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About
Fuda Gong is a researcher at the forefront of battery aging science, specializing in explainable machine learning and electrochemical modeling. His work centers on unraveling the complex mechanisms behind battery calendar aging—a critical factor for electric vehicle and grid storage longevity. Gong’s major contribution is the development of MELODI, an innovative framework that combines machine learning with mechanistic interpretation to disentangle the physical and chemical processes driving capacity fade over time. This approach not only predicts aging with high accuracy but also provides transparent, physics-informed explanations, bridging the gap between data-driven models and fundamental electrochemistry. His 2025 paper on MELODI has already garnered early citations, signaling its growing influence in the field. By enabling researchers to identify dominant aging pathways—such as lithium inventory loss or electrode degradation—Gong’s work empowers more targeted battery design and smarter battery management systems. His research stands out for its clarity and practical relevance, offering a powerful tool for accelerating the development of longer-lasting, safer batteries.
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Top Papers
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