001024892 001__ 1024892
001024892 005__ 20250203103202.0
001024892 0247_ $$2doi$$a10.1016/j.xcrp.2023.101720
001024892 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-02543
001024892 0247_ $$2WOS$$aWOS:001144156000001
001024892 037__ $$aFZJ-2024-02543
001024892 082__ $$a530
001024892 1001_ $$0P:(DE-HGF)0$$aWang, Qiushi$$b0
001024892 245__ $$aLarge-scale field data-based battery aging prediction driven by statistical features and machine learning
001024892 260__ $$a[New York, NY]$$bElsevier$$c2023
001024892 3367_ $$2DRIVER$$aarticle
001024892 3367_ $$2DataCite$$aOutput Types/Journal article
001024892 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1712733931_14826
001024892 3367_ $$2BibTeX$$aARTICLE
001024892 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001024892 3367_ $$00$$2EndNote$$aJournal Article
001024892 500__ $$aUnterstützt durch das Projekt ‘‘COBALT-P’’ (16BZF314C) des BMKW
001024892 520__ $$aAccurately predicting battery aging is critical for mitigating performance degradation during battery usage. While the automotive industry recognizes the importance of utilizing field data for battery performance evaluation and optimization, its practical implementation faces challenges in data collection and the lack of field data-based prognosis methods. To address this, we collect field data from 60 electric vehicles operated for over 4 years and develop a robust data-driven approach for lithium-ion battery aging prediction based on statistical features. The proposed pre-processing methods integrate data cleaning, transformation, and reconstruction. In addition, we introduce multi-level screening techniques to extract statistical features from historical usage behavior. Utilizing machine learning, we accurately predict aging trajectories and worst-lifetime batteries while quantifying prediction uncertainty. This research emphasizes a field data-based framework for battery health management, which not only provides a vital basis for onboard health monitoring and prognosis but also paves the way for battery second-life evaluation scenarios.
001024892 536__ $$0G:(DE-HGF)POF4-1223$$a1223 - Batteries in Application (POF4-122)$$cPOF4-122$$fPOF IV$$x0
001024892 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001024892 7001_ $$0P:(DE-HGF)0$$aWang, Zhenpo$$b1
001024892 7001_ $$0P:(DE-HGF)0$$aLiu, Peng$$b2
001024892 7001_ $$aZhang, Lei$$b3
001024892 7001_ $$0P:(DE-Juel1)172625$$aSauer, Dirk Uwe$$b4
001024892 7001_ $$00000-0002-2916-3968$$aLi, Weihan$$b5$$eCorresponding author
001024892 773__ $$0PERI:(DE-600)3015727-4$$a10.1016/j.xcrp.2023.101720$$gVol. 4, no. 12, p. 101720 -$$n12$$p101720 -$$tCell reports / Physical science$$v4$$x2666-3864$$y2023
001024892 8564_ $$uhttps://juser.fz-juelich.de/record/1024892/files/Large-scale%20field%20data-based%20battery%20aging%20prediction%20driven%20by%20statistical%20features%20and%20machine%20learning.pdf$$yOpenAccess
001024892 8564_ $$uhttps://juser.fz-juelich.de/record/1024892/files/Large-scale%20field%20data-based%20battery%20aging%20prediction%20driven%20by%20statistical%20features%20and%20machine%20learning.gif?subformat=icon$$xicon$$yOpenAccess
001024892 8564_ $$uhttps://juser.fz-juelich.de/record/1024892/files/Large-scale%20field%20data-based%20battery%20aging%20prediction%20driven%20by%20statistical%20features%20and%20machine%20learning.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001024892 8564_ $$uhttps://juser.fz-juelich.de/record/1024892/files/Large-scale%20field%20data-based%20battery%20aging%20prediction%20driven%20by%20statistical%20features%20and%20machine%20learning.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001024892 8564_ $$uhttps://juser.fz-juelich.de/record/1024892/files/Large-scale%20field%20data-based%20battery%20aging%20prediction%20driven%20by%20statistical%20features%20and%20machine%20learning.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001024892 909CO $$ooai:juser.fz-juelich.de:1024892$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
001024892 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172625$$aForschungszentrum Jülich$$b4$$kFZJ
001024892 9131_ $$0G:(DE-HGF)POF4-122$$1G:(DE-HGF)POF4-120$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1223$$aDE-HGF$$bForschungsbereich Energie$$lMaterialien und Technologien für die Energiewende (MTET)$$vElektrochemische Energiespeicherung$$x0
001024892 9141_ $$y2024
001024892 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2023-10-27
001024892 915__ $$0LIC:(DE-HGF)CCBYNCND4$$2HGFVOC$$aCreative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
001024892 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bCELL REP PHYS SCI : 2022$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bCELL REP PHYS SCI : 2022$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2023-05-02T08:54:40Z
001024892 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2023-05-02T08:54:40Z
001024892 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001024892 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2023-05-02T08:54:40Z
001024892 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-27
001024892 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-27
001024892 9201_ $$0I:(DE-Juel1)IEK-12-20141217$$kIEK-12$$lHelmholtz-Institut Münster Ionenleiter für Energiespeicher$$x0
001024892 9801_ $$aFullTexts
001024892 980__ $$ajournal
001024892 980__ $$aVDB
001024892 980__ $$aUNRESTRICTED
001024892 980__ $$aI:(DE-Juel1)IEK-12-20141217
001024892 981__ $$aI:(DE-Juel1)IMD-4-20141217