The electrification of the transportation industry requires increasing attention to battery life-cycle management. In particular, Lithium-ion batteries are often retired from Electric Vehicles (EVs) when they still have significant residual capacity. Therefore, they can be repurposed in less-demanding applications, where they are referred to as Second-Life Batteries (SLBs). A key challenge to the reliable use of SLBs is the accurate prediction of their Remaining Useful Life (RUL). This thesis proposes a Gaussian Process Regression (GPR) model for RUL prediction that explicitly accounts for Cell-to-Cell variation while requiring limited prior knowledge. The model is validated on both experimental and synthetic data. Due to the strong influence of input data on data-driven models, a comprehensive sensitivity analysis was conducted to evaluate the impact of different input features and data quantity on prediction accuracy.