Skip to main content

Advertisement

SpringerLink
Log in

Electric Vehicle Charging Scheduling Problem: Heuristics and Metaheuristic Approaches

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Adopting electric vehicles is essential to reducing greenhouse gas emissions and achieving climate goals. However, the existing electrical grid can easily be overloaded with more electric vehicles on the road. For this reason, many researchers are working on developing relevant charging infrastructures that include charging scheduling strategies. These strategies aim to control charging power efficiently and economically. This paper addresses the electric vehicle charging scheduling problem at a public charging station with a limited number of chargers and a limited overall power capacity. Before arriving at the charging station, electric vehicle drivers submit their charging demands. Then, the scheduler reserves a suitable charger for each vehicle and allocates the right amount of power so that the final state-of-charge at the departure time is as close as possible to the requested one. We present two variants of the problem, a constant output power model and a variable power model. We show that these charging scheduling problems are strongly NP-hard, and we propose heuristics and simulated annealing combined with linear programming to solve them. Simulation results show the efficiency of the proposed approaches for maximizing the state-of-charge of 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

Similar content being viewed by others

Data availability

The datasets generated and used during the current study are available on https://github.com/imyzz/EVCSP_RA_instances.

Notes

  1. https://www.climate-transparency.org/countries/europe/france.

References

  1. Chandran LS, Ibarra L, Ruskey F, Sawada J. Generating and characterizing the perfect elimination orderings of a chordal graph. Theoret Comput Sci. 2003;307(2):303–17.

    Article  MathSciNet  MATH  Google Scholar 

  2. Connolly DT. An improved annealing scheme for the qap. Eur J Oper Res. 1990;46(1):93–100.

    Article  MathSciNet  MATH  Google Scholar 

  3. Das HS, Rahman MM, Li S, Tan C. Electric vehicles standards, charging infrastructure, and impact on grid integration: a technological review. Renew Sustain Energy Rev. 2020;120: 109618.

    Article  Google Scholar 

  4. EVDB: Ev database. 2020. https://ev-database.org. Accessed 8 Jan 2020.

  5. Franco JF, Rider MJ, Romero R. An MILP model for the plug-in electric vehicle charging coordination problem in electrical distribution systems. In: 2014 IEEE PES General Meeting \(|\) Conference & Exposition, 2014; pp. 1–5. IEEE, National Harbor, MD, USA.

  6. García-Álvarez J, González MA, Vela CR. Metaheuristics for solving a real-world electric vehicle charging scheduling problem. Appl Soft Comput. 2018;65:292–306.

    Article  Google Scholar 

  7. Garey MR, Johnson DS. Computers and intractability, vol. 174. San Francisco: Freeman; 1979.

    MATH  Google Scholar 

  8. Gilmore PC, Hoffman AJ. A characterization of comparability graphs and of interval graphs. Can J Math. 1964;16:539–48.

    Article  MathSciNet  MATH  Google Scholar 

  9. Hardman S, Jenn A, Tal G, Axsen J, Beard G, Daina N, Figenbaum E, Jakobsson N, Jochem P, Kinnear N, et al. A review of consumer preferences of and interactions with electric vehicle charging infrastructure. Transp Res Part D Transp Environ. 2018;62:508–23.

    Article  Google Scholar 

  10. IEA. Global EV Outlook 2021: Accelerating ambitions despite the pandemic. International Energy Agency (IEA). 2021. https://www.iea.org/reports/global-ev-outlook-2021. Accessed 8 Jul 2021.

  11. Jin C, Tang J, Ghosh P. Optimizing electric vehicle charging: a customer’s perspective. IEEE Trans Veh Technol. 2013;62(7):2919–27.

    Article  Google Scholar 

  12. Kang Q, Wang J, Zhou M, Ammari AC. Centralized charging strategy and scheduling algorithm for electric vehicles under a battery swapping scenario. IEEE Trans Intell Transp Syst. 2016;17(3):659–69.

    Article  Google Scholar 

  13. Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science. 1983;220(4598):671–80.

    Article  MathSciNet  MATH  Google Scholar 

  14. Liu J, Lin G, Huang S, Zhou Y, Li Y, Rehtanz C. Optimal ev charging scheduling by considering the limited number of chargers. IEEE Trans Transport Electrif. 2020;7(3):1112–22.

    Article  Google Scholar 

  15. López-Ibáñez M, Dubois-Lacoste J, Cáceres LP, Birattari M, Stützle T. The irace package: iterated racing for automatic algorithm configuration. Oper Res Perspect. 2016;3:43–58.

    MathSciNet  Google Scholar 

  16. Lundy M, Mees A. Convergence of an annealing algorithm. Math Program. 1986;34(1):111–24.

    Article  MathSciNet  MATH  Google Scholar 

  17. Luo L, Gu W, Zhou S, Huang H, Gao S, Han J, Wu Z, Dou X. Optimal planning of electric vehicle charging stations comprising multi-types of charging facilities. Appl Energy. 2018;226:1087–99.

    Article  Google Scholar 

  18. Majhi RC, Ranjitkar P, Sheng M, Covic GA, Wilson DJ. A systematic review of charging infrastructure location problem for electric vehicles. Transp Rev. 2020;41(4):432–455.

  19. Mann HB, Whitney DR. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 1947; p. 50–60.

  20. Niu L, Zhang P, Wang X. Hierarchical power control strategy on small-scale electric vehicle fast charging station. J Clean Prod. 2018;199:1043–9.

    Article  Google Scholar 

  21. Nykvist B, Sprei F, Nilsson M. Assessing the progress toward lower priced long range battery electric vehicles. Energy Policy. 2019;124:144–55.

    Article  Google Scholar 

  22. Pflaum P, Alamir M, Lamoudi MY. Probabilistic energy management strategy for EV charging stations using randomized algorithms. IEEE Trans Control Syst Technol. 2018;26(3):1099–106.

    Article  Google Scholar 

  23. Rahman I, Vasant PM, Singh BSM, Abdullah-Al-Wadud M. On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles. Alex Eng J. 2016;55(1):419–26.

    Article  MATH  Google Scholar 

  24. Rose DJ, Tarjan RE, Lueker GS. Algorithmic aspects of vertex elimination on graphs. SIAM J Comput. 1976;5(2):266–83.

    Article  MathSciNet  MATH  Google Scholar 

  25. Std I. 61851-1: 2017 “electric vehicle conductive charging system-part 1: General requirements. In: The International Electrotechnical Commission, Geneva, Switzerland. Feb, 2017;7:292.

  26. Tang W, Zhang YJA. A model predictive control approach for low-complexity electric vehicle charging scheduling: optimality and scalability. IEEE Trans Power Syst. 2016;32(2):1050–63.

    Article  Google Scholar 

  27. Wu H, Pang GKH, Choy KL, Lam HY. Dynamic resource allocation for parking lot electric vehicle recharging using heuristic fuzzy particle swarm optimization algorithm. Appl Soft Comput. 2018;71:538–52.

    Article  Google Scholar 

  28. Wu W, Lin Y, Liu R, Li Y, Zhang Y, Ma C. Online EV charge scheduling based on time-of-use pricing and peak load minimization: Properties and efficient algorithms. IEEE Trans Intell Transport Syst. 2020.

  29. Yang S. Price-responsive early charging control based on data mining for electric vehicle online scheduling. Electric Power Syst Res. 2019;167:113–21.

    Article  Google Scholar 

  30. Yao L, Lim WH, Tsai TS. A real-time charging scheme for demand response in electric vehicle parking station. IEEE Trans Smart Grid. 2016;8(1):52–62.

    Article  Google Scholar 

  31. Zhang L, Li Y. Optimal management for parking-lot electric vehicle charging by two-stage approximate dynamic programming. IEEE Trans Smart Grid. 2015;8(4):1722–30.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université de Haute-Alsace, IRIMAS UR 7499, 68100, Mulhouse, France

    Imene Zaidi, Lhassane Idoumghar & Michel Basset

  2. LORIA Laboratory UMR7503, Université de Lorraine, 54506, Vandoeuvre-lès-Nancy, France

    Ammar Oulamara

Authors
  1. Imene Zaidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ammar Oulamara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lhassane Idoumghar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michel Basset
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Imene Zaidi.

Ethics declarations

Conflict of interest

The 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.

This article is part of the topical collection “Bio-inspired Algorithms for Combinatorial Optimization” guest edited by Aniko Ekart, Christine Zarges and Sébastien Verel.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zaidi, I., Oulamara, A., Idoumghar, L. et al. Electric Vehicle Charging Scheduling Problem: Heuristics and Metaheuristic Approaches. SN COMPUT. SCI. 4, 283 (2023). https://doi.org/10.1007/s42979-023-01708-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-01708-1

Keywords

Search

Navigation