University of Reading Research Data Archive

Elitist Genetic Algorithm (EGA)-based Energy Storage System (ESS)-facilitated Heavy Good Electric Vehicle (HGEV) charging station designer

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Description

This solution leverages an Elitist Genetic Algorithm (EGA) to tackle interconnected challenges in the Energy Storage System (ESS) design and demand-side management, particularly focusing on battery scheduling. It addresses these concerns simultaneously for diverse styles of Heavy Goods Electrical Vehicle (HGEV) Depots and on-route charging stations. The ESS design encompasses considerations like battery capacity and power electronic board rating power, while demand-side management involves a series of battery charging/discharging power scheduling within the evaluation time window.

Resource Type: Software
Creators: Shariati, Omid ORCID logoORCID: https://orcid.org/0000-0002-1790-7165
Rights-holders: University of Reading
Data Publisher: University of Reading
Publication Year: 2024
Data last accessed: 17 April 2024
DOI: https://doi.org/10.17864/1947.000526
Metadata Record URL: https://researchdata.reading.ac.uk/id/eprint/526
Organisational units: Science > School of the Built Environment > Energy and Environmental Engineering group
Participating Organisations: University of Reading, University of Nottingham
Keywords: charging station design, Heavy Good Electric Vehicle, Energy Storage System, Elitist Genetic Algorithm
Rights:
Data Availability: OPEN

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