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Technology learning for fuel cells: An assessment of past and potential cost reductions

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  • Schoots, K.
  • Kramer, G.J.
  • van der Zwaan, B.C.C.
Abstract
Fuel cells have gained considerable interest as a means to efficiently convert the energy stored in gases like hydrogen and methane into electricity. Further developing fuel cells in order to reach cost, safety and reliability levels at which their widespread use becomes feasible is an essential prerequisite for the potential establishment of a 'hydrogen economy'. A major factor currently obviating the extensive use of fuel cells is their relatively high costs. At present we estimate these at about 1100 [euro](2005)/kW for an 80Â kW fuel cell system but notice that specific costs vary markedly with fuel cell system power capacity. We analyze past fuel cell cost reductions for both individual manufacturers and the global market. We determine learning curves, with fairly high uncertainty ranges, for three different types of fuel cell technology - AFC, PAFC and PEMFC - each manufactured by a different producer. For PEMFC technology we also calculate a global learning curve, characterised by a learning rate of 21% with an error margin of 4%. Given their respective uncertainties, this global learning rate value is in agreement with those we find for different manufacturers. In contrast to some other new energy technologies, R&D still plays a major role in today's fuel cell improvement process and hence probably explains a substantial part of our observed cost reductions. The remaining share of these cost reductions derives from learning-by-doing proper. Since learning-by-doing usually involves a learning rate of typically 20%, the residual value for pure learning we find for fuel cells is relatively low. In an ideal scenario for fuel cell technology we estimate a bottom-line for specific (80Â kW system) manufacturing costs of 95 [euro](2005)/kW. Although learning curves observed in the past constitute no guarantee for sustained cost reductions in the future, when we assume global total learning at the pace calculated here as the only cost reduction mechanism, this ultimate cost figure is reached after a large-scale deployment about 10 times doubled with respect to the cumulative installed fuel cell capacity to date.

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  • Schoots, K. & Kramer, G.J. & van der Zwaan, B.C.C., 2010. "Technology learning for fuel cells: An assessment of past and potential cost reductions," Energy Policy, Elsevier, vol. 38(6), pages 2887-2897, June.
    • Handle: RePEc:eee:enepol:v:38:y:2010:i:6:p:2887-2897
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    References listed on IDEAS

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    8. Engelen, Peter-Jan & Kool, Clemens & Li, Ye, 2016. "A barrier options approach to modeling project failure: The case of hydrogen fuel infrastructure," Resource and Energy Economics, Elsevier, vol. 43(C), pages 33-56.
    9. Julien Brunet & Jean-Pierre Ponssard, 2016. "Policies and Deployment for Fuel Cell Electric Vehicles An Assessment of the Normandy Project," Working Papers hal-01366205, HAL.
    10. Jaakkola, Niko, 2019. "Carbon taxation, OPEC and the end of oil," Journal of Environmental Economics and Management, Elsevier, vol. 94(C), pages 101-117.
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    14. Basrawi, Mohamad Firdaus Bin & Yamada, Takanobu & Nakanishi, Kimio & Katsumata, Hideaki, 2012. "Analysis of the performances of biogas-fuelled micro gas turbine cogeneration systems (MGT-CGSs) in middle- and small-scale sewage treatment plants: Comparison of performances and optimization of MGTs," Energy, Elsevier, vol. 38(1), pages 291-304.
    15. Wei, Max & Smith, Sarah J. & Sohn, Michael D., 2017. "Experience curve development and cost reduction disaggregation for fuel cell markets in Japan and the US," Applied Energy, Elsevier, vol. 191(C), pages 346-357.
    16. Hardman, Scott & Shiu, Eric & Steinberger-Wilckens, Robert & Turrentine, Thomas, 2017. "Barriers to the adoption of fuel cell vehicles: A qualitative investigation into early adopters attitudes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 166-182.
    17. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    18. Densing, Martin & Turton, Hal & Bäuml, Georg, 2012. "Conditions for the successful deployment of electric vehicles – A global energy system perspective," Energy, Elsevier, vol. 47(1), pages 137-149.
    19. Teng, Fei & Zhang, Qi & Chen, Siyuan & Wang, Ge & Huang, Zhenyue & Wang, Lu, 2024. "Comprehensive effects of policy mixes on the diffusion of heavy-duty hydrogen fuel cell electric trucks in China considering technology learning," Energy Policy, Elsevier, vol. 185(C).
    20. Sansi Yang & C. Richard Shumway, 2020. "Knowledge accumulation in US agriculture: research and learning by doing," Journal of Productivity Analysis, Springer, vol. 54(2), pages 87-105, December.
    21. Singh, Surinder P. & Ohara, Brandon & Ku, Anthony Y., 2021. "Prospects for cost-competitive integrated gasification fuel cell systems," Applied Energy, Elsevier, vol. 290(C).
    22. Fukui, Rokuhei & Greenfield, Carl & Pogue, Katie & van der Zwaan, Bob, 2017. "Experience curve for natural gas production by hydraulic fracturing," Energy Policy, Elsevier, vol. 105(C), pages 263-268.
    23. van der Zwaan, Bob & Keppo, Ilkka & Johnsson, Filip, 2013. "How to decarbonize the transport sector?," Energy Policy, Elsevier, vol. 61(C), pages 562-573.
    24. Arias-Gaviria, Jessica & van der Zwaan, Bob & Kober, Tom & Arango-Aramburo, Santiago, 2017. "The prospects for Small Hydropower in Colombia," Renewable Energy, Elsevier, vol. 107(C), pages 204-214.
    25. Nakata, Toshihiko & Sato, Takemi & Wang, Hao & Kusunoki, Tomoya & Furubayashi, Takaaki, 2011. "Modeling technological learning and its application for clean coal technologies in Japan," Applied Energy, Elsevier, vol. 88(1), pages 330-336, January.

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