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Reinforcement Learning-Based Root Planner for Electric Vehicle

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Complex, Intelligent and Software Intensive Systems (CISIS 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 176))

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Abstract

Electric Vehicles (EVs) have been identified as the current innovation for sustainable mobility that reduces carbon emissions and pollution. The transition to electric mobility is accelerating, and this means that services and infrastructures must be ready to support the impact of such a change. Smart applications can leverage this transition contributing to a seamless integration of the expected increase of new energy loads into the electric grid assisting users’ behaviour. At the same time, they must comply with the strict regulations in terms of privacy and deal with limited users’ acceptance. In this context, we propose a Policy-Based Reinforcement Learning agent-based route planner that is able to suggest a route to a driver who starts from a location A traveling to a location B minimizing the number of re-charge sessions along the journey.

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Author information

Authors and Affiliations

  1. University of Campania “Luigi Vanvitelli”, Aversa, Italy

    Pietro Fusco, Dario Branco & Salvatore Venticinque

Authors
  1. Pietro Fusco
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  2. Dario Branco
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  3. Salvatore Venticinque
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Corresponding author

Correspondence to Pietro Fusco .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Fusco, P., Branco, D., Venticinque, S. (2023). Reinforcement Learning-Based Root Planner for Electric Vehicle. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 176. Springer, Cham. https://doi.org/10.1007/978-3-031-35734-3_35

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