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Integrated information service for plug-in electric vehicle users including smart grid functions

    Bálint Csonka Affiliation
    ; Csaba Csiszár Affiliation

Abstract

Information provision can mitigate the drawbacks of electric vehicle use. In this paper, we elaborate an integrated Information Service (IS) for Plug-in Electric Vehicle (PEV) users that cover each process of use. The system elements and their relations are modelled. We deduce the functions from the negatives of electric vehicles compared to conventional ones. The information management processes are elaborated in detail. We focus on the charging and recharging periods considering dynamic electricity rates in the Smart Grid in order to minimalize the cost of charging from the user’s perspective.  We elaborate a cost saving method and evaluate the effects of dynamic tariff and forethoughtful behaviour. Since our proposed information, service covers each phase of use and reduces charging costs the presented solution simplifies electric vehicle use and improves efficiency. Furthermore, we elaborate the automatic charging scheduling methods that significantly reduce the charging cost and the fluctuation of the electricity demand.

Keyword : plug-in electric vehicle, information service, charging scheduling method, sensitivity analysis, smart grid

How to Cite
Csonka, B., & Csiszár, C. (2019). Integrated information service for plug-in electric vehicle users including smart grid functions. Transport, 34(1), 135-145. https://doi.org/10.3846/transport.2019.8548
Published in Issue
Feb 21, 2019
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Caparello, N. D.; Kurani, K. S. 2012. Households’ stories of their encounters with a plug-in hybrid electric vehicle, Environment and Behavior 44(4): 493–508. https://doi.org/10.1177/0013916511402057

Cowan, K. R. 2013. A New roadmapping technique for creatively managing the emerging smart grid, Creativity and Innovation Management 22(1): 67–83. https://doi.org/10.1111/caim.12017

Csiszár, C.; Földes, D. 2015. Analysis and modelling methods of urban integrated information system of transportation, in 2015 Smart Cities Symposium Prague (SCSP), 24–25 June 2015, Prague, Czech Republic, 1–10. https://doi.org/10.1109/SCSP.2015.7181574

Dagsvik, J. K.; Wennemo, T.; Wetterwald, D. G.; Aaberge, R. 2002. Potential demand for alternative fuel vehicles, Transportation Research Part B: Methodological 36(4): 361–384. https://doi.org/10.1016/S0965-8564(01)00013-1

ESO. 2017. Electricity System Operator (ESO) in Great Britain. Available from Internet: https://www.nationalgrid.com

Esztergár-Kiss, D; Csiszár, C. 2016. Multicriteria analysis of Hungarian journey planners, Periodica Polytechnica Transportation Engineering 44(2): 97–104. https://doi.org/10.3311/PPtr.8570

Guille, C; Gross, G. 2009. A conceptual framework for the vehicle-to-grid (V2G) implementation, Energy Policy 37(11): 4379–4390. https://doi.org/10.1016/j.enpol.2009.05.053

Hernández, L.; Baladrón, C.; Aguiar, J. M.; Calavia, L.; Carro, B.; Sánchez-Esguevillas, A.; Cook, D. J.; Chinarro, D.; Gómez, J. 2012. A study of the relationship between weather variables and electric power demand inside a smart grid/smart world framework, Sensors 12(9): 11571–11591. https://doi.org/10.3390/s120911571

Herrala, M. 2007. The Value of Transport Information. VTT Technical Research Centre, Finland. 98 p. Available from Internet: https://www.vtt.fi/inf/pdf/tiedotteet/2007/T2394.pdf

Hidrue, M. K.; Parsons, G. R.; Kempton, W.; Gardner, M. P. 2011. Willingness to pay for electric vehicles and their attributes, Resource and Energy Economics 33(3): 686–705. https://doi.org/10.1016/j.reseneeco.2011.02.002

Hu, J.; Morais, H.; Sousa, T.; Lind, M. 2016. Electric vehicle fleet management in smart grids: a review of services, optimization and control aspects, Renewable and Sustainable Energy Reviews 56: 1207–1226. https://doi.org/10.1016/j.rser.2015.12.014

Khoo, H. L.; Asitha K. S. 2016. User requirements and route choice response to smart phone traffic applications (apps), Travel Behaviour and Society 3: 59–70. https://doi.org/10.1016/j.tbs.2015.08.004

Kiviluoma, J.; Meibom, P. 2011. Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a system with either smart or dumb electric vehicles, Energy 36(3): 1758–1767. https://doi.org/10.1016/j.energy.2010.12.053

Krupa, J. S.; Rizzo, D. M.; Eppstein, M. J.; Lanute, D. B.; Gaalema, D. E.; Lakkaraju, K.; Warrender, C. E. 2014. Analysis of a consumer survey on plug-in hybrid electric vehicles, Transportation Research Part A: Policy and Practice 64: 14–31. https://doi.org/10.1016/j.tra.2014.02.019

Lawrence, D. B. 1999. The Economic Value of Information. Springer. 393 p. https://doi.org/10.1007/978-1-4612-1460-1

Mal, S.; Chattopadhyay, A.; Yang, A.; Gadh, R. 2013. Electric vehicle smart charging and vehicle-to-grid operation, International Journal of Parallel, Emergent and Distributed Systems 28(3): 249–265. https://doi.org/10.1080/17445760.2012.663757

Philipsen, R.; Schmidt, T.; Ziefle, M. 2015. A charging place to be – users’ evaluation criteria for the positioning of fastcharging infrastructure for electro mobility, Procedia Manufacturing 3: 2792–2799. https://doi.org/10.1016/j.promfg.2015.07.742

Rietveld, P. 2011. The economics of information in transport, in A. de Palma, R. Lindsey, E. Quinet, R. Vickerman (Eds.). A Handbook of Transport Economics, 586–603. https://doi.org/10.4337/9780857930873.00033

Shao, S.; Pipattanasomporn, M.; Rahman, S. 2009. Challenges of PHEV penetration to the residential distribution network, in 2009 IEEE Power & Energy Society General Meeting, 26–30 July 2009, Calgary, Canada, 1–8. https://doi.org/10.1109/PES.2009.5275806

Sutherland, E. 2013. Forrester: The iPhone is Still Most-Used Smartphone. Available from internet: https://www.idownloadblog.com/2013/08/01/iphone-most-used-smartphone

ThinkMobile. 2011. The Mobile Movement: Understanding Smartphone Users. 39 p. Available from internet: https://ssl.gstatic.com/think/docs/the-mobile-movement_research-studies.pdf

Wang, T.-G.; Xie, C.; Xie, J.; Waller, T. 2016. Path-constrained traffic assignment: A trip chain analysis under range anxiety, Transportation Research Part C: Emerging Technologies 68: 447–461. https://doi.org/10.1016/j.trc.2016.05.003

Wydro, K. B. 2010. A measurement of the information value in transport processes, Communications in Computer and Information Science 104: 210–217. https://doi.org/10.1007/978-3-642-16472-9_23

Yang, Y.; Yao, E.; Yang, Z.; Zhang, R. 2016. Modeling the charging and route choice behavior of BEV drivers, Transportation Research Part C: Emerging Technologies 65: 190–204. https://doi.org/10.1016/j.trc.2015.09.008

Zheng, C.; Xu, G.; Xu, K.; Pan, Z.; Liang, Q. 2015. An energy management approach of hybrid vehicles using traffic preview information for energy saving, Energy Conversion and Management 105: 462–470. https://doi.org/10.1016/j.enconman.2015.07.061