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
Capturing lithium-ion battery dynamics with support vector machine-based battery model
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology, Applied Electrochemistry.ORCID iD: 0000-0001-5748-0226
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology, Applied Electrochemistry.ORCID iD: 0000-0002-9392-9059
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology, Applied Electrochemistry.ORCID iD: 0000-0001-9203-9313
2015 (English)In: Journal of Power Sources, ISSN 0378-7753, E-ISSN 1873-2755, Vol. 298, 92-101 p.Article in journal (Refereed) Published
Abstract [en]

During long and high current pulses, diffusion resistance becomes important in lithium-ion batteries. In such diffusion-intense situations, a static support vector machine-based battery model relying on instantaneous current, state-of-charge (SOC), and temperature is not sufficient to capture the time-dependent voltage characteristics. In order to account for the diffusion-related voltage dynamics, we suggest therefore the inclusion of current history in the data-driven battery model by moving averages of the recent current. The voltage estimation performance of six different dynamic battery models with additional current history input is studied during relevant test scenarios. All current history models improve the time-dependent voltage drop estimation compared to the static model, manifesting the beneficial effect of the additional current history input during diffusion-intense situations. The best diffusion resistance estimation results are obtained for the two-step voltage estimation models that incorporate a reciprocal square root of time weighing function for the current of the previous 100 s or an exponential time function with a 20 s time constant (1–8% relative error). Those current history models even improve the overall voltage estimation performance during the studied test scenarios (under 0.25% root-mean-square percentage error).

Place, publisher, year, edition, pages
2015. Vol. 298, 92-101 p.
National Category
Other Chemical Engineering
Research subject
Chemical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-173535DOI: 10.1016/j.jpowsour.2015.08.036ISI: 000362146800011Scopus ID: 2-s2.0-84939817512OAI: oai:DiVA.org:kth-173535DiVA: diva2:853524
Note

QC 20150914

Available from: 2015-09-14 Created: 2015-09-14 Last updated: 2017-12-04Bibliographically approved
In thesis
1. Battery Health Estimation in Electric Vehicles
Open this publication in new window or tab >>Battery Health Estimation in Electric Vehicles
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

For the broad commercial success of electric vehicles (EVs), it is essential to deeply understand how batteries behave in this challenging application. This thesis has therefore been focused on studying automotive lithium-ion batteries in respect of their performance under EV operation. Particularly, the  need  for  simple  methods  estimating  the  state-of-health  (SOH)  of batteries during EV operation has been addressed in order to ensure safe, reliable, and cost-effective EV operation. Within  the  scope  of  this  thesis,  a  method  has  been  developed  that  can estimate the SOH indicators capacity and internal resistance. The method is solely based on signals that are available on-board during ordinary EV operation  such  as  the  measured  current,  voltage,  temperature,  and  the battery  management  system’s  state-of-charge  estimate.  The  approach  is based on data-driven battery models (support vector machines (SVM) or system  identification)  and  virtual  tests  in  correspondence  to  standard performance  tests  as  established  in  laboratory  testing  for  capacity  and resistance determination. The proposed method has been demonstrated for battery data collected in field tests and has also been verified in laboratory. After a first proof-of-concept of the method idea with battery pack data from a plug-in hybrid electric vehicle (PHEV) field test, the method was improved with the help of a laboratory study where battery electric vehicle (BEV) operation of a battery  cell  was  emulated  under  controlled  conditions  providing  a thorough validation possibility. Precise partial capacity and instantaneous resistance  estimations  could  be  derived  and  an  accurate  diffusion resistance estimation was achieved by including a current history variable in the SVM-based model. The dynamic system identification battery model gave precise total resistance estimates as well. The SOH estimation method was also applied to a data set from emulated hybrid electric vehicle (HEV) operation of a battery cell on board a heavy-duty vehicle, where on-board standard  test  validation  revealed  accurate  dynamic  voltage  estimation performance of the applied model even during high-current situations. In order to exhibit the method’s intended implementation, up-to-date SOH indicators have been estimated from driving data during a one-year time period.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. 59 p.
Series
TRITA-CHE-Report, ISSN 1654-1081 ; 2015:45
Keyword
Lithium-ion battery, state-of-health, electric vehicle, support vector machine, resistance, capacity
National Category
Other Chemical Engineering
Research subject
Chemical Engineering
Identifiers
urn:nbn:se:kth:diva-173544 (URN)978-91-7595-671-8 (ISBN)
Public defence
2015-10-09, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 09:30 (English)
Opponent
Supervisors
Note

QC 20150914

Available from: 2015-09-14 Created: 2015-09-14 Last updated: 2015-09-14Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Klass, VerenaBehm, MårtenLindbergh, Göran

Search in DiVA

By author/editor
Klass, VerenaBehm, MårtenLindbergh, Göran
By organisation
Applied Electrochemistry
In the same journal
Journal of Power Sources
Other Chemical Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 167 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