State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer Learning
The state of charge (SOC) of a battery is a key parameter of electrical vehicles (EVs). However, limited by the lack of computing resources, the SOC estimation strategy used in vehicle-mounted battery management systems (V-BMS) is usually simplified. With the development of the new energy vehicle bi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/9/5/266 |
| _version_ | 1797601116494495744 |
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| author | Xiaoyu Li Jianhua Xu Xuejing Ding Hongqiang Lyu |
| author_facet | Xiaoyu Li Jianhua Xu Xuejing Ding Hongqiang Lyu |
| author_sort | Xiaoyu Li |
| collection | DOAJ |
| description | The state of charge (SOC) of a battery is a key parameter of electrical vehicles (EVs). However, limited by the lack of computing resources, the SOC estimation strategy used in vehicle-mounted battery management systems (V-BMS) is usually simplified. With the development of the new energy vehicle big data platforms, it is possible to obtain the battery SOC through cloud-based BMS (C-BMS). In this paper, a battery SOC estimation method based on common feature extraction and transfer learning is proposed for C-BMS applications. Considering the diversity of driving cycles, a common feature extraction method combining empirical mode decomposition (EMD) and a compensation strategy for C-BMS is designed. The selected features are treated as the new inputs of the SOC estimation model to improve the generalization ability. Subsequently, a long short-term memory (LSTM) recurrent neural network is used to construct a basic model for battery SOC estimation. A parameter-based transfer learning method and an adaptive weighting strategy are used to obtain the C-BMS battery SOC estimation model. Finally, the SOC estimation method is validated on laboratory datasets and cloud platform datasets. The maximum root-mean-square error (RMSE) of battery SOC estimation with the laboratory dataset is 2.2%. The maximum RMSE of battery pack SOC estimation on two different electric vehicles is 1.3%. |
| first_indexed | 2024-03-11T03:56:54Z |
| format | Article |
| id | doaj.art-9c7ae8cd359c4edc8bb2b1ca50454d68 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-11T03:56:54Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-9c7ae8cd359c4edc8bb2b1ca50454d682023-11-18T00:28:40ZengMDPI AGBatteries2313-01052023-05-019526610.3390/batteries9050266State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer LearningXiaoyu Li0Jianhua Xu1Xuejing Ding2Hongqiang Lyu3State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, ChinaCollege of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, ChinaShenzhen Aerospace Dongfanghong Satellite Co., Ltd., Shenzhen 518061, ChinaShenzhen Aerospace Dongfanghong Satellite Co., Ltd., Shenzhen 518061, ChinaThe state of charge (SOC) of a battery is a key parameter of electrical vehicles (EVs). However, limited by the lack of computing resources, the SOC estimation strategy used in vehicle-mounted battery management systems (V-BMS) is usually simplified. With the development of the new energy vehicle big data platforms, it is possible to obtain the battery SOC through cloud-based BMS (C-BMS). In this paper, a battery SOC estimation method based on common feature extraction and transfer learning is proposed for C-BMS applications. Considering the diversity of driving cycles, a common feature extraction method combining empirical mode decomposition (EMD) and a compensation strategy for C-BMS is designed. The selected features are treated as the new inputs of the SOC estimation model to improve the generalization ability. Subsequently, a long short-term memory (LSTM) recurrent neural network is used to construct a basic model for battery SOC estimation. A parameter-based transfer learning method and an adaptive weighting strategy are used to obtain the C-BMS battery SOC estimation model. Finally, the SOC estimation method is validated on laboratory datasets and cloud platform datasets. The maximum root-mean-square error (RMSE) of battery SOC estimation with the laboratory dataset is 2.2%. The maximum RMSE of battery pack SOC estimation on two different electric vehicles is 1.3%.https://www.mdpi.com/2313-0105/9/5/266cloud-based battery management systemstate of chargeempirical mode decompositiontransfer learning |
| spellingShingle | Xiaoyu Li Jianhua Xu Xuejing Ding Hongqiang Lyu State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer Learning Batteries cloud-based battery management system state of charge empirical mode decomposition transfer learning |
| title | State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer Learning |
| title_full | State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer Learning |
| title_fullStr | State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer Learning |
| title_full_unstemmed | State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer Learning |
| title_short | State of Charge Estimation for Batteries Based on Common Feature Extraction and Transfer Learning |
| title_sort | state of charge estimation for batteries based on common feature extraction and transfer learning |
| topic | cloud-based battery management system state of charge empirical mode decomposition transfer learning |
| url | https://www.mdpi.com/2313-0105/9/5/266 |
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