Title: Data supporting 'Simulation of electric vehicle driver behaviour in road transport and electric power networks'

Citation
Marmaras C, Xydas E, Cipcigan LM (2017). Data supporting 'Simulation of electric vehicle driver behaviour in road transport and electric power networks'. Cardiff University. http://doi.org/10.17035/d.2017.0034924002


Access Rights: Data can be made freely available subject to attribution
Access Method: Click to email a request for this data to opendata@cardiff.ac.uk

Dataset Details
Publisher: Cardiff University
Date (year) of data becoming publicly available: 2017
Data format: .xlsx
Estimated total storage size of dataset: Less than 100 megabytes
Number of Files In Dataset: 2
DOI: 10.17035/d.2017.0034924002

Description

Paper abstract:

This paper describes an integrated simulation-based approach, modelling the EV and its interactions in both road transport and electric power systems. The main components of both systems have been considered, and the EV driver behaviour was modelled using a multi-agent simulation platform. Considering a fleet of 1000 EV agents, two behavioural profiles were studied (Unaware/Aware) to model EV driver behaviour. The two behavioural profiles represent the EV driver in different stages of EV adoption starting with Unaware EV drivers when the public acceptance of EVs is limited, and developing to Aware EV drivers as the electrification of road transport is promoted in an overall context. The EV agents were modelled to follow a realistic activity-based trip pattern, and the impact of EV driver behaviour was simulated on a road transport and electricity grid. It was found that the EV agents’ behaviour has direct and indirect impact on both the road transport network and the electricity grid, affecting the traffic of the roads, the stress of the distribution network and the utilization of the charging infrastructure.

2 datasets are provided containing the input and output (result) data of the model presented in this publication.

The file "input_data.xlsx" contains the data from the modelling of the EV agent characteristics, as described in the paper. Data regarding the power consumption, discharge characteristics, battery voltage, battery current, battery soc and charger power are presented in separate tabs.

Power consumption (measured in kW) was calculated using the battery model described in the paper, and is split into 4 categories: P aerodynamics, P drivetrain, P tires and P ancillary.

P aerodynamics are the vehicle power losses due to the outside air friction, P drivetrain are the vehicle power losses due to the operation of the motor, P tires are the vehicle losses due to the vehicle weight and the rolling drag, while P ancillary reflects all the other electrical load of the vehicle (lights etc). Each category is given as a function of vehicle speed (in km/hour).

The Discharge Characteristics are the characteristic discharge curves calculated with the battery model described in the paper. The data include information about the terminal voltage of the battery (in Volts) as a function of the discharge level (in %) for 4 different discharge rates (0.2C, 0.5C, 1C and 2C). 

The battery voltage, battery current, battery soc and charger power show the calculated values of battery voltage (in Volts), battery current (in amperes), battery soc (in %) and charger power (in kW) as a function of time (in hours) for a full charging cycle using a Home and a Public Charger respectively. 

The above data were used as inputs to the multi-agent simulation model described in the paper.

The file "output_data.xlsx" contains the result data from the multi-agent simulation model, as described in the paper. Data regarding the home charging demand, public charging demand and traffic distribution are presented in separate tabs.

The Home charging demand data is the aggregated power demand (in MW) of the simulated network as a function of time (in minutes). Three cases are presented: the base case (without EV agents) and 2 simulation case studies (considering Home Chargers and 2 different types of EV agents).

The Public charging demand data is the aggregated charging energy (in kWh) from Public Chargers in the different nodes of the simulated road network for two different simulation study cases (with different types of EV agents).

The Traffic distribution data contain information about the average hourly density (in vehicles/hour) for every road of the simulated road network in two different simulation study cases (with different types of EV agents). The number of EVs on the road A1_2 are also provided (per minute) for two different simulation study cases (with different types of EV agents).

Research results based upon these data are published at https://doi.org/10.1016/j.trc.2017.05.004


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Last updated on 2019-01-08 at 08:36