Application of a new type of lithium‑sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management

Ye, Y; Zhang, J; Pilla, S; Rao, AM; Xu, B

Application of a new type of lithium‑sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management

Ye, Y Zhang, J

Pilla, S Rao, AM Xu, B

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Journal of Energy Storage, 2023, 59
Issue Date:: 2023-03-01

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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	Pilla, S
dc.contributor.author	Rao, AM
dc.contributor.author	Xu, B
dc.date.accessioned	2024-03-31T21:51:35Z
dc.date.available	2024-03-31T21:51:35Z
dc.date.issued	2023-03-01
dc.identifier.citation	Journal of Energy Storage, 2023, 59
dc.identifier.issn	2352-152X
dc.identifier.issn	2352-152X
dc.identifier.uri	http://hdl.handle.net/10453/177381
dc.description.abstract	The continuous increase in vehicle ownership has caused overall energy consumption to increase rapidly. Developing new energy vehicle technologies and improving energy utilization efficiency are significant in saving energy. Plug-in hybrid electric vehicles (PHEVs) present a practical solution to the arising energy shortage concerns. However, existing battery technologies restrict PHEV application as the most popular lithium-ion battery has a relatively high capital cost and degradation during service time. This paper studies the application of a new type of lithium‑sulfur (Li–S) battery with bilateral solid electrolyte interphases in the PHEV. Compared with metals such as cobalt and nickel used in conventional lithium-ion batteries, sulfur utilized in Li–S is cheaper and easier to manufacture. The high energy density of the new Li–S battery also provides a longer range for PHEVs. In this paper, a PHEV propulsion system model is introduced, which includes vehicle dynamics, engine, electric motor, and Li–S battery models. Dynamic programming is formulated as a benchmark energy management strategy to reduce energy consumption. Besides the offline global optimal benchmark from dynamic programming, the real-time performance of the Li–S battery is evaluated by Q-learning and rule-based strategies. For a more comprehensive validation, both light-duty vehicles and heavy-duty vehicles are considered. Compared with lithium-ion batteries, the new Li–S battery reduces the fuel consumption by up to 14.63 % and battery degradation by up to 82.37 %.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Journal of Energy Storage
dc.relation.isbasedon	10.1016/j.est.2022.106546
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	40 Engineering
dc.title	Application of a new type of lithium‑sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management
dc.type	Journal Article
utslib.citation.volume	59
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	2024-03-31T21:51:33Z
pubs.publication-status	Published
pubs.volume	59

Abstract:

The continuous increase in vehicle ownership has caused overall energy consumption to increase rapidly. Developing new energy vehicle technologies and improving energy utilization efficiency are significant in saving energy. Plug-in hybrid electric vehicles (PHEVs) present a practical solution to the arising energy shortage concerns. However, existing battery technologies restrict PHEV application as the most popular lithium-ion battery has a relatively high capital cost and degradation during service time. This paper studies the application of a new type of lithium‑sulfur (Li–S) battery with bilateral solid electrolyte interphases in the PHEV. Compared with metals such as cobalt and nickel used in conventional lithium-ion batteries, sulfur utilized in Li–S is cheaper and easier to manufacture. The high energy density of the new Li–S battery also provides a longer range for PHEVs. In this paper, a PHEV propulsion system model is introduced, which includes vehicle dynamics, engine, electric motor, and Li–S battery models. Dynamic programming is formulated as a benchmark energy management strategy to reduce energy consumption. Besides the offline global optimal benchmark from dynamic programming, the real-time performance of the Li–S battery is evaluated by Q-learning and rule-based strategies. For a more comprehensive validation, both light-duty vehicles and heavy-duty vehicles are considered. Compared with lithium-ion batteries, the new Li–S battery reduces the fuel consumption by up to 14.63 % and battery degradation by up to 82.37 %.

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http://hdl.handle.net/10453/177381