An open tool for creating battery-electric vehicle time series from empirical data, emobpy

Gaete-Morales, Carlos; Kramer, Hendrik; Schill, Wolf-Peter; Zerrahn, Alexander

doi:10.1038/s41597-021-00932-9

Cited by 75 publications

(33 citation statements)

References 39 publications

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“…The data used to compare model outputs were adopted in the following study [49]. This study utilised German automotive statistics from empirical sources to generate drive cycles of EV journeys using a mathematical model.…”

Section: Resultsmentioning

confidence: 99%

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An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Electric Energy Consumption in Urban Space

Olmez¹,

Thompson²,

Marfleet³

et al. 2022

Preprint

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By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

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Section: Resultsmentioning

confidence: 99%

“…This way, we can capture finer detail such as the impact of traffic lights on acceleration/deceleration and momentary traffic congestion. The second drawback of [49] was that trips are split into commuters and non-commuters. Thus, the modelled scenarios revolve around two profiles of drivers.…”

Section: Resultsmentioning

confidence: 99%

An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Electric Energy Consumption in Urban Space

Olmez¹,

Thompson²,

Marfleet³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It follows a similar pattern, consisting of sampling trips throughout the time horizon to specific times of the day and incorporating consistency checks to ensure the vehicles can reach the household within the trip while being able to drive the specified daily trips with the available battery capacity. It has been applied in conjunction with the Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) specifying around 200 profiles, which serve as representative data basis for energy systems modeling [9].…”

Section: State Of Researchmentioning

confidence: 99%

Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

et al. 2021

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As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

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“…Many of the works addressing even the most technical and low-level interfaces and phenomena are based on simulated data, for an obvious reason of availability and readiness [10][11][12][13]. It is recognized that simulated charging patterns and profiles on the one hand may be realistic, representative and quite close to the measured data available, and on the other hand, may allow the application of induced artificial variability, probabilistic behavior and statistical dispersion, which would not otherwise be possible with purely experimental data.…”

Section: Introductionmentioning

confidence: 99%

Harmonic and Supraharmonic Emissions of Plug-In Electric Vehicle Chargers

Mariscotti¹

2022

Smart Cities

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Electric vehicle (EV) charging represents a relevant electric load with a rapid evolution in terms of number, power rating and distortion, in particular, considering the connection to the low-voltage public grid: available short-circuit power may be limited and particularly susceptible loads may co-exist in the same grid portion. Standards can partially address the problem covering only the harmonic interval, but they necessitate significant extension and improvement in the supraharmonic range. In addition, EV chargers have been observed to violate in some scenarios the applicable harmonic limits, so that the mechanisms of emission and distortion should be better understood and evaluated, including phenomena of mutual influence between EV chargers and with pre-existing grid distortion. Although models can help simulate large-scale scenarios in terms of fundamental frequency phenomena, such as power flow, voltage fluctuation and imbalance, substantial and reliable information can come from experimental results, providing measured harmonic and supraharmonic emissions, accompanied by details on loads mix, grid characteristics and EV charger operating conditions. This work thus defines the applicable constraints in terms of limits and compatibility levels for public and light industrial low-voltage grids, discusses the available experimental results and datasets, analyzing the typical distortion behavior and providing indication of sources of information for further studies.

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An open tool for creating battery-electric vehicle time series from empirical data, emobpy

Cited by 75 publications

References 39 publications

An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Electric Energy Consumption in Urban Space

An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Electric Energy Consumption in Urban Space

Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

Harmonic and Supraharmonic Emissions of Plug-In Electric Vehicle Chargers

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