“Li-ion Cell modeling & machine learning based diagnosis" With the current energy transition towards renewable sources and the rise of electric vehicles, optimizing battery use and improving our understanding of their degradation is becoming increasingly important. This thesis focuses on the modeling and diagnosis of Li-ion cells, specifically on designing and training a machine learning (ML) model capable of quantifying and characterizing degradation modes within the cell. These modes consist of the loss of active material (within both anode and cathode) and the loss of cyclable lithium throughout the battery's life. The model uses the cell's voltage response during low C-rate charging as the only input, or more specifically, using the incremental capacity method.