Energy Management System for Hybrid Renewable Energy-Based Electric Vehicle Charging Station

Ashish Kumar Karmaker; Md Alamgir Hossain; Hemanshu Roy Pota; Ahmet Onen; Jaesung Jung

doi:10.1109/ACCESS.2023.3259232

Energy Management System for Hybrid Renewable Energy-Based Electric Vehicle Charging Station

Ashish Kumar Karmaker, Md Alamgir Hossain, Hemanshu Roy Pota, Ahmet Onen, Jaesung Jung^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

This paper introduces an energy management algorithm for a hybrid solar and biogas-based electric vehicle charging station (EVCS) that considers techno-economic and environmental factors. The proposed algorithm is designed for a 20-kW EVCS and uses a fuzzy inference system in MATLAB SIMULINK to manage power generation, EV power demand, charging periods, and existing charging rates to optimize real-time charging costs and renewable energy utilization. The results show that the proposed algorithm reduces energy costs by 74.67% compared to existing flat rate tariffs and offers lower charging costs for weekdays and weekends. The integration of hybrid renewables also results in a significant reduction in greenhouse gas emissions, with payback periods for charging station owners being relatively short, making the project profitable.

Original language	English
Pages (from-to)	27793-27805
Number of pages	13
Journal	IEEE Access
Volume	11
DOIs	https://doi.org/10.1109/ACCESS.2023.3259232
Publication status	Published - 2023

Keywords

Electric vehicle
electric vehicle charging station
fuzzy logic
renewable resources

ASJC Scopus subject areas

General Computer Science
General Materials Science
General Engineering
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ACCESS.2023.3259232

Cite this

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title = "Energy Management System for Hybrid Renewable Energy-Based Electric Vehicle Charging Station",

abstract = "This paper introduces an energy management algorithm for a hybrid solar and biogas-based electric vehicle charging station (EVCS) that considers techno-economic and environmental factors. The proposed algorithm is designed for a 20-kW EVCS and uses a fuzzy inference system in MATLAB SIMULINK to manage power generation, EV power demand, charging periods, and existing charging rates to optimize real-time charging costs and renewable energy utilization. The results show that the proposed algorithm reduces energy costs by 74.67% compared to existing flat rate tariffs and offers lower charging costs for weekdays and weekends. The integration of hybrid renewables also results in a significant reduction in greenhouse gas emissions, with payback periods for charging station owners being relatively short, making the project profitable.",

keywords = "Electric vehicle, electric vehicle charging station, fuzzy logic, renewable resources",

author = "Karmaker, {Ashish Kumar} and Hossain, {Md Alamgir} and Pota, {Hemanshu Roy} and Ahmet Onen and Jaesung Jung",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2023",

doi = "10.1109/ACCESS.2023.3259232",

language = "English",

volume = "11",

pages = "27793--27805",

journal = "IEEE Access",

issn = "2169-3536",

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T1 - Energy Management System for Hybrid Renewable Energy-Based Electric Vehicle Charging Station

AU - Karmaker, Ashish Kumar

AU - Hossain, Md Alamgir

AU - Pota, Hemanshu Roy

AU - Onen, Ahmet

AU - Jung, Jaesung

PY - 2023

Y1 - 2023

N2 - This paper introduces an energy management algorithm for a hybrid solar and biogas-based electric vehicle charging station (EVCS) that considers techno-economic and environmental factors. The proposed algorithm is designed for a 20-kW EVCS and uses a fuzzy inference system in MATLAB SIMULINK to manage power generation, EV power demand, charging periods, and existing charging rates to optimize real-time charging costs and renewable energy utilization. The results show that the proposed algorithm reduces energy costs by 74.67% compared to existing flat rate tariffs and offers lower charging costs for weekdays and weekends. The integration of hybrid renewables also results in a significant reduction in greenhouse gas emissions, with payback periods for charging station owners being relatively short, making the project profitable.

AB - This paper introduces an energy management algorithm for a hybrid solar and biogas-based electric vehicle charging station (EVCS) that considers techno-economic and environmental factors. The proposed algorithm is designed for a 20-kW EVCS and uses a fuzzy inference system in MATLAB SIMULINK to manage power generation, EV power demand, charging periods, and existing charging rates to optimize real-time charging costs and renewable energy utilization. The results show that the proposed algorithm reduces energy costs by 74.67% compared to existing flat rate tariffs and offers lower charging costs for weekdays and weekends. The integration of hybrid renewables also results in a significant reduction in greenhouse gas emissions, with payback periods for charging station owners being relatively short, making the project profitable.

KW - Electric vehicle

KW - electric vehicle charging station

KW - fuzzy logic

KW - renewable resources

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Energy Management System for Hybrid Renewable Energy-Based Electric Vehicle Charging Station

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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