https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-6279-4446
Show others and affiliations
2020 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 131, article id 110048Article in journal (Refereed) 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). 

Place, publisher, year, edition, pages
Elsevier Ltd , 2020. Vol. 131, article id 110048
Keywords [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
National Category
Energy Engineering
Identifiers
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
Available from: 2020-08-06 Created: 2020-08-06 Last updated: 2020-09-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Li, Hailong

Search in DiVA

By author/editor
Li, Hailong
By organisation
Future Energy Center
In the same journal
Renewable & sustainable energy reviews
Energy Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 244 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf