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http://dx.doi.org/10.7472/jksii.2020.21.5.39

Improved Slow Charge Scheme for non-communication Electric Vehiclesby Predicting Charge Demand  

Chang, Tae Uk (Department of Multimedia, Seoul Women's University)
Ryu, Young Su (Korea Electronics Technology Institute)
Kwon, Ki Won (Korea Electronics Technology Institute)
Paik, Jong Ho (Department of Multimedia, Seoul Women's University)
Publication Information
Journal of Internet Computing and Services / v.21, no.5, 2020 , pp. 39-48 More about this Journal
Abstract
Recently, the study and development of environment-friendly energy technique have increased in worldwide due to environmental pollution and energy resources problems. In vehicle industry, the development of electric vehicle(EV) is now on progress, and also, many other governments support the study and development and make an effort for EV to become widely available. In addition, though they strive to construct the EV infra such as a charge station for EV, the techniques related to managing charge demand and peak power are not enough. The standard of EV communication has been already established as ISO/IEC 15118, however, most of implemented EVs and EV charge stations do not support any communication between each of them. In this paper, an improved slow charge scheme for non-communication EVs is proposed and designed by using predicting charge demand. The proposed scheme consists of distributed charge model and charge demand prediction. The distributed charge model is designed to manage to distribute charge power depending on available charge power and charge demand. The charge demand prediction is designed to be used in the distributed charge model. The proposed scheme is based on the collected data which were from EV slow charge station in business building during the past 1 year. The system-level simulation results show that the waiting time of EV and the charge fee of the proposed scheme are better than those of the conventional scheme.
Keywords
Electric vehicle; charge schedule; distributed charge; demand prediction;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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1 M. Etezadi-Amoli, K. Choma, J. Stefani, "Rapid-Charge Electric-Vehicle Stations," IEEE Trans. on Power Delivery, Vol. 25, No. 3, pp. 1883-1887, 2010. https://doi.org/10.1109/TPWRD.2010.2047874   DOI
2 Green, Erin H., Steven J. Skerlos, and James J. Winebrake, "Increase electric vehicle policy efficiency and effectiveness by reducing mainstream market bias," Energy Policy, Vol. 65, pp. 262-566, 2014. https://doi.org/10.1016/j.enpol.2013.10.024
3 The Edison Electric Institute and the Institute for Electric Innovation, "Plug-in Electric Vehicle Sales Forecast Through 2025 and the Charging Infrastructure Required," 2017. http://www.edisonfoundation.net/iei/publications/Documents/IEI_EEI%20PEV%20Sales%20and%20Infrastructure%20thru%202025_FINAL%20(2).pdf
4 SNE Research, "Global EV Charger Industry, Standardization Trend and Market Forecast (2014-2023)," 2016. http://www.sneresearch.com/_new/eng/sub/sub1/sub1_01_view.php?mode=show&id=932&sub_cat=17
5 Y. W. Son, J. H. Cho, Y. E. Kim, "Distribution Switchboard for Slow Charger of EV able to Distribute as Power Capacity," Trans. of the Korea Society of Automotive Engineers, Vol. 26, No. 2, pp. 187-195, 2018. https://doi.org/10.7467/KSAE.2018.26.2.187   DOI
6 T. W. Chang, Y. S. Ryu, S. K. Song, K. W. Kwon, and J. H. Paik, "Design and Implementation of Distributed Charge Signal Processing Software for Smart Slow and Quick Electric Vehicle Charge," Trans. on Internet and Information Systems, Vol. 13, No. 3, pp. 1674-1687, 2019. https://doi.org/10.3837/tiis.2019.03.032
7 International Organization for Standardization, "ISO 15118." https://www.iso.org/ics/43.120/x/
8 S. G. Hong, and S. R. Jeong, "Development and Comparison of Data Mining-based Prediction Models of Building Fire Probability," Journal of Internet Computing and Services, Vol. 19, No. 6, pp. 101-112, 2018. https://doi.org/10.7472/jksii.2018.19.6.101   DOI
9 X. Zheng, Y. Zhao, H. Bai, and C. Lin, "Fast Algorithm for Intra Prediction of HEVC Using Adaptive Decision Trees," Trans. on Internet and Information Systems, Vol. 10, No. 7, pp. 3286-3300, 2016. https://doi.org/10.3837/tiis.2016.07.023
10 Tso, Geoffrey KF, and Kelvin KW Yau, "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Vol. 32, No. 9, pp. 1761-1768, 2007. https://doi.org/10.1016/j.energy.2006.11.010   DOI