A multi-physics model for a cylindrical Li-ion battery has been developed by coupling the thermal distribution in the radial direction to an electrochemical P2D model. The model can predict both terminal voltage and surface temperature, which has the advantage that it can be readily validated by measurement. A sensitivity analysis of up to 30 parameters was carried out using model simulation. A parameter sensitivity matrix was established to describe the parameter sensitivity under different operating conditions and the parameters were grouped according to their average sensitivity. The parameters were clustered based on their sensitivity matrix with Fuzzy C-Means (FCM) method. The cluster centers are special operating conditions on which the parameters in the same cluster have high identifiability. Finally, a stepwise experiment is designed based on the analysis results of parameter sensitivity, and the rationality and effectiveness are also validated. It was shown that the stepwise approach to parameter identification results in significantly higher accuracy.

Non-destructive analysis of degradation mechanisms can be very beneficial for the prognostics and health management (PHM) study of lithium-ion batteries. In this paper, a type of graphite/LiCoO₂ battery was cycle aged at high ambient temperature, then 25 parameters of the multi-physics model were identified. Nine key parameters degraded with the cycle life, and they were treated as indicators of battery degradation. Accordingly, the degradation mechanism was discussed by using the multi-physics model and key parameters, and the reasons for capacity fade and the internal resistance increase were analyzed in detail. All evidence indicates that the formation reaction of the solid electrolyte interface (SEI) film is the main cause of battery degradation at high ambient temperature.