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Article Dans Une Revue IEEE Vehicular Technology Magazine Année : 2021

Designing Hybrid Vehicle Architectures: Utilizing an Automatic Generation and Optimization Approach

Résumé

Different architectures for hybrid powertrains exist. To compare their efficiency, traditionally one energetic model is developed per architecture and is optimized. This heuristic approach can be replaced today by a systematic method that explores all possibilities in a defined search space. This article presents HEVs optimization and comparison using an automatic methodology for architectures generation. The components of series-parallel hybrid architecture, except planetary gears, are chosen as starting components. All the possible architectures are generated and then filtered to determine the most promising ones. The latter are automatically assessed and optimized using a general hybrid model. The process includes a genetic algorithm for the design optimization and dynamic programming for the control optimization. Given two different objectives to minimize, namely the battery size and the fuel consumption in our considered case, the Pareto fronts of the most promising architectures are presented and compared to the Pareto fronts of reference seriesparallel architectures. Several of the filtered architectures are found to be better than the references (consumption about 5% lower). In addition, a gain in powertrain sizing and cost is found. The best architecture is finally revealed and its operation is analyzed to determine the functionalities that lead to its energy efficiency.
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Dates et versions

hal-03328968 , version 1 (30-08-2021)

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Bilal Kabalan, Emmanuel Vinot, Rochdi Trigui, Clement Dumand. Designing Hybrid Vehicle Architectures: Utilizing an Automatic Generation and Optimization Approach. IEEE Vehicular Technology Magazine, 2021, 16 (2), pp.76-85. ⟨10.1109/MVT.2021.3061988⟩. ⟨hal-03328968⟩
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