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Master's Dissertation
DOI
https://doi.org/10.11606/D.3.2020.tde-05032021-091755
Document
Author
Full name
Patrick Cadier D'Aquino e Baroni Santos
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2020
Supervisor
Committee
Seabra, Antonio Carlos (President)
Maruyama, Newton
Sousa, Miguel Angelo de Abreu de
Title in Portuguese
Métodos para a estimativa da capacidade de baterias estacionárias com ênfase em aplicações de segurança.
Keywords in Portuguese
Baterias elétricas
Fuzzy (Inteligência artificial)
Redes neurais
Abstract in Portuguese
Neste projeto, é proposto métodos diferentes para medir a capacidade e o envelhecimento de baterias estacionárias com ênfase em aplicações de segurança. Para tanto, é primeiro descrito a importância de baterias em diversas aplicações, principalmente as baterias de íons de lítio. Um estudo das características elétricas e químicas de baterias de íons de lítio é apresentado, mostrando entre outras coisas as causas eletroquímicas do envelhecimento. Além disso, as diferentes técnicas para o monitoramento de sistemas são mostradas. Neste projeto pretende-se usar redes neurais, lógica fuzzy e adaptatividade para estimar a capacidade de uma bateria usando os valores da resistência e capacitância interna. Os dados recolhidos são apresentados e analisados. Os diferentes métodos são comparados e as vantagens e desvantagens de cada método são discutidas.
Title in English
Methods for the estimation of a stationary battery's capacity with enfasis on security aplications.
Keywords in English
Electrical batteries
Fuzzy logic
Neural networks
Abstract in English
This project proposes different methods for measuring the capacity and aging of stationary batteries on security sistems. For this, it is first described batteries' important role on many applications, especially concerning lithium-ion batteries. A study of electrical and chemical characteristics of lithium-ion batteries is presented, showing among other things the electrochemical causes for aging. Furthermore, different techniques for Battery Management System (BMS) are shown. In this project we intend to use a neural network, fuzzy logic and adaptability to estimate the battery's capacity using the internal resistance and capacitance values. The data collected is presented and analyzed. In the end, each method is compared with one another.
 
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Publishing Date
2021-03-10
 
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