Linear Battery Aging Model for Industrial Peak Shaving Applications

Recent attention to industrial peak shaving applications sparked an increased interest in battery energy storage systems. Among other studies, there have been several reports examining optimal sizing of such storage systems. Most such works make significant assumptions about the key factors that affect battery degradation. This work examines these assumptions using a linear aging model that considers a state of charge (SOC) dependent calendric aging. The linear model reveals the potential of an SOC-aware charge control strategy for peak shaving applications: with adequate forecasting, the battery storage system lifetime could be significantly prolonged. The results also show that while batteries used in peak shaving applications are sensitive to calendric aging, the depth of discharge cycling is much less relevant. This is an important observation that will simplify relevant optimization studies and thus contribute to more widespread application of industrial peak shaving systems.