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Machine learning-ba...
Machine learning-based fast charging of lithium-ion battery by perceiving and regulating internal microscopic states
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- Wei, Zhongbao (författare)
- Beijing Institute of Technology
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- Yang, Xiaofeng (författare)
- Beijing Institute of Technology
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- Li, Yang, 1984 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- He, Hongwen (författare)
- Beijing Institute of Technology
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- Li, Weihan (författare)
- Rheinisch-Westfaelische Technische Hochschule Aachen,RWTH Aachen University
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- Sauer, Dirk Uwe (författare)
- Rheinisch-Westfaelische Technische Hochschule Aachen,RWTH Aachen University
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(creator_code:org_t)
- Elsevier BV, 2023
- 2023
- Engelska.
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Ingår i: Energy Storage Materials. - : Elsevier BV. - 2405-8297. ; 56, s. 62-75
- Relaterad länk:
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- Fast charging of the lithium-ion battery (LIB) is an enabling technology for the popularity of electric vehicles. However, high-rate charging regardless of the physical limits can induce irreversible degradation or even hazardous safety issues to the LIB system. Motivated by this, this paper proposes a machine learning-based fast charging strategy with multi-physical awareness within a battery-to-cloud framework. In particular, a reduced-order electrochemical-thermal model is built in the cloud to perceive the microscopic states of LIB, leveraging which the soft actor-critic (SAC) deep reinforcement learning (DRL) algorithm is exploited for the first time to train a fast charging strategy. Hardware-in-Loop tests and experiments with practical LIBs are carried out for validation. Results suggest that the battery-to-cloud architecture can mitigate the risk of a heavy computing burden in the real-time controller. The proposed strategy can effectively mitigate the unfavorable over-temperature and lithium deposition, which benefits the safety and longevity during fast charging. Given a similar charging speed, the proposed machine learning approach extends the LIB cycle life by about 75% compared to the commonly-used empirical protocol. Meanwhile, the proposed strategy is proven superior to the state-of-the-art rule-based and the model-based strategies in terms of charging rapidity, charging safety and computational complexity. Moreover, the trained low-complexity strategy is highly adaptive to the ambient temperature and initial charging state, which promises robust performance in practical applications.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Annan teknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Other Engineering and Technologies (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- deep reinforcement learning
- machine learning
- fast charging
- battery
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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