Optimal Planning of Electrified Road Structures Using Queuing Models Article Swipe
Eiman ElGhanam
,
Mohamed S. Hassan
,
Ahmed M. Benaya
,
Ahmed Osman
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/ojvt.2025.3563109
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/ojvt.2025.3563109
Dynamic wireless charging (DWC) of electric vehicles (EVs) is an attractive solution to the EV driving range limitations and the associated range anxiety problem. In DWC, charging lanes are deployed along city roads to wirelessly supply the needed charging power to EVs during their motion. However, due to the high construction costs of electrified road structures (ERS) with wireless charging lanes and the likely increase in the energy demand by EV owners, an optimal deployment plan is essential to maximize the net returns to the infrastructure owners and ensure maximal demand coverage. Therefore, to formulate a reliable optimization framework, accurate modeling of the charging lane operation is needed at each potential lane location. In this work, the traffic behavior at different locations is modeled analytically using queuing theory. This accurately represents the desired flow of vehicles on the charging lanes and provides a reliable estimate of the EV charging demand, particularly due to the lack of EV traffic flow datasets with the currently low but expanding penetration of EVs. A multi-objective optimization framework is then developed based on the established traffic model to determine the most optimal locations for the deployment of DWC lanes within a smart city infrastructure. The model is tested on 24 candidate roads selected from the United Arab Emirates map and the corresponding optimal locations are determined by solving the optimization problem on GAMS/CONOPT solver. Sensitivity analysis is also conducted to validate the results of the proposed model.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ojvt.2025.3563109
- OA Status
- gold
- Cited By
- 2
- References
- 71
- Related Works
- 10
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All OpenAlex metadata
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https://openalex.org/W4409640719Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ojvt.2025.3563109Digital Object Identifier
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Optimal Planning of Electrified Road Structures Using Queuing ModelsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Eiman ElGhanam, Mohamed S. Hassan, Ahmed M. Benaya, Ahmed OsmanList of authors in order
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https://doi.org/10.1109/ojvt.2025.3563109Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/ojvt.2025.3563109Direct OA link when available
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Queueing theory, Computer science, Transport engineering, Operations research, Engineering, Computer networkTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.demand, | 149 |
| abstract_inverted_index.desired | 132 |
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| abstract_inverted_index.maximal | 89 |
| abstract_inverted_index.modeled | 123 |
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| abstract_inverted_index.optimal | 73, 186, 217 |
| abstract_inverted_index.owners, | 71 |
| abstract_inverted_index.problem | 225 |
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| abstract_inverted_index.results | 237 |
| abstract_inverted_index.returns | 82 |
| abstract_inverted_index.solver. | 228 |
| abstract_inverted_index.solving | 222 |
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| abstract_inverted_index.traffic | 117, 157, 180 |
| abstract_inverted_index.Emirates | 212 |
| abstract_inverted_index.However, | 45 |
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| abstract_inverted_index.analysis | 230 |
| abstract_inverted_index.behavior | 118 |
| abstract_inverted_index.charging | 2, 26, 38, 59, 103, 138, 148 |
| abstract_inverted_index.datasets | 159 |
| abstract_inverted_index.deployed | 29 |
| abstract_inverted_index.electric | 5 |
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| abstract_inverted_index.increase | 64 |
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| abstract_inverted_index.modeling | 100 |
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| abstract_inverted_index.proposed | 240 |
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| abstract_inverted_index.reliable | 96, 143 |
| abstract_inverted_index.selected | 207 |
| abstract_inverted_index.solution | 11 |
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| abstract_inverted_index.vehicles | 6, 135 |
| abstract_inverted_index.wireless | 1, 58 |
| abstract_inverted_index.candidate | 205 |
| abstract_inverted_index.conducted | 233 |
| abstract_inverted_index.coverage. | 91 |
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| abstract_inverted_index.expanding | 165 |
| abstract_inverted_index.formulate | 94 |
| abstract_inverted_index.framework | 172 |
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| abstract_inverted_index.locations | 121, 187, 218 |
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| abstract_inverted_index.potential | 110 |
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| abstract_inverted_index.determined | 220 |
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| abstract_inverted_index.wirelessly | 34 |
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| abstract_inverted_index.Sensitivity | 229 |
| abstract_inverted_index.electrified | 53 |
| abstract_inverted_index.established | 179 |
| abstract_inverted_index.limitations | 17 |
| abstract_inverted_index.penetration | 166 |
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| abstract_inverted_index.particularly | 150 |
| abstract_inverted_index.corresponding | 216 |
| abstract_inverted_index.infrastructure | 85 |
| abstract_inverted_index.infrastructure. | 198 |
| abstract_inverted_index.multi-objective | 170 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.89236734 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |