Synthesis of electric vehicle charging data: A real-world data-driven approach
Nowadays, electric vehicles (EVs) are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging data. The analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes, particularly in the deployment of...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Communications in Transportation Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772424724000118 |
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| author | Zhi Li Zilin Bian Zhibin Chen Kaan Ozbay Minghui Zhong |
| author_facet | Zhi Li Zilin Bian Zhibin Chen Kaan Ozbay Minghui Zhong |
| author_sort | Zhi Li |
| collection | DOAJ |
| description | Nowadays, electric vehicles (EVs) are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging data. The analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes, particularly in the deployment of charging infrastructure and the formulation of EV-focused policies. Nevertheless, the challenges of collecting these data are significant, primarily due to privacy concerns and the high costs associated with data access. In response, this study introduces an innovative methodology for generating large-scale and diverse EV charging data, mirroring real-world patterns for cost-efficient and privacy-compliant use. Specifically, this approach combines Gibbs sampling and conditional density networks and was trained and validated using a real-world dataset consisting of approximately 1.65 million charging events from 3,777 battery EVs (BEVs) in Shanghai over a year. Results illustrate that the proposed model can effectively capture the underlying distribution of the original charging data, enabling the generation of synthetic samples that closely resemble real-world charging events. The approach is readily employed for data imputation and augmentation, and it can also help simulate future charging distributions by conditional generation based on anticipated development premises. |
| format | Article |
| id | doaj-art-7db82def10e04f01a46e0d29bf902190 |
| institution | DOAJ |
| issn | 2772-4247 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Communications in Transportation Research |
| spelling | doaj-art-7db82def10e04f01a46e0d29bf9021902025-08-20T02:50:04ZengElsevierCommunications in Transportation Research2772-42472024-12-01410012810.1016/j.commtr.2024.100128Synthesis of electric vehicle charging data: A real-world data-driven approachZhi Li0Zilin Bian1Zhibin Chen2Kaan Ozbay3Minghui Zhong4Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, NYU Shanghai, Shanghai, 200126, China; Shanghai Key Laboratory of Urban Design and Urban Science, NYU Shanghai, Shanghai, 200126, ChinaDepartment of Civil and Urban Engineering, New York University, New York, 11201, USAShanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, NYU Shanghai, Shanghai, 200126, China; Shanghai Key Laboratory of Urban Design and Urban Science, NYU Shanghai, Shanghai, 200126, China; Corresponding author. Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, NYU Shanghai, Shanghai, 200126, China.C2SMARTER Center (A Tier 1 USDOT UTC), Department of Civil and Urban Engineering, New York University, New York, 11201, USAShanghai Electric Vehicle Public Data Collecting, Monitoring, and Research Center, Shanghai, 201805, ChinaNowadays, electric vehicles (EVs) are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging data. The analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes, particularly in the deployment of charging infrastructure and the formulation of EV-focused policies. Nevertheless, the challenges of collecting these data are significant, primarily due to privacy concerns and the high costs associated with data access. In response, this study introduces an innovative methodology for generating large-scale and diverse EV charging data, mirroring real-world patterns for cost-efficient and privacy-compliant use. Specifically, this approach combines Gibbs sampling and conditional density networks and was trained and validated using a real-world dataset consisting of approximately 1.65 million charging events from 3,777 battery EVs (BEVs) in Shanghai over a year. Results illustrate that the proposed model can effectively capture the underlying distribution of the original charging data, enabling the generation of synthetic samples that closely resemble real-world charging events. The approach is readily employed for data imputation and augmentation, and it can also help simulate future charging distributions by conditional generation based on anticipated development premises.http://www.sciencedirect.com/science/article/pii/S2772424724000118Electric vehiclesCharging dataData augmentationData generationGibbs samplingConditional density network |
| spellingShingle | Zhi Li Zilin Bian Zhibin Chen Kaan Ozbay Minghui Zhong Synthesis of electric vehicle charging data: A real-world data-driven approach Communications in Transportation Research Electric vehicles Charging data Data augmentation Data generation Gibbs sampling Conditional density network |
| title | Synthesis of electric vehicle charging data: A real-world data-driven approach |
| title_full | Synthesis of electric vehicle charging data: A real-world data-driven approach |
| title_fullStr | Synthesis of electric vehicle charging data: A real-world data-driven approach |
| title_full_unstemmed | Synthesis of electric vehicle charging data: A real-world data-driven approach |
| title_short | Synthesis of electric vehicle charging data: A real-world data-driven approach |
| title_sort | synthesis of electric vehicle charging data a real world data driven approach |
| topic | Electric vehicles Charging data Data augmentation Data generation Gibbs sampling Conditional density network |
| url | http://www.sciencedirect.com/science/article/pii/S2772424724000118 |
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