Predicting Si-anode calendar life using machine learning: Correlating electrolyte properties and electrochemical signals

Schulze, Maxwell C.; Prakash, Nina; Gering, Kevin L.; Colclasure, Andrew M.; Weddle, Peter J.

doi:10.1016/j.jpowsour.2025.238338

Title: Predicting Si-anode calendar life using machine learning: Correlating electrolyte properties and electrochemical signals

Journal Article · Wed Sep 24 00:00:00 UTC 2025 · Journal of Power Sources

DOI: https://doi.org/10.1016/j.jpowsour.2025.238338 · OSTI ID:3016206

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National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Idaho National Laboratory (INL), Idaho Falls, ID (United States)

This study evaluates novel electrolytes tailored for Si-containing anodes to promote calendar-life. Drawing inspiration from advancements in electrolytes for Li-metal cells, the work investigates correlations between predicted electrolyte properties and measured electrochemical performance using several machine-learning models. By leveraging machine learning and advanced modeling techniques, this study aims to establish predictive frameworks that accelerate calendar-aging experiments and inform rational electrolyte design for Si-containing cells. In the present study, fifteen different electrolytes are evaluated in a Si-containing cell using an accelerated calendar-life protocol. For each electrolyte considered, 87 properties (features) from the Advanced Electrolyte Model were produced to identify key property/performance relationships. In this study, the best performing electrolytes were generally those formulations that included non-coordinating fluoroether solvents, and the most predictive features for long-term calendar-life were features related to salt concentration and electrolyte viscosity as well as early capacity, ionic conductivity, and Coulombic efficiency measurements. The framework developed in this study correlating electrolyte properties to measured electrochemical performance is expected to accelerate electrolyte design for Si-containing anodes and ultimately enable high-energy-density, long-life Li-ion batteries.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office; USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517; AC36-08GO28308

OSTI ID:: 3016206

Report Number(s):: INL/JOU--26-90007; NREL/JA--5700-94337; NREL/JA-5700-94337

Journal Information:: Journal of Power Sources, Journal Name: Journal of Power Sources Vol. 659; ISSN 0378-7753

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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25 ENERGY STORAGE
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Advanced Electrolyte Model (AEM)
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Li-ion battery
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Title: Predicting Si-anode calendar life using machine learning: Correlating electrolyte properties and electrochemical signals

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