Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation

This paper proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model proposed in [1]. It is known that the original model can address the rate capacity and energy recovery effects inherent to batteries better than other models. However, it is a purely linear model and includes no representation of a battery’s nonlinear phenomena. Hence, this work transforms the original model by introducing a nonlinear-mapping-based voltage source and a serial RC circuit. The modification is justified by an analogy with the single-particle model. Two offline parameter estimation approaches, termed 1.0 and 2.0, are designed for the new model to deal with the scenarios of constant-current and variable-current charging/discharging, respectively. In particular, the 2.0 approach proposes the notion of Wiener system identification based on maximum a posteriori estimation, which allows all the parameters to be estimated in one shot while overcoming the nonconvexity or local minima issue to obtain physically reasonable estimates. An extensive experimental evaluation shows that the proposed model offers excellent accuracy and predictive capability. A comparison against the Rint and Thevenin models further points to its superiority. With high fidelity and low mathematical complexity, this model is beneficial for various real-time battery management applications.


  • Related Publication

  •  Tian, N., Fang, H., Wang, Y., "Parameter Identification of the Nonlinear Double-Capacitor Model for Lithium-Ion Batteries: From the Wiener Perspective", American Control Conference (ACC), DOI: 10.23919/ACC.2019.8814957, July 2019, pp. 897-902.
    BibTeX TR2019-065 PDF
    • @inproceedings{Tian2019jul,
    • author = {Tian, Ning and Fang, Huazhen and Wang, Yebin},
    • title = {Parameter Identification of the Nonlinear Double-Capacitor Model for Lithium-Ion Batteries: From the Wiener Perspective},
    • booktitle = {American Control Conference (ACC)},
    • year = 2019,
    • pages = {897--902},
    • month = jul,
    • doi = {10.23919/ACC.2019.8814957},
    • url = {}
    • }