Improved SOH Prediction of Lithium-Ion Batteries Based on Multi-Dimensional Feature Analysis and Transformer Framework
Data-driven state-of-health (SOH) prediction is increasingly critical for the effective management of lithium-ion batteries; however, challenges remain in practical applications. Traditional methods that rely on a single health indicator often fail to capture the complexity and multi-dimensional nat...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11015565/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850161460037025792 |
|---|---|
| author | Tianfeng Long Pengcheng Zhang Xiaoqi Liu Huaqing Shang Meiling Yue Xuesong Shen Jianwen Meng |
| author_facet | Tianfeng Long Pengcheng Zhang Xiaoqi Liu Huaqing Shang Meiling Yue Xuesong Shen Jianwen Meng |
| author_sort | Tianfeng Long |
| collection | DOAJ |
| description | Data-driven state-of-health (SOH) prediction is increasingly critical for the effective management of lithium-ion batteries; however, challenges remain in practical applications. Traditional methods that rely on a single health indicator often fail to capture the complexity and multi-dimensional nature of battery performance changes. To address these limitations, this paper presents a novel Transformer-based approach for accurate SOH prediction. The correlation between various measured and computed features extracted from battery charge/discharge curves and their impact on battery performance degradation are investigated using Pearson correlation coefficients. Three strongly correlated features are identified as multiple input variables for the Transformer framework. The effectiveness of this Transformer-based SOH prediction method is demonstrated using public datasets, revealing that predictions for internal resistance and capacity closely align with actual values, with most RMSE values falling below 0.01. Furthermore, validation with an additional laboratory dataset confirms the accuracy and adaptability of our proposed approach, highlighting its potential to enhance SOH prediction in real-world applications. |
| format | Article |
| id | doaj-art-a1014411084a45f08cfc5edba7e39318 |
| institution | OA Journals |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-a1014411084a45f08cfc5edba7e393182025-08-20T02:22:49ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-0161363137910.1109/OJVT.2025.357370511015565Improved SOH Prediction of Lithium-Ion Batteries Based on Multi-Dimensional Feature Analysis and Transformer FrameworkTianfeng Long0https://orcid.org/0009-0002-6297-8963Pengcheng Zhang1https://orcid.org/0009-0006-0314-452XXiaoqi Liu2https://orcid.org/0009-0003-9810-0279Huaqing Shang3Meiling Yue4https://orcid.org/0000-0002-4624-4743Xuesong Shen5Jianwen Meng6https://orcid.org/0000-0001-5539-464XSchool of Mechnical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Mechnical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Mechnical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Mechnical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Mechnical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, ChinaNational Center of Technology Innovation for Fuel Cell, Shandong Guochuang Fuel Cell Technology Innovation Center Company Ltd., Weifang, ChinaEcole Supérieure des Techniques Aéronautiques et de Construction Automobile, Montigny-le-Bretonneux, FranceData-driven state-of-health (SOH) prediction is increasingly critical for the effective management of lithium-ion batteries; however, challenges remain in practical applications. Traditional methods that rely on a single health indicator often fail to capture the complexity and multi-dimensional nature of battery performance changes. To address these limitations, this paper presents a novel Transformer-based approach for accurate SOH prediction. The correlation between various measured and computed features extracted from battery charge/discharge curves and their impact on battery performance degradation are investigated using Pearson correlation coefficients. Three strongly correlated features are identified as multiple input variables for the Transformer framework. The effectiveness of this Transformer-based SOH prediction method is demonstrated using public datasets, revealing that predictions for internal resistance and capacity closely align with actual values, with most RMSE values falling below 0.01. Furthermore, validation with an additional laboratory dataset confirms the accuracy and adaptability of our proposed approach, highlighting its potential to enhance SOH prediction in real-world applications.https://ieeexplore.ieee.org/document/11015565/Feature extractionlithium-ion batterySOH predictionTransformer |
| spellingShingle | Tianfeng Long Pengcheng Zhang Xiaoqi Liu Huaqing Shang Meiling Yue Xuesong Shen Jianwen Meng Improved SOH Prediction of Lithium-Ion Batteries Based on Multi-Dimensional Feature Analysis and Transformer Framework IEEE Open Journal of Vehicular Technology Feature extraction lithium-ion battery SOH prediction Transformer |
| title | Improved SOH Prediction of Lithium-Ion Batteries Based on Multi-Dimensional Feature Analysis and Transformer Framework |
| title_full | Improved SOH Prediction of Lithium-Ion Batteries Based on Multi-Dimensional Feature Analysis and Transformer Framework |
| title_fullStr | Improved SOH Prediction of Lithium-Ion Batteries Based on Multi-Dimensional Feature Analysis and Transformer Framework |
| title_full_unstemmed | Improved SOH Prediction of Lithium-Ion Batteries Based on Multi-Dimensional Feature Analysis and Transformer Framework |
| title_short | Improved SOH Prediction of Lithium-Ion Batteries Based on Multi-Dimensional Feature Analysis and Transformer Framework |
| title_sort | improved soh prediction of lithium ion batteries based on multi dimensional feature analysis and transformer framework |
| topic | Feature extraction lithium-ion battery SOH prediction Transformer |
| url | https://ieeexplore.ieee.org/document/11015565/ |
| work_keys_str_mv | AT tianfenglong improvedsohpredictionoflithiumionbatteriesbasedonmultidimensionalfeatureanalysisandtransformerframework AT pengchengzhang improvedsohpredictionoflithiumionbatteriesbasedonmultidimensionalfeatureanalysisandtransformerframework AT xiaoqiliu improvedsohpredictionoflithiumionbatteriesbasedonmultidimensionalfeatureanalysisandtransformerframework AT huaqingshang improvedsohpredictionoflithiumionbatteriesbasedonmultidimensionalfeatureanalysisandtransformerframework AT meilingyue improvedsohpredictionoflithiumionbatteriesbasedonmultidimensionalfeatureanalysisandtransformerframework AT xuesongshen improvedsohpredictionoflithiumionbatteriesbasedonmultidimensionalfeatureanalysisandtransformerframework AT jianwenmeng improvedsohpredictionoflithiumionbatteriesbasedonmultidimensionalfeatureanalysisandtransformerframework |