Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning
Achieving accurate battery state of health (SOH) estimation is crucial, but existing methods still face many challenges in terms of data quality, computational efficiency, and cross-scenario generalization capabilities. This study proposes a hybrid deep learning framework incorporating transfer lear...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/6/1491 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850205098487054336 |
|---|---|
| author | Jinling Ren Misheng Cai Dapai Shi |
| author_facet | Jinling Ren Misheng Cai Dapai Shi |
| author_sort | Jinling Ren |
| collection | DOAJ |
| description | Achieving accurate battery state of health (SOH) estimation is crucial, but existing methods still face many challenges in terms of data quality, computational efficiency, and cross-scenario generalization capabilities. This study proposes a hybrid deep learning framework incorporating transfer learning to address these challenges. The framework integrates inception depthwise convolution (IDC), channel reduction attention (CRA) mechanism, and staged training strategy to improve the accuracy and generalization ability of SOH estimation. The IDC module of the proposed model is capable of extracting battery degradation time series features from multiple scales while reducing the computational overhead. The CRA module effectively reduces the computational complexity and memory usage of global feature capture by compressing the channel dimensions. A well-designed pre-training/fine-tuning two-stage training strategy achieves accurate cross-scene SOH estimation by utilizing large-scale source-domain data to learn generalized aging features and then uses a small amount of new data to quickly fine-tune the base model. The proposed method is validated using two publicly available datasets, including 54 nickel cobalt manganese oxide (NCM) cells and 16 nickel manganese cobalt oxide (NMC) cells. The experimental results show that the root mean square error (RMSE) of the model on the NCM and NMC datasets is 0.522% and 0.283%, respectively, with a coefficient of determination (R<sup>2</sup>) not less than 0.98 and mean absolute percentage error (MAPE) of 0.431% and 0.22%, respectively. The proposed method not only achieves high-precision SOH estimation among the same type of batteries but also demonstrates strong generalization ability under different battery chemistries and scenarios. |
| format | Article |
| id | doaj-art-77e53bdeabff4854b9fe0ec19298ccb4 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-77e53bdeabff4854b9fe0ec19298ccb42025-08-20T02:11:09ZengMDPI AGEnergies1996-10732025-03-01186149110.3390/en18061491Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer LearningJinling Ren0Misheng Cai1Dapai Shi2Department of Automotive Engineering, Shandong Vocational College of Science and Technology, Weifang 261053, ChinaHubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441000, ChinaHubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441000, ChinaAchieving accurate battery state of health (SOH) estimation is crucial, but existing methods still face many challenges in terms of data quality, computational efficiency, and cross-scenario generalization capabilities. This study proposes a hybrid deep learning framework incorporating transfer learning to address these challenges. The framework integrates inception depthwise convolution (IDC), channel reduction attention (CRA) mechanism, and staged training strategy to improve the accuracy and generalization ability of SOH estimation. The IDC module of the proposed model is capable of extracting battery degradation time series features from multiple scales while reducing the computational overhead. The CRA module effectively reduces the computational complexity and memory usage of global feature capture by compressing the channel dimensions. A well-designed pre-training/fine-tuning two-stage training strategy achieves accurate cross-scene SOH estimation by utilizing large-scale source-domain data to learn generalized aging features and then uses a small amount of new data to quickly fine-tune the base model. The proposed method is validated using two publicly available datasets, including 54 nickel cobalt manganese oxide (NCM) cells and 16 nickel manganese cobalt oxide (NMC) cells. The experimental results show that the root mean square error (RMSE) of the model on the NCM and NMC datasets is 0.522% and 0.283%, respectively, with a coefficient of determination (R<sup>2</sup>) not less than 0.98 and mean absolute percentage error (MAPE) of 0.431% and 0.22%, respectively. The proposed method not only achieves high-precision SOH estimation among the same type of batteries but also demonstrates strong generalization ability under different battery chemistries and scenarios.https://www.mdpi.com/1996-1073/18/6/1491lithium-ion batterySOHtransfer learningdeep learninghybrid model |
| spellingShingle | Jinling Ren Misheng Cai Dapai Shi Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning Energies lithium-ion battery SOH transfer learning deep learning hybrid model |
| title | Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning |
| title_full | Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning |
| title_fullStr | Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning |
| title_full_unstemmed | Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning |
| title_short | Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning |
| title_sort | efficient hybrid deep learning model for battery state of health estimation using transfer learning |
| topic | lithium-ion battery SOH transfer learning deep learning hybrid model |
| url | https://www.mdpi.com/1996-1073/18/6/1491 |
| work_keys_str_mv | AT jinlingren efficienthybriddeeplearningmodelforbatterystateofhealthestimationusingtransferlearning AT mishengcai efficienthybriddeeplearningmodelforbatterystateofhealthestimationusingtransferlearning AT dapaishi efficienthybriddeeplearningmodelforbatterystateofhealthestimationusingtransferlearning |