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
State-of-health estimation of lithium-ion battery under emulated HEV operation on board heavy-duty truck
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology, Applied Electrochemistry.ORCID iD: 0000-0001-5748-0226
Scania CV AB.ORCID iD: 0000-0001-9559-0004
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
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper addresses the need for simple and cost-effective methods that detect the state-of-health (SOH) of batteries in vehicle applications solely based on data readily available from the battery management system without any knowledge of battery properties, prior laboratory measurements or additional equipment.

A power-optimized lithium-ion battery cell is operated in an emulated hybrid electric vehicle (HEV) environment on board a conventional heavy-duty truck. The HEV operation of the battery cell depends on the driving pattern of the truck within set limits. Beyond the HEV operation, the performance of the battery cell is periodically measured with on-board standard pulse and capacity tests. On basis of the battery operating data collected in the field test, support vector machine-based battery models are built. From a model input of current, temperature, state-of-charge, and current history, the model accurately estimates the battery voltage despite the tough HEV operating conditions with high current pulses. This data-driven battery model is used to estimate the battery cell’s charge and discharge resistance as well as capacity, i.e. the performance measures verified with the standard tests. These SOH indicators can be predicted by the model with adequate accuracy for on-board SOH detection and are followed throughout the one-year field test period.

National Category
Other Chemical Engineering
Research subject
Chemical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-173537OAI: oai:DiVA.org:kth-173537DiVA: diva2:853530
Note

QS 2015

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

Authority records BETA

Klass, VerenaSvens, PontusBehm, MårtenLindbergh, Göran

Search in DiVA

By author/editor
Klass, VerenaSvens, PontusBehm, MårtenLindbergh, Göran
By organisation
Applied Electrochemistry
Other Chemical Engineering

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 196 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