A Range Prediction Method for All-Electric Aircraft by Capacity Discretization-based Iterative Convex Programming

Mingkai Wang, Saulo O.D. Luiz, Shuguang Zhang, Antonio M.N. Lima

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The emerging all-electric aircraft (AEA), which usually uses lithium-ion batteries as the energy supply unit, has constant mass but variable propulsive performance. The available voltage and power of electrified propulsion system can vary regarding battery current and capacity consumption. The dynamic features distinguish AEA from its conventional counterpart driven by fossil fuel, thus necessitating the reconsideration of performance prediction. This paper proposes a capacity discretization-based method to efficiently predict range of AEA. The core idea is to discretize AEA states on capacity consumption nodes. At each node, the maximum horizontal flight distance is determined by numerical optimization and accumulated as range. To guarantee the computational tractability, the crude nonlinear programming problem is convexified iteratively based on previous solution. Compared to a benchmark Breguet equation, the proposed method reflects the influence of voltage drop feature on AEA range and improves the prediction precision by 15.22%. Further numerical cases show that the iterative convex programming method is superior to the nonlinear programming paradigm with computational time reduced by 77.60%.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Transportation Electrification
DOIs
StateAccepted/In press - 2023
Externally publishedYes

Keywords

  • Aircraft
  • Aircraft performance
  • Batteries
  • Convex optimization
  • Electric aircraft
  • Iterative methods
  • Lithium-ion battery
  • Optimization
  • Programming
  • Transportation
  • Voltage

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