Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/13415
Title: Charging policies for PREYs used for service delivery: a reinforcement learning approach
Authors: Panayiotou, Tania 
Chatzis, Sotirios P. 
Panayiotou, Christos 
Ellinas, Georgios 
metadata.dc.contributor.other: Χατζής, Σωτήριος Π.
Major Field of Science: Engineering and Technology
Field Category: Computer and Information Sciences
Keywords: Hybrid electric vehicles;Charging;Reinforcement learning approach;Markov processes
Issue Date: Nov-2018
Source: 21st IEEE International Conference on Intelligent Transportation Systems (ITSC), 2018, 4-7 November, Maui, United States
Conference: IEEE Conference on Intelligent Transportation Systems 
Abstract: This work examines a cost optimization problem for plug-in hybrid electric vehicles (PHEVs) used for service delivery, in the presence of energy consumption uncertainty. For the cost optimization problem, an optimal policy is found that dynamically decides, as the vehicle moves, at which charging station the vehicle should be charged, in order to minimize the service fuel cost. The problem is formulated as a Partially Observable Markov Decision Process (POMDP) and is solved by applying reinforcement learning (RL). The RL charging policy (RLCP), found after solving the POMDP, is compared to two benchmark policies and it is shown that RLCP outperforms both. Most importantly, RLCP can be automatically adjusted to significant variations on the vehicle's energy consumption behavior by continuously training the RLCP model according to the most recent information obtained from the vehicle's environment.
URI: https://hdl.handle.net/20.500.14279/13415
DOI: 10.1109/ITSC.2018.8569901
Rights: © 2018 IEEE.
Type: Conference Papers
Affiliation : University of Cyprus 
Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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