Abstract

With the accelerating adoption of lithium-ion battery systems, careful analysis of the performance evolution and failure mechanisms of commercially produced batteries is crucial for predicting the lifetime of battery systems, estimating operating and maintenance costs, and ensuring safe operation. Here, we present the results of a 2-year aging study conducted on large-format NMC-Gr pouch cells from a large battery manufacturer. Calendar and cycle aging tests were conducted on 23 cells, with calendar aging conducted at varying temperature and state-of-charge, and cycle aging conducted at varying temperatures, average voltage, voltage window, charge-discharge rates, using both constant-current cycling and drive cycles.The evolution of cell performance throughout aging was observed using electrochemical characterization tests. DC pulses show a general trend of decreasing then increasing resistance, while EIS measurements are used to deconvolute resistance evolution modes. Capacity checks show decreasing capacity throughout life for all cells. Fitting of C/20 charge and discharge curves using half-cell voltage data is used to quantify loss of lithium inventory and loss of active material degradation modes, as well as reveal the de/lithiation window experienced by electrodes throughout aging.While cell performance evolution is often neatly explained by a few key degradation modes, cell failures observed during testing were often the result of several interacting degradation mechanisms. Degradation mechanisms were identified by a variety of physical, electrochemical, and microstructural characterizations. Key degradation mechanisms observed were SEI growth at graphite/electrolyte interfaces, electrolyte decomposition and gassing leading, NMC particle cracking, Li plating, and electrode dry-out. Degradation mechanisms observable in early life often initiate new failure mechanism that lead to cell failure or safety concerns through physical or electrochemical interactions. The findings demonstrate the multi-faceted challenge of cell lifetime and failure prediction in real-world systems.

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