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arXiv · 2509.21116

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

Yang Wang·Riccardo M. G. Ferrari·Michel Verhaegen
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Abstract

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 presented in the battery. In this paper, we developed 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, achieving more accurate identification. In addition, we jointly identify the open-circuit voltage (OCV) and the state of charge (SOC) relation of the battery without utilizing offline OCV tests. By modeling the OCV-SOC curve as a cubic B-spline, we achieve a high-fidelity representation of the OCV curve, facilitating its estimation. Through solving a rank and L1 regularized least squares problem, we jointly identify battery parameters and the OCV-SOC relation from the battery's dynamic data. Simulated and real-life data demonstrate the effectiveness of the developed method.

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Yang Wang, Riccardo M. G. Ferrari, Michel Verhaegen. 2025-09-25. Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares. https://arxiv.org/abs/2509.21116

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