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Systematic methodology for generation and design of hybrid vehicle powertrains

Abstract : To meet the vehicle fleet-wide average CO2 targets, the stringent pollutant emissions standards, and the clients’ new demands, the automakers realized the inevitable need to offer more hybrid and electric powertrains. Designing a hybrid powertrain remains however a complex task. It is an intricate system involving numerous variables that are spread over different levels: architecture, component technologies, sizing, and control. The industry lacks frameworks or tools that help in exploring the entire design space and in finding the global optimal solution on all these levels. This thesis proposes a systematic methodology that tries to answer a part of this need. Starting from a set of chosen components, the methodology automatically generates all the possible graphs of architectures using constraint-programming techniques. A tailored representation is developed to picture these graphs. The gearbox elements (clutches, synchronizer units) are represented with a level of details appropriate to generate the new-trend dedicated hybrid gearboxes, without making the problem too complex. The graphs are then transformed into other types of representation: 0ABC Table (describing the mechanical connections between the components), Modes Table (describing the available modes in the architectures) and Modes Table + (describing for each available mode the global efficiency and ratio of the power flow between all the components). Based on these representations, the architectures are filtered and the most promising ones are selected. They are automatically assessed and optimized using a general hybrid model specifically developed to calculate the performance and fuel consumption of all the generated architectures. This model is inserted inside a bi-level optimization process: Genetic Algorithm GA is used on the sizing and components level, while Dynamic Programming DP is used on the control level. A case study is performed and the capability of the methodology is proven. It succeeded in automatically generating all the graphs of possible architectures, and filtering dismissed architectures that were then proven not efficient. It also selected the most promising architectures for optimization. The results show that the proposed methodology succeeded in finding an architecture better than the ones proposed without the methodology (consumption about 5% lower)
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Submitted on : Wednesday, June 10, 2020 - 10:26:58 AM
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Bilal Kabalan. Systematic methodology for generation and design of hybrid vehicle powertrains. Electric power. Université de Lyon, 2020. English. ⟨NNT : 2020LYSE1048⟩. ⟨tel-02863337⟩

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