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Model-Based Lithium-Ion Battery Resistance Estimation From Electric Vehicle Operating Data
Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden..
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology.ORCID iD: 0000-0001-5748-0226
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology.ORCID iD: 0000-0002-9392-9059
KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology.ORCID iD: 0000-0001-9203-9313
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2018 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 67, no 5, p. 3720-3728Article in journal (Refereed) Published
Abstract [en]

State-of-health estimates of batteries are essential for onboard electric vehicles in order to provide safe, reliable, and cost-effective battery operation. This paper suggests a method to estimate the 10-s discharge resistance, which is an established battery figure of merit from laboratory testing, without performing the laboratory test. Instead, a state-of-health estimate of batteries is obtained using data directly from their operational use, e.g., onboard electric vehicles. It is shown that simple dynamical battery models, based on a current input and a voltage output, with model parameters dependent on temperature and state of charge, can be derived using AutoRegressive with eXogenous input models, whose order can be adjusted to describe the complex battery behavior. Then, the 10-s discharge resistance can be conveniently computed from the identified model parameters. Moreover, the uncertainty of the estimated resistance values is provided by the method. The suggested method is validated with usage data from emulated electric vehicle operation of an automotive lithium-ion battery cell. The resistance values are estimated accurately for a state-of-charge and temperature range spanning typical electric vehicle operating conditions. The identification of the model parameters and the resistance computation are very fast, rendering the method suitable for onboard application.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 67, no 5, p. 3720-3728
Keywords [en]
State of health, autoregressive eXogenous model, dynamical models, electric vehicle, lithium-ion battery, resistance estimation, system identification
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-229023DOI: 10.1109/TVT.2018.2796723ISI: 000432310500002Scopus ID: 2-s2.0-85041012711OAI: oai:DiVA.org:kth-229023DiVA, id: diva2:1211568
Note

QC 20180531

Available from: 2018-05-31 Created: 2018-05-31 Last updated: 2018-05-31Bibliographically approved

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Klass, VerenaBehm, MårtenLindbergh, Göran

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