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A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation
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
2014 (English)In: Journal of Power Sources, ISSN 0378-7753, Vol. 270, 262-272 p.Article in journal (Refereed) Published
Abstract [en]

Capacity and resistance are state-of-health (SOH) indicators that are essential to monitor during the application of batteries on board electric vehicles. For state-of-health determination in laboratory environment, standard battery performance tests are established and well-functioning. Since standard performance tests are not available on-board a vehicle, we are developing a method where those standard tests are applied virtually to a support vector machine-based battery model. This data-driven model is solely based on variables available during ordinary electric vehicle (EV) operation such as battery current, voltage and temperature. This article contributes with a thorough experimental validation of this method, as well as the introduction of new features capacity estimation and temperature dependence. Typical EV battery usage data is generated and exposed to the suggested method in order to estimate capacity and resistance. These estimations are compared to direct measurements of the SOH indicators with standard tests. The obtained estimations of capacities and instantaneous resistances demonstrate good accuracy over a temperature and state-of-charge range typical for EV operating conditions and allow thus for online detection of battery degradation. The proposed method is also found to be suitable for on-board application in respect of processing power and memory restrictions.

Place, publisher, year, edition, pages
2014. Vol. 270, 262-272 p.
Keyword [en]
Support vector machine, State-of-health, Capacity, Resistance, Lithium-ion battery, Electric vehicle
National Category
Chemical Engineering
URN: urn:nbn:se:kth:diva-154734DOI: 10.1016/j.jpowsour.2014.07.116ISI: 000342245400031ScopusID: 2-s2.0-84905707183OAI: diva2:764310

QC 20141118

Available from: 2014-11-18 Created: 2014-10-27 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.
TRITA-CHE-Report, ISSN 1654-1081 ; 2015:45
Lithium-ion battery, state-of-health, electric vehicle, support vector machine, resistance, capacity
National Category
Other Chemical Engineering
Research subject
Chemical Engineering
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)

QC 20150914

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

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