In vehicle operation, in order to maximize the fuel economy, the propulsion system control can easily adapt to pressure and temperature variations as these variations can be measured by sensors. However, it is challenging to detect driving cycles. With growing progress made in the artificial intelligence field, pattern recognition gains momentum in various applications. This study presents a study on driving cycle pattern recognition based on supervised learning. Training data 2-D visualization is achieved by the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Ten out of 12 supervised learning algorithms predict driving conditions with an accuracy of 88% or higher, and the extra tree (ET) algorithm leads the recognition accuracy at 90.26%. To improve the recognition accuracy, two hierarchical frameworks are proposed by integrating multiple supervised learning methods using weighted average and vote methods. The two hierarchical frameworks boost the driving condition recognition accuracy from 90.26% to 90.43% (weighted average) and 91.76% (vote). The results are further validated in the holdout test. In addition, a plug-in hybrid electric vehicle simulation shows 3.88%–5.82% fuel economy improvement compared to the baseline method. Two hierarchical methods outperform the ET method by 2% fuel economy. In summary, supervised learning shows great potential to detect driving cycles for vehicle energy saving.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Study of Vehicle Driving Condition Recognition Using Supervised Learning Methods


    Contributors:
    Xu, Bin (author) / Shi, Junzhe (author) / Li, Sixu (author) / Li, Huayi (author)


    Publication date :

    2022-06-01


    Size :

    1892826 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    An approach to vehicle recognition using supervised learning

    Kato, T. / Ninomiya, Y. | Tema Archive | 2000


    Self-Driving Car Using Supervised Learning

    Collins, Ryan ;Kumar Maurya, Himanshu ;Ragul, Raj S.R. | Trans Tech Publications | 2023


    Self-driving commercial vehicle formation driving decision-making method based on semi-supervised learning

    HU WEIMING / LIU YAN / ZHOU JINYING et al. | European Patent Office | 2024

    Free access

    Vehicle driving condition construction method

    XIANG JIWEN / ZHOU ENYU / CHEN BAIFAN | European Patent Office | 2021

    Free access