Optimal Fleet Size for Cross-Route Dispatching in Electrified Bus Networks

This manuscript proposes a model for optimal fleet size in electrified bus networks using cross-route dispatching. Battery Electric Buses often require day-time charging, which might require long idle time. The longer total idle time results in a bigger fleet size. Here, we show that we can reduce the total idle time, and consequently the fleet size, by applying the cross-route dispatching method to the entire bus network. The proposed model can also find the location of fast en-route charging stations and manage their maximum required power. In addition, the model can adopt a network with different bus sizes, battery capacities and required charging loads. We verified the model on bus networks of New York City, USA, Melbourne, AU and Manchester, U.K., and showed that it can significantly reduce the fleet size in these cities.

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Publication Details

Journal
IEEE Transactions on Network Science and Engineering
Published
2024-08-20
DOI
https://doi.org/10.1109/tnse.2024.3445915
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimal Fleet Size for Cross-Route Dispatching in Electrified Bus Networks

Kianoosh Keshavarzian, Mahdi Jalili, Ali MoradiAmani
IEEE Transactions on Network Science and Engineering
Electric Vehicles and Infrastructure
article

Optimal Fleet Size for Cross-Route Dispatching in Electrified Bus Networks

Kianoosh Keshavarzian, Mahdi Jalili, Ali MoradiAmani
article en

Abstract

This manuscript proposes a model for optimal fleet size in electrified bus networks using cross-route dispatching. Battery Electric Buses often require day-time charging, which might require long idle time. The longer total idle time results in a bigger fleet size. Here, we show that we can reduce the total idle time, and consequently the fleet size, by applying the cross-route dispatching method to the entire bus network. The proposed model can also find the location of fast en-route charging stations and manage their maximum required power. In addition, the model can adopt a network with different bus sizes, battery capacities and required charging loads. We verified the model on bus networks of New York City, USA, Melbourne, AU and Manchester, U.K., and showed that it can significantly reduce the fleet size in these cities.

IEEE Transactions on Network Science and EngineeringVol. 11(6)
RMIT University (AU)
Australian Research Council
Affordable and clean energy
Openalex Percentile: Top 88%
Electric Vehicles and Infrastructure
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