This works studies the reduction of electrochemical lithium-ion battery cell models. Battery cell modeling is crucial to predicting battery behavior under different conditions and enables battery management system (BMS) control algorithms to ensure efficient and reliable operation. The finite difference method (FDM), commonly used for describing nonlinear battery dynamics, requires a high number of states to accurately capture the battery behavior. When computational resources are limited, model reduction is necessary to increase computational tractability and decrease runtime. The literature presents a variety of reduced-order modeling techniques, though these techniques do not commonly incorporate degradation dynamics. To address this gap, this study explores model reduction with the inclusion of degradation effects. Two methods are used to accomplish this. The first extends the application of the Padé approximation technique to incorporate degradation terms found in the boundary conditions of the single-particle model (SPM). This novel solution of the Padé approximation is compared against the Padé approximation solution that excludes degradation. The second uses optimization-based techniques to identify reduced-order models. The models produced are compared for different operating conditions of a lithium-ion battery cell. For the simulated case studies explored, optimization-based reduced-order models outperform Padé-based models for input conditions used for parameter optimization. However, the Padé-based models are more consistent with regards to negative electrode concentration accuracy for a separate validation input profile determined from a drive cycle's demanded current.