Predictive Energy Management for Hybrid Electric Aircraft Propulsion Systems

We present a model predictive control (MPC) algorithm for energy management in aircraft with hybrid electric propulsion systems consisting of gas turbine and electric motor components. Series and parallel configurations are considered. By combining a point-mass aircraft dynamical model with models o...

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Bibliographic Details
Published in:IEEE transactions on control systems technology Vol. 31; no. 2; pp. 602 - 614
Main Authors: Doff-Sotta, Martin, Cannon, Mark, Bacic, Marko
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aircraft
Aircraft propulsion
Algorithms
Alternating direction method of multipliers (ADMM)
Atmospheric modeling
Batteries
Control theory
convex programming
Dynamic models
Electric motors
Energy management
Fly by wire control
Fuels
Gas turbines
hybrid aircraft
Hybrid propulsion systems
model predictive control (MPC)
Nonlinear programming
Optimization
Predictive control
Solvers
ISSN:1063-6536, 1558-0865
Online Access:Get full text
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Summary:We present a model predictive control (MPC) algorithm for energy management in aircraft with hybrid electric propulsion systems consisting of gas turbine and electric motor components. Series and parallel configurations are considered. By combining a point-mass aircraft dynamical model with models of electrical losses and losses in the gas turbine, the fuel consumed over a given future flight path is minimized subject to constraints on the battery, electric motor, and gas turbine. The optimization is formulated as a convex problem under mild assumptions and its solution is used to define a predictive energy management control law that takes into account the variation in aircraft mass during flight. We investigate the performance of algorithms for solving this problem. An alternating direction method of multipliers (ADMM) algorithm is proposed and compared with a general purpose convex interior point solver. We also show that the ADMM implementation reduces the required computation time by orders of magnitude in comparison with a general purpose nonlinear programming solver, making it suitable for real-time supervisory energy management control.
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ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2022.3193295