State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model

An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra RC branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.

• Record URL:
  • https://doi.org/10.1109/TVT.2011.2132812
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • He, Hongwen
  • 0000-0003-2874-1858
  • Xiong, Rui
  • Zhang, Xiaowei
  • Sun, Fengchun
  • Fan, JinXin
• Publication Date: 2011-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1461-1469
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 60
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Algorithms; Electric vehicles; Estimating; Kalman filtering; Lithium batteries
• Uncontrolled Terms: State of charge
• Subject Areas: Design; Highways; Vehicles and Equipment; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01543582
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 18 2014 11:57AM