Academic Journal
Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging
| Τίτλος: | Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging |
|---|---|
| Συγγραφείς: | Zhang, Yizhou, 1991, Wik, Torsten, 1968, Bergström, John, Zou, Changfu, 1987 |
| Πηγή: | Datadriven förlängning av livslängden och optimering av prestanda för fordonsbatterisystem Energy Storage Materials. 74 |
| Θεματικοί όροι: | Lithium plating potential estimation, Lithium-ion battery, Fast charging, Data-driven models, Machine learning |
| Περιγραφή: | To enable a shift from fossil fuels to renewable and sustainable transport, batteries must allow fast charging and exhibit extended lifetimes---objectives that traditionally conflict. Current charging technologies often compromise one attribute for the other, leading to either inconvenience or diminished resource efficiency in battery-powered vehicles. For lithium-ion batteries, the way to meet both objectives is for the lithium plating potential at the anode surface to remain positive. In this study, we address this challenge by introducing a novel method that involves real-time monitoring and control of the plating potential in lithium-ion battery cells throughout their lifespan. Our experimental results on three-electrode cells reveal that our approach can enable batteries to charge at least 30% faster while almost doubling their lifetime. To facilitate the adoption of these findings in commercial applications, we propose a machine learning-based framework for lifelong plating potential estimation, utilizing readily available battery data from electric vehicles. The resulting model demonstrates high fidelity and robustness under diverse operating conditions, achieving a mean absolute error of merely 3.37 mV. This research outlines a practical methodology to prevent lithium plating and enable the fastest health-conscious battery charging. |
| Περιγραφή αρχείου: | electronic |
| Σύνδεσμος πρόσβασης: | https://research.chalmers.se/publication/544434 https://research.chalmers.se/publication/543531 https://research.chalmers.se/publication/544244 https://research.chalmers.se/publication/544434/file/544434_Fulltext.pdf |
| Βάση Δεδομένων: | SwePub |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://research.chalmers.se/publication/544434# Name: EDS - SwePub (ns324271) Category: fullText Text: View record in SwePub – Url: https://www.doi.org/10.1016/j.ensm.2024.103877? Name: ScienceDirect (all content) (s7799221) Category: fullText Text: View record from ScienceDirect MouseOverText: View record from ScienceDirect |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Yizhou%22">Zhang, Yizhou</searchLink>, 1991<br /><searchLink fieldCode="AR" term="%22Wik%2C+Torsten%22">Wik, Torsten</searchLink>, 1968<br /><searchLink fieldCode="AR" term="%22Bergström%2C+John%22">Bergström, John</searchLink><br /><searchLink fieldCode="AR" term="%22Zou%2C+Changfu%22">Zou, Changfu</searchLink>, 1987 – Name: TitleSource Label: Source Group: Src Data: <i>Datadriven förlängning av livslängden och optimering av prestanda för fordonsbatterisystem Energy Storage Materials</i>. 74 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Lithium+plating+potential+estimation%22">Lithium plating potential estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Lithium-ion+battery%22">Lithium-ion battery</searchLink><br /><searchLink fieldCode="DE" term="%22Fast+charging%22">Fast charging</searchLink><br /><searchLink fieldCode="DE" term="%22Data-driven+models%22">Data-driven models</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Description Group: Ab Data: To enable a shift from fossil fuels to renewable and sustainable transport, batteries must allow fast charging and exhibit extended lifetimes---objectives that traditionally conflict. Current charging technologies often compromise one attribute for the other, leading to either inconvenience or diminished resource efficiency in battery-powered vehicles. For lithium-ion batteries, the way to meet both objectives is for the lithium plating potential at the anode surface to remain positive. In this study, we address this challenge by introducing a novel method that involves real-time monitoring and control of the plating potential in lithium-ion battery cells throughout their lifespan. Our experimental results on three-electrode cells reveal that our approach can enable batteries to charge at least 30% faster while almost doubling their lifetime. To facilitate the adoption of these findings in commercial applications, we propose a machine learning-based framework for lifelong plating potential estimation, utilizing readily available battery data from electric vehicles. The resulting model demonstrates high fidelity and robustness under diverse operating conditions, achieving a mean absolute error of merely 3.37 mV. This research outlines a practical methodology to prevent lithium plating and enable the fastest health-conscious battery charging. – Name: Format Label: File Description Group: SrcInfo Data: electronic – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/544434" linkWindow="_blank">https://research.chalmers.se/publication/544434</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/543531" linkWindow="_blank">https://research.chalmers.se/publication/543531</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/544244" linkWindow="_blank">https://research.chalmers.se/publication/544244</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/544434/file/544434_Fulltext.pdf" linkWindow="_blank">https://research.chalmers.se/publication/544434/file/544434_Fulltext.pdf</link> |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ensm.2024.103877 Languages: – Text: English Subjects: – SubjectFull: Lithium plating potential estimation Type: general – SubjectFull: Lithium-ion battery Type: general – SubjectFull: Fast charging Type: general – SubjectFull: Data-driven models Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Yizhou – PersonEntity: Name: NameFull: Wik, Torsten – PersonEntity: Name: NameFull: Bergström, John – PersonEntity: Name: NameFull: Zou, Changfu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 24058297 – Type: issn-locals Value: SWEPUB_FREE – Type: issn-locals Value: CTH_SWEPUB Numbering: – Type: volume Value: 74 Titles: – TitleFull: Datadriven förlängning av livslängden och optimering av prestanda för fordonsbatterisystem Energy Storage Materials Type: main |
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