Consistency and Robustness in Forecasting for Emerging Technologies: The Case of Li-ion Batteries for Electric Vehicles

32 Pages Posted: 15 Nov 2016 Last revised: 23 Nov 2016

See all articles by Apurba Sakti

Apurba Sakti

MIT Energy Initiative

Ines Azevedo

Carnegie Mellon University

Erica R.H. Fuchs

Department of Engineering and Public Policy, Carnegie Mellon University

Jeremy J. Michalek

Carnegie Mellon University

Kevin Gallagher

Argonne National Laboratory - Chemical Sciences and Engineering Division

Jay Whitacre

Carnegie Mellon University - Department of Engineering & Public Policy; Aquion Energy

Date Written: November 12, 2016

Abstract

There are a large number of accounts about rapidly declining costs of batteries with potentially transformative effects, but these accounts often are not based on detailed design and technical information. Using a method ideally suited for that purpose, we find that when experts are free to assume any battery pack design, a majority of the cost estimates are consistent with the ranges reported in the literature, although the range is notably large. However, we also find that 55% of relevant experts’ component-level cost projections are inconsistent with their total pack-level projections, and 55% of relevant experts’ elicited cost projections are inconsistent with the cost projections generated by putting their design- and process-level assumptions into our process-based cost model (PBCM). These results suggest a need for better understanding of the technical assumptions driving popular consensus regarding future costs. Approaches focusing on technological details first, followed by non-aggregated and systemic cost estimates while keeping the experts aware of any discrepancies, should they arise, may result in more accurate forecasts.    

Keywords: Electric Vehicle, Lithium-Ion Battery, Battery Design, Expert Elicitation, Technology Forecasting

Suggested Citation

Sakti, Apurba and Azevedo, Ines and Fuchs, Erica Renee and Michalek, Jeremy J. and Gallagher, Kevin and Whitacre, Jay, Consistency and Robustness in Forecasting for Emerging Technologies: The Case of Li-ion Batteries for Electric Vehicles (November 12, 2016). Available at SSRN: https://ssrn.com/abstract=2868386 or http://dx.doi.org/10.2139/ssrn.2868386

Apurba Sakti (Contact Author)

MIT Energy Initiative ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Ines Azevedo

Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States

Erica Renee Fuchs

Department of Engineering and Public Policy, Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States

Jeremy J. Michalek

Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States

Kevin Gallagher

Argonne National Laboratory - Chemical Sciences and Engineering Division ( email )

9700 South Cass Avenue
Argonne, IL 60439
United States

Jay Whitacre

Carnegie Mellon University - Department of Engineering & Public Policy ( email )

129 Baker Hall
5000 Forbes Avenue
Pittsburgh, PA 15213
United States

Aquion Energy ( email )

Pittsburgh, PA
United States

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