DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation
Sodium batteries have emerged as a competitive energy storage candidate due to their low cost and abundant resources. The accurate estimation of the state of health (SOH) of sodium batteries is essential for their practical utilization. However, limited cycling data and rapid capacity decay pose sig...
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MDPI AG
2025-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/11/2792 |
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| author | Xikang Wang Bangyu Zhou Huan Xu Song Xu Tao Wan Wenjie Sun Yuanjun Guo Zuobin Ying Wenjiao Yao Zhile Yang |
| author_facet | Xikang Wang Bangyu Zhou Huan Xu Song Xu Tao Wan Wenjie Sun Yuanjun Guo Zuobin Ying Wenjiao Yao Zhile Yang |
| author_sort | Xikang Wang |
| collection | DOAJ |
| description | Sodium batteries have emerged as a competitive energy storage candidate due to their low cost and abundant resources. The accurate estimation of the state of health (SOH) of sodium batteries is essential for their practical utilization. However, limited cycling data and rapid capacity decay pose significant challenges for SOH prediction. This study proposes a data-driven approach for SOH estimation in sodium batteries. By analyzing first-cycle data, the method determines battery health factor ranges and extracts comprehensive features from limited charging data segments. A predictive model is then established using deep learning techniques, specifically a stacked, bidirectional, long short-term memory (SB-LSTM) network. Unlike conventional methodologies relying on filtering or curve smoothing, the proposed approach demonstrates exceptional robustness, particularly at high discharge rates of up to 5C. Moreover, it applies to a wider range of current rates and consumes fewer computational resources. The method’s effectiveness is validated on three different battery sets, achieving high accuracy with an average absolute error in SOH estimation below 0.86% and a root mean square error under 1.07%. These results highlight the potential of this data-driven approach for reliable SOH estimation in sodium batteries, contributing to their practical implementation in energy storage systems. |
| format | Article |
| id | doaj-art-d4728ca2f03d4e4c98b7eeb83bdbbae9 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-d4728ca2f03d4e4c98b7eeb83bdbbae92025-08-20T03:11:18ZengMDPI AGEnergies1996-10732025-05-011811279210.3390/en18112792DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health EstimationXikang Wang0Bangyu Zhou1Huan Xu2Song Xu3Tao Wan4Wenjie Sun5Yuanjun Guo6Zuobin Ying7Wenjiao Yao8Zhile Yang9Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaAdvanced Energy Storage Technology Research Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaState Grid Hunan Electric Power Co., Ltd. Research Institute, Changsha 410000, ChinaState Grid Hunan Electric Power Co., Ltd. Research Institute, Changsha 410000, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaFaculty of Data Science, City University of Macau, Taipa 999078, MacauShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSodium batteries have emerged as a competitive energy storage candidate due to their low cost and abundant resources. The accurate estimation of the state of health (SOH) of sodium batteries is essential for their practical utilization. However, limited cycling data and rapid capacity decay pose significant challenges for SOH prediction. This study proposes a data-driven approach for SOH estimation in sodium batteries. By analyzing first-cycle data, the method determines battery health factor ranges and extracts comprehensive features from limited charging data segments. A predictive model is then established using deep learning techniques, specifically a stacked, bidirectional, long short-term memory (SB-LSTM) network. Unlike conventional methodologies relying on filtering or curve smoothing, the proposed approach demonstrates exceptional robustness, particularly at high discharge rates of up to 5C. Moreover, it applies to a wider range of current rates and consumes fewer computational resources. The method’s effectiveness is validated on three different battery sets, achieving high accuracy with an average absolute error in SOH estimation below 0.86% and a root mean square error under 1.07%. These results highlight the potential of this data-driven approach for reliable SOH estimation in sodium batteries, contributing to their practical implementation in energy storage systems.https://www.mdpi.com/1996-1073/18/11/2792sodium batteriesstate-of-health predictioncapacity modelincremental capacity analysis |
| spellingShingle | Xikang Wang Bangyu Zhou Huan Xu Song Xu Tao Wan Wenjie Sun Yuanjun Guo Zuobin Ying Wenjiao Yao Zhile Yang DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation Energies sodium batteries state-of-health prediction capacity model incremental capacity analysis |
| title | DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation |
| title_full | DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation |
| title_fullStr | DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation |
| title_full_unstemmed | DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation |
| title_short | DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation |
| title_sort | di4she deep learning via incremental capacity analysis for sodium battery state of health estimation |
| topic | sodium batteries state-of-health prediction capacity model incremental capacity analysis |
| url | https://www.mdpi.com/1996-1073/18/11/2792 |
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