Characterizing Electric Vehicle Plug-in Behaviors Using Customer Classification Approach
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023, 2023, 00, pp. 1-6
- Issue Date:
- 2023-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Characterizing_Electric_Vehicle_Plug-in_Behaviors_Using_Customer_Classification_Approach.pdf | Published version | 2.52 MB |
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This paper proposes a customer classification approach to analyze the plug-in behavior of Electric Vehicle (EV) users for developing sustainable and efficient charging infrastructure. The features influencing EV adoption are selected from available EV models in Australia using a feature importance technique. The selected influential features are employed to classify EV customers using a k-means clustering algorithm into different clusters. Each cluster's energy demand and plug-in behavior are assessed considering EV uncertainties. The results exhibit the efficacy of the customer-segmentation approach for managing and deploying charging infrastructures for large-scale EV penetration. This study underscores the significance of EV users' plug-in behaviors and characteristics toward transport electrification for achieving net-zero emissions by 2050.
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