Context:
Lithium-ion and emerging Sodium-ion batteries are crucial for energy transition and transportation electrification. Ensuring battery longevity, performance, and safety requires understanding degradation mechanisms at multiple scales.
Research Objective:
Develop innovative battery diagnostic and prognostic methodologies by leveraging multi-sensor data fusion (acoustic sensors, strain gauge sensors, thermal sensors, electrical sensors, optical sensors) and Physics-Informed Machine Learning (PIML) approaches, combining physical battery models with deep learning algorithms.
Scientific Approach:

Establish correlations between multi-physical measurements (internal sensors using optical fiber modality, and external sensors Embedded on the cell packaging) and battery degradation mechanisms
Explore hybrid PIML approaches for multi-physical data fusion
Develop learning architectures integrating physical constraints while processing heterogeneous data
Extend methodologies to emerging Na-Ion battery technologies

Methodology:
The research will utilize an extensive multi-instrumented cell database, analyzing measurement signatures and developing innovative PIML algorithms that optimize multi-sensor data fusion and validate performance using real-world data.
Expected Outcomes:
The thesis aims to provide valuable recommendations for battery system instrumentation, develop advanced diagnostic algorithms, and contribute significantly to improving the reliability and sustainability of electrochemical storage systems, with potential academic and industrial impacts.