Authors:
Roman Liessner
;
Christian Schroer
;
Ansgar Dietermann
and
Bernard Bäker
Affiliation:
TU Dresden, Germany
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.