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Authors: Roman Liessner ; Christian Schroer ; Ansgar Dietermann and Bernard Bäker

Affiliation: TU Dresden, Germany

ISBN: 978-989-758-275-2

Keyword(s): Energy Management, Deep Learning, Reinforcement Learning, Hybrid Electric Vehicle.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: Machine Learning seizes a substantial role in the development of future low-emission automobiles, as manufacturers are increasingly reaching limits with traditional engineering methods. Apart from autonomous driving, recent advances in reinforcement learning also offer great benefit for solving complex parameterization tasks. In this paper, deep reinforcement learning is used for the derivation of efficient operating strategies for hybrid electric vehicles. There, for achieving fuel efficient solutions, a wide range of potential driving and traffic scenarios have to be anticipated where intelligent and adaptive processes could bring significant improvements. The underlying research proves the ability of a reinforcement learning agent to learn nearlyoptimal operating strategies without any prior route-information and offers great potential for the inclusion of further variables into the optimization process.

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Paper citation in several formats:
Liessner, R.; Schroer, C.; Dietermann, A. and Bäker, B. (2018). Deep Reinforcement Learning for Advanced Energy Management of Hybrid Electric Vehicles.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-275-2, pages 61-72. DOI: 10.5220/0006573000610072

@conference{icaart18,
author={Roman Liessner. and Christian Schroer. and Ansgar Dietermann. and Bernard Bäker.},
title={Deep Reinforcement Learning for Advanced Energy Management of Hybrid Electric Vehicles},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2018},
pages={61-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006573000610072},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Deep Reinforcement Learning for Advanced Energy Management of Hybrid Electric Vehicles
SN - 978-989-758-275-2
AU - Liessner, R.
AU - Schroer, C.
AU - Dietermann, A.
AU - Bäker, B.
PY - 2018
SP - 61
EP - 72
DO - 10.5220/0006573000610072

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