The performance of a power-split hybrid system and power coupling quality of its power components directly depend on the optimal design of its configuration and characteristic parameters. For numerous potential configurations, determining the best system configuration and corresponding planetary gear (PG) sets characteristic parameters to achieve the desired vehicle performance target is the key issue during a power-split system design. In this article, all potential configurations, different numbers of shifting actuators, connection positions of power components, and combinations of vehicle working modes are considered for a two-planetary-gear (2-PG) power-split hybrid system. A new algorithm, named Reborn after the Doom based on multi-objective particle swarm optimization (RAD-MOPSO) algorithm, is proposed to optimize the 2-PG system configuration and parameters. First, based on the analysis, evaluation, and screening of the system configuration, the design of the 2-PG hybrid system is considered as a multi-objective optimization design problem. The RAD-MOPSO algorithm is then proposed and applied, and the effectiveness and competitiveness of the proposed algorithm are validated by relevant benchmark functions. Finally, RAD-MOPSO is applied to the parameter optimization of the potential 2-PG system configuration. The calculation results demonstrate that this algorithm has faster convergence speed and better diversity performance compared with MOPSO and can also achieve efficient configuration screening and parameter optimization of a power-split hybrid system, and the Pareto-optimal solution set is obtained.


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    Title :

    Parameter optimization design of two-planetary-gear power-split hybrid system configuration using the RAD-MOPSO algorithm


    Contributors:
    Gu, Jiading (author) / Zhao, Zhiguo (author) / Chen, Yi (author) / Zhan, Xiaowen (author) / He, Lu (author)


    Publication date :

    2020-12-01


    Size :

    16 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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