Minggao Ouyang
Papers
1
Total Citations
3
H-Index
1
About
Dr. Minggao Ouyang is a leading figure in energy storage and battery aging research, with a particular focus on developing interpretable machine learning methods to unravel the complex degradation mechanisms of lithium-ion batteries. His most notable contribution is the creation of MELODI (Mechanistic Explainable Learning for Disentangling Interpretations), a groundbreaking framework that combines physical models with explainable AI to decouple calendar aging factors such as temperature, state of charge, and time. This work, published in 2025 and already garnering 3 citations, enables researchers to move beyond black-box predictions and understand the underlying electrochemical processes driving battery fade. Dr. Ouyang’s research bridges the gap between data-driven approaches and fundamental electrochemistry, offering practical insights for extending battery lifespan in electric vehicles and grid storage. His work is particularly impactful for students and engineers seeking to design more durable batteries, as it provides a transparent, mechanistic view of aging that can guide material selection and operational strategies. By integrating machine learning with physical principles, Dr. Ouyang is shaping the future of predictive battery health management.
Research Focus
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Top Papers
- 1