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Thesis
Home   /   Thesis   /   Predictive Diagnosis and Ageing Trajectory Estimation of New Generation Batteries through Multi-modalities Fusion and Physics-Informed Machine Learning

Predictive Diagnosis and Ageing Trajectory Estimation of New Generation Batteries through Multi-modalities Fusion and Physics-Informed Machine Learning

Artificial intelligence & Data intelligence Electrochemical energy storage incl. batteries for energy transition Technological challenges

Abstract

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 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.

Laboratory

Département de l’Electricité et de l’Hydrogène pour les Transports (LITEN)
Service Transverses pour Technologies de Conversion électrochimique et électrique
Laboratoire Electronique Enérgie et Puissance
Université Grenoble Alpes
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