Design and operation for an electric taxi fleet

Gacias, Bernat; Meunier, Frédéric

doi:10.1007/s00291-014-0362-y

Cited by 16 publications

(4 citation statements)

References 25 publications

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“…We distinguish between two approaches to fleet simulation. Object-oriented or agent-based models using a discrete-event framework were popularised in autonomous taxi fleet and general logistics simulation [39][40][41][42], but are also applied to bus fleets [14,15,43,44]. All objects in the simulation-vehicles, charging stations, depots, etc.-are simulated simultaneously in a shared environment with a central simulation clock, each object having its own individual state.…”

Section: Fleet Modellingmentioning

confidence: 99%

A Comprehensive TCO Evaluation Method for Electric Bus Systems Based on Discrete-Event Simulation Including Bus Scheduling and Charging Infrastructure Optimisation

Jefferies¹,

Göhlich²

2020

WEVJ

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Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies; however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to reschedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO; however, under some circumstances, the technologies are on par. This emphasises the need for a detailed analysis of the local bus network in order to make an informed procurement decision.

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Section: Fleet Modellingmentioning

confidence: 99%

A Comprehensive TCO Evaluation Method for Electric Bus Systems Based on Discrete-Event Simulation Including Bus Scheduling and Charging Infrastructure Optimisation

Jefferies¹,

Göhlich²

2020

WEVJ

View full text Add to dashboard Cite

show abstract

Section: Fleet Modellingmentioning

confidence: 99%

A Comprehensive TCO Evaluation Method for Electric Bus Systems Based on Discrete-Event Simulation Including Bus Scheduling and Charging Infrastructure Optimisation

Jefferies¹,

Göhlich²

2020

Preprint

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Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies, however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to re-schedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO, however, under some circumstances, the technologies are on par. This emphasises the need for detailed analysis of the local bus network in order to make an informed procurement decision.

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“…Gacias et al [28] study both the problem of locating charging stations (strategic level) and the charging management of an ET fleet (operational level).…”

Section: Literature Reviewmentioning

confidence: 99%

On the Selection of Charging Facility Locations for EV-Based Ride-Hailing Services: A Computational Case Study

et al. 2020

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The uptake of Electric Vehicles (EVs) is rapidly changing the landscape of urban mobility services. Transportation Network Companies (TNCs) have been following this trend by increasing the number of EVs in their fleets. Recently, major TNCs have explored the prospect of establishing privately owned charging facilities that will enable faster and more economic charging. Given the scale and complexity of TNC operations, such decisions need to consider both the requirements of TNCs and local planning regulations. Therefore, an optimisation approach is presented to model the placement of CSs with the objective of minimising the empty time travelled to the nearest CS for recharging as well as the installation cost. An agent based simulation model has been set in the area of Chicago to derive the recharging spots of the TNC vehicles, and in turn derive the charging demand. A mathematical formulation for the resulting optimisation problem is provided alongside a genetic algorithm that can produce solutions for large problem instances. Our results refer to a representative set of the total data for Chicago and indicate that nearly 180 CSs need to be installed to handle the demand of a TNC fleet of 3000 vehicles.

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Design and operation for an electric taxi fleet

Cited by 16 publications

References 25 publications

A Comprehensive TCO Evaluation Method for Electric Bus Systems Based on Discrete-Event Simulation Including Bus Scheduling and Charging Infrastructure Optimisation

A Comprehensive TCO Evaluation Method for Electric Bus Systems Based on Discrete-Event Simulation Including Bus Scheduling and Charging Infrastructure Optimisation

A Comprehensive TCO Evaluation Method for Electric Bus Systems Based on Discrete-Event Simulation Including Bus Scheduling and Charging Infrastructure Optimisation

On the Selection of Charging Facility Locations for EV-Based Ride-Hailing Services: A Computational Case Study

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