A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery
The prognostic and health management (PHM) of lithium-ion batteries has received increasing attention in recent years. The remaining useful life (RUL) prediction and state of health (SOH) monitoring are two important parts in PHM of the lithium-ion battery. Nowadays, the development of signal proces...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8747502/ |
| _version_ | 1818737262801715200 |
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| author | Jiantao Qu Feng Liu Yuxiang Ma Jiaming Fan |
| author_facet | Jiantao Qu Feng Liu Yuxiang Ma Jiaming Fan |
| author_sort | Jiantao Qu |
| collection | DOAJ |
| description | The prognostic and health management (PHM) of lithium-ion batteries has received increasing attention in recent years. The remaining useful life (RUL) prediction and state of health (SOH) monitoring are two important parts in PHM of the lithium-ion battery. Nowadays, the development of signal processing technology and neural network technology introduces new data-driven methods to RUL prediction and SOH monitoring of the lithium-ion battery. This paper presents a neural-network-based method that combines long short-term memory (LSTM) network with particle swarm optimization and attention mechanism for RUL prediction and SOH monitoring of the lithium-ion battery. Before predicting RUL of the lithium-ion battery, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized for the raw data denoising, which can improve the accuracy of prediction. A real-life cycle dataset of lithium-ion batteries from NASA is used to evaluate the proposed method, and the experiment results show that when compared with traditional methods, the proposed method has higher accuracy. |
| first_indexed | 2024-12-18T00:50:15Z |
| format | Article |
| id | doaj.art-b6578a913b4b4a0a8369908e54fae54a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:50:15Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b6578a913b4b4a0a8369908e54fae54a2022-12-21T21:26:42ZengIEEEIEEE Access2169-35362019-01-017871788719110.1109/ACCESS.2019.29254688747502A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion BatteryJiantao Qu0https://orcid.org/0000-0002-8664-2236Feng Liu1Yuxiang Ma2https://orcid.org/0000-0002-6131-2599Jiaming Fan3School of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaThe prognostic and health management (PHM) of lithium-ion batteries has received increasing attention in recent years. The remaining useful life (RUL) prediction and state of health (SOH) monitoring are two important parts in PHM of the lithium-ion battery. Nowadays, the development of signal processing technology and neural network technology introduces new data-driven methods to RUL prediction and SOH monitoring of the lithium-ion battery. This paper presents a neural-network-based method that combines long short-term memory (LSTM) network with particle swarm optimization and attention mechanism for RUL prediction and SOH monitoring of the lithium-ion battery. Before predicting RUL of the lithium-ion battery, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized for the raw data denoising, which can improve the accuracy of prediction. A real-life cycle dataset of lithium-ion batteries from NASA is used to evaluate the proposed method, and the experiment results show that when compared with traditional methods, the proposed method has higher accuracy.https://ieeexplore.ieee.org/document/8747502/Lithium-ion batteryprognostic and health management (PHM)long short-term memory (LSTM)attention mechanism |
| spellingShingle | Jiantao Qu Feng Liu Yuxiang Ma Jiaming Fan A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery IEEE Access Lithium-ion battery prognostic and health management (PHM) long short-term memory (LSTM) attention mechanism |
| title | A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery |
| title_full | A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery |
| title_fullStr | A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery |
| title_full_unstemmed | A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery |
| title_short | A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery |
| title_sort | neural network based method for rul prediction and soh monitoring of lithium ion battery |
| topic | Lithium-ion battery prognostic and health management (PHM) long short-term memory (LSTM) attention mechanism |
| url | https://ieeexplore.ieee.org/document/8747502/ |
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