A digital twin to quantitatively understand aging mechanisms coupled effects of NMC battery using dynamic aging profiles

Traditional lithium-ion battery modeling does not provide sufficient information to accurately verify battery performance under real-time dynamic operating conditions, particularly when considering various aging modes and mechanisms. To improve the current methods, this paper proposes a lithium-ion battery digital twin that can capture real-time data and integrate the strong coupling between SEI layer growth, anode crack propagation, and lithium plating. It can be utilized to estimate aging behavior from macroscopic full-cell level to microscopic particle level, including voltage-current profiles in dynamic aging conditions, predict the degradation behavior of Nickel-Manganese-Cobalt-Oxide (NMC)-based lithium-ion batteries, and assist in electrochemical analysis. This model can improve the root cause analysis of cell aging, enabling a quantitative understanding of aging mechanism coupled effects. Three charging protocols with dynamic discharging profiles are developed to simulate real vehicle operation scenarios and used to validate the digital twin, combining operando impedance measurements, post-mortem analysis, and SEM to further prove the conclusions. The digital twin can accurately predict battery capacity fade within 0.4% MAE. The results indicate that SEI layer growth is the primary contributor to capacity degradation and resistance increase. Based on the analysis of the model, it is concluded that one of the proposed multi-step charging protocols, in comparison to a standard continuous charging protocol, can reduce the degradation of NMC-based lithium-ion batteries. This paper represents a firm physical foundation for future physics-informed machine learning development.