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Energy management strategy for a fuel cell hybrid vehicle based on maximum efficiency and maximum power identification

Energy management strategy for a fuel cell hybrid vehicle based on maximum efficiency and maximum power identification

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This paper addresses the energy management strategy (EMS) for a fuel cell hybrid electric vehicle (FC-HEV). The fuel cell system (FCS) is a multi-physics system, and consequently, its energetic performances depend on the degradation and on the operating conditions. The maximum power (MP) and the maximum efficiency (ME) points of the FCS are unique but they move with operating condition variations. Thus, developing an extremum seeking process (ESP) for both MP and ME tracking is a challenging task. In the ESP, models are identified online by using an adaptive recursive least square (ARLS) method to seek a variation in the FCS performances. Then an optimisation algorithm is used on the updated model to find the MP and the ME points. The ESP is incorporated into a hysteresis power splitting control (HPSC). A MP mode or a ME mode can be set based on the energy storage level (battery pack). The effectiveness of the proposed MP- and ME-ESP EMS is demonstrated by conducting experimental studies on two FCSs with different levels of degradation. It was demonstrated that the classical EMS based on maps are not valid when the operating parameters vary because of the level of degradation change.

References

    1. 1)
      • 5. Payman, A., Pierfederici, S., Meibody-Tabar, F.: ‘Energy control of supercapacitor/fuel cell hybrid power source’, Energy Convers. Manag., 2008, 49, pp. 16371644.
    2. 2)
      • 19. Becherif, M., Hissel, D.: ‘MPPT of a PEMFC based on air supply control of the motocompressor group’, Int. J. Hydrog. Energy, 2010, 35, pp. 1252112530.
    3. 3)
      • 9. Farouk, O., Jürgen, R., Lars, W., et al: ‘Power management optimization of fuel cell/battery hybrid vehicles with experimental validation’, J. Power Sources, 2014, 252, pp. 333343.
    4. 4)
      • 13. Meiler, M., Schmid, O., Schudy, M., et al: ‘Dynamic fuel cell stack model for real-time simulation based on system identification’, J. Power Sources, 2008, 176, pp. 523528.
    5. 5)
      • 15. Zhong, Z.-d., Huo, H.-b., Zhu, X.-j., et al: ‘Adaptive maximum power point tracking control of fuel cell power plants’, J. Power Sources, 2008, 176, pp. 259269.
    6. 6)
      • 35. Lee, J.H., Lalk, T.R., Appleby, A.J.: ‘Modeling electrochemical performance in large scale proton exchange membrane fuel cell stacks’, J. Power Sources, 1998, 70, pp. 258268.
    7. 7)
      • 23. Gene, A.B., Zacharie, W., Grégory, F., et al: ‘Experimental real-time optimization of a solid oxide fuel cell stack via constraint adaptation’, Energy, 2012, 39, pp. 5462.
    8. 8)
      • 1. Sharaf, O.Z., Orhan, M.F.: ‘An overview of fuel cell technology: fundamentals and applications’, Renew. Sustain. Energy Rev., 2014, 32, pp. 810853.
    9. 9)
      • 29. Bonnans, J.-F., Gilbert, J.C., Lemaréchal, C., et al: ‘Numerical optimization: theoretical and practical aspects’ (Springer Science & Business Media, 2006).
    10. 10)
      • 4. Tie, S.F., Tan, C.W.: ‘A review of energy sources and energy management system in electric vehicles’, Renew. Sustain. Energy Rev., 2013, 20, pp. 82102.
    11. 11)
      • 36. Mann, R.F., Amphlett, J.C., Hooper, M.A.I., et al: ‘Development and application of a generalised steady-state electrochemical model for a PEM fuel cell’, J. Power Sources, 2000, 86, pp. 173180.
    12. 12)
      • 17. Benyahia, N., Denoun, H., Badji, A., et al: ‘MPPT controller for an interleaved boost dc–dc converter used in fuel cell electric vehicles’, Int. J. Hydrog. Energy, 2014, 39, pp. 1519615205.
    13. 13)
      • 27. Ettihir, K., Boulon, L., Becherif, M., et al: ‘Online identification of semi-empirical model parameters for PEMFCs’, Int. J. Hydrog. Energy, 2014, 39, pp. 2116521176.
    14. 14)
      • 14. Xu, L., Li, J., Hua, J., et al: ‘Adaptive supervisory control strategy of a fuel cell/battery-powered city bus’, J. Power Sources, 2009, 194, pp. 360368.
    15. 15)
      • 25. Jinfeng, W., Xiao-Zi, Y., Jonathan, J.M., et al: ‘Proton exchange membrane fuel cell degradation under close to open-circuit conditions: Part I: in situ diagnosis’, J. Power Sources, 2010, 195, pp. 11711176.
    16. 16)
      • 37. Bagotsky, V.S.: ‘The working principles of a fuel cell’, in (ed.): ‘Fuel cells’ (John Wiley & Sons, Inc., 2012), pp. 524.
    17. 17)
      • 6. Feroldi, D., Serra, M., Riera, J.: ‘Energy management strategies based on efficiency map for fuel cell hybrid vehicles’, J. Power Sources, 2009, 190, pp. 387401.
    18. 18)
      • 10. Boulon, L., Hissel, D., Bouscayrol, A., et al: ‘From modeling to control of a PEM fuel cell using energetic macroscopic representation’, IEEE Trans. Ind. Electron., 2010, 57, pp. 18821891.
    19. 19)
      • 16. Kelouwani, S., Adegnon, K., Agbossou, K., et al: ‘Online system identification and adaptive control for PEM fuel cell maximum efficiency tracking’, IEEE Trans. Energy Convers., 2012, 27, pp. 580592.
    20. 20)
      • 8. Bernard, J., Delprat, S., Guerra, T.M., et al: ‘Fuel efficient power management strategy for fuel cell hybrid powertrains’, Control Eng. Pract., 2010, 18, pp. 408417.
    21. 21)
      • 18. Methekar, R.N., Patwardhan, S.C., Gudi, R.D., et al: ‘Adaptive peak seeking control of a proton exchange membrane fuel cell’, J. Process Control, 2010, 20, pp. 7382.
    22. 22)
      • 2. Thomas, C.E.: ‘Fuel cell and battery electric vehicles compared’, Int. J. Hydrog. Energy, 2009, 34, pp. 60056020.
    23. 23)
      • 26. Plett, G.L.: ‘Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification’, J. Power Sources, 2004, 134, pp. 262276.
    24. 24)
      • 34. Pisani, L., Murgia, G., Valentini, M., et al: ‘A new semi-empirical approach to performance curves of polymer electrolyte fuel cells’, J. Power Sources, 2002, 108, pp. 192203.
    25. 25)
      • 30. T. Mathworks: ‘Matlab Optimization Toolbox User's Guide’. Available at: http://www.mathworks.co.uk/access/helpdesk/help/pdf_doc/optim/optim_tb.pdf.
    26. 26)
      • 22. Park, J.-D., Ren, Z.: ‘Hysteresis controller based maximum power point tracking energy harvesting system for microbial fuel cells’, J. Power Sources, 2012, 205, pp. 151156.
    27. 27)
      • 24. Ettihir, K., Boulon, L., Agbossou, K., et al: ‘Design of an energy management strategy for PEM fuel cell vehicles’. 2012 IEEE Int. Symp. on Industrial Electronics (ISIE), 2012, pp. 17141719.
    28. 28)
      • 21. Carlos Andrés, R.-P., Giovanni, S., Giovanni, P., et al: ‘A perturbation strategy for fuel consumption minimization in polymer electrolyte membrane fuel cells: analysis, design and FPGA implementation’, Appl. Energy, 2014, 119, pp. 2132.
    29. 29)
      • 33. Kim, J.: ‘Modeling of proton exchange membrane fuel cell performance with an empirical equation’, J. Electrochem. Soc., 1995, 142, p. 2670.
    30. 30)
      • 28. Kuen, H.Y., Mjalli, F.S., Koon, Y.H.: ‘Recursive least squares-based adaptive control of a biodiesel transesterification reactor’, Ind. Eng. Chem. Res., 2010, 49, pp. 1143411442.
    31. 31)
      • 31. Squadrito, G., Maggio, G., Passalacqua, E., et al: ‘An empirical equation for polymer electrolyte fuel cell (PEFC) behaviour’, J. Appl. Electrochem., 1999, 29, pp. 14491455.
    32. 32)
      • 32. Srinivasan, S., Ticianelli, E.A., Derouin, C.R., et al: ‘Advances in solid polymer electrolyte fuel cell technology with low platinum loading electrodes’, J. Power Sources, 1988, 22, pp. 359375.
    33. 33)
      • 20. Dazi, L., Yadi, Y., Qibing, J., et al: ‘Maximum power efficiency operation and generalized predictive control of PEM (proton exchange membrane) fuel cell’, Energy, 2014, 68, pp. 210217.
    34. 34)
      • 12. Raga, C., Barrado, A., Lazaro, A., et al: ‘Black-box model, identification technique and frequency analysis for PEM fuel cell with overshooted transient response’, IEEE Trans. Power Electron., 2014, 29, pp. 53345346.
    35. 35)
      • 7. Hemi, H., Ghouili, J., Cheriti, A.: ‘Combination of Markov chain and optimal control solved by Pontryagin's minimum principle for a fuel cell/supercapacitor vehicle’, Energy Convers. Manag., 2015, 91, pp. 387393.
    36. 36)
      • 3. Kumar, L., Jain, S.: ‘Electric propulsion system for electric vehicular technology: a review’, Renew. Sustain. Energy Rev., 2014, 29, pp. 924940.
    37. 37)
      • 11. Wang, L., Husar, A., Zhou, T., et al: ‘A parametric study of PEM fuel cell performances’, Int. J. Hydrog. Energy, 2003, 28, pp. 12631272.
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