Forecasting infrastructure needs, environmental impacts, and dynamic pricing for electric vehicle charging

Lists

Jabr, Osama, Ayaz, Ferheen, Nekovee, Maziar and Saeed, Nagham ORCID logoORCID: https://orcid.org/0000-0002-5124-7973 (2025) Forecasting infrastructure needs, environmental impacts, and dynamic pricing for electric vehicle charging. World Electric Vehicle Journal, 16 (8). pp. 1-26.

[thumbnail of PDF/A]
Preview
 PDF (PDF/A)
Forecasting Infrastructure Needs_SaeedN_accessible.pdf - Published Version
Available under License Creative Commons Attribution.
Download (1MB) | Preview

Abstract

In recent years, carbon dioxide (CO2) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO2 and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency.

Item Type: Article
Identifier: 10.3390/wevj16080410
Keywords: infrastructure forecasting; environmental impact assessment; system dynamics modelling; urban planning; sustainability; predictive analytics; policymodelling; electric vehicle
Subjects: Construction and engineering > Built environment
Depositing User: Nagham Saeed
Date Deposited: 25 Jul 2025 08:33
Last Modified: 25 Jul 2025 14:45
URI: https://repository.uwl.ac.uk/id/eprint/13917

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item
References
Tools
CORE (COnnecting REpositories)

Menu