  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.
