Findings offer insight for battery manufacturers and information provided to installers and consumers
Cell aging in manufacturer specification sheets and research publications is commonly reported as capacity fade, rather than energy fade, the irreversible loss of a battery cell’s ability to store and deliver energy over time. However, when results are scaled to full systems, this can lead to less accurate predictions about how much energy the system can store for how long once in use. Research conducted by Sandia National Laboratories with the University of Hawaiʻi found that the difference can cause up to a 15% error in energy storage system lifetime predictions. Results from the study suggest that cell-aging reports based on energy fade can more accurately predict cells lifetimes in real-world battery applications.
The paper has been among the top four most-read articles in the Journal of Electrochemical Energy Conversion and Storage for three months, and it was featured on batterydesign.net, a popular industry resource for battery design. The analysis is part of ongoing safety and reliability research to improve the understanding and predictability of energy storage systems and components under realistic grid conditions to stabilize, optimize, and grow the electricity system. Sandia National Laboratories’ Yuliya Preger led the research, which was funded by the Office of Electricity Energy Storage Division.
https://asmedigitalcollection.asme.org/electrochemical/article/23/2/021109/1231554/Are-Capacity-and-Energy-Loss-Equivalent-Metrics Preger, Y., Wittman, R., Harris, S. J., and Dubarry, M. (March 13, 2026). “Are capacity and energy loss equivalent metrics for battery aging reporting?” ASME. J. Electrochem. En. Conv. Stor. May 2026; 23(2): 021109. https://doi.org/10.1115/1.4071224
Photo: Research published in the Journal of Electrochemical Energy Conversion and Storage reinforces that capacity loss and energy loss are not equivalent metrics when reporting battery aging; distinguishing between the two can more reliably and accurately predict how much energy is available in a battery or system.
This material is based upon work supported by the U.S. Department of Energy, Office of Electricity (OE), Energy Storage Division.