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
Remaining useful life prediction of lithium-ion batteries using particle filter framework
Karlstad University, Faculty of Technology and Science. ISEA-RWTH.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Prediktion av återstående livslängd för litiumjonbatterier med användning av partikelfilter (Swedish)
Abstract [en]

A method for the prediction of remaining useful life (RUL) of li-ion battery for battery management system (BMS) is presented in this thesis work. Estimation of State of charge (SOC), state of health (SOH) and RUL are important tasks in BMS. For the prediction of RUL, information of aging parameter is required. Previously, in RUL prediction methods, aging parameter information was based on the battery tests data. For online estimation of RUL, ageing parameter is required and BMS does have a sensor system which could measure the battery ageing parameter online. So estimation techniques are utilized for the estimation of ageing parameters. Previously different methods were used such as Fuzzy logic, artificial neural network (ANN) and Kalman filters. Fuzzy logic and ANN methods are computationally heavy to implement them in BMS of EVs. Kalman filters methods are not very accurate for the estimation of an ageing parameter because batteries are highly nonlinear system and Kalman filters can only capture nonlinearity up to 3rd order Taylor series expression. This thesis work presents an algorithm for the prediction of RUL, which is based on the information of SOH. To predict the behaviour of the battery, Thevenin battery model is selected and internal resistance of the battery is considered as an ageing parameter. Internal resistance changes due to change in temperature. Particle filtering framework is implemented for estimation and is found that unscented particle filter gives better estimation of ageing parameter because it adapts the ageing parameter with respect to temperature change. RUL prediction consists of two steps. In first step, SOC and ageing parameter is estimated and then SOH is calculated from ageing parameter. In second step, RUL is predicted from the information of SOH. Variable load and effect of temperature are also considered for the estimation of ageing parameter. SOH is obtained from the ageing parameter and then a degradation model is developed from the information of SOH. Remaining cycles of the battery are then predicted from this degradation model by using particle filter.

Place, publisher, year, edition, pages
2014. , p. 114
Keywords [en]
Battery management system, remaining useful life, state of health, particle filter
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kau:diva-31806OAI: oai:DiVA.org:kau-31806DiVA, id: diva2:709276
Subject / course
Electrical Engineering
Presentation
2013-12-18, Karlstad University, SE-651 88, Karlstad, 10:23 (English)
Supervisors
Examiners
Available from: 2014-04-02 Created: 2014-04-01 Last updated: 2014-04-02Bibliographically approved

Open Access in DiVA

No full text in DiVA

By organisation
Faculty of Technology and Science
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 959 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