Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United States
Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced EV charging data pl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/17/5240 |
| _version_ | 1797521503626985472 |
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| author | David Trinko Emily Porter Jamie Dunckley Thomas Bradley Timothy Coburn |
| author_facet | David Trinko Emily Porter Jamie Dunckley Thomas Bradley Timothy Coburn |
| author_sort | David Trinko |
| collection | DOAJ |
| description | Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced EV charging data platform. Because prices in these data exist in a semi-structured textual format, an ad hoc text mining approach is used to extract quantitative price information. Descriptive analytics of the processed dataset demonstrate how the prices of EV charging vary with charging level (Direct Current Fast Charging versus Level 2), geographic location, network provider, and location type. Our research indicates that a great deal of diversity and flexibility exists in structuring the prices of EV charging to enable incentives for shaping charging behaviors, but that it has yet to be widely standardized or utilized. Comparisons with estimates of the levelized cost of EV charging illustrate some of the challenges associated with operating and using these stations. |
| first_indexed | 2024-03-10T08:12:28Z |
| format | Article |
| id | doaj.art-305614c7a0dd4b409953359eaf80171c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T08:12:28Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-305614c7a0dd4b409953359eaf80171c2023-11-22T10:31:43ZengMDPI AGEnergies1996-10732021-08-011417524010.3390/en14175240Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United StatesDavid Trinko0Emily Porter1Jamie Dunckley2Thomas Bradley3Timothy Coburn4Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USAElectric Power Research Institute, Palo Alto, CA 94304, USAElectric Power Research Institute, Palo Alto, CA 94304, USADepartment of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USADepartment of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USAElectric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced EV charging data platform. Because prices in these data exist in a semi-structured textual format, an ad hoc text mining approach is used to extract quantitative price information. Descriptive analytics of the processed dataset demonstrate how the prices of EV charging vary with charging level (Direct Current Fast Charging versus Level 2), geographic location, network provider, and location type. Our research indicates that a great deal of diversity and flexibility exists in structuring the prices of EV charging to enable incentives for shaping charging behaviors, but that it has yet to be widely standardized or utilized. Comparisons with estimates of the levelized cost of EV charging illustrate some of the challenges associated with operating and using these stations.https://www.mdpi.com/1996-1073/14/17/5240ad hoc text miningdescriptive analyticsdata wranglingEV charging costlevel 2 chargingDC fast charging |
| spellingShingle | David Trinko Emily Porter Jamie Dunckley Thomas Bradley Timothy Coburn Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United States Energies ad hoc text mining descriptive analytics data wrangling EV charging cost level 2 charging DC fast charging |
| title | Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United States |
| title_full | Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United States |
| title_fullStr | Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United States |
| title_full_unstemmed | Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United States |
| title_short | Combining Ad Hoc Text Mining and Descriptive Analytics to Investigate Public EV Charging Prices in the United States |
| title_sort | combining ad hoc text mining and descriptive analytics to investigate public ev charging prices in the united states |
| topic | ad hoc text mining descriptive analytics data wrangling EV charging cost level 2 charging DC fast charging |
| url | https://www.mdpi.com/1996-1073/14/17/5240 |
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