Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering.
2019 (English)In: Journal of Power Sources, ISSN 0378-7753, E-ISSN 1873-2755, Vol. 421, p. 56-67Article in journal (Refereed) Published
Abstract [en]

Precisely battery state of health estimation and remaining useful lifetime prediction are crucial factors in ensuring the reliability and safety for system operation. This paper thus focuses on the short-term battery state of health estimation and long-term battery remaining useful lifetime prediction. A novel hybrid method by fusion of partial incremental capacity and Gaussian process regression is proposed and dual Gaussian process regression models are employed to forecast battery health conditions. First, the initial incremental capacity curves are filtered by using the advanced signal process technology. Second, the important health feature variables are extracted from partial incremental capacity curves using correlation analysis method. Third, the Gaussian process regression is applied to model the short-term battery SOH estimation using the feature variables. Forth, an autoregressive long-term battery remaining useful lifetime model is established using the results of battery SOH values and previous output. The predictive capability and effectiveness of two models are demonstrated by four battery datasets under different cycling test conditions. Otherwise, the robustness of the two models is verified using four datasets with different health levels. The experimental results show that the proposed method can provide accurate battery state of health estimation and remaining useful lifetime.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 421, p. 56-67
Keywords [en]
Correlation coefficient, Gaussian regression process, Incremental capacity analysis, Lithium-ion batteries, State of health
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-246422DOI: 10.1016/j.jpowsour.2019.03.008ISI: 000464487000009Scopus ID: 2-s2.0-85062567256OAI: oai:DiVA.org:kth-246422DiVA, id: diva2:1300731
Note

QC 20190329

Available from: 2019-03-29 Created: 2019-03-29 Last updated: 2019-05-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Yan, Jinying
By organisation
Chemical Engineering
In the same journal
Journal of Power Sources
Energy Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 22 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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