How to cite this Dataset
Shariati, Omid (2024): Elitist Genetic Algorithm (EGA)-based Energy Storage System (ESS)-facilitated Heavy Good Electric Vehicle (HGEV) charging station designer. University of Reading. Software. https://doi.org/10.17864/1947.000526
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: https://orcid.org/0000-0002-1790-7165 |
| Rights-holders: | University of Reading |
| Data Publisher: | University of Reading |
| Publication Year: | 2024 |
| Data last accessed: | 31 October 2025 |
| 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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