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Model-based fully coupled propulsion-aerodynamics optimization for hybrid electric aircraft energy management strategy

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  • Zhang, Jinning
  • Roumeliotis, Ioannis
  • Zolotas, Argyrios

Abstract

Hybrid electric aircraft concepts are high in future aviation agenda to enabling reduced fuel consumption and emissions. However, the additional weight of the introduced battery and electrical components and sizing cascading effects will impact aircraft mission analysis performance. Therefore, the potential benefit of adopting hybrid electric aircraft will highly depend on the applied energy management strategy (EMS). This paper presents a three-layer propulsion-mission analysis-EMS integrated multi-objective optimization scheme for hybrid electric aircraft identifying feasible design aspects considering fully coupled propulsion-aerodynamics effects. The ‘propulsion’ layer comprises the propulsion system modelling, integration approaches, and performance synthesis using an Artificial Neural Network (ANN)-based gas turbine surrogate model. The ‘mission-analysis’ layer is designed for multi-energy sources hybrid electric aircraft mission analysis considering fully coupled propulsion-aerodynamic effects. The ‘EMS’ layer utilizes non-dominated sorting genetic algorithm II (NSGA-II) addressing the design trade-off, i.e., block fuel burn, energy consumption, emissions. The proposed scheme is applied to a typical narrow-body aircraft, Boeing 737–800, equipped with mechanically integrated hybrid electric parallel propulsion configuration to explore flight electrification in civil aviation. Moreover, sensitivity analysis of battery technology level and flight mission definition is followed providing insights to hybrid electric aircraft application scenarios. The EMS optimization results indicate that for short/medium haul aircrafts which operate in high altitude with long flight durations, fuel as consumable energy source is prone to be used in initial stages to reduce aircraft weight and lift-dependent drag, while battery as non-consumable energy is optimally allocated in final flight stages of descent and landing. The design of hybrid electric aircraft is highly sensitive to both flight mission definition and battery specific energy projections. With battery specific energy projections of 1500 Wh/kg for the year 2035, optimal block fuel burn reduction by −44.62%, −31.47% and −21.86% can be obtained at the flight range design of 1000 nmi, 1250 nmi and 1500 nmi respectively.

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  • Zhang, Jinning & Roumeliotis, Ioannis & Zolotas, Argyrios, 2022. "Model-based fully coupled propulsion-aerodynamics optimization for hybrid electric aircraft energy management strategy," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001426
    DOI: 10.1016/j.energy.2022.123239
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    References listed on IDEAS

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    Cited by:

    1. Jinning Zhang & Ioannis Roumeliotis & Argyrios Zolotas, 2022. "Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control," Sustainability, MDPI, vol. 14(10), pages 1-30, May.
    2. Wang, Bin & Wang, Chaohui & Wang, Zhiyu & Ni, Siliang & Yang, Yixin & Tian, Pengyu, 2023. "Adaptive state of energy evaluation for supercapacitor in emergency power system of more-electric aircraft," Energy, Elsevier, vol. 263(PA).
    3. Lyu, Chenghao & Zhang, Yuchen & Bai, Yilin & Yang, Kun & Song, Zhengxiang & Ma, Yuhang & Meng, Jinhao, 2024. "Inner-outer layer co-optimization of sizing and energy management for renewable energy microgrid with storage," Applied Energy, Elsevier, vol. 363(C).
    4. Zhang, Jinning & Roumeliotis, Ioannis & Zhang, Xin & Zolotas, Argyrios, 2023. "Techno-economic-environmental evaluation of aircraft propulsion electrification: Surrogate-based multi-mission optimal design approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    5. Wang, Mingkai & Xiaoyang, Guotai & He, Ruichen & Zhang, Shuguang & Ma, Jintao, 2023. "Bi-layer sizing and design optimization method of propulsion system for electric vertical takeoff and landing aircraft," Energy, Elsevier, vol. 283(C).
    6. Zheng, Fengying & Chen, Yuang & Zhang, Jingyang & Cheng, Fengna & Zhang, Jingzhou, 2023. "A two-stage energy management for integrated thermal/energy optimization of aircraft airborne system based on Stackelberg game," Energy, Elsevier, vol. 269(C).
    7. Yang, Chao & Lu, Zhexi & Wang, Weida & Wang, Muyao & Zhao, Jing, 2023. "An efficient intelligent energy management strategy based on deep reinforcement learning for hybrid electric flying car," Energy, Elsevier, vol. 280(C).

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