Politecnico di Torino (logo)

Multi-model approach for simulation of Li-ion batteries.

Luca Bussi

Multi-model approach for simulation of Li-ion batteries.

Rel. Alessandro Rizzo, Giovanni Guida. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022

PDF (Tesi_di_laurea) - Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives.

Download (7MB) | Preview

The global effort to reduce fossil fuel consumption implies massive energy storage exploitation. The only available technology able to satisfy the required performance is the Li-ion cell. To ensure a sustainable cycle life, it is necessary to exploit the batteries during their overall possible lifetime. BAT-MAN is a project by Brain Technologies to provide a product that offers an online, real-time, and non-intrusive estimation of a lead-acid battery's charge status and state of health. The thesis aims to provide an extension of BAT-MAN to Li-ion batteries. The thesis aims to investigate the behavior of different Li-ion cells to build a general model for simulation and identification purposes. A rigid methodology definition is needed to standardize the experiments and collect consistent data for the successive steps. Some different approaches are investigated to find the best trade-off model between accuracy and computational complexity. Candidate models are tested in closed-loop validation using an Extended Kalman Filter as an observer. The resulting parameters models are examined to extrapolate a simple relation between different SoC and SoH of the batteries. The last phase is the application of the ERMES algorithm, patented by Brain Technologies, to provide a fast and computational inexpensive estimation of the State of Health of the investigated cell.

Relators: Alessandro Rizzo, Giovanni Guida
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 148
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: Brain technologies
URI: http://webthesis.biblio.polito.it/id/eprint/25477
Modify record (reserved for operators) Modify record (reserved for operators)