A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks.

Li-ion batteries are the main power source used in electric propulsion applications (e.g., electric cars, unmanned aerial vehicles, and advanced air mobility aircraft).

Verification of Prognostic Algorithms to Predict Remaining Flying Time for Electric Unmanned Vehicles.

This paper addresses the problem of building trust in the online prediction of a battery powered UAV's remaining available flying time. A series of ground tests is described that make use of an electric unmanned aerial vehicle (eUAV) to verify the performance of remaining flying time predictions. The algorithm verification procedure described is implemented on a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure (charge depletion) experiments.

Experimental Validation of Model-Based Prognostics for Pneumatic Valves.

Because valves control many critical operations, they are prime candidates for deployment of prognostic algorithms. But, similar to the situation with most other components, examples of failures experienced in the field are hard to come by. This lack of data impacts the ability to test and validate prognostic algorithms.