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Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives
Beijing Inst Technol, China.
Beijing Inst Technol, China.
Swinburne Univ Technol, Fac Sci Engn & Technol, Hawthorn, Australia.
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.ORCID-id: 0000-0002-6279-4446
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2020 (engelsk)Inngår i: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 131, artikkel-id 110048Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Lithium-ion batteries decay every time as it is used. Aging-induced degradation is unlikely to be eliminated. The aging mechanisms of lithium-ion batteries are manifold and complicated which are strongly linked to many interactive factors, such as battery types, electrochemical reaction stages, and operating conditions. In this paper, we systematically summarize mechanisms and diagnosis of lithium-ion battery aging. Regarding the aging mechanism, effects of different internal side reactions on lithium-ion battery degradation are discussed based on the anode, cathode, and other battery structures. The influence of different external factors on the aging mechanism is explained, in which temperature can exert the greatest impact compared to other external factors. As for aging diagnosis, three widely-used methods are discussed: disassembly-based post-mortem analysis, curve-based analysis, and model-based analysis. Generally, the post-mortem analysis is employed for cross-validation while the curve-based analysis and the model-based analysis provide quantitative analysis. The challenges in the use of quantitative diagnosis and on-board diagnosis on battery aging are also discussed, based on which insights are provided for developing online battery aging diagnosis and battery health management in the next generation of intelligent battery management systems (BMSs). 

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Elsevier Ltd , 2020. Vol. 131, artikkel-id 110048
Emneord [en]
Accelerated aging tests, Aging mechanism, Diagnosis, Intelligent battery management systems, Lithium-ion battery, Automotive batteries, Battery management systems, Electrodes, Ions, Online systems, Automotive applications, Battery management systems (BMSs), Electrochemical reactions, Induced degradation, Model-based analysis, onboard diagnosis, Post mortem analysis, Quantitative diagnosis, Lithium-ion batteries
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URN: urn:nbn:se:mdh:diva-49490DOI: 10.1016/j.rser.2020.110048ISI: 000565620100002Scopus ID: 2-s2.0-85087996254OAI: oai:DiVA.org:mdh-49490DiVA, id: diva2:1456747
Tilgjengelig fra: 2020-08-06 Laget: 2020-08-06 Sist oppdatert: 2020-09-17bibliografisk kontrollert

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