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

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  • Xikang Wang

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    College of Engineering, Southern University of Science and Technology, Shenzhen 518055, China)

  • Bangyu Zhou

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    College of Engineering, Southern University of Science and Technology, 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
    College of Engineering, Southern University of Science and Technology, Shenzhen 518055, China)

  • Yuanjun Guo

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    Guangdong Institute of Carbon Neutrality (Shaoguan), Shaoguan 512000, 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
    Advanced Energy Storage Technology Research Center, 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
    Guangdong Institute of Carbon Neutrality (Shaoguan), Shaoguan 512000, China)

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.

Suggested Citation

  • Xikang Wang & Bangyu Zhou & Huan Xu & Song Xu & Tao Wan & Wenjie Sun & Yuanjun Guo & Zuobin Ying & Wenjiao Yao & Zhile Yang, 2025. "DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation," Energies, MDPI, vol. 18(11), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2792-:d:1665693
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    References listed on IDEAS

    as
    1. Wen, Jianping & Chen, Xing & Li, Xianghe & Li, Yikun, 2022. "SOH prediction of lithium battery based on IC curve feature and BP neural network," Energy, Elsevier, vol. 261(PA).
    2. Bai, Guangxing & Su, Yunsheng & Rahman, Maliha Maisha & Wang, Zequn, 2023. "Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Lin, Chuanping & Xu, Jun & Shi, Mingjie & Mei, Xuesong, 2022. "Constant current charging time based fast state-of-health estimation for lithium-ion batteries," Energy, Elsevier, vol. 247(C).
    4. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
    5. You, Gae-won & Park, Sangdo & Oh, Dukjin, 2016. "Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach," Applied Energy, Elsevier, vol. 176(C), pages 92-103.
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