The invention discloses a vehicle fuel consumption prediction method based on integrated learning, and the method comprises the steps: obtaining vehicle-mounted CAN data, selecting key features through employing an XGBoost method, introducing an improved pelican optimization algorithm to optimize the hyper-parameters of a deep echo state network, constructing a diesel truck fuel consumption prediction model, and obtaining a fuel consumption prediction result. High-precision prediction of the fuel consumption of the vehicle is realized, and the practicability and the economical efficiency of the fuel consumption prediction method are further improved.

    本发明公开了一种基于集成学习的车油耗预测方法,本发明通过获取车载CAN数据,使用XGBoost方法选择关键特征,引入改进的鹈鹕优化算法优化深度回声状态网络的超参数,并构建柴油卡车油耗预测模型,进而获得油耗预测结果,实现对车辆油耗的高精度预测,进一步提高了油耗预测方法的实用性和经济性。


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    Titel :

    Vehicle fuel consumption prediction method based on ensemble learning


    Weitere Titelangaben:

    一种基于集成学习的车油耗预测方法


    Beteiligte:
    WANG NING (Autor:in) / NIE LIAODONG (Autor:in) / LI XIUFENG (Autor:in) / SHANG KAI (Autor:in)

    Erscheinungsdatum :

    12.11.2024


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G07C TIME OR ATTENDANCE REGISTERS , Zeit- oder Anwesenheitskontrollgeräte




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    SCHOORS GEERT / HENSHER DAVID | Europäisches Patentamt | 2024

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