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A Bernoulli distribution model for plug-in electric vehicle charging based on time-use data for driving patterns
KTH, School of Electrical Engineering (EES), Electric Power Systems.
2014 (English)In: 2014 IEEE International Electric Vehicle Conference, IEVC 2014, IEEE conference proceedings, 2014Conference paper (Refereed)
Abstract [en]

This paper presents a Bernoulli distribution model for plug-in electric vehicle (PEV) charging based on high resolution activity data for Swedish driving patterns. Based on the activity 'driving vehicle' from a time diary study a Monte Carlo simulation is made of PEV state of charge which is then condensed down to Bernoulli distributions representing charging for each hour during weekday and weekend day. These distributions are then used as a basis for simulations of PEV charging patterns. Results regarding charging patterns for a number of different PEV parameters are shown along with a comparison with results from a different stochastic model for PEV charging. A convergence test for Monte Carlo simulations of the distributions is also provided. In addition to this we show that multiple PEV charging patterns are represented by Binomial distributions via convolution of Bernoulli distributions. Also the distribution for aggregate charging of many PEVs is shown to be normally distributed. Finally a few remarks regarding the applicability of the model are given along with a discussion on potential extensions.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014.
Keyword [en]
Bernoulli Distribution, Convolution, Plug-in Electric Vehicle Charging, Airships, Charging (batteries), Electric vehicles, Intelligent systems, Normal distribution, Research aircraft, Stochastic models, Stochastic systems, Vehicles, Bernoulli distributions, Binomial distribution, Charging patterns, Convergence test, Driving pattern, High resolution, Plug in Electric Vehicle (PEV), Monte Carlo methods
National Category
Transport Systems and Logistics Probability Theory and Statistics Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-174800DOI: 10.1109/IEVC.2014.7056224ScopusID: 2-s2.0-84934300673ISBN: 9781479960750OAI: oai:DiVA.org:kth-174800DiVA: diva2:878277
Conference
2014 IEEE International Electric Vehicle Conference, IEVC 2014, 17 December 2014 through 19 December 2014
Funder
Swedish Energy Agency
Note

QC 20151208

Available from: 2015-12-08 Created: 2015-10-07 Last updated: 2015-12-08Bibliographically approved

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