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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.22396 |
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| _version_ | 1866908781956300800 |
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| author | Yeregui, Josu Lopetegi, Iker Fernandez, Sergio Garayalde, Erik Iraola, Unai |
| author_facet | Yeregui, Josu Lopetegi, Iker Fernandez, Sergio Garayalde, Erik Iraola, Unai |
| contents | This paper presents a novel physical parameter estimation framework for on-site model characterization, using a two-phase modelling strategy with Physics-Informed Neural Networks (PINNs) and transfer learning (TL). In the first phase, a PINN is trained using only the physical principles of the single particle model (SPM) equations. In the second phase, the majority of the PINN parameters are frozen, while critical electrochemical parameters are set as trainable and adjusted using real-world voltage profile data. The proposed approach significantly reduces computational costs, making it suitable for real-time implementation on Battery Management Systems (BMS). Additionally, as the initial phase does not require field data, the model is easy to deploy with minimal setup requirements. With the proposed methodology, we have been able to effectively estimate relevant electrochemical parameters with operating data. This has been proved estimating diffusivities and active material volume fractions with charge data in different degradation conditions. The methodology is experimentally validated in a Raspberry Pi device using data from a standard charge profile with a 3.89\% relative accuracy estimating the active material volume fractions of a NMC cell with 82.09\% of its nominal capacity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach Yeregui, Josu Lopetegi, Iker Fernandez, Sergio Garayalde, Erik Iraola, Unai Machine Learning Artificial Intelligence This paper presents a novel physical parameter estimation framework for on-site model characterization, using a two-phase modelling strategy with Physics-Informed Neural Networks (PINNs) and transfer learning (TL). In the first phase, a PINN is trained using only the physical principles of the single particle model (SPM) equations. In the second phase, the majority of the PINN parameters are frozen, while critical electrochemical parameters are set as trainable and adjusted using real-world voltage profile data. The proposed approach significantly reduces computational costs, making it suitable for real-time implementation on Battery Management Systems (BMS). Additionally, as the initial phase does not require field data, the model is easy to deploy with minimal setup requirements. With the proposed methodology, we have been able to effectively estimate relevant electrochemical parameters with operating data. This has been proved estimating diffusivities and active material volume fractions with charge data in different degradation conditions. The methodology is experimentally validated in a Raspberry Pi device using data from a standard charge profile with a 3.89\% relative accuracy estimating the active material volume fractions of a NMC cell with 82.09\% of its nominal capacity. |
| title | On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.22396 |