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Consistency and robustness of forecasting for emerging technologies: The case of Li-ion batteries for electric vehicles

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  • Sakti, Apurba
  • Azevedo, Inês M.L.
  • Fuchs, Erica R.H.
  • Michalek, Jeremy J.
  • Gallagher, Kevin G.
  • Whitacre, Jay F.

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.

Suggested Citation

  • Sakti, Apurba & Azevedo, Inês M.L. & Fuchs, Erica R.H. & Michalek, Jeremy J. & Gallagher, Kevin G. & Whitacre, Jay F., 2017. "Consistency and robustness of forecasting for emerging technologies: The case of Li-ion batteries for electric vehicles," Energy Policy, Elsevier, vol. 106(C), pages 415-426.
    • Handle: RePEc:eee:enepol:v:106:y:2017:i:c:p:415-426
    DOI: 10.1016/j.enpol.2017.03.063
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    References listed on IDEAS

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    1. Catenacci, Michela & Verdolini, Elena & Bosetti, Valentina & Fiorese, Giulia, 2013. "Going electric: Expert survey on the future of battery technologies for electric vehicles," Energy Policy, Elsevier, vol. 61(C), pages 403-413.
    2. Edward S. Rubin & Sonia Yeh & David A. Hounshell & Margaret R. Taylor, 2004. "Experience curves for power plant emission control technologies," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 2(1/2), pages 52-69.
    3. Verdolini, Elena & Anadon, Laura Diaz & Lu, Jiaqi & Nemet, Gregory F., 2015. "The effects of expert selection, elicitation design, and R&D assumptions on experts' estimates of the future costs of photovoltaics," Energy Policy, Elsevier, vol. 80(C), pages 233-243.
    4. Baker, Erin & Bosetti, Valentina & Anadon, Laura Diaz & Henrion, Max & Aleluia Reis, Lara, 2015. "Future costs of key low-carbon energy technologies: Harmonization and aggregation of energy technology expert elicitation data," Energy Policy, Elsevier, vol. 80(C), pages 219-232.
    5. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    6. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    7. Mihai Burcea & Wing-Kai Hon & Hsiang-Hsuan Liu & Prudence W. H. Wong & David K. Y. Yau, 2016. "Scheduling for electricity cost in a smart grid," Journal of Scheduling, Springer, vol. 19(6), pages 687-699, December.
    8. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    9. Richard Green & Iain Staffell, 2016. "Electricity in Europe: exiting fossil fuels?," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 32(2), pages 282-303.
    10. Nemet, Gregory F. & Baker, Erin & Jenni, Karen E., 2013. "Modeling the future costs of carbon capture using experts' elicited probabilities under policy scenarios," Energy, Elsevier, vol. 56(C), pages 218-228.
    11. ., 2016. "Electric energy utilities," Chapters, in: Public Utilities, Second Edition, chapter 4, pages 69-88, Edward Elgar Publishing.
    12. Daschbach, J. M. & Apgar, Henry, 1988. "Design analysis through techniques of parametric cost estimation," Engineering Costs and Production Economics, Elsevier, vol. 14(2), pages 87-93, July.
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    Cited by:

    1. Cotterman, Turner & Fuchs, Erica R.H. & Whitefoot, Kate S. & Combemale, Christophe, 2024. "The transition to electrified vehicles: Evaluating the labor demand of manufacturing conventional versus battery electric vehicle powertrains," Energy Policy, Elsevier, vol. 188(C).
    2. Marc Wentker & Matthew Greenwood & Jens Leker, 2019. "A Bottom-Up Approach to Lithium-Ion Battery Cost Modeling with a Focus on Cathode Active Materials," Energies, MDPI, vol. 12(3), pages 1-18, February.
    3. Duffner, Fabian & Mauler, Lukas & Wentker, Marc & Leker, Jens & Winter, Martin, 2021. "Large-scale automotive battery cell manufacturing: Analyzing strategic and operational effects on manufacturing costs," International Journal of Production Economics, Elsevier, vol. 232(C).
    4. Whiston, Michael M. & Lima Azevedo, Inês M. & Litster, Shawn & Samaras, Constantine & Whitefoot, Kate S. & Whitacre, Jay F., 2021. "Paths to market for stationary solid oxide fuel cells: Expert elicitation and a cost of electricity model," Applied Energy, Elsevier, vol. 304(C).
    5. Xiaohong Wang & Shixiang Li & Lizhi Wang & Yaning Sun & Zhongxing Wang, 2020. "Degradation and Dependence Analysis of a Lithium-Ion Battery Pack in the Unbalanced State," Energies, MDPI, vol. 13(22), pages 1-25, November.
    6. Duffner, F. & Wentker, M. & Greenwood, M. & Leker, J., 2020. "Battery cost modeling: A review and directions for future research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    7. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    8. Doremus, Jacqueline & Helfand, Gloria & Liu, Changzheng & Donahue, Marie & Kahan, Ari & Shelby, Michael, 2019. "Simpler is better: Predicting consumer vehicle purchases in the short run," Energy Policy, Elsevier, vol. 129(C), pages 1404-1415.

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