Electric vehicle simulator for energy consumption studies in electric mobility systems

Maia, Ricardo; Silva, Marco; Araújo, Rui; Nunes, Urbano

doi:10.1109/fists.2011.5973655

Cited by 85 publications

(33 citation statements)

References 9 publications

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“…Some simulators or models can be found in previous research results [7][8][9][10][11][12][13]; however these tools are in most cases developed with quite different purposes from those of EVeSSi. In most of these models, the output is the individuals list of activities and trips that include detailed information about departure time, destination and mode for each trip.…”

Section: Open Accessmentioning

confidence: 99%

“…An extension of the Simulation of Urban Mobility (SUMO two-dimensional (2D) vehicular simulation package, allowing simulation of energy consumption of one EV, is described in [13], where the EV model and modelling of terrain altitude were incorporated in SUMO providing a three-dimensional simulator.…”

Section: Open Accessmentioning

confidence: 99%

“…This will allow analysing the impact on the electricity grid for different scenarios as well as the energy consumption of the EVs in general. Although, terrain slope has an important impact on EV performance in the way the battery discharges [13], this feature will not be implemented in EVeSSi. Nevertheless, the EVeSSi framework is designed to provide future expansion to integrate additional features with 3D capabilities.…”

Section: Open Accessmentioning

confidence: 99%

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Electric Vehicle Scenario Simulator Tool for Smart Grid Operators

et al. 2012

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This paper presents a simulator for electric vehicles in the context of smart grids and distribution networks. It aims to support network operators' planning and operations but can be used by other entities for related studies. The paper describes the parameters supported by the current version of the Electric Vehicle Scenario Simulator (EVeSSi) tool and its current algorithm. EVeSSi enables the definition of electric vehicles scenarios on distribution networks using a built-in movement engine. The scenarios created with EVeSSi can be used by external tools (e.g., power flow) for specific analysis, for instance grid impacts. Two scenarios are briefly presented for illustration of the simulator capabilities.

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Section: Open Accessmentioning

confidence: 99%

Section: Open Accessmentioning

confidence: 99%

Section: Open Accessmentioning

confidence: 99%

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Electric Vehicle Scenario Simulator Tool for Smart Grid Operators

et al. 2012

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“…[12], with new and fully charged batteries [13][14][15][16][17][18][19][20] and for urban driving with a short highway section [21][22][23][24], at the time of this study there is little information available on realistic EV use, energy consumption and vehicle range for travel on an electric highway between cities. In particular, there is a gap in the literature on the interaction of the combination of a limited fast-DC charge level of 80% capacity, increased energy consumption at highway speeds, increased loads due to headwinds, increased aerodynamic drag due to roof racks, additional vehicle weight, the absence of energy recovery and a battery discharge safety margin.…”

Section: Introductionmentioning

confidence: 99%

Driving electric vehicles at highway speeds: The effect of higher driving speeds on energy consumption and driving range for electric vehicles in Australia

Wäger

Whale

Bräunl

2016

Renewable and Sustainable Energy Reviews

126

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Keywords:Electric vehicles, Fast-DC charging, battery management system, cell balance, drivable range. 2 AbstractElectric vehicles (EVs) have the potential to operate emission free and thus overcome many environmental and health issues associated with cars run on fossil fuels. Recharging time and driving range are amongst the biggest hurdles for the mainstream acceptance and implementation of EV technology. Fast-DC charging significantly reduces the recharging time and can be used to make longer EV trips possible, e.g. on highways between cities. Although some EV and hybrid car studies have been conducted that address separately issues such as limited drivable ranges, charge stations, impact from auxiliary loads on vehicle energy consumption and emissions, there is currently limited research on the impact on drivable range from the combination of driving EVs at highway speeds, using auxiliary loads such as heating or air conditioning (AC), and reduced charge capacity from fast-DC charging and discharge safety margins. In this study we investigate these parameters and their impact on energy consumption and drivable range of EVs. Our results show a significantly reduced range under conditions relevant for highway driving and significant deviation from driving ranges published by EV manufacturers. The results and outcomes of this project are critical for the efficient design and implementation of so-called 'Electric Highways'. To prevent stranded cars and a possible negative perception of EVs, drivers and charging infrastructure planners need be aware of how EV energy and recharging demands can significantly change under different loads and driving patterns.

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“…Electric vehicle simulator for energy consumption studies in electric mobility systems In this work (Maia et al, 2011), an electric vehicle model implemented directly in SUMO is suggested, as opposed to the Simulink model mentioned in the previously. At the time of writing of that paper, SUMO was lacking a direct way to integrate electric vehicles in its simulation capabilities.…”

Section: Related Workmentioning

confidence: 99%

Modelling Altitude Information in Two-Dimensional Traffic Networks for Electric Mobility Simulation

Santos

Pinto

Rossetti

et al. 2016

Journal of Information Systems Engineering & Management

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Elevation data is important for electric vehicle simulation. However, traffic simulators are often two-dimensional and do not offer the capability of modelling urban networks taking elevation into account. Specifically, SUMO -Simulation of Urban Mobility, a popular microscopic traffic simulator, relies on networks previously modelled with elevation data as to provide this information during simulations. This work tackles the problem of adding elevation data to urban network models -particularly for the case of the Porto urban network, in Portugal. With this goal in mind, a comparison between different altitude information retrieval approaches is made and a simple tool to annotate network models with altitude data is proposed. The work starts by describing the methodological approach followed during research and development, then describing and analysing its main findings. This description includes an in-depth explanation of the proposed tool. Lastly, this work reviews some related work to the subject.

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Electric vehicle simulator for energy consumption studies in electric mobility systems

Cited by 85 publications

References 9 publications

Electric Vehicle Scenario Simulator Tool for Smart Grid Operators

Electric Vehicle Scenario Simulator Tool for Smart Grid Operators

Driving electric vehicles at highway speeds: The effect of higher driving speeds on energy consumption and driving range for electric vehicles in Australia

Modelling Altitude Information in Two-Dimensional Traffic Networks for Electric Mobility Simulation

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