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    Geo-Fence Based Route Tracking Diagnosis Strategy for Energy Prediction Strategies Applied to EV

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    2019_IECON_Geofence__.pdf (3.513Mb)
    Identifiers
    URI: http://hdl.handle.net/11556/1270
    ISSN: 1553-572X
    ISBN: 978-1-7281-4879-3
    DOI: 10.1109/IECON.2019.8927769
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    Author/s
    Prieto, P.; Trancho, E.; Arteta, B.; Parra, A.; Coupeau, A.; [et al.]
    Date
    2019
    Keywords
    BEV
    PHEV
    Energy consumption estimation
    Optimization
    Tracking diagnosis
    Abstract
    Nowadays, the shortage of energy and environmental pollution are considered as relevant problems due to the high amount of traditional automotive vehicles with internal combustion engines (ICEs). Electric vehicle (EV) is one of the solutions to localize the energy source and the best choice for saving energy and provide zero emission vehicles. However, their main drawback when compared to conventional vehicles is their limited energy storage capacity, resulting in poor driving ranges. In order to mitigate this issue, the scientific community is extensively researching on energy optimization and prediction strategies to extend the autonomy of EV. In general, such strategies require the knowledge of the route profile, being of capital importance to identify whether the vehicle is on route or not. Considering this, in this paper, a route tracking diagnosis strategy is proposed and tested. The proposed strategy relies on the information provided by the Google Maps API (Application Programming ...
    Type
    conferenceObject

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