Bad Cell Identification of Utility-Scale Battery Energy Storage System through Statistical Analysis of Electrical and Thermal Properties

High penetration of renewable integration with the grid has invoked the necessity for battery energy storage applications. This has been further escalated by technology advances and descending costs. Utility-Scale Battery Energy Storage Systems (BESS) are being deployed worldwide in numerous projects due to their ability to provide grid ancillary services such as frequency regulation, flexible ramping, and black start services besides their energy storing and shifting capabilities. To ensure reliable and safe operation, identifying the bad battery cells in a Utility-Scale BESS is of profound importance. This paper proposes a comprehensible method of bad cell identification for Utility-Scale BESS by evaluating the necessary electrical and thermal properties of the cells through statistical approaches. This method only requires data that is easily available and accessible and can save a lot of time and effort. A detailed formulation of the analysis process was carried out and applied to data from a Utility-Scale BESS. Results demonstrated that the method can successfully identify bad cells in a BESS containing a large number of cells. This approach can offer prospective economic benefits by reducing the Operation and Maintenance (O&M) costs associated with Utility-Scale BESS.