Direct Continuous-Time LPV System Identification of Li-Ion Batteries via L1-Regularized Least Squares

Abstract

Accurate identification of lithium-ion battery parameters is essential for estimating battery states and managing performance. However, the variation of battery parameters over the state of charge (SOC) and the nonlinear dependence of the open-circuit voltage (OCV) on the SOC complicate the identification process. In this work, we develop a continuous-time LPV system identification approach to identify the SOC-dependent battery parameters and the OCV-SOC mapping. We model parameter variations using cubic B-splines to capture the piecewise nonlinearity of the variations and estimate signal derivatives via state variable filters, facilitating CT-LPV identification. Battery parameters and the OCV-SOC mapping are jointly identified by solving L1-regularized least squares problems. Numerical experiments on a simulated battery and real-life data demonstrate the effectiveness of the developed method in battery identification, presenting improved performance compared to conventional RLS-based methods.