Analyzing Charging Behavior of Electric City Buses in Typical Chinese Cities
Electric city buses have potential to reduce greenhouse gases emission in case the majority of the electric power used in electric buses originate from the renewable sources or nuclear power plants. Their charging behaviors analysis is critical to their development and mass-adoption. To analyze char...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8946603/ |
| _version_ | 1819295737721126912 |
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| author | Wei Wei Zhaosheng Zhang Peng Liu Zhenpo Wang Lulu Xue |
| author_facet | Wei Wei Zhaosheng Zhang Peng Liu Zhenpo Wang Lulu Xue |
| author_sort | Wei Wei |
| collection | DOAJ |
| description | Electric city buses have potential to reduce greenhouse gases emission in case the majority of the electric power used in electric buses originate from the renewable sources or nuclear power plants. Their charging behaviors analysis is critical to their development and mass-adoption. To analyze charging behavior characteristics of electric city buses at different locations, the datasets collected from 17576 electric buses operating in 14 cities are used based on the probability statistics method. Then, the characteristic parameters including the charging power and charging duration are utilized to cluster the cities into 5 clusters based on the K-means algorithm. The results enrich the traditional research conducted only under limited test routes and provide the comparison of key characteristic parameters among different clusters. The analysis results are useful in studying the connection between the operational efficiency and the charging behaviors, optimizing the charging scheduling, evaluation of charging load and planning charging infrastructures construction. |
| first_indexed | 2024-12-24T04:46:58Z |
| format | Article |
| id | doaj.art-7f28bd61e9fb4f599a084edc6b840668 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T04:46:58Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7f28bd61e9fb4f599a084edc6b8406682022-12-21T17:14:39ZengIEEEIEEE Access2169-35362020-01-0184466447410.1109/ACCESS.2019.29632588946603Analyzing Charging Behavior of Electric City Buses in Typical Chinese CitiesWei Wei0https://orcid.org/0000-0002-3466-3449Zhaosheng Zhang1https://orcid.org/0000-0003-0591-9641Peng Liu2https://orcid.org/0000-0001-9702-6888Zhenpo Wang3https://orcid.org/0000-0002-1396-906XLulu Xue4School of Mechanical Engineering, Beijing Institute of Technology, Beijing, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, ChinaWorld Resources Institute (USA) Beijing Office, Beijing, ChinaElectric city buses have potential to reduce greenhouse gases emission in case the majority of the electric power used in electric buses originate from the renewable sources or nuclear power plants. Their charging behaviors analysis is critical to their development and mass-adoption. To analyze charging behavior characteristics of electric city buses at different locations, the datasets collected from 17576 electric buses operating in 14 cities are used based on the probability statistics method. Then, the characteristic parameters including the charging power and charging duration are utilized to cluster the cities into 5 clusters based on the K-means algorithm. The results enrich the traditional research conducted only under limited test routes and provide the comparison of key characteristic parameters among different clusters. The analysis results are useful in studying the connection between the operational efficiency and the charging behaviors, optimizing the charging scheduling, evaluation of charging load and planning charging infrastructures construction.https://ieeexplore.ieee.org/document/8946603/Charging behaviorsK-means algorithmelectric city busbig data analytic |
| spellingShingle | Wei Wei Zhaosheng Zhang Peng Liu Zhenpo Wang Lulu Xue Analyzing Charging Behavior of Electric City Buses in Typical Chinese Cities IEEE Access Charging behaviors K-means algorithm electric city bus big data analytic |
| title | Analyzing Charging Behavior of Electric City Buses in Typical Chinese Cities |
| title_full | Analyzing Charging Behavior of Electric City Buses in Typical Chinese Cities |
| title_fullStr | Analyzing Charging Behavior of Electric City Buses in Typical Chinese Cities |
| title_full_unstemmed | Analyzing Charging Behavior of Electric City Buses in Typical Chinese Cities |
| title_short | Analyzing Charging Behavior of Electric City Buses in Typical Chinese Cities |
| title_sort | analyzing charging behavior of electric city buses in typical chinese cities |
| topic | Charging behaviors K-means algorithm electric city bus big data analytic |
| url | https://ieeexplore.ieee.org/document/8946603/ |
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