Multi-stage state of health estimation of lithium-ion battery with high tolerance to heavily-partial charging

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Fältnamn	Indikatorer	Metadata
000		03740naa a2200445 4500
001		oai:research.chalmers.se:eaf7cb7d-603b-491b-9637-93ef99bba852
003		SwePub
008		220127s2022 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://doi.org/10.1109/TPEL.2022.31445042 DOI
024	7	a https://research.chalmers.se/publication/5284042 URI
040		a (SwePub)cth
041		a engb eng
042		9 SwePub
072	7	a art2 swepub-publicationtype
072	7	a ref2 swepub-contenttype
100	1	a Wei, Zhongbaou Beijing Institute of Technology4 aut
245	1 0	a Multi-stage state of health estimation of lithium-ion battery with high tolerance to heavily-partial charging
264	1	c 2022
338		a electronic2 rdacarrier
520		a State of health (SOH) is critical to the management of lithium-ion battery (LIB) due to its deep insight into the health diagnostic and protection. However, the absence of complete charging data, which is common in practice, risks impairing the charging-based SOH estimators. This paper proposes a multi-stage SOH estimation method with a broad scope of applications, including the unfavorable but practical scenarios of heavily-partial charging. In particular, different sets of health indicators (HIs), covering both the morphological IC features and the voltage entropy information, are extracted from the partial CC charging data with different initial charging voltage to characterize the aging status. Following this endeavor, artificial neural network (ANN)-based HI fusion is proposed to estimate the SOH of LIB precisely in real time. The proposed method is evaluated with long-term aging experiments performed on different types of LIBs. Results validate several superior merits of the proposed method, including high estimation accuracy, high tolerance to partial charging, strong robustness to cell inconsistency, and wide generality to different battery types.
650	7	a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Reglerteknik0 (SwePub)202022 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Control Engineering0 (SwePub)202022 hsv//eng
650	7	a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Signalbehandling0 (SwePub)202052 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Signal Processing0 (SwePub)202052 hsv//eng
650	7	a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Annan elektroteknik och elektronik0 (SwePub)202992 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Other Electrical Engineering, Electronic Engineering, Information Engineering0 (SwePub)202992 hsv//eng
653		a health indicators
653		a lithium-ion battery
653		a partial charging
653		a state of health
700	1	a Ruan, Haokaiu Beijing Institute of Technology4 aut
700	1	a Li, Yang,d 1984u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)yanu
700	1	a Li, Jianweiu Beijing Institute of Technology4 aut
700	1	a Zhang, Caizhiu Chongqing University4 aut
700	1	a He, Hongwenu Beijing Institute of Technology4 aut
710	2	a Beijing Institute of Technologyb Chalmers tekniska högskola4 org
773	0	t IEEE Transactions on Power Electronicsg 37:6, s. 7432-7442q 37:6<7432-7442x 0885-8993x 1941-0107
856	4	u https://research.chalmers.se/publication/528977/file/528977_Fulltext.pdfx primaryx freey FULLTEXT
856	4 8	u https://doi.org/10.1109/TPEL.2022.3144504
856	4 8	u https://research.chalmers.se/publication/528404

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Av författaren/redakt...: Wei, Zhongbao; Ruan, Haokai; Li, Yang, 1984; Li, Jianwei; Zhang, Caizhi; He, Hongwen

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