Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle

  • Pandian VasantEmail author
  • Jose Antonio Marmolejo
  • Igor Litvinchev
  • Roman Rodriguez Aguilar


Currently, there is a remarkable focus on green technologies for taking steps towards more use of renewable energy sources within the sector of transportation and also decreasing pollution. At this point, employment of plug-in hybrid electric vehicles (PHEVs) needs sufficient charging allocation strategy, by running smart charging infrastructures and smart grid systems. In order to daily usage of PHEVs, daytime charging stations are required and at this point, only an appropriate charging control and a management of the infrastructure can lead to wider employment of PHEVs. In this study, four swarm intelligence based optimization techniques: particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization, and hybrid version of PSO and GSA (PSOGSA) have been applied for the state-of-charge optimization of PHEVs. In this research, hybrid PSOGSA has performed very well in producing better results than other stand-alone optimization techniques.


Nature-inspire metaheuristics Hybrid optimization Swarm intelligence Artificial intelligence State-of-charge optimization Plug-in hybrid electric vehicle 



The authors would like to sincerely thank Mr. Imran Rahman, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Malaysia for his great help and support in this research work. This research project also supported by Modeling Evolutionary Algorithms Simulation and Artificial Intelligence (MERLIN), Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam and Faculty of Science and Information Technology, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


  1. 1.
    Lund, H., & Kempton, W. (2008). Integration of renewable energy into the transport and electricity sectors through V2G. Energy Policy, 36, 3578–3587.CrossRefGoogle Scholar
  2. 2.
    Hota, A. R., Juvvanapudi, M., & Bajpai, P. (2014). Issues and solution approaches in PHEV integration to the smart grid. Renewable and Sustainable Energy Reviews, 30, 217–229.CrossRefGoogle Scholar
  3. 3.
    Soares, J., Sousa, T., Morais, H., Vale, Z., Canizes, B., & Silva, A. (2013). Application specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid. Applied Soft Computing, 13, 4264–4280.CrossRefGoogle Scholar
  4. 4.
    Su, W., & Chow, M.-Y. (2012). Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck. Applied Energy, 96, 171–182.CrossRefGoogle Scholar
  5. 5.
    Su, W., & Chow, M.-Y. (2011). Performance evaluation of a PHEV parking station using particle swarm optimization. In Power and energy society general meeting, 2011 IEEE (pp. 1–6).Google Scholar
  6. 6.
    Su, W., Eichi, H., Zeng, W., & Chow, M.-Y. (2012). A survey on the electrification of transportation in a smart grid environment. IEEE Transactions on Industrial Informatics, 8, 1–10.CrossRefGoogle Scholar
  7. 7.
    Morrow, K., Karner, D., & Francfort, J. (2008). Plug-in hybrid electric vehicle charging infrastructure review. US Department of Energy-Vehicle Technologies Program.Google Scholar
  8. 8.
    Mayfield, D. (2012). Site design for electric vehicle charging stations, ver. 1.0. Sustainable transportation strategies.Google Scholar
  9. 9.
    Boyle, G. (2007). Renewable electricity and the grid: The challenge of variability. Earthscan: Routledge.Google Scholar
  10. 10.
    Hess, A., Malandrino, F., Reinhardt, M. B., Casetti, C., Hummel, K. A., & Barceló-Ordinas, J. M. (2012). Optimal deployment of charging stations for electric vehicular networks. In Proceedings of the first workshop on urban networking (pp. 1–6).Google Scholar
  11. 11.
    Li, Z., Sahinoglu, Z., Tao, Z., & Teo, K. H. (2010). Electric vehicles network with nomadic portable charging stations. In 2010 IEEE 72nd vehicular technology conference fall (VTC 2010-fall) (pp. 1–5).Google Scholar
  12. 12.
    Dorigo, M. (2006). Ant colony optimization and swarm intelligence. In Proceedings of the 5th international workshop, ANTS 2006, Brussels, Belgium, September 4–7, 2006 (Vol. 4150). Springer.Google Scholar
  13. 13.
    Eberhart, R. C., & Yuhui, S. (2001). Particle swarm optimization: developments, applications and resources. In Proceedings of the 2001 congress on evolutionary computation, 2001 (Vol. 1, pp. 81–86).Google Scholar
  14. 14.
    Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39, 459–471.MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179, 2232–2248.CrossRefzbMATHGoogle Scholar
  16. 16.
    Martens, D., Baesens, B., & Fawcett, T. (2011). Editorial survey: Swarm intelligence for data mining. Machine Learning, 82, 1–42.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Chiasson, J., & Vairamohan, B. (2005). Estimating the state of charge of a battery. IEEE Transactions on Control Systems Technology, 13, 465–470.CrossRefGoogle Scholar
  18. 18.
    Piller, S., Perrin, M., & Jossen, A. (2001). Methods for state-of-charge determination and their applications. Journal of Power Sources, 96, 113–120.CrossRefGoogle Scholar
  19. 19.
    Yang, J., He, L., & Fu, S. (2014). An improved PSO-based charging strategy of electric vehicles in electrical distribution grid. Applied Energy, 128, 82–92.CrossRefGoogle Scholar
  20. 20.
    Ul-Haq, A., Buccella, C., Cecati, C., & Khalid, H. A. (2013). Smart charging infrastructure for electric vehicles. In 2013 international conference on clean electrical power (ICCEP) (pp. 163–169).Google Scholar
  21. 21.
    Su, W., & Chow, M.-Y. (2011). Sensitivity analysis on battery modeling to large-scale PHEV/PEV charging algorithms. In IECON 2011-37th annual conference on IEEE industrial electronics society (pp. 3248–3253).Google Scholar
  22. 22.
    Wu, X., Cao, B., Wen, J., & Bian, Y. (2008). Particle swarm optimization for plug-in hybrid electric vehicle control strategy parameter. In Vehicle power and propulsion conference, 2008. VPPC’08. IEEE (pp. 1–5).Google Scholar
  23. 23.
    Soares, J., Morais, H., & Vale, Z. (2012). Particle swarm optimization based approaches to vehicle-to-grid scheduling. In Power and energy society general meeting, 2012 IEEE (pp. 1–8).Google Scholar
  24. 24.
    Yang, X.-S., Deb, S., & Fong, S. (2011). Accelerated particle swarm optimization and support vector machine for business optimization and applications. In Networked digital technologies (pp. 53–66). Springer.Google Scholar
  25. 25.
    Fergany, A. E. (2013). Accelerated particle swarm optimization-based approach to the optimal design of substation grounding grid. Przegląd Elektrotechniczny, 89, 30–34.Google Scholar
  26. 26.
    Reddy, B. R. (2012). Performance analysis of MIMO radar waveform using accelerated particle swarm optimization algorithm. arXiv preprint arXiv:1209.4015.
  27. 27.
    Talatahari, S., Khalili, E., & Alavizadeh, S. M. (2013). Accelerated particle swarm for optimum design of frame structures. Mathematical Problems in Engineering, 2013, 649857. Scholar
  28. 28.
    Prajna, K., Rao, G. S. B., Reddy, K., & Maheswari, R. U. (2014). A new dual channel speech enhancement approach based on accelerated particle swarm optimization (APSO). International Journal of Intelligent Systems and Applications (IJISA), 6, 1.CrossRefGoogle Scholar
  29. 29.
    Mohamed, A. Z., Lee, S. H., Hsu, H. Y., & Nath, N. (2012). A faster path planner using accelerated particle swarm optimization. Artificial Life and Robotics, 17, 233–240.CrossRefGoogle Scholar
  30. 30.
    Gandomi, A. H., Yun, G. J., Yang, X.-S., & Talatahari, S. (2013). Chaos-enhanced accelerated particle swarm optimization. Communications in Nonlinear Science and Numerical Simulation, 18, 327–340.MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Ganesan, T., Elamvazuthi, I., Ku Shaari, K. Z., & Vasant, P. (2013). Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production. Applied Energy, 103, 368–374.CrossRefzbMATHGoogle Scholar
  32. 32.
    Roy, P. K. (2013). Solution of unit commitment problem using gravitational search algorithm. International Journal of Electrical Power and Energy Systems, 53, 85–94.CrossRefGoogle Scholar
  33. 33.
    Duman, S., Güvenç, U., Sönmez, Y., & Yörükeren, N. (2012). Optimal power flow using gravitational search algorithm. Energy Conversion and Management, 59, 86–95.CrossRefGoogle Scholar
  34. 34.
    Weber, G. W., Vasant, P., & Saucedo, J. A. M. (2018). Computer science and engineering. Journal of Computational Science, 25, 416–418.CrossRefGoogle Scholar
  35. 35.
    Dubey, H. M., Pandit, M., Panigrahi, B., & Udgir, M. (2013). Economic load dispatch by hybrid swarm intelligence based gravitational search algorithm. International Journal of Intelligent Systems and Applications, 5, 21–32.CrossRefGoogle Scholar
  36. 36.
    Mallick, S., Ghoshal, S. P., Acharjee, P., & Thakur, S. S. (2013). Optimal static state estimation using improved particle swarm optimization and gravitational search algorithm. International Journal of Electrical Power and Energy Systems, 52, 254–265.CrossRefGoogle Scholar
  37. 37.
    Kunche, P., Rao, G. S. B., Reddy, K., & Maheswari, R. U. (2014). A new approach to dual channel speech enhancement based on hybrid PSOGSA. International Journal of Speech Technology, 18, 1–12.Google Scholar
  38. 38.
    Mirjalili, S., Mohd Hashim, S. Z., & Moradian Sardroudi, H. (2012). Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Applied Mathematics and Computation, 218, 11125–11137.MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Tan, W. S., Hassan, M. Y., Rahman, H. A., Abdullah, M. P., & Hussin, F. (2013). Multi-distributed generation planning using hybrid particle swarm optimisation-gravitational search algorithm including voltage rise issue. IET Generation, Transmission and Distribution, 7, 929–942.CrossRefGoogle Scholar
  40. 40.
    Vasant, P., Litvinchev, I., & Marmolejo-Saucedo, J. A. (2017). Modeling, simulation, and optimization. New York: Springer. ISBN 978-3-319-70541-5.Google Scholar
  41. 41.
    Beyer, H.-G. (2014). Convergence analysis of evolutionary algorithms that are based on the paradigm of information geometry. Evolutionary Computation, 22, 679–709.CrossRefGoogle Scholar
  42. 42.
    Barr, R. S., Golden, B. L., Kelly, J. P., Resende, M. G., & Stewart, W. R., Jr. (1995). Designing and reporting on computational experiments with heuristic methods. Journal of Heuristics, 1, 9–32.CrossRefzbMATHGoogle Scholar
  43. 43.
    Vasant, P., Litvinchev, I. S., & Marmolejo-Saucedo, J. A. (2019). Innovative computing trends and applications. New York: Springer. ISBN 978-3-030-03897-7.CrossRefGoogle Scholar
  44. 44.
    Mernik, M., Liu, S.-H., Karaboga, D., & Črepinšek, M. (2015). On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation. Information Sciences, 291, 115–127.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Pandian Vasant
    • 1
    • 2
    Email author
  • Jose Antonio Marmolejo
    • 3
  • Igor Litvinchev
    • 4
  • Roman Rodriguez Aguilar
    • 5
  1. 1.Modeling Evolutionary Algorithms Simulation and Artificial Intelligence (MERLIN), Faculty of Electrical and Electronics EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Universiti Teknologi PetronasSeri IskandarMalaysia
  3. 3.Facultad de IngenieriaUniversidad PanamericanaMexico CityMexico
  4. 4.Nuevo Leon State UniversityMonterreyMexico
  5. 5.Escuela de Ciencias Económicas y EmpresarialesUniversidad PanamericanaMexico CityMexico

Personalised recommendations