Bayesian analysis of interpretable aging across thousands of lithium-ion battery cycles

Abstract

The Doyle–Fuller–Newman (DFN) model is a common mechanistic model for lithium-ion batteries. The reaction rate constant and diffusivity are key parameters that directly affect the movement of lithium ions, thereby offering explanations for cell aging. This work investigates the ability to uniquely estimate each electrode’s diffusion coefficients and reaction rate constants of 95 T Model 3 cells with a nickel cobalt aluminum oxide (NCA) cathode and silicon oxide–graphite (${\mathrm{LiC}}_{\text{6}}$–${\mathrm{SiO}}_{\text{x}}$) anode. The four parameters are estimated using Markov chain Monte Carlo (MCMC) for a total of 7776 cycles at various discharge C-rates. While one or more anode parameters are uniquely identifiable over every cell’s lifetime, cathode parameters become identifiable at mid- to end-of-life, indicating measurable resistive growth in the cathode. The contribution of key parameters to the state of health (SOH) is expressed as a power law. This model for SOH shows a high consistency with the MCMC results performed over the overall lifespan of each cell. Our approach suggests that effective diagnosis of aging can be achieved by predicting the trajectories of the aging parameters. As such, extending our analysis with more physically accurate models building on DFN may lead to more identifiable parameters and further improved aging predictions.