Aging is an unavoidable process caused by side reactions present in all electrochemical devices including battery cells. It may result in significant changes of capacity and resistance of a device over time and must, therefore, be considered in the system layout phase (e.g., necessity of over-sizing initial capacity) as well as in the system operation phase (e.g., adapting maximum allowed cell dispatch power).

In fact, in contrast to lesser demanding applications in portable devices, a profitable usage of Lithium iron Phosphate battery in stationary applications requires a detailed understanding and modeling of battery degradation: A long-lasting and demanding application will cause both performance and capacity reduction of the storage system and may significantly affect the overall business case via increased operational cost (OPEX) and particularly high degradation induced replacement cost.

It is common to monitor a battery State of Health (SOH) by an advance BMS to quantify the continued evolution of battery degradation resulting in both capacity fade and internal resistance increase (linked to decreasing peak power performance). The battery remaining capacity can be related to its nominal value derived in new/used state under standard test conditions. Due to transport regulations and application-specific minimum power requirements a replacement indicator SOH replace cap is defined. In automotive, often a SOH replace cap = 0.8 is applied, but for stationary applications and particularly in the context of second-life concepts lower values have been proposed.

Despite being studied for many years with continued effort we know that LFP life time is much more superior to VRLA, but still understanding and modeling the lifetime of LFP is a field of continued research.

In a challenging environment, if the user doesn't follow the operating instruction from the manufacturer, or if the battery and BMS quality is not up to the mark, then various degradation mechanisms including electrolyte decomposition, passive film formation, particle cracking, and active material dissolution can be individually addressed on the material and battery cell level often leading to increased resistance, reduced capacity retention and/or an increased risk of an unsafe battery state.

Conventional analysis and modeling approaches are based on extensive battery tests and derive empirical models often compatible with an Equivalent Circuit Model (ECM) approach for system performance determination. With an improved understanding of the cell-internal loss mechanisms, an increasing number of semi-empirical and physical models have been developed and successfully used for cell modeling. Recently, non-empirical Physical-Chemical Models (PCM) has gained increasing interest. Despite the usage of PCM models for aging prediction may allow giving a more detailed insight to cell internal loss mechanisms and how to circumvent these, it remains most challenging to find a valid parameterization of such models and to scale the cell internal models to the application relevant level of a full battery system.

With increasing capabilities of data logging and data management, data-driven approaches on the storage system level have also gained increasing interest recently. Despite improved capabilities of these emerging approaches, it is still believed, that for simulations of the aging behavior of a full