Emanuele Gravante

Machine learning-based fault diagnosis for lithium-ion battery systems

An experimental/synthetic hybrid, data-driven FDI framework that leverages supervised machine learning (ML) integrated with an experimentally validated second-order electro-thermal battery model to generate a mixed experimental–synthetic dataset enables fast, real-time diagnosis of complex multi-fault scenarios at the cell or module level in series–parallel LIB pack architectures.