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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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 |
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Engineering::Electrical and electronic engineering Chee, Nigel Zachary State of health estimation for lithium-ion batteries based on data driven techniques |
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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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1772828532609646592 |