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Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships

Wu, P; Partridge, J; Bucknall, R; (2020) Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships. Applied Energy , 275 , Article 115258. 10.1016/j.apenergy.2020.115258. Green open access

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Abstract

Hybrid fuel cell and battery propulsion systems have the potential to offer improved emission performance for coastal ships with access to H2 replenishment and battery charging infrastructures in ports. However, such systems could be constrained by high power source degradation and energy costs. Cost-effective energy management strategies are essential for such hybrid systems to mitigate the high costs. This article presents a Double Q reinforcement learning based energy management system for such systems to achieve near-optimal average voyage cost. The Double Q agent is trained using stochastic power profiles collected from continuous monitoring of a passenger ferry, using a plug-in hybrid fuel cell and battery propulsion system model. The energy management strategies generated by the agent were validated using another test dataset collected over a different period. The proposed methodology provides a novel approach to optimal use hybrid fuel cell and battery propulsion systems for ships. The results show that without prior knowledge of future power demands, the strategies can achieve near-optimal cost performance (96.9%) compared to those derived from using dynamic programming with the equivalent state space resolution.

Type: Article
Title: Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.apenergy.2020.115258
Publisher version: https://doi.org/10.1016/j.apenergy.2020.115258
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: Coastal ferry, Hybrid fuel cell and battery, Continuous monitoring, Energy management system, Reinforcement learning
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/10098265
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