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Energies
Volume 18
Issue 11
10.3390/en18112792
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Xikang Wang
1,2,
Bangyu Zhou
1,2,
Huan Xu
2,
Song Xu
3,
Tao Wan
4,
Wenjie Sun
1,2,
Yuanjun Guo
1,5,
Zuobin Ying
6,
Wenjiao Yao
1,2,* and
Zhile Yang
1,3,*
1
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2
College of Engineering, Southern University of Science and Technology, Shenzhen 518055, China
3
Advanced Energy Storage Technology Research Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
4
State Grid Hunan Electric Power Co., Ltd. Research Institute, Changsha 410000, China
5
Guangdong Institute of Carbon Neutrality (Shaoguan), Shaoguan 512000, China
6
Faculty of Data Science, City University of Macau, Taipa 999078, Macau
*
Authors to whom correspondence should be addressed.
Energies 2025, 18(11), 2792; https://doi.org/10.3390/en18112792
Submission received: 22 April 2025 / Revised: 20 May 2025 / Accepted: 23 May 2025 / Published: 27 May 2025
(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation: 3rd Edition)
Versions Notes

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 sodium batteries; state-of-health prediction; capacity model; incremental capacity analysis

Share and Cite

MDPI and ACS Style

Wang, X.; Zhou, B.; Xu, H.; Xu, S.; Wan, T.; Sun, W.; Guo, Y.; Ying, Z.; Yao, W.; Yang, Z. DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation. Energies 2025, 18, 2792. https://doi.org/10.3390/en18112792

AMA Style

Wang X, Zhou B, Xu H, Xu S, Wan T, Sun W, Guo Y, Ying Z, Yao W, Yang Z. DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation. Energies. 2025; 18(11):2792. https://doi.org/10.3390/en18112792

Chicago/Turabian Style

Wang, Xikang, Bangyu Zhou, Huan Xu, Song Xu, Tao Wan, Wenjie Sun, Yuanjun Guo, Zuobin Ying, Wenjiao Yao, and Zhile Yang. 2025. "DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation" Energies 18, no. 11: 2792. https://doi.org/10.3390/en18112792

APA Style

Wang, X., Zhou, B., Xu, H., Xu, S., Wan, T., Sun, W., Guo, Y., Ying, Z., Yao, W., & Yang, Z. (2025). DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation. Energies, 18(11), 2792. https://doi.org/10.3390/en18112792

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