Reinforcement Learning-Based Energy Management System Enhancement Using Digital Twin for Electric Vehicles

Ye, Y; Xu, B; Zhang, J; Lawler, B; Ayalew, B

Reinforcement Learning-Based Energy Management System Enhancement Using Digital Twin for Electric Vehicles

Ye, Y Xu, B Zhang, J

Lawler, B Ayalew, B

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings, 2022, 00, pp. 1-6
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Ye, Y
dc.contributor.author	Xu, B
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722
dc.contributor.author	Lawler, B
dc.contributor.author	Ayalew, B
dc.date	2022-11-01
dc.date.accessioned	2023-04-13T04:05:40Z
dc.date.available	2023-04-13T04:05:40Z
dc.date.issued	2022-01-01
dc.identifier.citation	2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings, 2022, 00, pp. 1-6
dc.identifier.isbn	9781665475877
dc.identifier.uri	http://hdl.handle.net/10453/169766
dc.description.abstract	Compared to conventional engine-based powertrains, electrified powertrain exhibit increased energy efficiency and reduced emissions, making electrification a key goal for the automotive industry. For a vehicle with hybrid energy storage system, its performance and lifespan are substantially affected by the energy management system. Reinforcement learning-based methods are gaining popularity in vehicle energy management, but most of the literature in this area focus on pure simulation while hardware implementation is still limited. This paper introduces the digital twin methodology to enhance the Q-learning-based energy management system for battery and ultracapacitor electric vehicles. The digital twin model can exploit the bilateral interdependency between the virtual model and the actual system, which improves the control performance of the energy management system. The physical model is established based on a hardware-in-the-loop simulation platform. In addition, battery degradation is also considered for prolonging the battery lifespan to reduce the operating cost. The validation results of the trained reinforcement learning agent illustrate that the digital twin-enhanced Q-learning energy management system improves the energy efficiency by 4.36% and the battery degradation is reduced by 25.28%.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings
dc.relation.ispartof	2022 IEEE Vehicle Power and Propulsion Conference (VPPC)
dc.relation.isbasedon	10.1109/VPPC55846.2022.10003411
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Reinforcement Learning-Based Energy Management System Enhancement Using Digital Twin for Electric Vehicles
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-13T04:05:39Z
pubs.finish-date	2022-11-04
pubs.publication-status	Published
pubs.start-date	2022-11-01
pubs.volume	00

Abstract:

Compared to conventional engine-based powertrains, electrified powertrain exhibit increased energy efficiency and reduced emissions, making electrification a key goal for the automotive industry. For a vehicle with hybrid energy storage system, its performance and lifespan are substantially affected by the energy management system. Reinforcement learning-based methods are gaining popularity in vehicle energy management, but most of the literature in this area focus on pure simulation while hardware implementation is still limited. This paper introduces the digital twin methodology to enhance the Q-learning-based energy management system for battery and ultracapacitor electric vehicles. The digital twin model can exploit the bilateral interdependency between the virtual model and the actual system, which improves the control performance of the energy management system. The physical model is established based on a hardware-in-the-loop simulation platform. In addition, battery degradation is also considered for prolonging the battery lifespan to reduce the operating cost. The validation results of the trained reinforcement learning agent illustrate that the digital twin-enhanced Q-learning energy management system improves the energy efficiency by 4.36% and the battery degradation is reduced by 25.28%.

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http://hdl.handle.net/10453/169766