Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations With Nested Genetic Algorithm
The technology of dynamic wireless power transfer (DWPT) has been recognized as an effective way to recharge battery electric bus and overcome some drawbacks (e.g. high battery cost and limited service range) with opportunity charging. This study develops a mixed integer non-linear model to optimize...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9055438/ |
| _version_ | 1819275086083915776 |
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| author | Gang Chen Dawei Hu Steven Chien |
| author_facet | Gang Chen Dawei Hu Steven Chien |
| author_sort | Gang Chen |
| collection | DOAJ |
| description | The technology of dynamic wireless power transfer (DWPT) has been recognized as an effective way to recharge battery electric bus and overcome some drawbacks (e.g. high battery cost and limited service range) with opportunity charging. This study develops a mixed integer non-linear model to optimize a feeder bus transit powered by DWPT. The decision variables consist of bus route networks, service frequency, locations of DWPT devices and battery capacity. The objective is to minimize total cost, including the costs of charging devices, battery, operation and travel time. A tangible nested genetic algorithm (NGA) is developed to find the optimal solution. The computational efficiency of NGA is demonstrated through numerical comparisons to the solutions founded by LINGO and GA. It was found that with NGA the solution converges to an acceptable level faster than using LINGO and GA. A real-world bus network is employed to explore the relation between the minimized costs and decision variables. The result suggested that DWPT outperforms terminal charging technology in terms of the least total cost, and that the yielded total infrastructure cost with DWPT is 16.6% less than that with terminal charging technology. |
| first_indexed | 2024-12-23T23:18:44Z |
| format | Article |
| id | doaj.art-f3f2f9b04c054cf0b75fa625096c3459 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:18:44Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f3f2f9b04c054cf0b75fa625096c34592022-12-21T17:26:24ZengIEEEIEEE Access2169-35362020-01-018671666717810.1109/ACCESS.2020.29851689055438Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations With Nested Genetic AlgorithmGang Chen0https://orcid.org/0000-0002-3462-0086Dawei Hu1https://orcid.org/0000-0002-0126-007XSteven Chien2https://orcid.org/0000-0001-8771-8194School of Automobile, Chang’an University, Xi’an, ChinaSchool of Automobile, Chang’an University, Xi’an, ChinaSchool of Automobile, Chang’an University, Xi’an, ChinaThe technology of dynamic wireless power transfer (DWPT) has been recognized as an effective way to recharge battery electric bus and overcome some drawbacks (e.g. high battery cost and limited service range) with opportunity charging. This study develops a mixed integer non-linear model to optimize a feeder bus transit powered by DWPT. The decision variables consist of bus route networks, service frequency, locations of DWPT devices and battery capacity. The objective is to minimize total cost, including the costs of charging devices, battery, operation and travel time. A tangible nested genetic algorithm (NGA) is developed to find the optimal solution. The computational efficiency of NGA is demonstrated through numerical comparisons to the solutions founded by LINGO and GA. It was found that with NGA the solution converges to an acceptable level faster than using LINGO and GA. A real-world bus network is employed to explore the relation between the minimized costs and decision variables. The result suggested that DWPT outperforms terminal charging technology in terms of the least total cost, and that the yielded total infrastructure cost with DWPT is 16.6% less than that with terminal charging technology.https://ieeexplore.ieee.org/document/9055438/Electric busesmixed non-linear programmingwireless chargingroute optimization |
| spellingShingle | Gang Chen Dawei Hu Steven Chien Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations With Nested Genetic Algorithm IEEE Access Electric buses mixed non-linear programming wireless charging route optimization |
| title | Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations With Nested Genetic Algorithm |
| title_full | Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations With Nested Genetic Algorithm |
| title_fullStr | Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations With Nested Genetic Algorithm |
| title_full_unstemmed | Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations With Nested Genetic Algorithm |
| title_short | Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations With Nested Genetic Algorithm |
| title_sort | optimizing battery electric feeder service and wireless charging locations with nested genetic algorithm |
| topic | Electric buses mixed non-linear programming wireless charging route optimization |
| url | https://ieeexplore.ieee.org/document/9055438/ |
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