The state-of-health (SoH) of a battery is a number between 0 % and 100 %, which provides information on the battery’s health condition based on a pre-selected SoH definition. The capacity and power degradation of a battery depend on both the number of charge/discharge cycles and storage time, as well as on the internal and external state conditions. Being able to estimate the SoH is very important, as the battery is one of the most expensive components in an electric vehicle. Research has so far mainly been focusing on SoH modelling and estimation at individual cell level in controlled laboratory environments. However, performing SoH estimation at pack level in an uncontrolled environment is much more difficult than at cell level as components like fuses, relays, cables, connectors, and the battery-management-system are affecting the measurements. In addition, there can be variations between the cells, which needs to be considered when performing SoH estimation at battery pack level. The overall goal of this research project is therefore to identify and propose new methods/techniques that are able to estimate the SoH at battery pack level with sufficient accuracy. As a new approach, the SoH estimation will be supported by statistical data from a database, which will continuously be fed (updated) with data. Furthermore, the SoH estimation algorithm, which will be developed in the framework of this project, will be implemented in an EV/HEV DC fast charging station and installed in a workshop for verification and demonstration purposes.