Nourhan Ali Kamel Abdelrahman
Machine learning approach for online estimation of Li-ion battery State of Health.
Rel. Angelo Bonfitto. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022
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Abstract
As the world moves more towards sustainability and reducing greenhouse effects, the age of electric cars is approaching in a quick pace and, batteries, which are the electric heart of these cars, are gaining more and more attention. This includes not only developments related to batteries’ material and structure but also the ones related to charging and monitoring battery health. The analysis and monitoring of cell degradation in batteries is crucial for assuring the safety of those electric vehicles. Alongside this, the expanding advances made in machine learning techniques and their wide range of applications has made it a very powerful tool that can be utilized in understanding the complex behavior of systems like batteries.
The main objective of this thesis is to develop and test a machine learning algorithm capable of monitoring and estimating the battery State of Health (SOH) in real-time, with the possibility of deploying this algorithm on a hardware, like in the case of the battery management system of an electric vehicle
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