Healthy lithium nickel manganese cobalt oxide (NMC) battery using machine-learning method
Nowadays, batteries are experiencing fast development and have a wide range of applications. However, due to their complex characteristics, it is still challenging for battery health estimation. The purpose of this project was to determine the correlations between the parameters and battery health....
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157674 |
| _version_ | 1824454841923010560 |
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| author | Wu, Xumin |
| author2 | Soong Boon Hee |
| author_facet | Soong Boon Hee Wu, Xumin |
| author_sort | Wu, Xumin |
| collection | NTU |
| description | Nowadays, batteries are experiencing fast development and have a wide range of applications. However, due to their complex characteristics, it is still challenging for battery health estimation. The purpose of this project was to determine the correlations between the parameters and battery health. Despite the fact that there are some methods for predicting battery health such as physics-based models and empirical models. While machine-learning-based method has good accuracy in estimation of battery health management. This project use machine-learning techniques to predict the health state of the Lithium Nickel Manganese Cobalt Oxide (NMC) battery. A variety of circuit models for battery modelling that are equivalent have been given and analysed. In addition, a battery modelling framework is presented for estimating Lithium-Ion Battery modelling parameters. The efficiency of the model will be demonstrated using NMC battery pack experiment data. Besides, this project also analyse the relationships between the parameters and the health state of battery. |
| first_indexed | 2025-02-19T03:28:44Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157674 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:28:44Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1576742023-07-07T19:00:03Z Healthy lithium nickel manganese cobalt oxide (NMC) battery using machine-learning method Wu, Xumin Soong Boon Hee School of Electrical and Electronic Engineering EBHSOONG@ntu.edu.sg Engineering::Electrical and electronic engineering Nowadays, batteries are experiencing fast development and have a wide range of applications. However, due to their complex characteristics, it is still challenging for battery health estimation. The purpose of this project was to determine the correlations between the parameters and battery health. Despite the fact that there are some methods for predicting battery health such as physics-based models and empirical models. While machine-learning-based method has good accuracy in estimation of battery health management. This project use machine-learning techniques to predict the health state of the Lithium Nickel Manganese Cobalt Oxide (NMC) battery. A variety of circuit models for battery modelling that are equivalent have been given and analysed. In addition, a battery modelling framework is presented for estimating Lithium-Ion Battery modelling parameters. The efficiency of the model will be demonstrated using NMC battery pack experiment data. Besides, this project also analyse the relationships between the parameters and the health state of battery. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-20T05:23:25Z 2022-05-20T05:23:25Z 2021 Final Year Project (FYP) Wu, X. (2021). Healthy lithium nickel manganese cobalt oxide (NMC) battery using machine-learning method. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157674 https://hdl.handle.net/10356/157674 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Wu, Xumin Healthy lithium nickel manganese cobalt oxide (NMC) battery using machine-learning method |
| title | Healthy lithium nickel manganese cobalt oxide (NMC) battery using machine-learning method |
| title_full | Healthy lithium nickel manganese cobalt oxide (NMC) battery using machine-learning method |
| title_fullStr | Healthy lithium nickel manganese cobalt oxide (NMC) battery using machine-learning method |
| title_full_unstemmed | Healthy lithium nickel manganese cobalt oxide (NMC) battery using machine-learning method |
| title_short | Healthy lithium nickel manganese cobalt oxide (NMC) battery using machine-learning method |
| title_sort | healthy lithium nickel manganese cobalt oxide nmc battery using machine learning method |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/157674 |
| work_keys_str_mv | AT wuxumin healthylithiumnickelmanganesecobaltoxidenmcbatteryusingmachinelearningmethod |