Using Dfferential Evolution Techniques for Management of a Hybrid-Electric Propulsion System

Abstract

The air transportation industry contributes with 2% of the total greenhouse gas emissions, and there is a demand from global aviation regulators for reducing this percentage. Hybrid-electric propulsion systems (HEPS) for aircraft is an area of increasing interest for achieving these goals. It is a multidisciplinary research that involves internal combustion engines (ICE), electric motors (EM), power electronic converters, energy storage devices, propeller design, monitoring and control systems, management, etc. The Electromechanical Energy Conversion Group (GCEME) in Brazil developed a complete HEPS test bench and Laboratory. The facility will be able to test three different topologies: Series, full-electric and turbo-electric. The present work employs a detailed model for the test bench, and given a mission prole and the energy consumption as optimization function, it applies Dierential Evolution (DE) techniques in the energy management code. The results highlight the nonlinear nature of the HEPS model, and the worth of this methodology in looking for an optimal solution to reduce the computing processing time.

Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control

“…In another study [142], the synergy effects between powertrain, structure, and mission are exploited by applying the multiobjective optimization method, NSGA-II, and the S-metric selection evolutionary multiobjective algorithm (SMS-EMOA), and the potential benefit of structural integration and multifunctionalization are addressed. The nonlinear nature of the HEPS for aircraft is highlighted in [143], and DEA techniques are applied for power management in series-, full-electric-, and turboelectric-powered UAVs, respectively. An active energy management strategy is proposed for a fuel-cell/battery/supercapacitor-powered aircraft to control the battery and supercapacitor SOCs as well as to minimize hydrogen consumption by applying metaheuristic algorithms including the ant lion optimizer (ALO), moth-flame optimization (MFO), the dragonfly algorithm (DA), the sine cosine algorithm (SCA), the multi-verse optimizer (MVO), particle swarm optimization (PSO), and the whale optimization algorithm (WOA) [144].…”