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Development of an algorithm to estimate the State of Health and State of Charge of a lead-acid battery

Hassan Jadayel

Development of an algorithm to estimate the State of Health and State of Charge of a lead-acid battery.

Rel. Marcello Chiaberge. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021


This thesis presents a part of the BAT-MAN research/industrial project conducted by Brain Technologies s.r.l. The project is a collaborative effort, including the contributions of Brain Technologies, S.I.V.E s.p.a, and Politecnico di Torino. The ultimate objective of this project is to develop and produce a battery management device capable of both the estimation and prediction of Lead-Acid battery working parameters, in real-time. The scope of this thesis is on the mathematical algorithm used to compute the parameters in question, namely the State of Charge (SoC) and State of Health (SoH). This algorithm stands out amongst others available in literature as it is a variable-SoH algorithm, whereas others rely on a fixed SoH (usually that of a new battery, i.e. 100%). The thesis will begin by presenting a literature review of the concepts required to form an understanding of the project, including an introduction as well as a brief overview of the state of the art in estimation/prediction methodologies. It will then present the student’s work as follows; firstly, it will explain the data acquisition procedure used for the identification of the parameters to be used for estimation/prediction. It will then describe the data driven parameter identification used to construct the mathematical model. Afterwards, it will describe the implementation of the model into Simulink along with a description of the prediction methodology to be used.

Relators: Marcello Chiaberge
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 70
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/17888
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