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Variable-Weight Optimization for More-Electric Aircraft Energy Management

  • Teng Lu
  • , Yang Qi
  • , Wei Chen
  • , Dongpo Deng
  • Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a variable weight coefficient-based dynamic programming energy management strategy for a hybrid-powered aircraft system integrating hydrogen fuel cells, lithium batteries, and supercapacitors. The strategy aims to synergistically optimize hydrogen consumption, fuel cell efficiency, and lithium battery lifespan under varying load and state-of-charge (SOC) conditions. By adaptively adjusting the weight coefficients of multiple objectives according to real-time flight power demand and battery SOC, the method achieves a dynamic balance among economic performance, and sustainability. Simulation results demonstrate the superiority of the proposed approach over the fixed-weight strategy. Specifically, it improves average fuel cell efficiency by 1.22%, reduces hydrogen consumption by 0.24%, and decreases lithium battery capacity degradation by 6.31%. These improvements collectively enhance the overall performance and operational economy of the aircraft. The study provides an effective solution for multi-objective cooperative management of complex aviation hybrid energy systems.

Original languageEnglish
Title of host publication2025 6th International Conference on Power Engineering, ICPE 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-68
Number of pages5
ISBN (Electronic)9798331570033
DOIs
StatePublished - 2025
Event2025 6th International Conference on Power Engineering, ICPE 2025 - Xi'an, China
Duration: 5 Dec 20257 Dec 2025

Publication series

Name2025 6th International Conference on Power Engineering, ICPE 2025

Conference

Conference2025 6th International Conference on Power Engineering, ICPE 2025
Country/TerritoryChina
CityXi'an
Period5/12/257/12/25

Keywords

  • Hybrid Energy Storage System
  • Multi-Objective Optimization
  • Variable Weight Coefficients

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