Predictive energy management for hybrid electric aircraft propulsion systems

TL;DR

This paper develops a convex Model Predictive Control algorithm for hybrid electric aircraft, optimizing fuel consumption by managing energy flow between gas turbines and electric motors in real-time.

Contribution

It introduces a convex optimization-based MPC for hybrid aircraft energy management, including an ADMM solver that enables real-time application.

Findings

ADMM reduces computation time significantly.

Convex formulation ensures efficient optimization.

Effective energy management improves fuel efficiency.

Abstract

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 minimised 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.