This work proposes a load allocation decision strategy based on deterioration prognostics information for multi-stack fuel cell systems. The fuel cell deterioration is characterized by the overall resistance value, as it carries the key aging information of a fuel cell. The fuel cell deterioration dynamics is then modeled as an increasing stochastic process whose trend is a function of the fuel cell output power. Combining system deterioration and fuel cell consumption, a multi-objective optimization (MOO) based decision-making strategy is proposed to manage the operation of a multi-stack fuel cell system. Based on this algorithm, the optimal operating power load is computed for each stack. Finally, the performance of the proposed approach is compared to the case without post-prognostics decision for a three-stack fuel cell system. The simulation results show that the proposed post-prognostics decision-making strategy can manage fuel cell system operating in real time by scheduling the optimal load allocation among stacks.
BorupRLKusogluANeyerlinKC, et al. Recent developments in catalyst-related pem fuel cell durability. Curr Opin Electrochem2020; 21: 192–200.
3.
WangYSeoBWangB, et al. Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology. Energy and AI2020; 1: 100014.
4.
JouinMGouriveauRHisselD, et al. Prognostics and health management of PEMFC–state of the art and remaining challenges. Int J Hydrog Energy2013; 38(35): 15307–15317.
5.
YueMAl MasryZJemeiS, et al. An online prognostics-based health management strategy for fuel cell hybrid electric vehicles. Int J Hydrog Energy2021; 46(24): 13206–13218.
6.
HuaZZhengZPahonE, et al. Lifespan prediction for proton exchange membrane fuel cells based on wavelet transform and echo state network. IEEE Trans Transp Electrification. Epub ahead of print 18 October 2021. DOI: 10.1109/TTE.2021.3121179.
7.
AtamuradovVMedjaherKDersinP, et al. Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation. Int J Progn Health Manag2017; 8(60): 1–31.
8.
LiQWangTDaiC, et al. Power management strategy based on adaptive droop control for a fuel cell-battery-supercapacitor hybrid tramway. IEEE Trans Veh Technol2017; 67(7): 5658–5670.
9.
WuXHuXYinX, et al. Convex programming energy management and components sizing of a plug-in fuel cell urban logistics vehicle. J Power Sources2019; 423: 358–366.
10.
YueMJemeiSGouriveauR, et al. Review on health-conscious energy management strategies for fuel cell hybrid electric vehicles: degradation models and strategies. Int J Hydrog Energy2019; 44(13): 6844–6861.
11.
YueMJemeiSZerhouniN. Health-conscious energy management for fuel cell hybrid electric vehicles based on prognostics-enabled decision-making. IEEE Trans Veh Technol2019; 68(12): 11483–11491.
12.
MarxNBoulonLGustinF, et al. A review of multi-stack and modular fuel cell systems: Interests, application areas and on-going research activities. Int J Hydrog Energy2014; 39(23): 12101–12111.
13.
LiuJZioE. Prognostics of a multistack pemfc system with multiagent modeling. Energy Sci Eng2019; 7(1): 76–87.
14.
MarxNCárdenasDCTBoulonL, et al. Degraded mode operation of multi-stack fuel cell systems. IET Electr Syst Transp2016; 6(1): 3–11.
15.
CalderónAJVivasFJSeguraF, et al. Integration of a multi-stack fuel cell system in microgrids: A solution based on model predictive control. Energies2020; 13(18): 4924.
16.
WangTLiQYinL, et al. Hydrogen consumption minimization method based on the online identification for multi-stack pemfcs system. Int J Hydrog Energy2019; 44(11): 5074–5081.
17.
YanYLiQChenW, et al. Online control & power coordination method for multi-stack fuel cells system based on optimal power allocation. IEEE Trans Ind Electron2020; 68(9): 8158–8168.
18.
HerrNNicodJMVarnierC, et al. Decision process to manage useful life of multi-stacks fuel cell systems under service constraint. Renew Energy2017; 105: 590–600.
19.
ZhouSZhangGFanL, et al. Scenario-oriented stacks allocation optimization for multi-stack fuel cell systems. Appl Energy2022; 308: 118328.
20.
LiuYLiJChenZ, et al. Research on a multi-objective hierarchical prediction energy management strategy for range extended fuel cell vehicles. J Power Sources2019; 429: 55–66.
21.
KonakACoitDWSmithAE. Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf2006; 91(9): 992–1007.
22.
LiHXuBLuG, et al. Multi-objective optimization of pem fuel cell by coupled significant variables recognition, surrogate models and a multi-objective genetic algorithm. Energy Convers Manag2021; 236: 114063.
23.
KimJLeeSMSrinivasanS, et al. Modeling of proton exchange membrane fuel cell performance with an empirical equation. J Electrochem Soc1995; 142(8): 2670.
24.
GouriveauRHilairetMHisselD, et al. IEEE PHM 2014 data challenge: outline, experiments, scoring of results, winners. In: Proceedings of IEEE Conference on Prognostics Health Management, Nantes, France, 8–10 July 2014. New York: IEEE, pp.1–6.
25.
LiuHChenJHisselD, et al. Prognostics methods and degradation indexes of proton exchange membrane fuel cells: a review. Renew Sustain Energy Rev2020; 123: 109721.
26.
BresselMHilairetMHisselD, et al. Extended kalman filter for prognostic of proton exchange membrane fuel cell. Appl Energy2016; 164: 220–227.
27.
LiuDLinRFengB, et al. Investigation of the effect of cathode stoichiometry of proton exchange membrane fuel cell using localized electrochemical impedance spectroscopy based on print circuit board. Int J Hydrog Energy2019; 44(14): 7564–7573.
28.
ChenFGaoY. An algorithm for on-line measurement of the internal resistance of proton exchange membrane fuel cell. Fuel Cells2015; 15(2): 337–343.
29.
MaTZhangZLinW, et al. Impedance prediction model based on convolutional neural networks methodology for proton exchange membrane fuel cell. Int J Hydrog Energy2021;46: 18534–18545.
30.
ZaccariaVTuckerDTraversoA. A distributed real-time model of degradation in a solid oxide fuel cell, part i: model characterization. J Power Sources2016; 311: 175–181.
31.
Laguna-BerceroMCampanaRLarreaA, et al. Electrolyte degradation in anode supported microtubular yttria stabilized zirconia-based solid oxide steam electrolysis cells at high voltages of operation. J Power Sources2011; 196(21): 8942–8947.
32.
TakaoSSekizawaOSamjeskéG, et al. Observation of degradation of pt and carbon support in polymer electrolyte fuel cell using combined nano-x-ray absorption fine structure and transmission electron microscopy techniques. ACS Appl Mater Interfaces2018; 10(33): 27734–27744.
33.
PeiPChangQTangT. A quick evaluating method for automotive fuel cell lifetime. Int J Hydrog Energy2008; 33(14): 3829–3836.
34.
ChenHPeiPSongM. Lifetime prediction and the economic lifetime of proton exchange membrane fuel cells. Appl Energy2015; 142: 154–163.
35.
YangSChoiSKimY, et al. Improvement of fuel cell durability performance by avoiding high voltage. Int J Automot Technol2019; 20(6): 1113–1121.
36.
SharafOZOrhanMF. An overview of fuel cell technology: fundamentals and applications. Renew Sustain Energy Rev2014; 32: 810–853.
37.
DepatureCJemeiSBoulonL, et al. Ieee vts motor vehicles challenge 2017-energy management of a fuel cell/battery vehicle. In 2016 IEEE Vehicle Power and Propulsion Conference (VPPC), Hangzhou, China, 17–20 October 2016. New York: IEEE, pp.1–6.
38.
van NoortwijkJM. A survey of the application of gamma processes in maintenance. Reliab Eng Syst Saf2009; 94(1): 2–21.
39.
FlammiaDGuarinoAPetroneG, et al. Enhanced kalman filter-based identification of a fuel cell circuit model in impedance spectroscopy tests. In: ZamboniWPetroneG (eds.) ELECTRIMACS 2019. Heidelberg: Springer, 2020, pp.117–128.
40.
ZuoJCadetCLiZ, et al. Post-prognostics decision making for a two-stacks fuel cell system based on a load-dependent deterioration model. PHM Soc European Conf2020; 5: 9.
41.
DebKPratapAAgarwalS, et al. A fast and elitist multiobjective genetic algorithm: nsga-ii. IEEE Trans Evol Comput2002; 6(2): 182–197.
42.
WierzbickiAP. The use of reference objectives in multiobjective optimization. In FandelGGalT (eds.) Multiple criteria decision making theory and application. Berlin, Heidelberg: Springer, 1980, pp.468–486.
43.
BlankJDebK. Pymoo: multi-objective optimization in python. IEEE Access2020; 8: 89497–89509.
44.
BlankJDebKRoyPC. Investigating the normalization procedure of NSGA-III. In: International conference on evolutionary multi-criterion optimization. Cham: Springer, 2019, pp.229–240.
45.
CinlarEBažantZPOsmanE. Stochastic process for extrapolating concrete creep. J Eng Mech Division1977; 103(6): 1069–1088.
46.
BlankJDebK. A running performance metric and termination criterion for evaluating evolutionary multi- and many-objective optimization algorithms. In: 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 19–24 July 2020, pp.1–8. New York: IEEE.
