Local model network based dynamic battery cell model identification

  • Authors:

  • Christoph Hametner;Johannes Unger;Stefan Jakubek

  • Affiliations:

  • Christian Doppler Laboratory for Model Based Calibration Methodologies, Vienna University of Technology, Vienna, Austria;Christian Doppler Laboratory for Model Based Calibration Methodologies, Vienna University of Technology, Vienna, Austria;Christian Doppler Laboratory for Model Based Calibration Methodologies, Vienna University of Technology, Vienna, Austria

  • Venue:
  • IMMURO'12 Proceedings of the 11th WSEAS international conference on Instrumentation, Measurement, Circuits and Systems, and Proceedings of the 12th WSEAS international conference on Robotics, Control and Manufacturing Technology, and Proceedings of the 12th WSEAS international conference on Multimedia Systems & Signal Processing
  • Year:
  • 2012

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Abstract

In this paper the local model network (LMN) based dynamic battery cell model identification is presented. Such a model describes the nonlinear dynamic behaviour of the cell terminal voltage in dependance of the charge/discharge current and can be used for the state of charge (SoC) estimation in hybrid electrical vehicles. For that purpose, the model must be accurate at high C-rates in combination with a highly dynamic excitation. The LMN construction, related SoC observer structures and the appropriate experiment design are discussed in the present paper. The proposed concepts and the performance of the LMN is validated by means of real measurement data from a Lithium Ion power cell.