Skip to main content

Advertisement

SpringerLink
Log in

Fuzzy Logic Controllers for Charging/Discharging Management of Battery Electric Vehicles in a Smart Grid

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

This paper presents the energy management tool of a power system operating in a smart grid that contains electric vehicles. The intention of this work is to make a comparison between a metaheuristic optimization technique and two fuzzy logic controllers, and with that highlight the advantages of using fuzzy logic and validate it to the detriment of other metaheuristic techniques. The optimization technique used was simulated annealing, in order to minimize the total energy cost of the system being studied. Tree charging strategies were adopted: peak charging, off-peak charging, and smart charging besides demand-side management techniques. In addition to the charging process will also be studied the battery electric vehicles discharging, preferably at the peak of the load curve, through the creation of a charging/discharging station. In this work, the system used is the IEEE-39 bus New England power system. Two fuzzy logic controllers have been developed, namely the charging station controller and the vehicle-to-grid controller. Together they decide the proper energy flow between the EVs and the grid. Energy discharge to the grid from EVs or energy required for charging EVs is controlled and tested for the real-time scenario. The results proved the effectiveness of the proposed method in studies of planning and expansion of electric energy systems that contain electric vehicles.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  • Al-OgailiI, A. S., Hashim, T. J. T., Rahmat, N. A., Ramasamy, A. K., Marsadek, M. B., Faisal, M., & Hannan, M. A. (2019). Review on Scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations. IEEE Acess, 7, 128353–128371.

    Article  Google Scholar 

  • Aghamohammadi, M., Parto, M., Ghods, A., & Ghazizadeh, S. (2012). Effects of V2G aggregators on the economic and loading performance of restructured power systems. In Cigré, 2012.

  • Braga, J. (2010). Integração de Veículos Elétricos no Sistema Elétrico Nacional. Dissertação. Departamento de Engenharia Eletrotécnico, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa. Monte da Caparica, Portugal.

  • Dharmakeerthi, C. H., Mithulananthan, N., & Saha, T. K. (2014). Impact of electric vehicle fast charging on power system voltage stability. International Journal of Electrical Power and Energy Systems, 57, 241–249.

    Article  Google Scholar 

  • Energy Information and Administration Web Site. (2010). [Online]. Available in: https://www.eia.gov/electricity/monthly/epm_table_grapher.cfm?t=epmt_5_6_a.

  • Faia, R., Soares, J., Vale, Z., & Corchado, J. M. (2021). An optimization model for energy community costs minimization considering a local electricity market between prosumers and electric vehicles. Electronics, 10, 129.

    Article  Google Scholar 

  • Gao, Y., Guo, S., Ren, J., Zhao, Z., Ehsan, A., & Zheng, Y. (2018). An electric bus power consumption model and optimization of charging scheduling concerning multi-external factors. Energies, 11(8), 2060.

    Article  Google Scholar 

  • Garcia-Valle, R., & Vlachogiannis, J. (2009). Letter to the Editor: Electric vehicle demand model for load flow studies. Electric Power Components and Systems, 37, 577–582.

    Article  Google Scholar 

  • Hadley, S., & Tsvetkova, A. (2008). Potential impacts of plug-in hybrid electric vehicles on regional power generation. Oak Ridge National Laboratory, Oak Ridge, TN, ORNL/TM-2007/150.

  • Kipkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.

    Article  MathSciNet  Google Scholar 

  • Kongjeen, Y., & Bhumkittipich, K. (2016). Modeling of electric vehicle loads for power flow analysis based on PSAT. In 2016 13th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON) (pp. 1–6). IEEE.

  • Lopez, K. L., Gagne, C., & Gardner, M. A. (2019). Demand-side management using deep learning for smart charging of electric vehicles. IEEE Transactions on Smart Grid, 10(3), 2683–2691.

    Article  Google Scholar 

  • Mithulananthan, N., & Saha, T. (2012). Impact of electric vehicle load on power system oscillatory stability. In Australasian Universities Power Engineering Conference, Hobart, TAS, Australia.

  • Milano, F. (2005). An open source power system analysis toolbox. IEEE Transactions on Power Systems, 20(3), 1199–1206.

    Article  Google Scholar 

  • Nissan Leaf Web Site. (2018). [Online]. Disponfvelem: https://www.nissanusa.com/ev/media/pdf/specs/FeaturesAndSpecs.pdf.

  • Pai, M. (1989). Energy function analysis for power system stability. London: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Pang, C., Dutta, P., & Kezunovic, M. (2011). BEVs/PHEVs as dispersed energy storage for V2B uses in the smart grid. IEEE Transactions on Smart Grid, 3(1), 473–482.

    Article  Google Scholar 

  • SAE. (2010). Recommended practice for electric vehicle and plug in hybrid electric vehicle conductive charger coupler. SAE Standard J1772.

  • Sousa, T., Morais, H., & Vale, Z. (2012). Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach. IEEE Transactions on Smart Grid, 3, 535–542.

    Article  Google Scholar 

  • Sousa, T., Morais, H., & Vale, Z. (2016). Simulated annealing to handle energy and ancillary services joint management considering electric vehicles. Electric Power Systems Research, 136, 383–397.

    Article  Google Scholar 

  • Siebert, L., Aoki, A., Yamakawa, E., & Toledo, F. (2012). Gerenciamento pelo Lado da Demanda em Redes Inteligentes Utilizando Algoritmos Genéticos, IV SBSE–Simpósio Brasileiro de Sistemas Elétricos, 2012.

  • Singh, M., Kumar, P., & Kar, I. (2012). Implementation of vehicle to grid infrastructure using fuzzy logic controller. IEEE Transactions on Smart Grid, 3(1), 565–577.

    Article  Google Scholar 

  • Sullivan, J., Salmeen, I., & Simon, C. (2009). PHEV marketplace penetration: An agent based simulation. University of Michigan, Ann Arbor, Transportation Research Institute. UMTRI-2009-32.

  • Xiong, Y., Wang, B., Chu, C.-C., & Gadh, R. (2018). Vehicle grid integration for demand response with mixture user model and decentralized optimization. Applied Energy, 231, 481–493.

    Article  Google Scholar 

  • Wang, L. (1997). A course in fuzzy systems and control. Prentice-Hall.

    MATH  Google Scholar 

  • Wu, T., Yang, Q., Bao, Z., & Yan, W. (2013). Coordinated energy dispatching in microgrid with wind power generation and plug-in electric vehicles. IEEE Transactions on Smart Grid, 4(3), 1453–1463.

    Article  Google Scholar 

Download references

Acknowledgements

This present work is funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES). Cód. de Financiamento 001.

Author information

Authors and Affiliations

  1. Institute of Technology, Federal University of Para, Belém, PA, Brazil

    Marcel Augusto Alvarenga Viegas & Carlos Tavares da Costa Jr.

Authors
  1. Marcel Augusto Alvarenga Viegas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos Tavares da Costa Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.A.A.V. contributed to Conceptualization, methodology, formal analysis and investigation, writing—original draft preparation. C.T.dC.Jr. was involved in writing—review and editing, acquisition, resources, supervision.

Corresponding author

Correspondence to Marcel Augusto Alvarenga Viegas.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Viegas, M.A.A., da Costa, C.T. Fuzzy Logic Controllers for Charging/Discharging Management of Battery Electric Vehicles in a Smart Grid. J Control Autom Electr Syst 32, 1214–1227 (2021). https://doi.org/10.1007/s40313-021-00741-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-021-00741-w

Keywords

Search

Navigation