State of health estimation for lithium-ion batteries based on data driven techniques

Lithium-Ion batteries (LIBs) have an increasingly critical role in the daily lives of people with their applications in renewable and non-renewable energy systems as an energy storage solution. As a result, the importance of accurate on-board estimations of LIBs has increased in criticality. Due to...

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Main Author: Chee, Nigel Zachary
Other Authors: Gooi Hoay Beng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/166771
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1667712023-07-07T16:25:26Z State of health estimation for lithium-ion batteries based on data driven techniques Chee, Nigel Zachary Gooi Hoay Beng School of Electrical and Electronic Engineering University of Oxford, Oxford University Research Archive Dr Xiong Binyu EHBGOOI@ntu.edu.sg Engineering::Electrical and electronic engineering Lithium-Ion batteries (LIBs) have an increasingly critical role in the daily lives of people with their applications in renewable and non-renewable energy systems as an energy storage solution. As a result, the importance of accurate on-board estimations of LIBs has increased in criticality. Due to the complex ageing mechanism of LIBs, this report presents a simple data-driven technique involving Gaussian Process Regression (GPR), which estimates the battery capacities using time-series voltage measurements over a period of galvanostatic operation. The GPR operation is applied to 8 cells from the University of Oxford dataset with 3 variables, the duration of galvanostatic operation, number of datapoints and the lower limit of galvanostatic operation voltage. The final selected model parameters have a model Root Mean Squared Percentage Error (RMSPE) or between 0.33-0.6%. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-10T06:56:29Z 2023-05-10T06:56:29Z 2023 Final Year Project (FYP) Chee, N. Z. (2023). State of health estimation for lithium-ion batteries based on data driven techniques. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166771 https://hdl.handle.net/10356/166771 en A1070-221 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Chee, Nigel Zachary
State of health estimation for lithium-ion batteries based on data driven techniques
description Lithium-Ion batteries (LIBs) have an increasingly critical role in the daily lives of people with their applications in renewable and non-renewable energy systems as an energy storage solution. As a result, the importance of accurate on-board estimations of LIBs has increased in criticality. Due to the complex ageing mechanism of LIBs, this report presents a simple data-driven technique involving Gaussian Process Regression (GPR), which estimates the battery capacities using time-series voltage measurements over a period of galvanostatic operation. The GPR operation is applied to 8 cells from the University of Oxford dataset with 3 variables, the duration of galvanostatic operation, number of datapoints and the lower limit of galvanostatic operation voltage. The final selected model parameters have a model Root Mean Squared Percentage Error (RMSPE) or between 0.33-0.6%.
author2 Gooi Hoay Beng
author_facet Gooi Hoay Beng
Chee, Nigel Zachary
format Final Year Project
author Chee, Nigel Zachary
author_sort Chee, Nigel Zachary
title State of health estimation for lithium-ion batteries based on data driven techniques
title_short State of health estimation for lithium-ion batteries based on data driven techniques
title_full State of health estimation for lithium-ion batteries based on data driven techniques
title_fullStr State of health estimation for lithium-ion batteries based on data driven techniques
title_full_unstemmed State of health estimation for lithium-ion batteries based on data driven techniques
title_sort state of health estimation for lithium-ion batteries based on data driven techniques
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166771
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