Novel Fitting Algorithm for Parametrization of Equivalent Circuit Model of Li-Ion Battery from Broadband Impedance Measurements

The impedance of Li-ion batteries contains information about the dynamics and state parameters of the battery. This information can be utilized to improve the performance and safety of the battery application. The battery impedance is typically modeled by an equivalent-circuit-model (ECM) which provides the dynamic information of the battery. In addition, the variations in the model parameters can be used for the battery state-estimation. A fitting algorithm is required to parametrize the ECM due to the non-linearity of both the battery impedance and ECM. However, conventional fitting algorithms, such as the complex-nonlinear-least-squares (CNLS) algorithm, often have a high computational burden and require selection of initial conditions which can be difficult to obtain adaptively. This paper proposes a novel fitting algorithm for the parametrization of battery ECM based on the geometric shape of the battery impedance in the complex-plane. The algorithm is applied to practical and fast broadband pseudo random sequence impedance measurements carried out at various state-of-charges (SOC) and temperatures for lithium-iron-phosphate cell. The performance of the method is compared to conventional CNLS algorithm with different initial conditions. The results show that the proposed method provides fast and accurate fit with low computational effort. Moreover, specific ECM parameters are found to be dependent on the battery SOC at various temperature.