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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.04268 |
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| _version_ | 1866912523093016576 |
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| author | Previtali, Davide Masti, Daniele Mazzoleni, Mirko Previdi, Fabio |
| author_facet | Previtali, Davide Masti, Daniele Mazzoleni, Mirko Previdi, Fabio |
| contents | This paper addresses the estimation of the State Of Charge (SOC) of lithium-ion cells via the combination of two widely used paradigms: Kalman Filters (KFs) equipped with Equivalent Circuit Models (ECMs) and machine-learning approaches. In particular, a recent Virtual Sensor (VS) synthesis technique is considered, which operates as follows: (i) learn an Affine Parameter-Varying (APV) model of the cell directly from data, (ii) derive a bank of linear observers from the APV model, (iii) train a machine-learning technique from features extracted from the observers together with input and output data to predict the SOC. The SOC predictions returned by the VS are supplied to an Extended KF (EKF) as output measurements along with the cell terminal voltage, combining the two paradigms. A data-driven calibration strategy for the noise covariance matrices of the EKF is proposed. Experimental results show that the designed approach is beneficial w.r.t. SOC estimation accuracy and smoothness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04268 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A virtual sensor fusion approach for state of charge estimation of lithium-ion cells Previtali, Davide Masti, Daniele Mazzoleni, Mirko Previdi, Fabio Systems and Control Machine Learning This paper addresses the estimation of the State Of Charge (SOC) of lithium-ion cells via the combination of two widely used paradigms: Kalman Filters (KFs) equipped with Equivalent Circuit Models (ECMs) and machine-learning approaches. In particular, a recent Virtual Sensor (VS) synthesis technique is considered, which operates as follows: (i) learn an Affine Parameter-Varying (APV) model of the cell directly from data, (ii) derive a bank of linear observers from the APV model, (iii) train a machine-learning technique from features extracted from the observers together with input and output data to predict the SOC. The SOC predictions returned by the VS are supplied to an Extended KF (EKF) as output measurements along with the cell terminal voltage, combining the two paradigms. A data-driven calibration strategy for the noise covariance matrices of the EKF is proposed. Experimental results show that the designed approach is beneficial w.r.t. SOC estimation accuracy and smoothness. |
| title | A virtual sensor fusion approach for state of charge estimation of lithium-ion cells |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2508.04268 |