Model and Graph Theory-based Differential-evolution Optimization Framework for Power, Propulsion and Energy Storage Systems of Electric Aircrafts

The proposed research will develop new scientific and engineering methodologies, based on the latest advancements in computational artificial intelligence stochastic multi-objective evolutionary algorithms such as differential evolution for the optimal design of electric aircraft. The distribution, power rating, specific power, and the positioning of the main components, including the electric generator, turbo engine, energy storage and power electronics converters, which would result in minimum overall mass, fuel consumption, cost, and maximum reliability, will be determined. This process will involve the evaluation of thousands of candidate designs for which advanced parallel processing techniques will be employed. The calculations will initially be based on high-level equivalent system models. The results will be used to identify critical issues and further develop detailed multi-domain multiphysics simulation models for specific system components, particularly, the power electronics converters and the battery energy storage subsystem. The effect that the design parameter tolerances have on the performance of the overall system will be quantified based on varieties of Monte Carlo and Design of Experiments (DOE) type methods.