A Temperature-Dependent State of Charge Estimation Method including Hysteresis for Lithium-Ion Batteries in Hybrid Electric Vehicles

Eunseok Choi, Sekchin Chang

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Currently, lithium-ion batteries are mainly used as central power components in electric vehicles. Usually, accurate battery cell modeling and state of charge estimation are required in order to effectively use the lithium-ion batteries in electrified vehicles. For an elaborate battery cell modeling on a wide temperature range, we select a temperature-dependent battery cell modeling approach, which relies on one resistance plus second-order resistance-capacitance equivalent circuit model. In order to corporate some temperature effects into the modeling, we derive the sixth-order polynomial functions using the curve fitting algorithm. The functions contribute to the accurate battery cell modeling on the wide temperature range. For a practical usage of the functions in real-time embedded systems, we propose an offset-based lookup table technique. In addition, we propose a novel hysteresis voltage unit model for more accurate parameter estimation of hysteresis voltages. This also leads to more accurate battery cell modeling. Based on the battery cell modeling, we propose a temperature-dependent estimation method for state of charge. This approach exploits the extended Kalman filter, which is suitable for nonlinear characteristics such as the hysteresis effect. Experimental evaluation exhibits that the proposed estimation method outperforms the conventional approaches in the wide temperature range.

Original languageEnglish
Article number9139945
Pages (from-to)129857-129868
Number of pages12
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Equivalent circuit model
  • electrified vehicle
  • extended Kalman filter
  • hysteresis model
  • lithium-ion battery
  • state of charge

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