TR2016-143

Extremum Seeking-based Parameter Identification for State-of-Power Prediction of Lithium-ion Batteries

    •  Benosman, M., Wei, C., "Extremum Seeking-based Parameter Identification for State-of-Power Prediction of Lithium-ion Batteries", IEEE International Conference on Renewable Energy Research and Applications (ICRERA), DOI: 10.1109/​ICRERA.2016.7884376, November 2016, pp. 67-72.
      BibTeX TR2016-143 PDF
      • @inproceedings{Benosman2016nov,
      • author = {Benosman, Mouhacine and Wei, Chun},
      • title = {Extremum Seeking-based Parameter Identification for State-of-Power Prediction of Lithium-ion Batteries},
      • booktitle = {IEEE International Conference on Renewable Energy Research and Applications (ICRERA)},
      • year = 2016,
      • pages = {67--72},
      • month = nov,
      • doi = {10.1109/ICRERA.2016.7884376},
      • url = {https://www.merl.com/publications/TR2016-143}
      • }
  • Research Area:

    Dynamical Systems

Abstract:

Accurate state-of-power (SOP) estimates are critical for building battery systems with optimized performance and longer life in electric vehicles (EVs) and hybrid electric vehicles (HEVs). This paper proposes a novel parameter identification method and its implementation on SOP prediction for lithium-ion batteries. The extremum seeking algorithm is developed for identifying the parameters of batteries on the basis of an electrical circuit model incorporating hysteresis effect. The estimated battery parameters can then be used for online stage -of-charge, state-of-health, and SOP estimation for lithium-ion batteries. In addition, based on the electrical circuit model with the identified parameters, a battery SOP prediction algorithm is derived, which considers both the voltage and current limitations of the battery. The proposed method is suitable for real operation of embedded battery management system (BMS) due to its low complexity and numerical stability. Simulation results for lithium-ion batteries are provided to validate the proposed parameter identification and SOP prediction methods.