Green Logistics Tram Charging and Path Optimization Considering Urban Impedance
The aim of this article is to investigate the optimization of electric vehicle charging and swapping for green logistics, as well as path planning considering urban impedance. We improved a road impedance function model suitable for urban road traffic in China to calculate the actual traffic time ba...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10472501/ |
| _version_ | 1797236622108917760 |
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| author | Lei Fu Yan Xu Aobei Zhang |
| author_facet | Lei Fu Yan Xu Aobei Zhang |
| author_sort | Lei Fu |
| collection | DOAJ |
| description | The aim of this article is to investigate the optimization of electric vehicle charging and swapping for green logistics, as well as path planning considering urban impedance. We improved a road impedance function model suitable for urban road traffic in China to calculate the actual traffic time based on real-time traffic data and the intricate urban road environment. This model is then applied to address the delivery optimization problem. Furthermore, a robust computational approach is introduced to estimate battery degradation costs, accounting for environmental temperature and depth of discharge. The logistics delivery model is effectively tackled using genetic algorithms, and simulation results demonstrate the considerable advantages of electric vehicle swapping, effectively mitigating energy wastage and environmental pollution. Additionally, the integration of road impedance modeling for path optimization proves to significantly reduce logistics costs, time expenditures, and enhance logistics efficiency. A comprehensive sensitivity analysis is also conducted to elucidate the factors influencing electric vehicle battery degradation, revealing a direct correlation between higher temperatures, deeper discharge depth, and increased battery loss. The study underscores the paramount significance of this research for the development and optimization of urban green logistics systems. |
| first_indexed | 2024-04-24T17:06:46Z |
| format | Article |
| id | doaj.art-fc4eeee2baa941f58f5869de7c1c8357 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T17:06:46Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fc4eeee2baa941f58f5869de7c1c83572024-03-28T23:00:42ZengIEEEIEEE Access2169-35362024-01-0112437584377110.1109/ACCESS.2024.337724410472501Green Logistics Tram Charging and Path Optimization Considering Urban ImpedanceLei Fu0https://orcid.org/0009-0005-5758-9885Yan Xu1Aobei Zhang2https://orcid.org/0000-0001-8875-8825School of Science and Technology, Shenyang Polytechnic College, Shenyang, ChinaSchool of Science and Technology, Shenyang Polytechnic College, Shenyang, ChinaSchool of Management, Shenyang University of Technology, Shenyang, ChinaThe aim of this article is to investigate the optimization of electric vehicle charging and swapping for green logistics, as well as path planning considering urban impedance. We improved a road impedance function model suitable for urban road traffic in China to calculate the actual traffic time based on real-time traffic data and the intricate urban road environment. This model is then applied to address the delivery optimization problem. Furthermore, a robust computational approach is introduced to estimate battery degradation costs, accounting for environmental temperature and depth of discharge. The logistics delivery model is effectively tackled using genetic algorithms, and simulation results demonstrate the considerable advantages of electric vehicle swapping, effectively mitigating energy wastage and environmental pollution. Additionally, the integration of road impedance modeling for path optimization proves to significantly reduce logistics costs, time expenditures, and enhance logistics efficiency. A comprehensive sensitivity analysis is also conducted to elucidate the factors influencing electric vehicle battery degradation, revealing a direct correlation between higher temperatures, deeper discharge depth, and increased battery loss. The study underscores the paramount significance of this research for the development and optimization of urban green logistics systems.https://ieeexplore.ieee.org/document/10472501/Green logisticsroad impedance functionelectric vehiclegenetic algorithmsbattery degradation costs |
| spellingShingle | Lei Fu Yan Xu Aobei Zhang Green Logistics Tram Charging and Path Optimization Considering Urban Impedance IEEE Access Green logistics road impedance function electric vehicle genetic algorithms battery degradation costs |
| title | Green Logistics Tram Charging and Path Optimization Considering Urban Impedance |
| title_full | Green Logistics Tram Charging and Path Optimization Considering Urban Impedance |
| title_fullStr | Green Logistics Tram Charging and Path Optimization Considering Urban Impedance |
| title_full_unstemmed | Green Logistics Tram Charging and Path Optimization Considering Urban Impedance |
| title_short | Green Logistics Tram Charging and Path Optimization Considering Urban Impedance |
| title_sort | green logistics tram charging and path optimization considering urban impedance |
| topic | Green logistics road impedance function electric vehicle genetic algorithms battery degradation costs |
| url | https://ieeexplore.ieee.org/document/10472501/ |
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