Efficient Estimation of Battery Degradation Bounds from Cycling Data for Use in Planning Tools

Advanced energy storage systems (ESS) are vital for grid stability, peak shaving, and load shifting as power systems change over time. However, evaluating the technoeconomic viability of ESS is complex and often excludes battery degradation, which significantly impacts both lifespan and long-term costeffectiveness. Sandia National Laboratories’ QuESt 2.0 tool assists with ESS analysis but currently lacks integrated degradation modeling. This study introduces a computationally efficient method to incorporate battery degradation into technoeconomic analyses by leveraging real-world cycling data from BatteryArchive.org. The proposed model estimates capacity loss and efficiency decline, enabling more accurate performance forecasting. In addition, this work introduces a percentile band derived from pointwise degradation rates which are used to provide uncertainty bounds around the degradation prediction. These envelopes aid in displaying variability in the expected degradation over time to communicate the range of plausible degradation outcomes and to improve the representation of prediction. This enhancement supports better investment decisions in energy storage technologies.