Abstract
This paper presents a system for calculating the optimum velocities and trajectories of an electric vehicle for a specific route. Our objective is to minimize the consumption over a trip without impacting the overall trip time. The system uses a particular segmentation of the route and involves a three-step procedure. In the first step, a neural network is trained on telemetry data to model the consumption of the vehicle based on its velocity and the surface gradient. In the second step, two Q-learning algorithms compute the optimum velocities and the racing line in order to minimize the consumption. In the final step, the computed data is presented to the driver through an interactive application. This system was installed on a light electric vehicle (LEV) and by adopting the suggested driving strategy we reduced its consumption by 24.03% with respect to the classic constant-speed control technique.
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Acknowledgements
We would like to thank Prometheus research team of National Technical University of Athens for providing the LEV for the research.
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Bougiouklis, A., Korkofigkas, A., Stamou, G. (2018). Improving Fuel Economy with LSTM Networks and Reinforcement Learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_23
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DOI: https://doi.org/10.1007/978-3-030-01421-6_23
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