Electrochemistry Group @PoliTO
Machine learning in the batteries field
Reference persons FEDERICO BELLA
Research Groups Electrochemistry Group @PoliTO
Thesis type BIBLIOGRAPHIC
Description Technology advancement demands energy storage devices with better performance, longer life, higher reliability and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This Thesis aims at reviewing recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (batteries).
This bibliographic thesis involves the drafting of a document in English, starting from a collection of scientific articles (provided by the supervisor) on the subject of machine learning in the lithium battery sector.
Required skills CapacitÓ di leggere e scrivere in inglese. VolontÓ di concludere la tesi entro 6 mesi dall'inizio.
Deadline 30/09/2022 PROPONI LA TUA CANDIDATURA