Given the longitudinal average velocity and energy consumption of a Full Electric Vehicle (FEV) for any given part of a targeted road trip, this work solves the problem of online remaining range estimation, i.e., predicting, at any given travelled distance from the beginning of the trip, the actual distance the vehicle can still be driven before recharging is required. Modelling the remaining range is closely related with modelling the energy consumption of an electric vehicle. The latter remains an open problem due to unknown context data that may apply such as driving trip speed, vehicle load and topographical characteristics. In this work, a regression model is formulated in order to learn, from time/location-variant real driving data, a relationship between the future energy consumption on one side, and the following related factors, which are considered known, on the other side: i) the difference in average velocity between the future and the past ii) the difference in elevation rate between the future and the past and iii) the recent past energy consumption. Experimental results onaround 2000km of discharge trips, demonstrate the effectiveness of the method over a conventional method that is based solely on historic energy usage evidence. An average Mean Absolute Error (MAE) of 1.64 km and of 1.95 km is obtained when the regression model is evaluated on a model trained without and with elevation respectively.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Online prediction of an electric vehicle remaining range based on regression analysis




    Publication date :

    2014-12-01


    Size :

    1159578 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Enhancing Electric Vehicle Remaining Range Prediction Through Machine Learning

    Kim, Byunggun / Kim, Haeyoun | Springer Verlag | 2024

    Free access

    Remaining driving range estimation of electric vehicle

    Zhang, Yuhe / Wang, Wenjia / Kobayashi, Yuichi et al. | IEEE | 2012


    Electric vehicle remaining driving range prediction method and device considering road environment

    XU HANG / LIU YU / ZHU XIANGLEI et al. | European Patent Office | 2024

    Free access

    Electric Vehicle Range Prediction using Random Forest Regression

    Sagar, B S / Rajasekaran, Mothiram / Sakthisaravanan, B et al. | IEEE | 2024


    Digital Twin-Based Remaining Driving Range Prediction for Connected Electric Vehicles

    Li, Heng / Peng, Hui / Bin Kaleem, Muaaz et al. | SAE Technical Papers | 2023