As part of international strategies towards high penetration of renewable energy sources, the implementation of energy storage systems in power grids becomes increasingly necessary for short- and long-term energy deferral. Due to technical advantages in short-term and overall practical advantages, Lithium-Ion batteries are excellent storage mediums and already serve today widespread in a variety of applications both stationary and mobile. However, most storage systems are privately funded. To investors, battery storage is considered an asset with high capital expenditure and slow, only partially predictable return on investment. The value estimation of storage systems is intercepted by two major difficulties. The first issue is the inherent complexity of batteries regarding performance and aging, which requires in-depth knowledge for accurate statements. The second issue is the inability to determine the optimal operation strategy due to the sizable portfolio of potential applications, reaching from frequency response over arbitrage to peak-shaving, and the potential impact of each on the storage system. Investors are commonly provided with very few details on the battery itself and don’t always have the background knowledge necessary to accurately predict battery behavior and value generation in long term due to this complexity. This makes battery storage less tangible and less attractive than other assets. This presentation introduces a tool for potential customers of battery storage that can be applied to any market-available carbon-based Li-Ion battery technology. It aims to provide an approximation for the battery’s lifetime behavior based on a multi-physics model. Electrical, thermal and aging models are constructed from literature and scaled and enhanced through experimental data. The tool, which is developed in Python, takes as input the maximum amount of information available to customers in advance while making the minimum amount of assumptions about the storage system. The tool produces an approximation for the degradation of the storage system on an absolute and linear scale and can therefore be used inside optimization algorithms or as pre-processing tool. It is designed to be easily transferable and computationally efficient enough to allow for flexible parameter adjustment and multi-case comparison between storage providers and applications on any scale. Figure 1