The safety and economy of ship navigation will be impacted by the randomness of meteorology and the timeliness of the forecasting system, thus it is essential to take this into account when optimizing ship speed. A mathematical model of a hybrid diesel-electric ship is first established. Using the k-means clustering method, typical scenarios of prediction mistakes are produced while taking into account the uncertainty of wind speed and wave height predictions in meteorological data. Based on this, a stochastic optimization-based energy management and speed optimization model for hybrid ships is established and solved by an improved particle swarm algorithm. The simulation results confirm the superiority of the suggested model in lowering the ship's fuel consumption and enhancing safety and stability.


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    Titel :

    Energy management and speed optimization of diesel-electric hybrid ships considering meteorological uncertainties


    Beteiligte:
    Khan, Zeashan Hameed (Herausgeber:in) / Zhang, Junxing (Herausgeber:in) / Zeng, Pengfei (Herausgeber:in) / Huang, Jiantao (Autor:in) / Qiao, Yining (Autor:in) / Ai, Qian (Autor:in) / Zhao, Yu (Autor:in)

    Kongress:

    Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024) ; 2024 ; Xi'an, China


    Erschienen in:

    Proc. SPIE ; 13163


    Erscheinungsdatum :

    05.06.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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