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Identification and Predictive Analysis of Storage Systems

Marco Colabella

Identification and Predictive Analysis of Storage Systems.

Rel. Carlo Novara. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2019


The work done in the thesis is placed as a continuation of the development of the experimental and industrial project BAT-MAN. The project team is composed by: ??Brain Technologies s.r.l. ??S.I.V.E. s.p.a. ??Politecnico di Torino (Research Organization) The proposal of BAT-MAN is to make significant technologic innovations (especially relative to the techniques of estimation and diagnostics), realizing at the same time a product idea (realizing a prototype) that, on the one hand it can offer immediate and large-scale feedback on the solutions developed, and on the other can act as a forerunner to a series of applications based on the same technologies, either in areas closely related to accumulation systems, or in areas where advanced diagnostic and estimation techniques can bring a significant added value. The objective, therefore, concerns the realization of a new product idea, at low cost, that “measures” the State of Charge (SoC) and the State of Health (SoH) of a battery. For this purpose, it has been implemented a method which make use of “Predictive Analysis” Theory. In the first two chapters, they will be presented a brief introduction to the proposed problem, and the actual ‘State of the Art’ related to this field. In the next chapters, the improvements made will be exposed: in particular, in the third it will be set a solid base for the modeling of the batteries, in the fourth and fifth they will be explained the method used for the prediction of the states, and the algorithm used for the improvements of the estimates.

Relators: Carlo Novara
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 108
Additional Information: Tesi secretata. Fulltext non presente
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/13105
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