The partial-update filter is a Kalman filter modification that can accommodate higher nonlinearities and uncertainties than a nominal and Schmidt-Kalman filter. This robustness enhancement of the partial-update filter is attributed to its capability to limit the impact of incorrect updates by applying user-selected static percentages of the nominal Kalman update, to user-selected states at any time step.To further extend the partial-update capabilities and applicability, this paper presents two methods for dynamically and automatically selecting the partial-update percentages based on nonlinearity metrics of the process and measurement model. By enabling dynamic update percentages, the filter automatically leverages situations where higher updates can be applied and lower updates are deemed suitable. This leads to higher statistical consistency and accuracy with respect to the nominal Kalman and static partial-update filters. The superior accuracy and consistency of the proposed nonlinearity-aware partial-update methods are shown via a numerical example.