Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation

Vidura Sumanasena, Lakshitha Gunasekara, Sachin Kahawala, Nishan Mills, Daswin De Silva (), Mahdi Jalili, Seppo Sierla and Andrew Jennings
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Vidura Sumanasena: Centre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, Australia
Lakshitha Gunasekara: Centre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, Australia
Sachin Kahawala: Centre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, Australia
Nishan Mills: Centre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, Australia
Daswin De Silva: Centre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, Australia
Mahdi Jalili: School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Seppo Sierla: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Andrew Jennings: Centre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, Australia

Energies, 2023, vol. 16, issue 5, 1-18

Abstract: Electric vehicles (EVs) are advancing the transport sector towards a robust and reliable carbon-neutral future. Given this increasing uptake of EVs, electrical grids and power networks are faced with the challenges of distributed energy resources, specifically the charge and discharge requirements of the electric vehicle infrastructure (EVI). Simultaneously, the rapid digitalisation of electrical grids and EVs has led to the generation of large volumes of data on the supply, distribution and consumption of energy. Artificial intelligence (AI) algorithms can be leveraged to draw insights and decisions from these datasets. Despite several recent work in this space, a comprehensive study of the practical value of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability and charge optimisation of the EVI has not been formally investigated. The objective of this study was to design, develop and evaluate a comprehensive AI framework that addresses this gap in EVI. Results from the empirical evaluation of this AI framework on a real-world EVI case study confirm its contribution towards addressing the emerging challenges of distributed energy resources in EV adoption.

Keywords: artificial intelligence; electric vehicles; demand profiling; demand forecasting; demand explainability; charge optimisation; EV data augmentation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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