Electrical Engineering

, Volume 101, Issue 3, pp 759–770 | Cite as

A predictive energy management system for hybrid energy storage systems in electric vehicles

  • Qiao ZhangEmail author
  • Gang Li
Original Paper


Energy management system plays a vital role in exploiting advantages of battery and supercapacitor hybrid energy storage systems in electric vehicles. Various energy management systems have been reported in the literature, of which the model predictive control is attracting more attentions due to its advantage in deal with system constraints. In this paper, a predictive energy management system is proposed based on a combination of Haar wavelet transform and model predictive control. Different from prior publications, the main contribution of this study is that the wavelet transform algorithm is introduced for power demand decomposition. At the same time, the power errors of the model predictive controllers are also fed to the wavelet transform algorithm for coefficient regulation. In this way, the power components distributed to the battery and supercapacitor can better match to their individual characteristics. The proposed method can reduce the maximum voltage drop of the battery up to 10.53%, 9.09% and 23.53%, the battery life cost up to 9.09%, 6.52% and 2.82%, respectively, as compared with the sole model predictive controller without wavelet transform based on NYCC, UDDS and NurembergR36 three driving cycles.


Hybrid energy storage system Battery Supercapacitor Wavelet transform and model predictive control 



Energy management system


Hybrid energy storage system


Model predictive control


Wavelet transform


Electric vehicle




Dynamic programming


Particle swarm optimization


Genetic algorithm


Simulated annealing


Direct current


New York City Cycle


Urban Dynamometer Driving Schedule


Equivalent consumption minimization strategy


Pontryagin’s minimum principle

List of symbols


Control command of DC/DC connected to SC


Resistance of DC/DC connected to battery


Inductance of DC/DC connected to battery


Control command of DC/DC connected to battery


Inductance of DC/DC converter connected to SC


Resistance of DC/DC connected to SC


Main cells of SC


Middle cells of SC


Slow cells of SC


Loss resistance of SC


Main cells voltage


Middle cells voltage


Slow cells voltage


SC output voltage


SC output current


Proportion coefficient


Original signal


Low-pass filter coefficient


High-pass filter coefficient


Prediction horizon of MPC controller


Control horizon of MPC controller


Output value of MPC controller


Large capacitor


Characteristic capacitor


Terminal resistance


Characteristic capacitor voltage


Battery output current


Surface resistance


Large capacitor voltage


Bus voltage


End resistance


Battery output voltage



This work is supported by Project of Liaoning Province Major Technology Platform Grant JP2017002, Guidance Plan of Natural Science Foundation of Liaoning Province Grant 20180551280, National Science Foundation of China Grant 51675257, Project of Liaoning Province Innovative Talents Grant LR2016054 and Overseas Training Program for Colleges and Universities of Liaoning Province Grant 2018LNGXGJWPY-YB014.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


  1. 1.
    Manzetti S, Mariasiu F (2015) Electric vehicle battery technologies: from present state to future systems. Renew Sustain Energy Rev 51:1004–1012CrossRefGoogle Scholar
  2. 2.
    Andwari AM, Pesiridis A, Rajoo S, Ricardo MB, Esfahanian V (2017) A review of battery electric vehicle technology and readiness levels. Renew Sustain Energy Rev 78:414–430CrossRefGoogle Scholar
  3. 3.
    Hannan M, Azidin F, Mohamed A (2014) Hybrid electric vehicles and their challenges: a review. Renew Sustain Energy Rev 29:135–150CrossRefGoogle Scholar
  4. 4.
    Hannan M, Hoque M, Mohamed A, Ayob A (2017) Review of energy storage systems for electric vehicle applications: issues and challenges. Renew Sustain Energy Rev 69:771–789CrossRefGoogle Scholar
  5. 5.
    Kouchachvili L, Yaïci W, Entchev E (2018) Hybrid battery/supercapacitor energy storage system for the electric vehicles. J Power Sources 374:237–248CrossRefGoogle Scholar
  6. 6.
    Shen J, Dusmez S, Khaligh A (2014) Optimization of sizing and battery cycle life in battery/ultracapacitor hybrid energy storage systems for electric vehicle applications. IEEE Trans Ind Inform 10(4):2112–2121CrossRefGoogle Scholar
  7. 7.
    Tie SF, Tan CW (2013) A review of energy sources and energy management system in electric vehicles. Renew Sustain Energy Rev 20:82–102CrossRefGoogle Scholar
  8. 8.
    Holland C, Weidner J, Dougal R, White R (2002) Experimental characterization of hybrid power systems under pulse current loads. J Power Sources 109:32–37CrossRefGoogle Scholar
  9. 9.
    Omar N, Daowd M, Hegazy O, Bossche P, Coosemans T, Mierlo J (2012) Electrical double-layer capacitors in hybrid topologies—assessment and evaluation of their performance. Energies 5:4533–4568CrossRefGoogle Scholar
  10. 10.
    Kuperman A, Aharon I (2011) Battery–ultracapacitor hybrids for pulsed current loads: a review. Renew Sustain Energy Rev 15:981–992CrossRefGoogle Scholar
  11. 11.
    Lam L, Louey R (2006) Development of ultra-battery for hybrid-electric vehicle applications. J Power Sources 158:1140–1148CrossRefGoogle Scholar
  12. 12.
    Bentley P, Stone DA, Schofield N (2005) The parallel combination of a VRLA cell and supercapacitor for use as a hybrid vehicle peak power buffer. J Power Sources 147:288–294CrossRefGoogle Scholar
  13. 13.
    Choi ME, Kim SW, Seo S-W (2012) Energy management optimization in a battery/supercapacitor hybrid energy storage system. IEEE Trans Smart Grid 3:463–472CrossRefGoogle Scholar
  14. 14.
    Burke A, Miller M (2011) The power capability of ultracapacitors and lithium batteries for electric and hybrid vehicle applications. J Power Sources 196:514–522CrossRefGoogle Scholar
  15. 15.
    Song Z, Li J, Han X, Xu L, Lu L, Ouyang M, Hofmann H (2014) Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles. Appl Energy 135:212–224CrossRefGoogle Scholar
  16. 16.
    Pay S, Baghzouz Y (2003) Effectiveness of BAT-supercapacitor combination in electric vehicles. In: Proceedings of IEEE Bologna PowerTech conference, pp 1–6Google Scholar
  17. 17.
    Chau K, Wong Y (2001) Hybridization of energy sources in electric vehicles. Energy Convers Manag 42:1059–1069CrossRefGoogle Scholar
  18. 18.
    Armenta J, Núñez C, Visairo N, Lázaro I (2015) An advanced energy management system for controlling the ultracapacitor discharge and improving the electric vehicle range. J Power Sources 284:452–458CrossRefGoogle Scholar
  19. 19.
    Wang B, Xu J, Cao B, Zhou X (2015) A novel multimode hybrid energy storage system and its energy management strategy for electric vehicles. J Power Sources 281:432–443CrossRefGoogle Scholar
  20. 20.
    Ferreira A, Pomilio J, Spiazzi G, Silva LA (2008) Energy management fuzzy logic supervisory for electric vehicle power supplies system. IEEE Trans Power Electron 23:107–115CrossRefGoogle Scholar
  21. 21.
    Michalczuk M, Ufnalski B, Grzesiak LM (2015) Fuzzy logic based power management strategy using topographic data for an electric vehicle with a battery-ultracapacitor energy storage. Int J Comput Math Electr Electron Eng 34:173–188CrossRefGoogle Scholar
  22. 22.
    Wang Y, Wang W, Zhao Y, Yang L, Chen W (2016) A fuzzy-logic power management strategy based on markov random prediction for hybrid energy storage systems. Energies 9:1–20Google Scholar
  23. 23.
    Zhang Q, Ju F, Zhang S, Deng W, Wu J, Gao C (2017) Power management for hybrid energy storage system of electric vehicles considering inaccurate terrain information. IEEE Trans Autom Sci Eng 14:608–618CrossRefGoogle Scholar
  24. 24.
    Florescu A, Bacha S, Munteanu I, Bratcu AI, Rumeau A (2015) Adaptive frequency-separation-based energy management system for electric vehicles. J Power Sources 280:410–421CrossRefGoogle Scholar
  25. 25.
    Song Z, Hofmann H, Li J, Han X, Ouyang M (2015) Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach. Appl Energy 139:151–162CrossRefGoogle Scholar
  26. 26.
    Abido MA (2002) Optimal power flow using particle swarm optimization. Int J Electr Power Energy Syst 24(7):563–571CrossRefGoogle Scholar
  27. 27.
    Piccolo A, Ippolito L, Galdi V, Vaccaro A (2001) Optimization of energy flow management in hybrid electric vehicles via genetic algorithms. In: Proceedings of IEEE/ASME international conference on advanced intelligent mechatronics, Corno, Italy, July 2001, pp 434–439Google Scholar
  28. 28.
    Trovão JP, Pereirinha PG, Jorge HM, Antunes CH (2013) A multi-level energy management system for multi-source electric vehicles—an integrated rule-based meta-heuristic approach. Appl Energy 105:304–318CrossRefGoogle Scholar
  29. 29.
    Jones DR (2003) Direct global optimization algorithm. In: Floudas CA, Pardalos PM (eds) Encyclopedia of optimization. Springer, New York, pp 431–440Google Scholar
  30. 30.
    Moreno J, Ortúzar ME, Dixon LW (2006) Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks. IEEE Trans Ind Electron 53:614–623CrossRefGoogle Scholar
  31. 31.
    Rodatz P, Paganelli G, Sciarretta A, Guzzella L (2005) Optimal power management of an experimental fuel cell/supercapacitor-powered hybrid vehicle. Control Eng Pract 13:41–53CrossRefGoogle Scholar
  32. 32.
    Zheng CH, Kim NW, Cha SW (2012) Optimal control in the power management of fuel cell hybrid vehicles. Int J Hydrog Energy 37(1):655–663CrossRefGoogle Scholar
  33. 33.
    Vahidi A, Stefanopoulou A, Peng H (2006) Current management in a hybrid fuel cell power system: A model predictive control approach. IEEE Trans Control Syst Technol 14:1047–1057CrossRefGoogle Scholar
  34. 34.
    Jayachandran M, Ravi G (2019) Decentralized model predictive hierarchical control strategy for islanded AC microgrids. Electr Power Syst Res 170:92–100CrossRefGoogle Scholar
  35. 35.
    Hredzak B, Agelidis VG, Jang M (2014) A model predictive control system for a hybrid battery-ultracapacitor power source. IEEE Trans Power Electron 29:1469–1479CrossRefGoogle Scholar
  36. 36.
    Zhang Q, Li G (2019) Experimental study on a semi-active battery-supercapacitor hybrid energy storage system for electric vehicle application. IEEE Trans Power Electron (Early Access). CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Automobile and Traffic EngineeringLiaoning University of TechnologyJinzhouChina

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