Technology learning for fuel cells: An assessment of past and potential cost reductions
AbstractFuel 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Energy Policy.
Volume (Year): 38 (2010)
Issue (Month): 6 (June)
Contact details of provider:
Web page: http://www.elsevier.com/locate/enpol
Technology innovation Learning curve Fuel cell;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Lipman, Timothy E. & Edwards, Jennifer L. & Kammen, Daniel M., 2004. "Fuel cell system economics: comparing the costs of generating power with stationary and motor vehicle PEM fuel cell systems," Energy Policy, Elsevier, vol. 32(1), pages 101-125, January.
- McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
- Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
- Tribe, M. A. & Alpine, R. L. W., 1986. "Scale economies and the "0.6 rule"," Engineering Costs and Production Economics, Elsevier, vol. 10(4), pages 271-278, March.
- John F. Muth, 1986. "Search Theory and the Manufacturing Progress Function," Management Science, INFORMS, vol. 32(8), pages 948-962, August.
- Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
- Ferioli, F. & Schoots, K. & van der Zwaan, B.C.C., 2009. "Use and limitations of learning curves for energy technology policy: A component-learning hypothesis," Energy Policy, Elsevier, vol. 37(7), pages 2525-2535, July.
- Sagar, Ambuj D. & van der Zwaan, Bob, 2006. "Technological innovation in the energy sector: R&D, deployment, and learning-by-doing," Energy Policy, Elsevier, vol. 34(17), pages 2601-2608, November.
- Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
- van der Zwaan, Bob & Keppo, Ilkka & Johnsson, Filip, 2013. "How to decarbonize the transport sector?," Energy Policy, Elsevier, vol. 61(C), pages 562-573.
- Ye Li & Peter Jan Engelen & Clemens Kool, 2012. "A Barrier Options Approach to Modeling Project Failure: The Case of Hydrogen Fuel Infrastructure," Working Papers 13-01, Utrecht School of Economics.
- Niko Jaakkola, 2013. "Green Technologies and the Protracted End to the Age of Oil: A strategic analysis," OxCarre Working Papers 099, Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.