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Réduction d'un modèle 0D instationnaire et non-linéaire de thermique habitacle pour l'optimisation énergétique des véhicules automobiles

Abstract : The use of automotive air conditioning leads to a fuel overconsumption. To reduce this overconsumption, we can either work upstream on the technical definitions of the cabin and the HVAC system or optimize control strategies. In both cases, it is essential to build a cabin thermal model that well balances accuracy and complexity. This is the topic of this PhD thesis driven by Renault Group. First, a model reduction methodology is used to build a 0D model starting from a 3D finite element cabin thermal model. This 0D model is based on mass and energy balances on the different cabin walls and air zones. It consists of a nonlinear differential algebraic equations system which can be reinterpreted as a Bond Graph. In addition, the 0D model is based on a weak coupling between the thermal equations and the fluid mechanics ones resulting from CFD calculations (internal airflow and external aerodynamics). Secondly, we apply a machine learning method to the data generated by the 0D model in order to build a reduced 0D model. A design of experiment is considered at this stage. Due to the nonlinearity of the heat exchanges, we have developed an approach which is inspired by the Gappy POD and EIM methods. We use a multiphysics reduced basis that takes several contributions into account (temperatures, enthalpies, heat fluxes and humidities). The resulting reduced model is a hybrid model that couples some of the original physical equations to an artificial neural network. The reduction methodology has been validated on Renault vehicles. The reduced order models have been integrated into a vehicle system-level energetic simulation platform (GREEN) which models different thermics (engine, transmission, cooling system, battery, HVAC, refrigerant circuit, underhood) in order to perform thermal management studies which are of particular importance for electric and hybrid vehicles. The reduced order models have been validated on several scenarios (temperature control for thermal comfort, driving cycles, HVAC coupling) and have achieved CPU gains of up to 99% with average errors of 0.5 °C on temperatures and 0.6% on relative humidities.
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https://hal-mines-paristech.archives-ouvertes.fr/tel-03012674
Contributor : Youssef Hammadi <>
Submitted on : Wednesday, December 2, 2020 - 12:24:52 AM
Last modification on : Friday, December 4, 2020 - 3:29:05 AM

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  • HAL Id : tel-03012674, version 1

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Youssef Hammadi. Réduction d'un modèle 0D instationnaire et non-linéaire de thermique habitacle pour l'optimisation énergétique des véhicules automobiles. Thermique [physics.class-ph]. Université Paris sciences et lettres, 2020. Français. ⟨NNT : 2020UPSLM027⟩. ⟨tel-03012674⟩

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