The market for electric vehicles (EV) is growing rapidly. The rise of EVs is most prominent for passenger vehicles, but trucks and busses are also quickly becoming electrified. Scania aims to be the leader of this transition. A central part of the EV is the Lithium-ion battery. In order to use the battery in the most efficient manner a Battery Management System (BMS) is needed. A key part of the BMS is a model that describes the battery as a system where the input is the current and the output is the terminal voltage. The dynamics of the battery is affected by external factors, called scheduling variables, that should be taken in to account in order to acquire an accurate model. This thesis aims to capture this behavior by the identification of a Linear Parameter Varying (LPV) model that has State of Charge (SOC) and temperature as scheduling variables. The LPV model was identified by first performing a set of local system identifications at varying levels of the scheduling variables. From this, a set of different LPV model structures were set up and then optimized with the use of datasets with a wider coverage of the scheduling variables. The results showed that there are clear advantages in using an LPV model compared to a traditional constant model, but that the robustness of the model largely is dependent on the choice of the data used for optimization.