Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence
This study investigates the spatiotemporal distribution of electric vehicle (EV) charging demands and operating efficiency of the charging system. The travel behavior of EV drivers is analyzed by considering the heterogeneity of range anxiety and bound rationality. Building on existing charging choi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/2309376 |
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| _version_ | 1832553712922394624 |
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| author | Shuai Ling Xiang Zhu Qing Wang Paul Schonfeld |
| author_facet | Shuai Ling Xiang Zhu Qing Wang Paul Schonfeld |
| author_sort | Shuai Ling |
| collection | DOAJ |
| description | This study investigates the spatiotemporal distribution of electric vehicle (EV) charging demands and operating efficiency of the charging system. The travel behavior of EV drivers is analyzed by considering the heterogeneity of range anxiety and bound rationality. Building on existing charging choice models, our more holistic perspective of charging demand distribution is obtained through multiagent system (MAS) modeling. In our study, the charging demand distribution in different areas is compared by considering two charging price schemes. The performance of the charging system is then evaluated based on three indicators, the charging request rejection rate, charging pile utilization rate, and charging load deviation, thereby verifying the effectiveness of our charging demand prediction model. This is done using multiagent-based simulations applied to the National Household Travel Survey (NHTS) 2017 dataset. The results are analyzed according to different key indicators, mainly indicating (a) the limitations of the time-of-use (TOU) pricing strategy in reducing the peak-valley difference, (b) the transferability of charging demand among different functional areas, and (c) that there is less demand for charging piles in the workplace, as users mainly rely on home charging. These results facilitate further analyses to help the design and operation of the EV charging infrastructure. |
| format | Article |
| id | doaj-art-d960eee3b28a4f23b6dacae65dfeb96e |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d960eee3b28a4f23b6dacae65dfeb96e2025-02-03T05:53:30ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/2309376Prediction of Charging Requirements for Electric Vehicles Based on Multiagent IntelligenceShuai Ling0Xiang Zhu1Qing Wang2Paul Schonfeld3College of Management and EconomicsCollege of Management and EconomicsSchool of Modern PostsDepartment of Civil and Environmental EngineeringThis study investigates the spatiotemporal distribution of electric vehicle (EV) charging demands and operating efficiency of the charging system. The travel behavior of EV drivers is analyzed by considering the heterogeneity of range anxiety and bound rationality. Building on existing charging choice models, our more holistic perspective of charging demand distribution is obtained through multiagent system (MAS) modeling. In our study, the charging demand distribution in different areas is compared by considering two charging price schemes. The performance of the charging system is then evaluated based on three indicators, the charging request rejection rate, charging pile utilization rate, and charging load deviation, thereby verifying the effectiveness of our charging demand prediction model. This is done using multiagent-based simulations applied to the National Household Travel Survey (NHTS) 2017 dataset. The results are analyzed according to different key indicators, mainly indicating (a) the limitations of the time-of-use (TOU) pricing strategy in reducing the peak-valley difference, (b) the transferability of charging demand among different functional areas, and (c) that there is less demand for charging piles in the workplace, as users mainly rely on home charging. These results facilitate further analyses to help the design and operation of the EV charging infrastructure.http://dx.doi.org/10.1155/2022/2309376 |
| spellingShingle | Shuai Ling Xiang Zhu Qing Wang Paul Schonfeld Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence Journal of Advanced Transportation |
| title | Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence |
| title_full | Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence |
| title_fullStr | Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence |
| title_full_unstemmed | Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence |
| title_short | Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence |
| title_sort | prediction of charging requirements for electric vehicles based on multiagent intelligence |
| url | http://dx.doi.org/10.1155/2022/2309376 |
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