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A reduced battery system model and sizing algorithm for future hybrid electric airplanes architectures studies

Abstract : The path to climate-friendly aviation is a difficult way that needs to be traveled fast. A considerable and immediate drop of fossil fuels consumption is necessary to respect the Paris Agreement. Hybrid-electric and full-electric aircraft are part of the solution but developing a reliable and profitable hybrid-electric regional aircraft is a complex task. The European research project FUTPRINT50 aims at developing methods and tools to help to achieve this goal. The battery system is one of the key elements of aircraft electric architectures as it stores energy for propulsion and auxiliary systems. Among other challenges, optimal sizing (including mass, electric performances and aging) and safety are the two most difficult aspects to overcome. Herein, we propose a battery sizing tool which, for a given power solicitation, optimizes battery systems sizing including all above cited parameters. Based on a reduced battery cell model, this tool is fast enough to be run in a high-level general optimization at aircraft level. The reduced battery cell model is built up using, either a reference cell model (fixed chemistry, format and specifications) parametrized by testing, or a parametric cell model based on physical modeling (possibility to tune several chemistry parameters). Integrating a response surface of cell voltage, the reduced model is tuned thanks to a large set of dynamic simulation. This model is then used to run an optimal battery sizing, finally taking into account performance targets (mass, volume, voltage, power), aging and safety.
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https://hal-cea.archives-ouvertes.fr/cea-03552217
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Submitted on : Wednesday, February 2, 2022 - 10:58:09 AM
Last modification on : Thursday, February 3, 2022 - 3:43:09 AM

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  • HAL Id : cea-03552217, version 1

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Boris Berseneff, Sébastien Fiette, Anh-Linh Bui Van. A reduced battery system model and sizing algorithm for future hybrid electric airplanes architectures studies. 11th EASN International Conference, Sep 2021, Virtual, France. pp.0. ⟨cea-03552217⟩

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