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Near-optimal energy management for plug-in hybrid fuel cell and battery propulsion using deep reinforcement learning

Wu, P; Partridge, J; Anderlini, E; Liu, Y; Bucknall, R; (2021) Near-optimal energy management for plug-in hybrid fuel cell and battery propulsion using deep reinforcement learning. International Journal of Hydrogen Energy (In press).

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Abstract

Plug-in hybrid fuel cell and battery propulsion systems appear promising for decarbonising transportation applications such as road vehicles and coastal ships. However, it is challenging to develop optimal or near-optimal energy management for these systems without exact knowledge of future load profiles. Although efforts have been made to develop strategies in a stochastic environment with discrete state space using Q-learning and Double Q-learning, such tabular reinforcement learning agents’ effectiveness is limited due to the state space resolution. This article aims to develop an improved energy management system using deep reinforcement learning to achieve enhanced cost-saving by extending discrete state parameters to be continuous. The improved energy management system is based upon the Double Deep Q-Network. Real-world collected stochastic load profiles are applied to train the Double Deep Q-Network for a coastal ferry. The results suggest that the Double Deep Q-Network acquired energy management strategy has achieved a further 5.5% cost reduction with a 93.8% decrease in training time, compared to that produced by the Double Q-learning agent in discrete state space without function approximations. In addition, this article also proposes an adaptive deep reinforcement learning energy management scheme for practical hybrid-electric propulsion systems operating in changing environments.

Type: Article
Title: Near-optimal energy management for plug-in hybrid fuel cell and battery propulsion using deep reinforcement learning
Publisher version: https://www.journals.elsevier.com/international-jo...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Hybrid fuel cell and battery propulsion, Coastal ferry, Continuous monitoring, Deep reinforcement learning, Energy Management System
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10135003
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