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Development of Urban Air Mobility (UAM) concepts has been primarily focused on electric vertical takeoff and landing aircraft (eVTOLs), small aircraft which can land and takeoff vertically, and which are powered by rechargeable (typically lithium-ion) batteries. Design, analysis, and operation of eVTOLs requires fast and accurate prediction of Li-ion battery performance throughout the lifetime of the battery. eVTOL battery performance modeling must be particularly accurate at high discharge rates to ensure accurate simulation of the high power takeoff and landing portions of the flight. In this work, we generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning in the form of Universal Ordinary Differential Equations (U-ODE's) combined with an electrochemical cell model and degradation models which include solid electrolyte interphase (SEI) growth, lithium plating, and charge loss. We show that Cellfit with U-ODE's is better able to predict battery degradation than a mechanistic battery degradation model. We show that the improved accuracy of the degradation model improves the accuracy of the performance model. We believe that Cellfit will prove to be a valuable tool for eVTOL designers.