Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912604826370048 |
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| author | Wang, Yang Ferrari, Riccardo M. G. Verhaegen, Michel |
| author_facet | Wang, Yang Ferrari, Riccardo M. G. Verhaegen, Michel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares Wang, Yang Ferrari, Riccardo M. G. Verhaegen, Michel Systems and Control 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. |
| title | Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.21116 |