This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. The result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Learning electric vehicle driver range anxiety with an initial state of charge-oriented gradient boosting approach


    Beteiligte:
    Song, Yang (Autor:in) / Hu, Xianbiao (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-03-04


    Format / Umfang :

    19 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





    Driver-selectable electric vehicle charger information system based on state of charge

    YOUNJAE HEO | Europäisches Patentamt | 2023

    Freier Zugriff

    SYSTEM AND METHOD FOR ESTIMATING A REMAINING ENERGY RANGE OF A VEHICLE AND REDUCING DRIVER RANGE ANXIETY

    OLIN PETER M / TANG LI / HUSTED HARRY | Europäisches Patentamt | 2024

    Freier Zugriff

    Electric Driving without Range Anxiety

    Schaeffler Technologies GmbH | Springer Verlag | 2014

    Freier Zugriff

    Analyzing the range barrier to electric vehicle adoption : the case of range anxiety

    Eisel, Matthias | GWLB - Gottfried Wilhelm Leibniz Bibliothek | 2016