Despite its fully commercial development status, lithium-ion battery (LIB) degradation understanding and diagnostics is still not fully consolidated, involving a number of interconnected mechanisms whose effect combines into a complexely mixed performance fade. State-of-health (SoH), while lacking a harmonized definition, is usually referred as a mere fade of capacity, lacking physical background and any possibility to distinguish between different degradation mechanisms. In such scenario, residual lifetime reliable estimation becomes an extremely challenging task.
In this work, degradation related to automotive-specific application have been investigated by applying several appositely developed dynamic load profiles (driving cycles, such as WLTP reported in Attachment 1) to commercial battery samples, replicating real-world and accelerated aging, under laboratory-controlled environment, for several hundreds of equivalent full cycles (EFCs). The contribution of calendar aging has been appositely characterized to enable its discretization from the overall fading. A multi-measurement diagnostic protocol combining discharge test, electrochemical impedance spectroscopy (EIS) and voltage relaxation, previously developed to maximize the sensitivity of the pseudo-2D Doyle-Fuller-Newman (P2D-DFN) model parameters, was periodically applied to thoroughly characterize cells aging modes (as reported in Attachment 2 for EIS during WLTP aging). Also, several online fast diagnostics have been developed and performed to track battery performance fading during actual real-world operation. Hence, a previously developed P2D-DFN model, implemented with heat transfer, has been applied to interpret the periodic aging diagnostics by means of an optimized stepwise calibration algorithm, translating battery performance fade into a variation of their key physical parameters.
Such physical-based approach enabled the interpretation of battery aging, distinguishing the underlying degradation mechanisms and identifying and quantifying the impact of operation-specific aging stressors, such as average C-rate, State of Charge (SOC), depth of discharge (DOD) and temperature. Also, the methodology permitted the validation of the physical consistency of accelerated stress cycles with respect to real-world driving cycles and permitting to identify fast experimental diagnostics which may enable a physically-sound online tracking of battery state of health during its operation.