Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation
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 o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/5/2245 |
| _version_ | 1797615435550556160 |
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| author | Vidura Sumanasena Lakshitha Gunasekara Sachin Kahawala Nishan Mills Daswin De Silva Mahdi Jalili Seppo Sierla Andrew Jennings |
| author_facet | Vidura Sumanasena Lakshitha Gunasekara Sachin Kahawala Nishan Mills Daswin De Silva Mahdi Jalili Seppo Sierla Andrew Jennings |
| author_sort | Vidura Sumanasena |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T07:26:17Z |
| format | Article |
| id | doaj.art-2e6a07805db64380ac9f2a3bb2af3316 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T07:26:17Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2e6a07805db64380ac9f2a3bb2af33162023-11-17T07:36:07ZengMDPI AGEnergies1996-10732023-02-01165224510.3390/en16052245Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge OptimisationVidura Sumanasena0Lakshitha Gunasekara1Sachin Kahawala2Nishan Mills3Daswin De Silva4Mahdi Jalili5Seppo Sierla6Andrew Jennings7Centre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, AustraliaCentre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, AustraliaCentre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, AustraliaCentre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, AustraliaCentre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, AustraliaSchool of Engineering, RMIT University, Melbourne, VIC 3000, AustraliaDepartment of Electrical Engineering and Automation, Aalto University, 02150 Espoo, FinlandCentre for Data Analytics and Cognition (CDAC), La Trobe University, Bundoora, VIC 3086, AustraliaElectric 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.https://www.mdpi.com/1996-1073/16/5/2245artificial intelligenceelectric vehiclesdemand profilingdemand forecastingdemand explainabilitycharge optimisation |
| spellingShingle | Vidura Sumanasena Lakshitha Gunasekara Sachin Kahawala Nishan Mills Daswin De Silva Mahdi Jalili Seppo Sierla Andrew Jennings Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation Energies artificial intelligence electric vehicles demand profiling demand forecasting demand explainability charge optimisation |
| title | Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation |
| title_full | Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation |
| title_fullStr | Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation |
| title_full_unstemmed | Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation |
| title_short | Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation |
| title_sort | artificial intelligence for electric vehicle infrastructure demand profiling data augmentation demand forecasting demand explainability and charge optimisation |
| topic | artificial intelligence electric vehicles demand profiling demand forecasting demand explainability charge optimisation |
| url | https://www.mdpi.com/1996-1073/16/5/2245 |
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