Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares

Accurate identification of lithium-ion (Li-ion) battery parameters is essential for managing and predicting battery behavior. However, existing discrete-time methods hinder the estimation of physical parameters and face the fast-slow dynamics problem of the battery. In this paper, we develop a continuous-time approach that enables the estimation of battery parameters directly from sampled data. This method avoids discretization errors in converting continuous-time models into discrete-time ones. Moreover, the developed method is capable of jointly identifying the open-circuit voltage (OCV) and the state of charge (SOC) relation of batteries without utilizing offline OCV tests. By modeling the OCV-SOC curve as a cubic B-spline, we represent the piecewise nonlinearity of the OCV curve with high fidelity, facilitating its estimation. By solving a rank and L1 regularized least squares problem, we identify battery parameters and the OCV-SOC relation directly from the battery’s dynamic data. Simulated and real-life data validate the effectiveness of the developed method.