Residual learning rates in lead-acid batteries: Effects on emerging technologies |
| |
| Affiliation: | 1. Massachusetts Institute of Technology, SUTD-MIT International Design Center, 77 Massachusetts Avenue, Cambridge, MA 02139, USA;1. Group for Sustainability and Technology, ETH Zurich, Zürich, Switzerland;2. Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States;3. Department of City and Regional Planning, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States;4. Environment, Ecology, and Energy Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States;5. Grantham Institute for Climate Change and the Environment, Imperial College London, London, United Kingdom;6. Centre for Environmental Policy, Imperial College London, London, United Kingdom;7. Energy and Resources Group, UC Berkeley, Berkeley, CA, United States;8. Renewable and Appropriate Energy Laboratory, UC Berkeley, Berkeley, CA, United States;9. Goldman School of Public Policy, UC Berkeley, Berkeley, CA, United States |
| |
| Abstract: | The low price of lead-acid, the most popular battery, is often used in setting cost targets for emerging energy storage technologies. Future cost reductions in lead acid batteries could increase investment and time scales needed for emerging storage technologies to reach cost-parity. In this paper the first documented model of cost reductions for lead-acid batteries is developed. Regression to a standard experience curve using 1989–2012 data yield a poor fit, with R2 values of 0.17 for small batteries and 0.05 for larger systems. To address this problem, battery costs are separated into material and residual costs, and experience curves developed for residual costs. Depending on the year, residual costs account for 41–86% of total battery cost. Using running-time averages to address volatility in material costs, a 4-year time average experience curve for residual costs yield much higher R2, 0.78 for small and 0.74 for large lead-acid batteries. The learning rate for residual costs in lead-acid batteries is 20%, a discovery with policy implications. Neglecting to consider cost reductions in lead-acid batteries could result in failure of energy storage start-ups and public policy programs. Generalizing this result, learning in incumbent technologies must be understood to assess the potential of emerging ones. |
| |
| Keywords: | Experience curve Lead-acid battery Material costs |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
