汽车工程

汽车工程 ›› 2025, Vol. 47 ›› Issue (10): 1963-1972.doi: 10.19562/j.chinasae.qcgc.2025.10.012

• • 上一篇    

容量增量与弛豫电压特征融合的钠离子电池SOH估计

顾辰言,彭俊(),王辉,赵轩(),马建,孟杰伦,颜思骞   

  1. 长安大学汽车学院，西安 710000
  • 收稿日期:2025-03-11 修回日期:2025-04-14 出版日期:2025-10-25 发布日期:2025-10-20
  • 通讯作者: 彭俊,赵轩 E-mail:pengjun@chd.edu.cn;zhaoxuan@chd.edu.cn
  • 基金资助:
    国家自然科学基金（52172362）、陕西省博士后基金（2023BSHEDZZ222）、陕西省科技成果转化计划项目（2024CG-CGZH-19）、陕西省重点研发计划项目（2024GX-YBXM-260）和中央高校基本业务费（CHD 300102223103）资助。

State of Health Estimation for Sodium-Ion Batteries Based on Features Fusion of Incremental Capacity and Relaxation Voltage

Chenyan Gu,Jun Peng(),Hui Wang,Xuan Zhao(),Jian Ma,Jielun Meng,Siqian Yan   

  1. School of Automobile，Chang’an University，Xi’an 710000
  • Received:2025-03-11 Revised:2025-04-14 Online:2025-10-25 Published:2025-10-20
  • Contact: Jun Peng,Xuan Zhao E-mail:pengjun@chd.edu.cn;zhaoxuan@chd.edu.cn

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摘要：

电池健康状态（SOH）作为衡量电池老化程度与性能衰退的核心指标，其精准估计是保障电池系统安全运行和寿命管理的关键。相较于成熟的锂离子电池体系，钠离子电池老化机制和容量衰减机理尚未明晰。本文提出一种容量增量（IC）与弛豫电压（RV）特征融合的钠离子电池SOH估计方法，通过IC曲线解析充放电过程中的相变动力学特性，结合RV特征提取电池静置期的电极极化恢复规律，从多维度揭示电池老化机制；在此基础上构建特征融合模型，增强健康因子的敏感性与抗干扰能力。采用机器学习算法建立IC和RV数据特征与SOH的映射关系，构建基于长短记忆网络结合注意力机制（LSTM-Attention）的SOH估计模型。实验结果表明：该模型的SOH估计误差（RMSE<0.51%，MAE<0.40%）显著优于单一特征模型，为钠离子电池健康管理及工程化应用提供了可靠解决方案。

关键词: 钠离子电池, 健康状态估计, 容量增量, 弛豫电压, LSTM-Attention

Abstract:

The State of Health （SOH）， a critical metric for evaluating battery aging and performance degradation， requires accurate estimation to ensure the safe operation and lifespan management of battery systems. Compared to the well-established lithium-ion battery systems， the aging mechanism and capacity degradation behavior of sodium-ion batteries remains insufficiently understood. In this study， a SOH estimation method for sodium-ion batteries is proposed by fusing incremental capacity （IC） and relaxation voltage （RV） features. The IC curves are employed to analyze phase transition dynamics during charge/discharge processes， while RV features are utilized to characterize electrode polarization recovery patterns during resting periods， thereby comprehensively revealing multi-dimensional aging mechanism. A feature fusion model is developed to enhance the sensitivity and noise immunity of health indicators. By leveraging machine learning algorithms， the mapping relationship between IC/RV-derived features and SOH is established， constructing an LSTM-Attention （Long Short-Term Memory network integrated with an attention mechanism） based estimation model. The experimental results show that the proposed method achieves superior SOH estimation accuracy （RMSE<0.51%，MAE<0.40%） compared to single-feature approaches， providing a robust solution for real-time health monitoring and industrial deployment of sodium-ion batteries.

Key words: Sodium-ion batteries, SOH estimation, incremental capacity, relaxation voltage, LSTM-Attention