Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells
Abstract: Precise lifetime predictions for lithium‐ion cells are crucial for efficient battery development and thus enable profitable electric vehicles and a sustainable transformation towards zero‐emission mobility. However, limitations remain due to the complex degradation of lithium‐ion cells, strongly influenced by cell design as well as operating and storage conditions. To overcome them, a machine learning framework is developed based on symbolic regression via genetic programming. This evolutionary algorithm is capable of inferring physically interpretable models from cell aging data without requiring domain knowledge. This novel approach is compared against established approaches in case studies, which represent common tasks of lifetime prediction based on cycle and calendar aging data of 104 automotive lithium‐ion pouch‐cells. On average, predictive accuracy for extrapolations over storage time and energy throughput is increased by 38% and 13%, respectively. For predictions over other stress factors, error reductions of up to 77% are achieved. Furthermore, the evolutionary generated aging models meet requirements regarding applicability, generalizability, and interpretability. This highlights the potential of evolutionary algorithms to enhance cell aging predictions as well as insights.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells ; day:26 ; month:08 ; year:2022 ; extent:12
Advanced science ; (26.08.2022) (gesamt 12)
- Creator
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Schofer, Kai
Laufer, Florian
Stadler, Jochen
Hahn, Severin
Gaiselmann, Gerd
Latz, Arnulf
Birke, Kai P.
- DOI
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10.1002/advs.202200630
- URN
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urn:nbn:de:101:1-2022083015124000922020
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Schofer, Kai
- Laufer, Florian
- Stadler, Jochen
- Hahn, Severin
- Gaiselmann, Gerd
- Latz, Arnulf
- Birke, Kai P.
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Lifetime prediction on lithium-ion battery cell and system level
Lithium‐Ion Conductive Coatings for Nickel‐Rich Cathodes for Lithium‐Ion Batteries
Influence of Cell Size on Performance and Lifetime of Lithium-Ion Batteries
Prolonging lithium-ion battery lifetime: How operating conditions affect solid electrolyte interphase growth
From Lithium-Ion to Sodium-Ion Batteries
Amorphous Materials for Lithium‐Ion and Post‐Lithium‐Ion Batteries
Lithium-Ion Batteries: basics and applications
New materials for lithium-ion batteries
New Materials for Lithium-Ion Batteries
Lithium-ion and beyond: safer alternatives
Energy consumption of current and future production of lithium-ion and post lithium-ion battery cells
Lithium-Ion Charged Polymer Channels Flattening Lithium Metal Anode
Lifetime prediction on lithium-ion battery cell and system level
Lithium‐Ion Conductive Coatings for Nickel‐Rich Cathodes for Lithium‐Ion Batteries
Influence of Cell Size on Performance and Lifetime of Lithium-Ion Batteries
Prolonging lithium-ion battery lifetime: How operating conditions affect solid electrolyte interphase growth
From Lithium-Ion to Sodium-Ion Batteries
Amorphous Materials for Lithium‐Ion and Post‐Lithium‐Ion Batteries
Lithium-Ion Batteries: basics and applications
New materials for lithium-ion batteries
New Materials for Lithium-Ion Batteries
Lithium-ion and beyond: safer alternatives
Energy consumption of current and future production of lithium-ion and post lithium-ion battery cells