A Fast Q-learning Energy Management Strategy for Battery/Supercapacitor Electric Vehicles Considering Energy Saving and Battery Aging

Ye, Y; Zhang, J; Xu, B

A Fast Q-learning Energy Management Strategy for Battery/Supercapacitor Electric Vehicles Considering Energy Saving and Battery Aging

Ye, Y Zhang, J

Xu, B

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021, 2022, 00, pp. 1639-1644
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Ye, Y
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722
dc.contributor.author	Xu, B
dc.date	2021-12-09
dc.date.accessioned	2023-04-06T03:55:50Z
dc.date.available	2023-04-06T03:55:50Z
dc.date.issued	2022-01-01
dc.identifier.citation	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021, 2022, 00, pp. 1639-1644
dc.identifier.isbn	9781665442312
dc.identifier.uri	http://hdl.handle.net/10453/169318
dc.description.abstract	Electrified powertrain system brings advantages of improved energy efficiency and reduced fossil fuel consumption, which leads to the electrification of powertrain systems as a key target of automotive industry. Advanced battery technology has been exploited in electric vehicle application and has made considerable progress. However, the degradation of batteries in the vehicle operation could cause adverse effects on the performance and lifespan of electric vehicles. Research on battery/supercapacitor hybrid energy storage systems of electric vehicles that considers energy saving and battery degradation is also lacking. This paper presents a fast Q-learning based energy management strategy to maximize energy saving and minimize battery degradation. Besides, battery only electric vehicle is also studied, and acts as a baseline vehicle. An electrified powertrain system model considering the battery degradation effect is established to form the environment for the Q-learning strategy. Under the training and validation driving cycles, the comparison indicates that the fast Q-learning strategy improves the energy efficiency by 3.83%, and the battery degradation is relieved by 26.36%.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
dc.relation.ispartof	International Conference on Electrical, Computer, and Energy Technologies
dc.relation.isbasedon	10.1109/ICECET52533.2021.9698682
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Fast Q-learning Energy Management Strategy for Battery/Supercapacitor Electric Vehicles Considering Energy Saving and Battery Aging
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Cape Town, South Africa
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	*
pubs.consider-herdc	false
dc.date.updated	2023-04-06T03:55:49Z
pubs.finish-date	2021-12-10
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-12-09
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Electrified powertrain system brings advantages of improved energy efficiency and reduced fossil fuel consumption, which leads to the electrification of powertrain systems as a key target of automotive industry. Advanced battery technology has been exploited in electric vehicle application and has made considerable progress. However, the degradation of batteries in the vehicle operation could cause adverse effects on the performance and lifespan of electric vehicles. Research on battery/supercapacitor hybrid energy storage systems of electric vehicles that considers energy saving and battery degradation is also lacking. This paper presents a fast Q-learning based energy management strategy to maximize energy saving and minimize battery degradation. Besides, battery only electric vehicle is also studied, and acts as a baseline vehicle. An electrified powertrain system model considering the battery degradation effect is established to form the environment for the Q-learning strategy. Under the training and validation driving cycles, the comparison indicates that the fast Q-learning strategy improves the energy efficiency by 3.83%, and the battery degradation is relieved by 26.36%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/169318