020

Electric Vehicle Charging with Battery Scheduling and Multicriteria Optimization using Genetic Algorithm

https://doi.org/10.36548/jeea.2020.3.003
Nayana Shetty
Associate Professor, Dept. of EE Engineering NMAMIT, Nitte, Udupi, Karnataka, India
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How to Cite

Shetty, Nayana. 2021. “Electric Vehicle Charging With Battery Scheduling and Multicriteria Optimization Using Genetic Algorithm”. Journal of Electrical Engineering and Automation 2 (3): 123-28. https://doi.org/10.36548/jeea.2020.3.003.
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Keywords

— Battery scheduling
— multicriteria optimization
— electric vehicle charging
— genetic algorithm
— energy optimization
Published: 19-01-2021

Abstract

The existing charging infrastructure needs expansion and upgrade with the growing fleet of electric vehicles (EV). The electric grids are largely affected by the uncontrolled charging cycles. To overcome this drawback, the hybrid charging stations are incorporated with battery storage and renewable energy sources. The power necessary from the grid can be buffered using a battery and renewable source attached to the charging station thereby avoiding the grid constraints and peaks. It has been a challenge to trace the origin of the battery's energy till date. The battery energy storage and a simple photovoltaic system is incorporated in a hybrid EV charging station. Uncontrolled EV charging and its adverse effects can be overcome by this technology by accurately calculating the share of renewable energy derived from the battery. Multi-attribute utility theory is used for optimizing the EV charging level and scheduling the battery charging and discharging. Minimizing battery degradation and charging cost while maximizing the renewable energy from the battery and PV sources are the major criteria of optimization. Multicriteria optimization function is used along with the genetic algorithm optimization scheme to address the optimization issues. Optimal capacity of the battery and optimization strategy is affected by the preferences in decision making.

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