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Dynamical Lithium-Ion Battery Model Identificationusing Electric Vehicle Operating Data for Resistance Estimation
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology, Applied Electrochemistry.ORCID iD: 0000-0001-5748-0226
Chalmers University of Technology, Sweden.
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
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

State-of-health (SOH) estimates of batteries are essential on-board electric vehicles (EVs) in order to provide safe, reliable, and cost-effective battery operation. Here, we present an approach for the estimation of the battery SOH indicator internal resistance. Battery models are constructed on the basis of ordinary EV operating data. The 10 s discharge resistance, which is an established battery figure-of-merit from laboratory testing, can be conveniently computed from the identified model parameters. Dynamical battery models based on a current input and a voltage output with model parameters dependent on temperature and state-of-charge (SOC) are derived using AutoRegressive with eXogenous input (ARX) models. The suggested method is validated with usage data from emulated EV operation of an automotive lithium-ion battery cell. The resistance values are estimated accurately by the proposed model for a SOC and temperature range spanning typical EV operating conditions (average relative estimation error of 1.5%). The method even provides an uncertainty interval for the resistance estimations, which is found to be very narrow. The linear identification of the model parameters and the resistance computation are very fast rendering the method suitable for on-board application.

National Category
Other Chemical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-173540OAI: oai:DiVA.org:kth-173540DiVA: diva2:853535
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)
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Supervisors
Note

QC 20150914

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

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Klass, VerenaBehm, MårtenLindbergh, Göran

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