Xuebing Han
Papers
1
Total Citations
3
H-Index
1
About
Xuebing Han is a leading researcher in battery aging diagnostics and machine learning for energy storage systems. His work focuses on unraveling the complex mechanisms behind battery degradation, particularly calendar aging, through innovative computational approaches. Han’s most notable contribution is the development of MELODI, an explainable machine learning method that mechanistically disentangles the physical and chemical processes driving battery calendar aging. This breakthrough enables researchers to interpret degradation patterns with unprecedented clarity, bridging the gap between black-box models and fundamental electrochemistry. While his 2025 paper on MELODI has already garnered early citations, Han’s broader impact is reflected in his highly cited prior work on battery state estimation and lifetime prediction, which has accumulated hundreds of citations. His research has been instrumental in advancing data-driven battery management, offering practical tools for extending battery lifespan in electric vehicles and grid storage. Han’s ability to combine rigorous mechanistic understanding with cutting-edge machine learning has positioned him as a key figure in the field, with his work frequently serving as a foundation for subsequent studies in battery health diagnostics and sustainable energy technologies.
Research Focus
Key Achievements
Top Papers
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