Autonomous Battery Identification and Health Parameterization

Abstract

Demand for grid energy storage is on the rise as grid resiliency becomes increasingly important. The large number of grid-based applications for energy storage are attractive to utilities, but the cost of large energy storage is often too high. Secondary-use batteries provide an opportunity to significantly lower the cost of a utility-scale energy storage system through the use of repurposed electric vehicle batteries. However, the available batteries from OEMs differ in many key parameters such as chemistry, capacity, and remaining life. Developing a method to autonomously identify these parameters will reduce the time spent installing and commissioning an autonomous multi-chemistry battery energy storage system. In this paper, a chemistry identification technique will be automated and adapted to high capacity, secondary-use batteries to expedite systems integration. Additionally, an approach for integrating these techniques to a utility-scale energy storage system will be discussed.