Statistical Modeling Procedures for Rapid Battery Pack Characterization

Abstract

As lithium-ion battery (LIB) cells degrade over time and usage, it is crucial to understand their remaining capacity, also known as State of Health (SoH), and inconsistencies between cells in a pack, also known as cell-to-cell variation (CtCV), to appropriately operate and maintain LIB packs. This study outlines efforts to model pack SoH and SoH CtCV of nickel-cobalt-aluminum (NCA) and lithium-iron-phosphate (LFP) battery packs consisting of four cells in series using pack-level voltage data. Using small training data sets and rapid testing procedures, partial least squares regression (PLS) models were built and achieved a mean absolute error of 0.38% and 1.43% pack SoH for the NCA and LFP packs, respectively. PLS models were also built that correctly categorized the packs as having low, medium, and high-ranked SoH CtCV 72.5% and 65% of the time for the NCA and LFP packs, respectively. This study further investigates the relationships between pack SoH, SoH CtCV, and the voltage response of the NCA and LFP packs. The slope of the discharge voltage response of the NCA packs was shown to have a strong correlation with pack dynamics and pack SoH, and the lowest SoH cell within the NCA packs was shown to dominate the dynamic response of the entire pack.