Feasibility study of 2020 target costs for PEM fuel cells and lithium-ion batteries: A two-factor experience curve approach

Abstract Vehicles with electric drive trains are currently the subject of intense discussion by society. The cost trends of the individual components in the electric drive train are a central aspect of the future market success of the different vehicle drive systems. An innovative two-factor experience curve approach was developed to facilitate the generation of the most meaningful cost forecasts for these components. This enables the creation of a flexible cost forecast model that supplements the two-factor experience curve approach by an analogous technology component. The performance of the model was demonstrated using alternative drive components, namely the proton exchange membrane (PEM) fuel cell stack, a high energy lithium-ion battery and a high power lithium-ion battery. A comparison of the forecast values calculated using this model with the industry targets determined by McKinsey in the study “A portfolio of power-trains for Europe” [1] shows that the realization of these targets for the fuel cell stack is possible if the product volume increases rapidly enough. For the high energy and high power lithium-ion battery targets, the product volume and research and development activity, measured here in terms of patent growth, need to grow compared to the trend of the last years.

[1]  Ulrich Fahl,et al.  Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model , 2009 .

[2]  Sven Ulrich,et al.  Lithium-Ionen Batterien: Stand der Technik und Anwendungspotenzial in Hybrid-, Plug-In Hybrid- und Elektrofahrzeugen , 2009 .

[3]  T. P. Wright,et al.  Factors affecting the cost of airplanes , 1936 .

[4]  Karl Heinrich Oppenländer,et al.  Patentwesen, technischer Fortschritt und Wettbewerb , 1984 .

[5]  Peter Mock,et al.  Entwicklung eines Szenariomodells zur Simulation der zukünftigen Marktanteile und CO2-Emissionen von Kraftfahrzeugen (VECTOR21) , 2010 .

[6]  Leo Schrattenholzer,et al.  Experiments with a methodology to model the role of RD first results , 2004 .

[7]  S. Kahouli-Brahmi Technological learning in energy–environment–economy modelling: A survey , 2008 .

[8]  W.G.J.H.M. van Sark,et al.  Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects , 2011 .

[9]  C. Wene Experience Curves for Energy Technology Policy , 2000 .

[10]  A. Griffin Metrics for Measuring Product Development Cycle Time , 1993 .

[11]  Rolf Steinhilper,et al.  Management des Produktlebenslaufs , 2009 .

[12]  Thomas Günther,et al.  Kostenrechnung und Kostenanalyse , 2011 .

[13]  Antonio Soria,et al.  Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching , 2000 .

[14]  Patrik Söderholm,et al.  Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies , 2007 .

[15]  R. J. Brodd,et al.  Lithium-ion batteries : science and technologies , 2009 .

[16]  U. Lindemann,et al.  Cost-Efficient Design , 2007 .

[17]  Bruce D. Henderson,et al.  Die Erfahrungskurve in der Unternehmensstrategie , 1974 .