Li-ion battery aging tests and data analytics
This project focuses on predicting the discharge capacity of lithium-ion batteries using data-driven models, with specific attention to two health indicators: Time Interval of Equal Discharge Voltage Difference (TIEDVD) and Incremental Capacity Peak (ICpeak). The study investigates the efficacy of t...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/181653 |
| _version_ | 1826124455704788992 |
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| author | Tan, Zheng Nan |
| author2 | Xu Yan |
| author_facet | Xu Yan Tan, Zheng Nan |
| author_sort | Tan, Zheng Nan |
| collection | NTU |
| description | This project focuses on predicting the discharge capacity of lithium-ion batteries using data-driven models, with specific attention to two health indicators: Time Interval of Equal Discharge Voltage Difference (TIEDVD) and Incremental Capacity Peak (ICpeak). The study investigates the efficacy of these indicators in tracking battery degradation over repeated charge-discharge cycles. The TIEDVD was used to measure Voltage drop over time, while ICpeak was derived from incremental capacity analysis, capturing shifts in battery behavior.
Using a framework, a Gaussian Process Regression (GPR) model was developed using data from cells 1 to 4 for training and cells 5 to 8 for testing, to assess the generalizability of the model. Results demonstrated that both TIEDVD and ICpeak are strong predictors of battery degradation, as evidenced by high R-squared values and low error metrics (RMSE, MAE, and MAPE). The model accurately predicted the discharge capacity across test sets, showing good performance in unseen data. This project provides a foundation for further research into battery health monitoring and predictive modelling using machine learning and health indicators, with potential applications in extending the lifetime of battery systems. |
| first_indexed | 2025-03-09T13:46:31Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/181653 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T13:46:31Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1816532024-12-13T15:45:20Z Li-ion battery aging tests and data analytics Tan, Zheng Nan Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering Li-ion battery data analytics Li-ion battery aging tests This project focuses on predicting the discharge capacity of lithium-ion batteries using data-driven models, with specific attention to two health indicators: Time Interval of Equal Discharge Voltage Difference (TIEDVD) and Incremental Capacity Peak (ICpeak). The study investigates the efficacy of these indicators in tracking battery degradation over repeated charge-discharge cycles. The TIEDVD was used to measure Voltage drop over time, while ICpeak was derived from incremental capacity analysis, capturing shifts in battery behavior. Using a framework, a Gaussian Process Regression (GPR) model was developed using data from cells 1 to 4 for training and cells 5 to 8 for testing, to assess the generalizability of the model. Results demonstrated that both TIEDVD and ICpeak are strong predictors of battery degradation, as evidenced by high R-squared values and low error metrics (RMSE, MAE, and MAPE). The model accurately predicted the discharge capacity across test sets, showing good performance in unseen data. This project provides a foundation for further research into battery health monitoring and predictive modelling using machine learning and health indicators, with potential applications in extending the lifetime of battery systems. Bachelor's degree 2024-12-11T23:36:56Z 2024-12-11T23:36:56Z 2024 Final Year Project (FYP) Tan, Z. N. (2024). Li-ion battery aging tests and data analytics. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181653 https://hdl.handle.net/10356/181653 en A1194-232 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Li-ion battery data analytics Li-ion battery aging tests Tan, Zheng Nan Li-ion battery aging tests and data analytics |
| title | Li-ion battery aging tests and data analytics |
| title_full | Li-ion battery aging tests and data analytics |
| title_fullStr | Li-ion battery aging tests and data analytics |
| title_full_unstemmed | Li-ion battery aging tests and data analytics |
| title_short | Li-ion battery aging tests and data analytics |
| title_sort | li ion battery aging tests and data analytics |
| topic | Engineering Li-ion battery data analytics Li-ion battery aging tests |
| url | https://hdl.handle.net/10356/181653 |
| work_keys_str_mv | AT tanzhengnan liionbatteryagingtestsanddataanalytics |