Estimating the remaining useful life of lithium-ion batteries is crucial for their application as energy storage devices in stationary and automotive applications. It is therefore important to understand battery degradation based on chemistry, usage patterns, and operating environment. Different degradation mechanisms that affect performance and durability of lithium-ion batteries have been identified over the past decades. Amongst them, the solid-electrolyte interface (SEI) layer growth has been observed to be the most influential cause of capacity fading. In this paper, we introduce for the very first time, a framework that evaluates the predictive ability of physics-based macroscopic models in capturing battery dynamics as function of their state-of-health (SoH). Using data from accelerated aging experiments, we identify the applicability conditions of classical electrochemical models. This analysis is performed using a phase diagram approach that involves parameters controlling the micro-scale dynamics inside the lithium-ion cell.

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