Lithium-Ion Battery Degradation Based on the CNN-Transformer Model
Due to its innovative structure and superior handling of long time series data with parallel input, the Transformer model has demonstrated a remarkable effectiveness. However, its application in lithium-ion battery degradation research requires a massive amount of data, which is disadvantageous for...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/2/248 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588637478322176 |
|---|---|
| author | Yongsheng Shi Leicheng Wang Na Liao Zequan Xu |
| author_facet | Yongsheng Shi Leicheng Wang Na Liao Zequan Xu |
| author_sort | Yongsheng Shi |
| collection | DOAJ |
| description | Due to its innovative structure and superior handling of long time series data with parallel input, the Transformer model has demonstrated a remarkable effectiveness. However, its application in lithium-ion battery degradation research requires a massive amount of data, which is disadvantageous for the online monitoring of batteries. This paper proposes a lithium-ion battery degradation research method based on the CNN-Transformer model. By leveraging the efficiency of the CNN model in feature extraction, it reduces the dependency of the Transformer model on data volume, thereby ensuring faster overall model training without a significant loss in model accuracy. This facilitates the online monitoring of battery degradation. The dataset used for training and validation consists of charge–discharge data from 124 lithium iron phosphate batteries. The experimental results include an analysis of the model training results for both single-battery and multiple-battery data, compared with commonly used models such as LSTM and Transformer. Regarding the instability of single-battery data in the CNN-Transformer model, statistical analysis is conducted to analyze the experimental results. The final model results indicate that the root mean square error (RMSE) of capacity predictions for the majority of batteries among the 124 batteries is within 3% of the actual values. |
| format | Article |
| id | doaj-art-90eb4e4d700f4a7691d8cea2c9eaed94 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-90eb4e4d700f4a7691d8cea2c9eaed942025-01-24T13:30:46ZengMDPI AGEnergies1996-10732025-01-0118224810.3390/en18020248Lithium-Ion Battery Degradation Based on the CNN-Transformer ModelYongsheng Shi0Leicheng Wang1Na Liao2Zequan Xu3School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, ChinaSchool of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, ChinaSchool of Engineering, Xi’an International University, Xi’an 710071, ChinaSchool of Computer Science, South China Normal University, Guangzhou 510631, ChinaDue to its innovative structure and superior handling of long time series data with parallel input, the Transformer model has demonstrated a remarkable effectiveness. However, its application in lithium-ion battery degradation research requires a massive amount of data, which is disadvantageous for the online monitoring of batteries. This paper proposes a lithium-ion battery degradation research method based on the CNN-Transformer model. By leveraging the efficiency of the CNN model in feature extraction, it reduces the dependency of the Transformer model on data volume, thereby ensuring faster overall model training without a significant loss in model accuracy. This facilitates the online monitoring of battery degradation. The dataset used for training and validation consists of charge–discharge data from 124 lithium iron phosphate batteries. The experimental results include an analysis of the model training results for both single-battery and multiple-battery data, compared with commonly used models such as LSTM and Transformer. Regarding the instability of single-battery data in the CNN-Transformer model, statistical analysis is conducted to analyze the experimental results. The final model results indicate that the root mean square error (RMSE) of capacity predictions for the majority of batteries among the 124 batteries is within 3% of the actual values.https://www.mdpi.com/1996-1073/18/2/248lithium-ion batteryCNN-Transformer modelSOC predictionparallelized data input |
| spellingShingle | Yongsheng Shi Leicheng Wang Na Liao Zequan Xu Lithium-Ion Battery Degradation Based on the CNN-Transformer Model Energies lithium-ion battery CNN-Transformer model SOC prediction parallelized data input |
| title | Lithium-Ion Battery Degradation Based on the CNN-Transformer Model |
| title_full | Lithium-Ion Battery Degradation Based on the CNN-Transformer Model |
| title_fullStr | Lithium-Ion Battery Degradation Based on the CNN-Transformer Model |
| title_full_unstemmed | Lithium-Ion Battery Degradation Based on the CNN-Transformer Model |
| title_short | Lithium-Ion Battery Degradation Based on the CNN-Transformer Model |
| title_sort | lithium ion battery degradation based on the cnn transformer model |
| topic | lithium-ion battery CNN-Transformer model SOC prediction parallelized data input |
| url | https://www.mdpi.com/1996-1073/18/2/248 |
| work_keys_str_mv | AT yongshengshi lithiumionbatterydegradationbasedonthecnntransformermodel AT leichengwang lithiumionbatterydegradationbasedonthecnntransformermodel AT naliao lithiumionbatterydegradationbasedonthecnntransformermodel AT zequanxu lithiumionbatterydegradationbasedonthecnntransformermodel |