For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%. Today, stationary energy storage systems utilizing lithium-ion batteries account for the majority of new storage capacity installed. 1In order to meet technical and economic requirements, the specified system lifetime has to be ensured.For reliable lifetime predictions, cell degradation models are necessary. Physicochemical models that include aging mechanisms are based on a detailed set of parameters which are often not readily available, computationally costly and require experimental parameterization of degradation rates.2-4 Instead, purely empirical models can be parameterized without knowledge of internal cell setup through extensive testing. Several purely empirical studies capture calendar aging 5,6 or cycle aging 7,8 without evaluating interdependencies. Through superposition, some empirical model approaches combine calendar and cycle aging 9-12 but tend to neglect the temperature dependence of the cycle aging mechanisms and are prone to extrapolation errors due to the utilized mathematical functions.Due to the limited knowledge about degradation mechanisms, empirically based models conventionally lump multiple degradation effects into single functions. This leads to the aforementioned prediction errors when deviating from the parameterization test conditions. E.g. for cycle aging, Waldmann et al. reported a transition of dominating aging mechanisms at 25• C. 13 The aging for temperatures above 25• C was attributed to the solid-electrolyte interface (SEI) growth and cathode degradation, while below 25• C the aging was attributed to lithium plating. In fact, for an improved understanding of cell internal degradation, model development should aim for a separation of the degradation mechanisms wherever possible. The respective mechanisms can then be modeled through functions that are suitable for the degradation driving factors.In this work, ...

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.