Residual learning rates in lead-acid batteries: Effects on emerging technologies Schuyler Matteson, Eric Williams n Golisano Institute for Sustainability, Rochester Institute of Technology, One Lomb Memorial Drive, Rochester, NY 14623, USA HIGHLIGHTS  We analyze potential cost reductions in lead-acid batteries.  Modified experience curve for non-material costs gives good empirical fit.  Historical learning rate for non-material costs from 1985–2012 is 19–24%.  Progress in incumbent technology raises barrier to new entrants. article info Article history: Received 29 July 2014 Received in revised form 11 April 2015 Accepted 20 May 2015 Keywords: Experience curve Lead-acid battery Material costs 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 docu- mented model of cost reductions for lead-acid batteries is developed. Regression to a standard experi- ence curve using 1989–2012 data yield a poor fit, with R 2 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 R 2 , 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 technolo- gies must be understood to assess the potential of emerging ones. & 2015 Elsevier Ltd. All rights reserved. 1. Introduction As we move into a data-driven future immersed in digital technology, new constraints are imposed on our infrastructure systems. In the case of electricity, reliability has become a pre- mium service, with governments, hospitals, data centers, cor- porations, and personal mobile technologies requiring a higher quantity, and a better quality of service than ever before. Many organizations, including electric utilities themselves, are now turning to energy storage systems to provide much needed energy security. The energy storage sector is a burgeoning market, with con- tinuing introductions of new technologies and applications. A recent report predicts that the global market for energy storage for grid use alone could rise from $200 million in 2012 to over $10 billion in 2017 (Warshay, 2013). Even though new systems based on lithium based batteries, flywheels, or compressed air technol- ogy have performance qualities distinct from lead-acid, the main contributor to market success is still cost. More mature technol- ogies, namely lead-acid batteries, remain the system of choice for stationary energy storage. In the world of batteries, the lead-acid chemistry is the most common (Haas and Cairns, 1999; Linden, 2010). Lead-acid batteries were first developed in 1860 by Gaston Plante, and have grown into the most widely used electrical energy storage system due to their high reliability and low cost (Huggins and Robert, 2010). As shown in Table 1, compared to other energy storage technologies, lead-acid batteries remain one of the cheapest options, giving them a distinct advantage in popular applications. The two primary uses for lead-acid batteries are in automobiles Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/enpol Energy Policy http://dx.doi.org/10.1016/j.enpol.2015.05.014 0301-4215/& 2015 Elsevier Ltd. All rights reserved. n Corresponding author. E-mail addresses: swm3850@rit.edu (S. Matteson), exwgis@rit.edu (E. Williams). Energy Policy 85 (2015) 71–79