Browse > Article
http://dx.doi.org/10.12815/kits.2018.17.4.150

Deep Learning Based Error Control in Electric Vehicle Charging Systems Using Power Line Communication  

Sun, Young Ghyu (Dept. of Wireless Communications Eng., Univ. of Kwangwoon)
Hwang, Yu Min (Dept. of Wireless Communications Eng., Univ. of Kwangwoon)
Sim, Issac (Dept. of Wireless Communications Eng., Univ. of Kwangwoon)
Kim, Jin Young (Dept. of Wireless Communications Eng., Univ. of Kwangwoon)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.4, 2018 , pp. 150-158 More about this Journal
Abstract
In this paper, we introduce an electric vehicle charging system using power line communication and propose a method to correct the error by applying a deep learning algorithm when an error occurs in the control signal of an electric vehicle charging system using power line communication. The error detection and correction of the control signal can be solved through the conventional error correcting code schemes, but the error is detected and corrected more efficiently by using the deep learning based error correcting code scheme. Therefore, we introduce deep learning based error correction code scheme and apply this scheme to electric vehicle charging system using power line communication. we proceed simulation and confirm performance with bit error rate. we judge whether the deep learning based error correction code scheme is more effective than the conventional schemes.
Keywords
Deep learning; Electric vehicles charging system; Power line communication; Error control; Channel decoding;
Citations & Related Records
연도 인용수 순위
  • Reference
1 IEC(2010), Electric vehicle conductive charging system - Part 1: General requirements, ISO/IEC 61851-1: 2010.
2 IEC(2011), Plugs, socket-outlets, vehicle connectors and vehicle inlets - Conductive charging of electric vehicles - Part 1: General requirements, ISO/IEC 62196-1: 2011.
3 Lin M. B.(2000), "New Path History Management Circuits for Viterbi Decoders," IEEE Trans. Communications, vol. 48, pp.1605-1608.   DOI
4 Prasanna G. N. S., Lakshmi A., Sumanth S., Simha V., Bapat J. and Koomullil G.(2009), "Data Communication over the Smart Grid," in Proc. IEEE International Symposium on Power Line Communications and Its Applications ISPLC'2009, Dresden, Germany, pp.273-279.
5 Toeltsch M. and Molisch A. F.(2001), "Equalization of OFDM-systems by interference cancellation techniques," in IEEE Int. Conf. Commun., vol. 6, pp.1950-1954.
6 Wells and Richard B.(1999), Applied Coding and Information Theory for Engineers, Upper Saddle River, NJ: Printice-Hall.
7 Berber S. M., and Kecman V.(2004), "Convolutional decoders based on artificial neural networks," IEEE Trans. Communications, vol. 2, pp.1551-1556.
8 Ferreira H. C., Grove H. M., Hooijen O. and HanVinck A. J.(1996), "Powerline communications: an overview," 4th IEEEAFRICON, vol. 2, pp.558-563.
9 Galli S., Scaglione A. and Wang Z.(2011), "For the Grid and Through the Grid: The Role of Power Line Communications in the Smart Grid," Proceedings of the IEEE, pp.998-1027.
10 Gomez, Lopez J. M., Montes O., Bota M., Juvells SA., Herms I., Fac A. and Fascia de(1996), "Implementation and design of new model of neural network with application on typographical character recognition," IEEE international conference on image processing.
11 Gruber T., Cammerer S., Hoydis J. and Brink S. T.(2017), "On deep learning-based channel decoding," CoRR. [Online]. Available: https://arxiv.org/abs/1701.07738.
12 Haroon A., Hussain F. and Bajwa M. R.(2013), Decoding of error correcting codes using neural networks, Degree of master of sciences in electrical engineering, School of engineering Blekinge Instutute of Technology, Sweden.