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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166771 |
| _version_ | 1826130342498533376 |
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| author | Chee, Nigel Zachary |
| author2 | Gooi Hoay Beng |
| author_facet | Gooi Hoay Beng Chee, Nigel Zachary |
| author_sort | Chee, Nigel Zachary |
| collection | NTU |
| 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%. |
| first_indexed | 2024-10-01T07:54:54Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166771 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:54:54Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Engineering::Electrical and electronic engineering Chee, Nigel Zachary State of health estimation for lithium-ion batteries based on data driven techniques |
| title | 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_short | 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 |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/166771 |
| work_keys_str_mv | AT cheenigelzachary stateofhealthestimationforlithiumionbatteriesbasedondatadriventechniques |