Electric Vehicle Fleet and Charging Infrastructure Planning
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866911159905419264 |
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| author | Varma, Sushil Mahavir Castro, Francisco Maguluri, Siva Theja |
| author_facet | Varma, Sushil Mahavir Castro, Francisco Maguluri, Siva Theja |
| contents | We study electric vehicle (EV) fleet and charging infrastructure planning in a spatial setting. With customer requests arriving continuously at rate $λ$ throughout the day, we determine the minimum number of vehicles and chargers for a target service level, along with matching and charging policies. While non-EV systems require extra $Θ(λ^{2/3})$ vehicles due to pickup times, EV systems differ. Charging increases nominal capacity, enabling pickup time reductions and allowing for an extra fleet requirement of only $Θ(λ^ν)$ for $ν\in (1/2, 2/3]$, depending on charging infrastructure and battery pack sizes. We propose the Power-of-$d$ dispatching policy, which achieves this performance by selecting the closest vehicle with the highest battery level from $d$ options. We extend our results to accommodate time-varying demand patterns and discuss conditions for transitioning between EV and non-EV capacity planning. Extensive simulations verify our scaling results, insights, and policy effectiveness while also showing the viability of low-range, low-cost fleets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_10178 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Electric Vehicle Fleet and Charging Infrastructure Planning Varma, Sushil Mahavir Castro, Francisco Maguluri, Siva Theja Optimization and Control We study electric vehicle (EV) fleet and charging infrastructure planning in a spatial setting. With customer requests arriving continuously at rate $λ$ throughout the day, we determine the minimum number of vehicles and chargers for a target service level, along with matching and charging policies. While non-EV systems require extra $Θ(λ^{2/3})$ vehicles due to pickup times, EV systems differ. Charging increases nominal capacity, enabling pickup time reductions and allowing for an extra fleet requirement of only $Θ(λ^ν)$ for $ν\in (1/2, 2/3]$, depending on charging infrastructure and battery pack sizes. We propose the Power-of-$d$ dispatching policy, which achieves this performance by selecting the closest vehicle with the highest battery level from $d$ options. We extend our results to accommodate time-varying demand patterns and discuss conditions for transitioning between EV and non-EV capacity planning. Extensive simulations verify our scaling results, insights, and policy effectiveness while also showing the viability of low-range, low-cost fleets. |
| title | Electric Vehicle Fleet and Charging Infrastructure Planning |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2306.10178 |