A parametric design method can be used to determine hybrid vehicle component sizes. There are a variety of optimization algorithms available for hybrid electric vehicle (HEV) design. This chapter explores the feasibility of different global optimization algorithms by comparing their performance and accuracy. It reviews the principles and procedures of four global optimization algorithms, namely divided rectangles (DIRECT), simulated annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO). The chapter presents the advantages and disadvantages of these different optimization algorithms and the methodology of the model‐in‐the‐loop design process used for the study. It describes the constrained HEV design optimization problem and suggests the results with the associated comparison. The chapter also presents another design example of a series HEV using the non‐dominated sorting genetic algorithm (NSGA). Powertrain system analysis toolkit (PSAT) and Advanced Vehicle Simulator (ADVISOR) are chosen as HEV modeling tools for the optimization study.


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    Titel :

    HEV Component Sizing and Design Optimization


    Beteiligte:
    Mi, Chris (Autor:in) / Masrur, M. Abul (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2017-10-26


    Format / Umfang :

    28 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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