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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Vidura Sumanasena & Lakshitha Gunasekara & Sachin Kahawala & Nishan Mills & Daswin De Silva & Mahdi Jalili & Seppo Sierla & Andrew Jennings, 2023. "Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation," Energies, MDPI, vol. 16(5), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2245-:d:1080934
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    References listed on IDEAS

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    Cited by:

    1. Kinga Stecuła & Radosław Wolniak & Wieslaw Wes Grebski, 2023. "AI-Driven Urban Energy Solutions—From Individuals to Society: A Review," Energies, MDPI, vol. 16(24), pages 1-34, December.

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