基于电压数据片段混合模型的锂离子电池剩余寿命预测与健康状态估计

doi:10.11930/j.issn.1004-9649.202208122

中国电力 ›› 2023, Vol. 56 ›› Issue (7): 163-174.DOI: 10.11930/j.issn.1004-9649.202208122

基于电压数据片段混合模型的锂离子电池剩余寿命预测与健康状态估计

岳家辉^1,3, 夏向阳^1,3, 蒋戴宇^1,3, 周冠东^2,3, 徐志强², 张媛^1,3, 吕崇耿^1,3

1. 长沙理工大学电气与信息工程学院，湖南长沙 410114;
2. 国网湖南省电力有限公司，湖南长沙 410004;
3. 规模化电池储能应用技术湖南省工程研究中心，湖南长沙 410004

收稿日期:2022-08-31 修回日期:2023-05-15 发布日期:2023-07-28
作者简介:岳家辉(1994-),男,博士研究生,从事储能电站安全监测、运行与控制等研究,E-mail:243952600@qq.com;夏向阳(1968-),男,通信作者,教授,博士生导师,从事柔性直流输电控制和储能安全控制研究,E-mail:307351045@qq.com
基金资助:
国家自然科学基金资助项目（51977014）；国网湖南省电力有限公司科技项目（大规模储能电站电池安全运行及并网调试关键技术研究，5216A220000X）。

Remaining Useful Life Prediction and State of Health Estimation of Lithium-Ion Batteries Based on Voltage Data Segment Hybrid Model

YUE Jiahui^1,3, XIA Xiangyang^1,3, JIANG Daiyu^1,3, ZHOU Guandong^2,3, XU Zhiqiang², ZHANG Yuan^1,3, LV Chonggeng^1,3

1. School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China;
2. State Grid Hunan Electric Power Company Limited, Changsha 410004, China;
3. Hunan Engineering Research Center of Large-Scale Battery Energy Storage Application Technology, Changsha 410004, China

Received:2022-08-31 Revised:2023-05-15 Published:2023-07-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.51977014), Science and Technology Project of State Grid Hunan Electric Power Company Limited (Research on Key Technologies for Safe Running and Grid Connection Debugging of Batteries in Large Scale Energy Storage Power Stations, No.5216A220000X).

摘要/Abstract

摘要： 锂离子电池剩余寿命预测及健康状态估计作为储能安全中最为核心的问题，其重要指标往往集中在电池的容量与内阻，但在实际工作中，测量容量需要完整的充放电周期，测量内阻多须外加设备。针对上述问题，基于锂离子电池恒流放电工况，将放电初始片段瞬时压降幅值作为新健康因子，在面对新健康因子数据受噪声污染时，通过多阶Bezier曲线对新健康因子数据进行重构降噪并与循环圈数构建电池经验退化模型；在此基础上，以电压片段来定义电池健康状态，提出新的健康状态评估模型；最后，通过NASA公开的老化数据集与实验平台验证了所提退化模型与估计模型的可行性与有效性。

关键词: 锂离子电池, 剩余寿命, 健康状态, 电压片段数据, 混合模型

Abstract: The remaining useful life (RUL) prediction and state of health (SOH) estimation of lithium-ion batteries are essential for energy storage safety. The important indicators often focus on battery capacity and internal resistance. However, in practical operation, the capacity measurement requires a complete charge/discharge cycle, and the measurement of internal resistance requires additional equipment. To this end, based on the voltage segment in the constant current discharge condition of the lithium-ion battery, the sharp voltage drop of the initial discharge segment is taken as a new healthy factor. Confronted with the possibility that data of the new healthy factor is polluted by noise, this paper rebuilds the factor data to reduce noise through a multi-order Bezier curve. Then an empirical degradation hybrid model is built with the number of cycles. On this basis, the SOH of the battery is defined by voltage segment, and a new SOH estimation model is proposed. The feasibility and effectiveness of the proposed degradation model and estimation model are verified by the aging data published by NASA and the experimental platform.

Key words: lithium-ion battery, remaining useful life, state of health, voltage segment data, hybrid model

岳家辉, 夏向阳, 蒋戴宇, 周冠东, 徐志强, 张媛, 吕崇耿. 基于电压数据片段混合模型的锂离子电池剩余寿命预测与健康状态估计[J]. 中国电力, 2023, 56(7): 163-174.

YUE Jiahui, XIA Xiangyang, JIANG Daiyu, ZHOU Guandong, XU Zhiqiang, ZHANG Yuan, LV Chonggeng. Remaining Useful Life Prediction and State of Health Estimation of Lithium-Ion Batteries Based on Voltage Data Segment Hybrid Model[J]. Electric Power, 2023, 56(7): 163-174.

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https://www.electricpower.com.cn/CN/Y2023/V56/I7/163

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基于电压数据片段混合模型的锂离子电池剩余寿命预测与健康状态估计

Remaining Useful Life Prediction and State of Health Estimation of Lithium-Ion Batteries Based on Voltage Data Segment Hybrid Model

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基于电压数据片段混合模型的锂离子电池剩余寿命预测与健康状态估计

Remaining Useful Life Prediction and State of Health Estimation of Lithium-Ion Batteries Based on Voltage Data Segment Hybrid Model

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