Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data
Electric vehicle (EV) has been popularized and promoted on a large scale because of its clean and efficient features. Charging this increasing number of EVs is expected to have an impact on the electricity grid and traffic network. Therefore, it is necessary to model and forecast the EV charging dem...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8830478/ |
| _version_ | 1831809481652043776 |
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| author | Qiang Xing Zhong Chen Ziqi Zhang Xueliang Huang Zhaoying Leng Kehui Sun Yanxi Chen Haiwei Wang |
| author_facet | Qiang Xing Zhong Chen Ziqi Zhang Xueliang Huang Zhaoying Leng Kehui Sun Yanxi Chen Haiwei Wang |
| author_sort | Qiang Xing |
| collection | DOAJ |
| description | Electric vehicle (EV) has been popularized and promoted on a large scale because of its clean and efficient features. Charging this increasing number of EVs is expected to have an impact on the electricity grid and traffic network. Therefore, it is necessary to model and forecast the EV charging demand. Most of the existing researches have not utilized real-world traffic data to analyze the EV charging demand. Few researches have considered and analyzed the characteristics of space-time transfer of charging load in urban functional areas. As an emerging mode of transportation, however, online ride-hailing trip data provide an ideal source for analyzing traffic planning and operation. On the basis of this, a charging demand forecasting model of EVs based on a data-driven approach was presented in this paper. In this methodology, it is firstly assumed that residents' transportation trip demand is not restricted by vehicle categories(electric or fuel vehicles). The original trip trajectory data of Didi online ride-hailing were conducted to model via data mining and fusion. And the process of data analysis included region-scope selection, spatial grid modeling, trajectory data mapping, retrieval data identification and urban functional area clustering as well as traffic network modeling. Through modeling and processing, the following regenerative feature data were obtained: functional regional division (i.e., residential areas, industrial areas, commercial areas, and public service areas), trip rule distribution (i.e., temporal distribution and spatial distribution on weekdays, weekends and holidays) and actual driving path (i.e., driving path with the shortest distance or with the minimum time-consuming). And then, considering the movable load feature of EVs, vehicles were subdivided into three kinds such as private vehicles, taxis and other public vehicles, and the single EV model with driving and charging characteristic parameters was established. Furthermore, the regeneration data obtained from modeling and analysis along with the determined single EV model were supported as data sources and model for the charging demand forecast architecture. At last, the actual urban traffic network in Nanjing, China was selected as an example to design the path planning experiments and charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to realistically simulate the actual dynamic driving process of EVs, and effectively predict the spatial-temporal distribution characteristics and load transfer trends of charging demands in different date type as well as different functional areas. The model also lays a theoretical foundation for the subsequent research on investment and construction of charging facilities, as well as charging control and charging guidance of EVs. |
| first_indexed | 2024-12-22T20:42:11Z |
| format | Article |
| id | doaj.art-98163d1f328a48399b66198447f2b4ab |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:42:11Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-98163d1f328a48399b66198447f2b4ab2022-12-21T18:13:19ZengIEEEIEEE Access2169-35362019-01-01713739013740910.1109/ACCESS.2019.29405978830478Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip DataQiang Xing0https://orcid.org/0000-0003-1926-2783Zhong Chen1Ziqi Zhang2Xueliang Huang3Zhaoying Leng4Kehui Sun5Yanxi Chen6Haiwei Wang7School of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaState Grid Anhui Electric Power Company, Electric Power Research Institute, Hefei, ChinaElectric vehicle (EV) has been popularized and promoted on a large scale because of its clean and efficient features. Charging this increasing number of EVs is expected to have an impact on the electricity grid and traffic network. Therefore, it is necessary to model and forecast the EV charging demand. Most of the existing researches have not utilized real-world traffic data to analyze the EV charging demand. Few researches have considered and analyzed the characteristics of space-time transfer of charging load in urban functional areas. As an emerging mode of transportation, however, online ride-hailing trip data provide an ideal source for analyzing traffic planning and operation. On the basis of this, a charging demand forecasting model of EVs based on a data-driven approach was presented in this paper. In this methodology, it is firstly assumed that residents' transportation trip demand is not restricted by vehicle categories(electric or fuel vehicles). The original trip trajectory data of Didi online ride-hailing were conducted to model via data mining and fusion. And the process of data analysis included region-scope selection, spatial grid modeling, trajectory data mapping, retrieval data identification and urban functional area clustering as well as traffic network modeling. Through modeling and processing, the following regenerative feature data were obtained: functional regional division (i.e., residential areas, industrial areas, commercial areas, and public service areas), trip rule distribution (i.e., temporal distribution and spatial distribution on weekdays, weekends and holidays) and actual driving path (i.e., driving path with the shortest distance or with the minimum time-consuming). And then, considering the movable load feature of EVs, vehicles were subdivided into three kinds such as private vehicles, taxis and other public vehicles, and the single EV model with driving and charging characteristic parameters was established. Furthermore, the regeneration data obtained from modeling and analysis along with the determined single EV model were supported as data sources and model for the charging demand forecast architecture. At last, the actual urban traffic network in Nanjing, China was selected as an example to design the path planning experiments and charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to realistically simulate the actual dynamic driving process of EVs, and effectively predict the spatial-temporal distribution characteristics and load transfer trends of charging demands in different date type as well as different functional areas. The model also lays a theoretical foundation for the subsequent research on investment and construction of charging facilities, as well as charging control and charging guidance of EVs.https://ieeexplore.ieee.org/document/8830478/Electric vehiclecharging demand forecastingonline ride-hailing datadata mining and fusiontrip rulespatial-temporal distribution characteristic |
| spellingShingle | Qiang Xing Zhong Chen Ziqi Zhang Xueliang Huang Zhaoying Leng Kehui Sun Yanxi Chen Haiwei Wang Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data IEEE Access Electric vehicle charging demand forecasting online ride-hailing data data mining and fusion trip rule spatial-temporal distribution characteristic |
| title | Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data |
| title_full | Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data |
| title_fullStr | Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data |
| title_full_unstemmed | Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data |
| title_short | Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data |
| title_sort | charging demand forecasting model for electric vehicles based on online ride hailing trip data |
| topic | Electric vehicle charging demand forecasting online ride-hailing data data mining and fusion trip rule spatial-temporal distribution characteristic |
| url | https://ieeexplore.ieee.org/document/8830478/ |
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