This thesis investigates the physics-based modeling and active control schemes of the lithium-ion battery rate-of-degradation (ROD) for EV applications. An integrated methodology is proposed. It models battery electro-thermal responses during drive cycles, and analyzes different drive cycle current properties’ effect on battery ROD during drive cycles. Also, it simplifies the complicated lithium-ion battery ROD model to a reduced-order model suitable to dynamically estimate battery ROD during drive cycles, and develops the active degradation control methodology via controlling the ROD for lithium-ion batteries. A power-commanded lithium-ion battery electro-thermal model combining a recurrent neural network (RNN) electrical model with a lumped thermal model is developed to model battery electrical-thermal responses. Current properties associated with drive cycles such as current rate, the current RMS value, the presence of regeneration and their interactions with the temperature are experimentally analyzed. A reduced-order battery ROD model without partial differential equations (PDE) with accurate exchange current estimations is developed. Active degradation control schemes during drive cycles via controlling the ROD are developed, incorporating the proposed models and emphasizing on manipulating the aging factors associated with the drive cycle current properties. Trade-off between the degradation reduction and battery performance compromise is addressed and an adjustable ROD-limit boundary is proposed to help achieve better balances. The ROD control system is also integrated with conventional battery state estimation systems to extend the overall battery management system capability. This research lays a foundation for applying active degradation control based on ROD control for lithium-ion batteries during drive cycles. This work can be valuable for EV applications for extending the battery life while maximizing the battery performance.