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Analyzing Electric Vehicle Charging Behaviour Using Advanced Clustering Tools

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Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference (DCAI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 741))

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Abstract

Electric vehicle (EV) owners often have electric vehicle charging stations (EVCS) in their homes and leave the EV connected when it remains in the garage. This procedure does not guarantee that EV charging is adequate, and monitoring this period is necessary to identify anomalies. A study was developed where the EV charging curves was analyzed by clustering with K-means. The advantage presented in this work is that the necessary data for the charging analysis come from the EVCS power supply circuit. This proposal allows monitoring and rapidly classifying the data according to the form of the EV charging curves, with no need for the supervision of the input data. The unsupervised machine learning method can classify any situation by collecting data on the electrical power supplied to the EVCS. In this study, the classification was applied with learning performed in the history, identifying the clusters representing abnormal situations to identify anomalies in future charging curves. The results of the proposed approach presented around 27.8% as an anomaly in the EV charging, allowing to identify the moment in which it occurs and to make the necessary adjustments to avoid them.

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References

  1. Rashidi, A., Namazi, M.M., Karshenas, H.R., et al.: An optimized simple current sharing function of SRM with intrgrated battery charger for EV drive. In: 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), pp 1–5. IEEE (2016)

    Google Scholar 

  2. German, R., Delarue, P., Bouscayrol, A.: Battery pack self-heating during the charging process. In: Proceedings IEEE International Conference Industrial Technology 2018-February, pp. 2049–2054 (2018). https://doi.org/10.1109/ICIT.2018.8352504

  3. Vu, V.B., Tran, D.H., Choi, W.: Implementation of the constant current and constant voltage charge of inductive power transfer systems with the double-sided lcc compensation topology for electric vehicle battery charge applications. IEEE Trans. Power Electron 33, 7398–7410 (2018). https://doi.org/10.1109/TPEL.2017.2766605

    Article  Google Scholar 

  4. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31, 651–666 (2010). https://doi.org/10.1016/j.patrec.2009.09.011

    Article  Google Scholar 

  5. Shateri, N., Shi, Z., Auger, D.J., Fotouhi, A.: Lithium-sulfur cell state of charge estimation using a classification technique. IEEE Trans. Veh. Technol. 70, 212–224 (2020)

    Article  Google Scholar 

  6. Beltran, H., Sansano, E., Pecht, M.: Machine learning techniques suitability to estimate the retained capacity in lithium-ion batteries from partial charge/discharge curves. J. Energy Storage 59, 106346 (2023). https://doi.org/10.1016/j.est.2022.106346

    Article  Google Scholar 

  7. Chen, K., Zheng, F., Jiang, J., et al.: Practical failure recognition model of lithium-ion batteries based on partial charging process. Energy 138, 1199–1208 (2017)

    Article  Google Scholar 

  8. Sanz-Gorrachategui, I., Pastor-Flores, P., Guillén-Asensio, A., et al.: Unsupervised clustering of battery waveforms in off-grid PV installations. In: 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), pp. 1–6 (2020)

    Google Scholar 

  9. Balasubramanian, S., Balachandra, P.: Characterising electricity demand through load curve clustering: a case of Karnataka electricity system in India. Comput. Chem. Eng. 150, 107316 (2021)

    Article  Google Scholar 

  10. Pedregosa, F., Michel, V., Grisel, O., et al.: Scikit-learn: Machine learning in python. Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    Google Scholar 

  11. Certification L (2020) Pecan Street Dataport. In: Pecan Str. Inc. https://www.pecanstreet.org/dataport/. Accessed 15 Nov 2021

  12. Kong, Q., Siauw, T., Bayen, A.M.: Python programming and numerical methods : a guide for engineers and scientists, 1st Editio. © Academic Press 2020 (2020)

    Google Scholar 

  13. Davies, E.R.: Computer and Machine Vision, 4th edn. Academic Press (2012)

    Google Scholar 

  14. Lee, G., et al.: PyWavelets - Wavelet Transforms in Python — PyWavelets Documentation. https://pywavelets.readthedocs.io/en/latest/index.html. (2006). Accessed 17 Nov 2021

  15. Chan, K.P., Fu, A.W.-C.: Efficient time series matching by wavelets. In: Proceedings International Conference Data Engineering, pp. 126–133 (1999). https://doi.org/10.1109/icde.1999.754915

  16. Nobre, J., Neves, R.F.: Combining principal component analysis, discrete wavelet transform and XGBoost to trade in the financial markets. Expert. Syst. Appl. 125, 181–194 (2019). https://doi.org/10.1016/j.eswa.2019.01.083

    Article  Google Scholar 

  17. Steinarsson, S.: Downsampling Time Series for Visual Representation. University of Iceland (2013)

    Google Scholar 

  18. Xu, J., Qiu, Y., Zhang, H., et al.: Large-scale time series data down-sampling based on map-reduce programming mode. In: Proceedings 2017 IEEE 2nd Advanced Information Technology Electronic Automation Control Conference IAEAC 2017, pp. 409–413 (2017). https://doi.org/10.1109/IAEAC.2017.8054047

  19. Burstein, M.: Introduction to graph theory: finding the shortest path - max Burstein’s Blog. In: Max Burstein. https://www.maxburstein.com/blog/introduction-to-graph-theory-finding-shortest-path/. (2013). Accessed 22 Nov 2021

  20. Raschka, S.: Python machine learning: unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics (2015)

    Google Scholar 

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Acknowledgment

This work has received funding from National Funds through FCT -- Portugal and CAPES -- Brazil, under the project 2019.00141.CBM Desenvolvimento de Técnicas de Inteligência Artificial para a Otimização de Sistemas de Distribuição de Energia Elétrica. It was also supported in part by the Coordination for the Improvement of Higher Education Personnel (CAPES) -- Finance Code 001, and by the São Paulo Research Foundation (FAPESP), under grants 2015/21972-6 and 2018/20355-1.

The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team and also acknowledge the support provided by the Thematic Network 723RT0150 “Red para la integración a gran escala de energías renovables en sistemas eléctricos (RIBIERSE-CYTED)” financed by the call for Thematic Networks of the CYTED (Ibero-American Program of Science and Technology for Development) for 2022. João Soares is also supported by National funds from FCT CEECIND/00420/2022.

Author information

Authors and Affiliations

  1. DAE - Academic Department of Electrotechnics, IFSC - Federal Institute of Santa Catarina, Florianopolis, Brazil

    Carlos Ernani da Veiga & Piara Fernandes

  2. GECAD - Research Group On Intelligent Engineering and Computing for Advanced Innovation and Development, LASI - Intelligent Systems Associate Laboratory, Polytechnic of Port, Porto, Portugal

    Carlos Ramos & João Soares

  3. BISITE Research Group, University of Salamanca, Edificio I+D+I, 37008, Salamanca, Spain

    Juan Manuel Corchado

Authors
  1. Carlos Ernani da Veiga
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  2. Carlos Ramos
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  3. Juan Manuel Corchado
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  4. Piara Fernandes
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  5. João Soares
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Corresponding author

Correspondence to Carlos Ernani da Veiga .

Editor information

Editors and Affiliations

  1. High Performance Computing Center, King Abdulaziz University, Jeddah, Saudi Arabia

    Rashid Mehmood

  2. University of Minho, Braga, Portugal

    Victor Alves

  3. ISEP/GECAD, Instituto Superior de Engenharia do Port, Porto, Portugal

    Isabel Praça

  4. Kielce University of Technology, Kielce, Poland

    Jarosław Wikarek

  5. BISITE, University of Salamanca, Salamanca, Spain

    Javier Parra-Domínguez

  6. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Roussanka Loukanova

  7. Universidad de Valladolid, Valladolid, Spain

    Ignacio de Miguel

  8. INESC-TEC and GECAD/ISEP, Universidade de Trás-os-Montes e Alto Douro, Vila Real, Portugal

    Tiago Pinto

  9. Universidade de Trás-os-Montes e Alto Douro, Vila Real, Portugal

    Ricardo Nunes

  10. University of Calabria, Arcavacata, Rende CS, Cosenza, Italy

    Michela Ricca

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da Veiga, C.E., Ramos, C., Corchado, J.M., Fernandes, P., Soares, J. (2023). Analyzing Electric Vehicle Charging Behaviour Using Advanced Clustering Tools. In: Mehmood, R., et al. Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 741. Springer, Cham. https://doi.org/10.1007/978-3-031-38318-2_24

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