Probabilistic attainability maps: Efficiently predicting driver-specific electric vehicle range

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Abstract

This paper concerns the efficient computation of a confidence level with which a particular driver will be able to reach a particular destination given the current state of charge of the battery of an electric vehicle. This probability of attainability is simultaneously computed for all destinations in a realistically sized map while taking into account the driver, the environment, on-board auxiliary systems and the vehicle battery system as potential sources of estimation noise. The model uses a feature-based linear regression framework which allows for a computationally efficient implementation capable of providing real-time updates of the resulting probabilistic attainability map. It was deployed on an all-electric Nissan Leaf and evaluated using data from over 140 miles of driving. The system proposed produces results of a quality commensurate with state-of-the-art approaches in terms of prediction accuracy.

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