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Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares

Journal Article


Abstract


  • This paper deals with the contradiction between simplicity and accuracy of the LiFePO4 battery states estimation in the electric vehicles (EVs) battery management system (BMS). State of charge (SOC) and state of health (SOH) are normally obtained from estimating the open circuit voltage (OCV) and the internal resistance of the equivalent electrical circuit model of the battery, respectively. The difficulties of the parameters estimation arise from their complicated variations and different dynamics which require sophisticated algorithms to simultaneously estimate multiple parameters. This, however, demands heavy computation resources. In this paper, we propose a novel technique which employs a simplified model and multiple adaptive forgetting factors recursive least-squares (MAFF-RLS) estimation to provide capability to accurately capture the real-time variations and the different dynamics of the parameters whilst the simplicity in computation is still retained. The validity of the proposed method is verified through two standard driving cycles, namely Urban Dynamometer Driving Schedule and the New European Driving Cycle. The proposed method yields experimental results that not only estimated the SOC with an absolute error of less than 2.8% but also characterized the battery model parameters accurately.

Authors


  •   Duong, Van Huan (external author)
  •   Bastawrous, Hany Ayad. (external author)
  •   Lim, Kai
  •   See, Khay W.
  •   Zhang, Peng (external author)
  •   Dou, Shi Xue

Publication Date


  • 2015

Citation


  • Duong, V., Bastawrous, H. Ayad., Lim, K., See, K., Zhang, P. & Dou, S. Xue. (2015). Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares. Journal of Power Sources, 296 215-224.

Scopus Eid


  • 2-s2.0-84937916952

Ro Metadata Url


  • http://ro.uow.edu.au/aiimpapers/1546

Number Of Pages


  • 9
  • 9

Start Page


  • 215

End Page


  • 224

Volume


  • 296

Abstract


  • This paper deals with the contradiction between simplicity and accuracy of the LiFePO4 battery states estimation in the electric vehicles (EVs) battery management system (BMS). State of charge (SOC) and state of health (SOH) are normally obtained from estimating the open circuit voltage (OCV) and the internal resistance of the equivalent electrical circuit model of the battery, respectively. The difficulties of the parameters estimation arise from their complicated variations and different dynamics which require sophisticated algorithms to simultaneously estimate multiple parameters. This, however, demands heavy computation resources. In this paper, we propose a novel technique which employs a simplified model and multiple adaptive forgetting factors recursive least-squares (MAFF-RLS) estimation to provide capability to accurately capture the real-time variations and the different dynamics of the parameters whilst the simplicity in computation is still retained. The validity of the proposed method is verified through two standard driving cycles, namely Urban Dynamometer Driving Schedule and the New European Driving Cycle. The proposed method yields experimental results that not only estimated the SOC with an absolute error of less than 2.8% but also characterized the battery model parameters accurately.

Authors


  •   Duong, Van Huan (external author)
  •   Bastawrous, Hany Ayad. (external author)
  •   Lim, Kai
  •   See, Khay W.
  •   Zhang, Peng (external author)
  •   Dou, Shi Xue

Publication Date


  • 2015

Citation


  • Duong, V., Bastawrous, H. Ayad., Lim, K., See, K., Zhang, P. & Dou, S. Xue. (2015). Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares. Journal of Power Sources, 296 215-224.

Scopus Eid


  • 2-s2.0-84937916952

Ro Metadata Url


  • http://ro.uow.edu.au/aiimpapers/1546

Number Of Pages


  • 9
  • 9

Start Page


  • 215

End Page


  • 224

Volume


  • 296