Li-ion dynamics and state of charge estimation

Volume 100, January 2017, Pages 44–52

Special Issue: Control and Optimization of Renewable Energy Systems

Edited By Nael H. El-Farra and Panagiotis D. Christofides


Characterized Li-ion dynamics by a simulated electrochemical impedance spectroscopy

Developed equivalent circuit model from system identification and EIS.

Applied extended Kalman filtering for State of Charge estimation.

Joint state/parameter estimation is superior to state estimation given varying parameters in the model.


This paper focuses on real-time estimation of Li-ion State of Charge (SoC). A first-principles model validated by experimental data from literature is chosen to mimic a real Li-ion cell. Its impedance responses at different SoCs are studied by a simulated electrochemical impedance spectroscopy (EIS). An equivalent circuit model is developed for estimator design in which the parameters (including lumped series resistances R1, lumped interfacial resistances R2 and time constant τ) are derived from system identification and compared with the EIS results. The estimator is designed using extended Kalman filtering (EKF) and is implemented in the first-principles model. It is demonstrated by computer simulation that the SoC during charge/discharge cycles can be estimated with a relative error <3%. The accuracy of SoC tracking is improved if it is jointly estimated along with either R1 or R2 given that these model parameters vary with SoC as revealed by EIS.


  • Li-ion;
  • State of Charge;
  • Electrochemical impedance spectroscopy;
  • Extended Kalman filtering;
  • Joint state and parameter estimation

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