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Updated: May 22, 2025
11:04
Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature
Published on: December 20, 2016
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Multi-Objective Optimization of Ionic Polymer Electrolytes for High-Voltage Fast-Charging and Versatile Lithium
Yuanyuan Song1,2, Jiazhe Ju1, Jifeng Wang1
1State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, AI Research Center for Polymer Science, Fudan University, Shanghai, 200438, China.
Advanced Materials (Deerfield Beach, Fla.)
|March 17, 2025
Summary
Bayesian optimization accelerates the discovery of ionic polymer electrolytes (IPEs) for high-voltage lithium batteries. This method efficiently targets promising materials, enabling faster charging and enhanced performance.
Area of Science:
- Materials Science
- Electrochemistry
- Computational Chemistry
Background:
- Designing ionic polymer electrolytes (IPEs) for high-voltage and fast-charging lithium batteries is challenging due to the complex chemical space.
- Traditional material discovery methods are slow and costly, hindering rapid advancement.
Purpose of the Study:
- To develop a kernel-based Bayesian optimization approach for multi-objective optimization of IPEs.
- To simultaneously consider ionic conductivity, electrochemical stability, and discharge capacity.
- To enable efficient material discovery for advanced lithium batteries.
Main Methods:
- Kernel-based Bayesian optimization utilizing a union set of acquisition functions.
- Multi-objective optimization targeting ionic conductivity, electrochemical stability, and discharge capacity.
- Development of aqueous and high-voltage lithium-ion batteries using modified IPEs.
Main Results:
- Promising IPEs were identified within 2.8% of the chemical space in three iterations.
- Achieved lithium metal batteries demonstrated high performance at ultrahigh cutoff voltages (4.8 V and 4.92 V).
- An aqueous, high-voltage lithium-ion battery was successfully developed, showing suppressed water reactivity and boosted ionic conductivity.
Conclusions:
- The developed multi-objective optimization effectively handles complex, discontinuous parameter spaces and multiple targets.
- This approach offers critical insights for material discovery and property optimization in advanced lithium batteries.
- The findings pave the way for more versatile and cost-effective lithium battery development.
