Real-time monitoring of the SoH of lithium-ion batteries
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911045149261824 |
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| author | Jammes, Bruno Sepúlveda-Oviedo, Edgar Hernando Alonso, Corinne |
| author_facet | Jammes, Bruno Sepúlveda-Oviedo, Edgar Hernando Alonso, Corinne |
| contents | Real-time monitoring of the state of health (SoH) of batteries remains a major challenge, particularly in microgrids where operational constraints limit the use of traditional methods. As part of the 4BLife project, we propose an innovative method based on the analysis of a discharge pulse at the end of the charge phase. The parameters of the equivalent electrical model describing the voltage evolution across the battery terminals during this current pulse are then used to estimate the SoH. Based on the experimental data acquired so far, the initial results demonstrate the relevance of the proposed approach. After training using the parameters of two batteries with a capacity degradation of around 85%, we successfully predicted the degradation of two other batteries, cycled down to approximately 90% SoH, with a mean absolute error of around 1% in the worst case, and an explainability score of the estimator close to 0.9. If these performances are confirmed, this method can be easily integrated into battery management systems (BMS) and paves the way for optimized battery management under continuous operation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05765 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-time monitoring of the SoH of lithium-ion batteries Jammes, Bruno Sepúlveda-Oviedo, Edgar Hernando Alonso, Corinne Artificial Intelligence Real-time monitoring of the state of health (SoH) of batteries remains a major challenge, particularly in microgrids where operational constraints limit the use of traditional methods. As part of the 4BLife project, we propose an innovative method based on the analysis of a discharge pulse at the end of the charge phase. The parameters of the equivalent electrical model describing the voltage evolution across the battery terminals during this current pulse are then used to estimate the SoH. Based on the experimental data acquired so far, the initial results demonstrate the relevance of the proposed approach. After training using the parameters of two batteries with a capacity degradation of around 85%, we successfully predicted the degradation of two other batteries, cycled down to approximately 90% SoH, with a mean absolute error of around 1% in the worst case, and an explainability score of the estimator close to 0.9. If these performances are confirmed, this method can be easily integrated into battery management systems (BMS) and paves the way for optimized battery management under continuous operation. |
| title | Real-time monitoring of the SoH of lithium-ion batteries |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.05765 |