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Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging

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Titel: Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging
Autoren: Zhang, Yizhou, 1991, Wik, Torsten, 1968, Bergström, John, Zou, Changfu, 1987
Quelle: Datadriven förlängning av livslängden och optimering av prestanda för fordonsbatterisystem Energy Storage Materials. 74
Schlagwörter: Lithium plating potential estimation, Lithium-ion battery, Fast charging, Data-driven models, Machine learning
Beschreibung: 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.
Dateibeschreibung: electronic
Zugangs-URL: 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
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  Data: Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging
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  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
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  Data: <i>Datadriven förlängning av livslängden och optimering av prestanda för fordonsbatterisystem Energy Storage Materials</i>. 74
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  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>
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  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.
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