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    Adaptive Design of Experiments for Automotive Engine Applications Using Concurrent Bayesian Optimization

    Source: ASME Letters in Dynamic Systems and Control:;2022:;volume( 002 ):;issue: 003::page 31005
    Author:
    Zhu, Ling;Wang, Yan;Pal, Anuj;Zhu, Guoming G.
    DOI: 10.1115/1.4054222
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Most practical automotive problems require the design of experiments (DoEs) over a number of different operating conditions to deliver optimal calibration parameters. DoE is especially crucial for automotive engine calibration problems due to its increasing complexity and nonlinearity. As the complexity of the system increases, the DoE applications require a significant amount of expensive testing. However, only a limited number of testings are available and desired. The current work addresses this issue by presenting an adaptive DoE method based on Bayesian optimization to find optimal parameter settings with a significantly reduced number of physical testings (or function evaluations). To further improve optimization efficiency, this work presents a new approach: concurrent Bayesian optimization, which searches for optimal DoE under multiple operating conditions simultaneously. The method utilizes a surrogate model and a novel concurrent evolutionary multi-objective optimization method: concurrent non-dominated sorting genetic algorithm-II, to solve adaptive DoE in multiple operating conditions with a limited number of function evaluations. The experimental study is carried out on a gasoline engine calibration problem using a high-fidelity GT-SUITE™ engine model. The experimental results demonstrate the effectiveness of the algorithm by optimizing engine performance with a significantly reduced number of expensive testings to achieve accurate optimal solutions. The method simultaneously performs engine calibration at eight different operating conditions using only 500–600 testings, compared to the traditional approach, where each operating condition requires 300–500 testings independently to achieve optimal results.
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      Adaptive Design of Experiments for Automotive Engine Applications Using Concurrent Bayesian Optimization

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4288029
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    contributor authorZhu, Ling;Wang, Yan;Pal, Anuj;Zhu, Guoming G.
    date accessioned2022-12-27T23:10:40Z
    date available2022-12-27T23:10:40Z
    date copyright4/26/2022 12:00:00 AM
    date issued2022
    identifier issn2689-6117
    identifier otheraldsc_2_3_031005.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288029
    description abstractMost practical automotive problems require the design of experiments (DoEs) over a number of different operating conditions to deliver optimal calibration parameters. DoE is especially crucial for automotive engine calibration problems due to its increasing complexity and nonlinearity. As the complexity of the system increases, the DoE applications require a significant amount of expensive testing. However, only a limited number of testings are available and desired. The current work addresses this issue by presenting an adaptive DoE method based on Bayesian optimization to find optimal parameter settings with a significantly reduced number of physical testings (or function evaluations). To further improve optimization efficiency, this work presents a new approach: concurrent Bayesian optimization, which searches for optimal DoE under multiple operating conditions simultaneously. The method utilizes a surrogate model and a novel concurrent evolutionary multi-objective optimization method: concurrent non-dominated sorting genetic algorithm-II, to solve adaptive DoE in multiple operating conditions with a limited number of function evaluations. The experimental study is carried out on a gasoline engine calibration problem using a high-fidelity GT-SUITE™ engine model. The experimental results demonstrate the effectiveness of the algorithm by optimizing engine performance with a significantly reduced number of expensive testings to achieve accurate optimal solutions. The method simultaneously performs engine calibration at eight different operating conditions using only 500–600 testings, compared to the traditional approach, where each operating condition requires 300–500 testings independently to achieve optimal results.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleAdaptive Design of Experiments for Automotive Engine Applications Using Concurrent Bayesian Optimization
    typeJournal Paper
    journal volume2
    journal issue3
    journal titleASME Letters in Dynamic Systems and Control
    identifier doi10.1115/1.4054222
    journal fristpage31005
    journal lastpage31005_8
    page8
    treeASME Letters in Dynamic Systems and Control:;2022:;volume( 002 ):;issue: 003
    contenttypeFulltext
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