Adaptive Estimation of State of Charge for Lithium-ion Batteries

    •  Fang, H.; Wang, Y.; Sahinoglu, Z.; Wanda, T.; Hara, S., "Adaptive Estimation of State of Charge for Lithium-ion Batteries", American Control Conference (ACC), June 2013.
      BibTeX Download PDF
      • @inproceedings{Fang2013jun,
      • author = {Fang, H. and Wang, Y. and Sahinoglu, Z. and Wanda, T. and Hara, S.},
      • title = {Adaptive Estimation of State of Charge for Lithium-ion Batteries},
      • booktitle = {American Control Conference (ACC)},
      • year = 2013,
      • month = jun,
      • url = {}
      • }
  • MERL Contact:
  • Research Areas:

    Control, Machine Learning, Signal Processing, Electric Systems

State of charge (SoC) estimation is a fundamental challenge in designing battery management systems. An adaptive SoC estimator, named as the AdaptSoC, is developed in this paper. It is able to estimate the SoC when the model parameters are unknown, through joint SoC and parameter estimation. Design of the AdaptSoC builds up on (1) a reduced complexity battery model that is developed from the well known single particle model (SPM) and, (2) joint local observability/identifiability analysis of the SoC and the unknown model parameters. Shown to be strongly observable, the SoC is estimated jointly with the parameters by the AdaptSoC using the iterated extended Kalman filter (IEKF). Simulation and experimental results exhibit the effectiveness of the AdaptSoC.