TR2014-094

Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach


    •  Wang, Y.; Fang, H.; Sahinoglu, Z.; Wada, T.; Hara, S., "Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2014.2356503, ISSN: 1063-6536, Vol. PP, No. 99, September 2014.
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      • @article{Wang2014sep1,
      • author = {Wang, Y. and Fang, H. and Sahinoglu, Z. and Wada, T. and Hara, S.},
      • title = {Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2014,
      • volume = {PP},
      • number = 99,
      • month = sep,
      • publisher = {IEEE},
      • doi = {10.1109/TCST.2014.2356503},
      • issn = {1063-6536},
      • url = {http://www.merl.com/publications/TR2014-094}
      • }
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  • Research Areas:

    Advanced Control Systems, Electronics & Communications, Mechatronics, Power


This paper considers the state of charge (SoC) and parameter estimation of lithium-ion batteries. Different from various prior arts, where estimation is based on local linearization of a nonlinear battery model, nonlinear geometric observer approach is followed to design adaptive observers for the SoC and parameter estimation based on nonlinear battery models. A major advantage of the proposed approach is the possibility to establish the exponential stability of the resultant error dynamics of state and parameter estimation. The proposed adaptive observers are shown to be robust with respect to unmodeled process uncertainties. Analysis also shows the design tradeoff between the convergence rate and the robustness of the estimation error dynamics with respect to the measurement noise. Simulation and experimental results validate the effectiveness and main advantages of the proposed approach. Error analysis is presented and explains the experimental results.