Machine learning-based fast charging of lithium-ion battery by perceiving and regulating internal microscopic states

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Wei, ZhongbaoBeijing Institute of Technology (author)

Machine learning-based fast charging of lithium-ion battery by perceiving and regulating internal microscopic states

Article/chapterEnglish2023

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Elsevier BV,2023

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LIBRIS-ID:oai:research.chalmers.se:45e08f68-415d-4b3a-8cd6-743997413557
https://doi.org/10.1016/j.ensm.2022.12.034DOI
https://research.chalmers.se/publication/534128URI

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Language:English
Summary in:English

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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.

Subject headings and genre

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
deep reinforcement learning
machine learning
fast charging
battery

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Yang, XiaofengBeijing Institute of Technology (author)
Li, Yang,1984Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)yanu (author)
He, HongwenBeijing Institute of Technology (author)
Li, WeihanRheinisch-Westfaelische Technische Hochschule Aachen,RWTH Aachen University (author)
Sauer, Dirk UweRheinisch-Westfaelische Technische Hochschule Aachen,RWTH Aachen University (author)
Beijing Institute of TechnologyChalmers tekniska högskola (creator_code:org_t)

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In:Energy Storage Materials: Elsevier BV56, s. 62-752405-8297

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By the author/editor: Wei, Zhongbao; Yang, Xiaofeng; Li, Yang, 1984; He, Hongwen; Li, Weihan; Sauer, Dirk Uwe

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NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Enginee ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Other Engineerin ...

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By the university: Chalmers University of Technology

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