DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation

Xikang Wang, Bangyu Zhou, Huan Xu, Song Xu, Tao Wan, Wenjie Sun, Yuanjun Guo, Zuobin Ying, Wenjiao Yao () and Zhile Yang ()
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Xikang Wang: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Bangyu Zhou: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Huan Xu: Advanced Energy Storage Technology Research Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Song Xu: State Grid Hunan Electric Power Co., Ltd. Research Institute, Changsha 410000, China
Tao Wan: State Grid Hunan Electric Power Co., Ltd. Research Institute, Changsha 410000, China
Wenjie Sun: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Yuanjun Guo: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Zuobin Ying: Faculty of Data Science, City University of Macau, Taipa 999078, Macau
Wenjiao Yao: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Zhile Yang: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Energies, 2025, vol. 18, issue 11, 1-22

Abstract: 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.

Keywords: sodium batteries; state-of-health prediction; capacity model; incremental capacity analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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