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

This article proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model in the literature. It is known that the original model can address the rate capacity effect and energy recovery effect 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 article 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 off-line 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 the 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. 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.

Automated summary

This article introduces a new equivalent circuit model for rechargeable batteries by modifying an existing double-capacitor model, incorporating a nonlinear-mapping-based voltage source and serial RC circuit to better represent battery nonlinear phenomena. Two offline parameter estimation approaches, 1.0 and 2.0, are designed for constant-current and variable-current charging/discharging scenarios. The proposed model demonstrates excellent accuracy and predictive capability in extensive experimental evaluation, surpassing the Rint and Thevenin models in accuracy and complexity.