Highlights Mixed logit model is used to explore choice behavior related to recharging timing. SOC, interval days and VKT are main predictors for recharging or not. Experience of fast charging negatively affects normal charging. Users tend to recharge during the nighttime in the latter half of the trial. Nighttime charging is significantly correlated with other alternatives.

    Abstract This paper aims to examine choice behavior in respect of the time at which battery electric vehicle users charge their vehicles. The focus is on normal charging after the last trip of the day, and the alternatives presented are no charging, charging immediately after arrival, nighttime charging, and charging at other times. A mixed logit model with unobserved heterogeneity is applied to panel data extracted from a two-year field trial on battery electric vehicle usage in Japan. Estimation results, obtained using separate models for commercial and private vehicles, suggest that state of charge, interval in days before the next travel day, and vehicle-kilometers to be traveled on the next travel day are the main predictors for whether a user charges the vehicle or not, that the experience of fast charging negatively affects normal charging, and that users tend to charge during the nighttime in the latter half of the trial. On the other hand, the probability of normal charging after the last trip of a working day is increased for commercial vehicles, while is decreased for private vehicles. Commercial vehicles tend not to be charged when they arrival during the nighttime, while private vehicles tend to be charged immediately. Further, the correlations of nighttime charging with charging immediately and charging at other times reveal that it may be possible to encourage charging during off-peak hours to lessen the load on the electricity grid. This finding is supported by the high variance for the alternative of nighttime charging.


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    Title :

    Charge timing choice behavior of battery electric vehicle users


    Contributors:


    Publication date :

    2015-01-01


    Size :

    11 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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