Parameter Identification of PEM Fuel Cell Using Quantum-Based Optimization Method

Abstract

Parameter identification of proton exchange membrane (PEM) fuel cells using quantum-based optimization method (QBOM) is presented in this paper. The QBOM is an algorithm that is adapted from certain elements of quantum computing aimed for use in a wider class of search and optimization problems. QBOM is composed of qubits and quantum gates. The quantum gate evolves the qubits until the desired objective is achieved, while qubits maintain the information in a superposition for all states. This novel optimization technique presents innovative insight in finding the best answer. Unlike other evolutionary search mechanism philosophies, the QBOM utilizes quantum phenomena to allocate the optimum, while the evolutionary algorithms seek to find the optimal solution using the available information including the best found to assemble the search mechanism with certain rules to avoid trapping in local minima. The proposed method is applied to 1.2 kW Ballard Nexa fuel cell to identify the exact parameters and has been successfully tested experimentally. Results based on parameter identification, simulation and experimental measurements are compared for validation purposes. The outcomes are very encouraging and prove that QBOM is very applicable in parameter optimization of PEM fuel cell.

This is a preview of subscription content, access via your institution.

References

  1. 1

    Larminie, J.; Dicks, A.: Fuel Cell Systems, 2nd edn. Wiley (2003)

  2. 2

    Mazumder S.K.; Acharya K.; Haynes C.L.; Williams R.; Spakovsky M.R.; Nelson D.J.; Rancruel D.F.; Hartvigsen J.; Gemmen R.S.: Solid-oxide-fuel-cell performance and durability: resolution of the effects of power conditioning systems and application loads. IEEE Trans. Power Electron. 19(5), 1263–1278 (2004)

    Article  Google Scholar 

  3. 3

    Choe S.-Y.; Ahn J.-W.; Lee J.-G.; Baek S.-H.: Dynamic simulator for a PEM fuel cell system with a PWM DC/DC converter. IEEE Trans. Energy Convers. 23(2), 669–680 (2008)

    Article  Google Scholar 

  4. 4

    Vepa R.: Adaptive state estimation of a PEM fuel cell. IEEE Trans. Energy Convers. 27(2), 457–467 (2012)

    Article  Google Scholar 

  5. 5

    Wang C.; Nehrir H.: Fuel cells and load transients. IEEE Power Energy Mag. 5(1), 58–63 (2007)

    Article  Google Scholar 

  6. 6

    Uzunoglu M.; Alam M.S.: Dynamic modeling, design and simulation of a PEM fuel cell/ultra-capacitor hybrid system for vehicular applications. J. Energy Convers. Manag. 48(5), 1544–1553 (2007)

    Article  Google Scholar 

  7. 7

    Paja Carlos A.R.; Bordons C.; Romero A.; Giral R.; Martinez-Salamero L: Minimum fuel consumption strategy for PEM fuel cells. IEEE Trans. Ind. Electron. 56(3), 685–696 (2009)

    Article  Google Scholar 

  8. 8

    Ahmed N.A.: Computational modelling and polarization characteristics of proton exchange membrane fuel cell with evaluation of the interface systems. J. Eur. Power Electron. EPE 18(1), 32–40 (2008)

    Google Scholar 

  9. 9

    Kim Y.H.; Kim S.S.: An electrical modeling and fuzzy logic control of a fuel cell generation system. IEEE Trans. Energy Convers. 14(2), 239–244 (1999)

    Article  Google Scholar 

  10. 10

    Ohenoja, M.; Leiviskä, K.: Identification of electrochemical model parameters in PEM fuel cells. In: International Conference of POWERENG 2009, pp. 363–368. Lisbon, 18–20 March 2009

  11. 11

    Wang C.; Nehrir M.H.; Shaw S.: Dynamic models and model validation for PEM fuel cells using electrical circuits. IEEE Trans. Energy Convers. 20(2), 442–451 (2005)

    Article  Google Scholar 

  12. 12

    Jia J.; Li Q.; Wang Y.; Cham Y.T.; Han M.: Modeling and dynamic characteristic simulation of a proton exchange membrane fuel cell. IEEE Trans. Energy Convers. 24(1), 283–291 (2009)

    Article  Google Scholar 

  13. 13

    Forrai A.; Funato H.; Yanagita Y.; Kato Y.: Fuel-cell parameter estimation and diagnostics. IEEE Trans. Energy Convers. 20(3), 668–675 (2005)

    Article  Google Scholar 

  14. 14

    Li Q.; Chen W.; Wang Y.; Liu S.; Jia J.: Parameter identification for PEM fuel-cell mechanism model based on effective informed adaptive particle swarm optimization. IEEE Trans. Ind. Electron. 58(6), 2410–2419 (2011)

    Article  Google Scholar 

  15. 15

    Lopes, Vitor V.; Novais, Augusto Q.; Rangel, Carmen M.: Novel data-driven methodologies for parameter estimation and interpretation of fuel cells performance. In: International Conference of Electrical Power Quality and Utilization, pp. 1–6. Lisbon, 17–19 Oct 2011

  16. 16

    Askarzadeh A.; Rezazadeh A.: An innovative global harmony search algorithm for parameter identification of a PEM fuel cell model. IEEE Trans. Ind. Electron. 59(9), 3473–3480 (2013)

    Article  Google Scholar 

  17. 17

    Corrêa Jeferson M.; Farret Felix A.; Popov Vladimir A.; Simões Marcelo G.: Sensitivity analysis of the modeling parameters used in simulation of proton exchange membrane fuel cells. IEEE Trans. Energy Convers. 20(1), 2011–2018 (2005)

    Google Scholar 

  18. 18

    Puranik Sachin V.; Keyhani A.; Khorrami F.: State-space modeling of proton exchange membrane fuel cell. IEEE Trans. Energy Convers. 25(3), 804–813 (2005)

    Article  Google Scholar 

  19. 19

    Askarzadeh A.; Rezazadeh A.: A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters. J. Zhejiang Univ. Sci. (Comput. Electron.) Part C 12(8), 638–646 (2011)

    Article  Google Scholar 

  20. 20

    She Y.; Baran Mesut E.; She X.: Multi objective control of PEM fuel cell system with improved durability. IEEE Trans. Sustain. Energy 4(1), 127–135 (2013)

    Article  Google Scholar 

  21. 21

    Marcello, P.; Pericle, Z.: Model parameters estimation of PEM fuel-cell systems using genetic algorithms. In: International Conference on Industrial Technology, pp. 1206–1212, Vi a del Mar (2010)

  22. 22

    Chibante, R.; Campos, D.: An experimentally optimized PEM fuel cell model using PSO algorithm. In: International Symposium on Industrial Electronics, pp. 2281–2285, Bari (2010)

  23. 23

    Hogg T.; Portnov D.S.: Quantum Optimization. Inf. Soc. 128, 81–97 (2000)

    MathSciNet  Google Scholar 

  24. 24

    Protopescu V.; Barhen J.: Solving a class of continuous global optimization problems using quantum algorithms. Phys. Lett. 296, 9–14 (2002)

    MathSciNet  Article  Google Scholar 

  25. 25

    Bulger D.; Baritompa W.P.; Wood G.R.: Implementing pure adaptive search with Grover’s quantum algorithm. J. Optim. Theory Appl. 116(3), 517–529 (2003)

    MathSciNet  Article  Google Scholar 

  26. 26

    Gruska Jozef: Quantum Computing (Advanced Topics in Computer Science Series). McGraw-Hill, New York (1999)

    Google Scholar 

  27. 27

    Valle, C.: Shor’s algorithm and Grover’s Algorithm in Quantum Computing. UMI Dissertation Publishing, 2 Sept 2011

  28. 28

    Nielsen M.A.; Chuang I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  29. 29

    Alfares, F.; Esat, I.: Real-coded quantum inspired evolution algorithm applied to engineering optimization problems. In: Proceedings of International Symposium on Leveraging Applications of Formal Methods, ISoLA, pp. 169–176, Cyprus, 15–19 Nov 2006

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Nabil A. Ahmed.

Additional information

Nabil A. Ahmed—On leave from Assiut University, Egypt.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Al-Othman, A.K., Ahmed, N.A., Al-Fares, F.S. et al. Parameter Identification of PEM Fuel Cell Using Quantum-Based Optimization Method. Arab J Sci Eng 40, 2619–2628 (2015). https://doi.org/10.1007/s13369-015-1711-0

Download citation

Keywords

  • Fuel cell
  • Proton exchange membrane
  • Parameter identification
  • Quantum optimization
  • Qubits
  • search mechanism