Digital Twin-Driven Multi-Physics and Nano/Microstructural Analysis for Lithium-Ion and All-Solid-State Batteries: Design, Optimization, and Lifetime Prediction
Special Issue Editor
Special Issue Information
Dear Colleagues,
The ever-increasing demand for advanced energy storage solutions, in electric vehicles, grid-scale storage, and portable electronics, drives continuous innovation in battery technology. While lithium-ion batteries (LIBs) have significantly transformed numerous sectors, the ongoing pursuit of superior energy density, faster charging capabilities, enhanced safety, and extended cycle life remains critical. Next-generation chemistries, such as all-solid-state batteries (ASSBs), offer immense promise but are confronted with substantial scientific and engineering complexities. The intricate interplay of electrochemical, thermal, mechanical, and microstructural phenomena, combined with progressive degradation mechanisms, poses considerable challenges for effective design, precise performance forecasting, and reliable lifetime estimation. Traditional empirical approaches are often insufficient, necessitating the use of advanced computational tools that integrate data-driven and physics-based modeling to accelerate battery research and development.
This Special Issue aims to provide cutting-edge studies in digital twin technology, multi-physics modeling, and nano/micro-scale structural analysis, significantly advancing the understanding, design, optimization, and lifetime prediction of both current lithium-ion and emerging all-solid-state battery technologies.
The editor encourages diverse contributions from researchers across academia, industry, and national laboratories worldwide. Both experimental studies, numerical simulations, and theoretical frameworks are welcome to foster a comprehensive and inclusive dialogue.
Potential topics include, but are not limited to, the following:
- Multi-physics modeling (electrochemical, thermal, and mechanical coupling) for lithium-ion and all-solid-state batteries.
- Nano/microstructural characterization, modeling, and its impact on battery performance and degradation.
- Degradation mechanism analysis, lifetime prediction, and accelerated aging models.
- Battery safety mechanisms, thermal runaway, and mitigation strategies through modeling.
- Physics-informed AI for battery modeling, diagnostics, and prediction.
- Advanced characterization techniques for understanding the multi-physics and microstructure of batteries.
- Modeling of interfacial phenomena and transport in all-solid-state batteries.
- Design and optimization methodologies for battery materials, components, cells, modules, and packs.
- Digital Twin frameworks for battery health management, performance prediction, and real-time optimization.
Dr. Joonam Park
Guest Editor
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Keywords
- lithium-ion batteries
- all-solid-state batteries
- digital twin
- multi-physics modeling
- nano/microstructural analysis
- physics-informed AI
- battery performance/degradation/safety
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