基于相平面二维样本熵的锂离子电池剩余寿命预测方法研究

李志明; 夏向阳; 赵晓悦; 卢启付; 夏天; 蔡昱宽

doi:10.19912/j.0254-0096.tynxb.2025-0289

PDF(2314 KB)

太阳能学报 ›› 2026, Vol. 47 ›› Issue (7) : 296-305. DOI: 10.19912/j.0254-0096.tynxb.2025-0289

基于相平面二维样本熵的锂离子电池剩余寿命预测方法研究

李志明¹, 夏向阳¹, 赵晓悦¹, 卢启付², 夏天¹, 蔡昱宽³

作者信息 +

RESEARCH ON LITHIUM-ION BATTERY REMAINING USEFUL LIFE PREDICTION METHOD BASED ON PHASE PLANE TWO-DIMENSIONAL SAMPLE ENTROPY

Li Zhiming¹, Xia Xiangyang¹, Zhao Xiaoyue¹, Lu Qifu², Xia Tian¹, Cai Yukuan³

Author information +

文章历史 +

摘要

针对锂离子电池老化过程可视化不足和剩余寿命（RUL）难以准确估计的问题,该文提出一种基于相平面二维样本熵（PP-2DSE）的锂离子电池剩余寿命预测方法。该方法首先通过构建电池相平面（PP）将老化过程可视化,将电池监测量变化数据转为二维时间序列;其次计算二维样本熵（2DSE）值,从而将电池老化过程量化为可观测指标;最后通过湖南某储能电站实际运行数据和NASA公开数据集的验证,结果表明,随着电池逐渐老化,PP-2DSE曲线特定区间峰值提前出现且幅值增大,并将该峰值与其对应时间的比值作为健康因子实现电池剩余寿命准确预测。该方法在定性和定量分析中均表现出良好的有效性和鲁棒性,为锂离子电池剩余寿命预测提供一种新的解决方案,可为不同工况下电力部门安全调度控制储能电站提供准确信息。

Abstract

Aiming at the problems of insufficient visualization of the aging process of lithium-ion batteries and difficulty in accurately estimating remaining useful life, this paper proposes a lithium-ion battery remaining useful life prediction method based on phase plane two-dimensional sample entropy （PP-2DSE）. The method first visualizes the aging process by constructing the battery phase plane （PP） and transforms battery monitoring data into a two-dimensional time series; secondly, calculates the two-dimensional sample entropy （2DSE） value, thereby quantifying the battery aging process into an observable indicator; and finally, through the validation of the actual operation data of an energy storage plant in Hunan Province and the NASA public dataset, the results show that, with gradual battery aging, the peak value in the specific interval of the PP-2DSE curve appears earlier and its amplitude increases, and the ratio of the peak value to its corresponding time is used as a health factor to achieve accurate prediction of the remaining battery life. The method shows good effectiveness and robustness in both qualitative and quantitative analyses and provides a new solution for lithium-ion battery remaining useful life prediction, which can provide accurate information for safe scheduling and control of energy storage plants by power departments under different working conditions.

导出引用

李志明, 夏向阳, 赵晓悦, 卢启付, 夏天, 蔡昱宽. 基于相平面二维样本熵的锂离子电池剩余寿命预测方法研究[J]. 太阳能学报. 2026, 47(7): 296-305 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0289

Li Zhiming, Xia Xiangyang, Zhao Xiaoyue, Lu Qifu, Xia Tian, Cai Yukuan. RESEARCH ON LITHIUM-ION BATTERY REMAINING USEFUL LIFE PREDICTION METHOD BASED ON PHASE PLANE TWO-DIMENSIONAL SAMPLE ENTROPY[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 296-305 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0289

中图分类号： TM911

参考文献

[1] 陈凌彬, 夏向阳, 廖一丁, 等. 考虑多变量协同保护的储能变流器故障穿越策略[J]. 太阳能学报, 2023, 44(1): 442-450.
Chen L B, Xia X Y, Liao Y D, et al.Fault ride through strategy of power conversion system considering multi-variable cooperative protection[J]. Acta Energiae Solaris Sinica, 2023, 44(1): 442-450.
[2] 夏向阳, 谭欣欣, 单周平, 等. 储能电站锂离子电池本体安全关键技术及新技术应用情况[J]. 中国电力, 2024, 57(11): 1-17.
Xia X Y, Tan X X, Shan Z P, et al.Key technology and development prospect of ontology safety for lithium-ion battery storage power stations[J]. Electric Power, 2024, 57(11): 1-17.
[3] 蔡敏怡, 张娥, 林靖, 等. 串联锂离子电池组均衡拓扑综述[J]. 中国电机工程学报, 2021, 41(15): 5294-5311.
Cai M Y, Zhang E, Lin J, et al.Review on balancing topology of lithium-ion battery pack[J]. Proceedings of the CSEE, 2021, 41(15): 5294-5311.
[4] 孟国栋, 李雨珮, 唐佳, 等. 锂离子电池储能电站的热失控状态检测与安全防控技术研究进展[J]. 高电压技术, 2024, 50(7): 3105-3127.
Meng G D, Li Y P, Tang J, et al.Research progress of thermal runaway detection and safety control technology for lithi-um-ion battery energy storage power stations[J]. High Voltage Engineering, 2024, 50(7): 3105-3127.
[5] 严干贵, 王铭岐, 段双明, 等. 考虑荷电状态恢复的储能一次调频控制策略[J]. 电力系统自动化, 2022, 46(21): 52-61.
Yan G G, Wang M Q, Duan S M, et al.Primary frequency regulation control strategy of energy storage considering state of charge recovery[J]. Automation of Electric Power Systems, 2022, 46(21): 52-61.
[6] 熊庆, 邸振国, 汲胜昌. 锂离子电池健康状态估计及寿命预测研究进展综述[J]. 高电压技术, 2024, 50(3): 1182-1195.
Xiong Q, Di Z G, Ji S C.Review on health state estimation and life prediction of lithium-ion batteries[J]. High Voltage Engineering, 2024, 50(3): 1182-1195.
[7] Zhang Q, White R E.Capacity fade analysis of a lithium ion cell[J]. Journal of Power Sources, 2008, 179(2): 793-798.
[8] 郭喜峰, 王凯泽, 单丹, 等. 多角度基于CEEMDAN-CNN-BiLSTM模型的锂离子电池RUL预测[J]. 太阳能学报, 2024, 45(7): 181-189.
Guo X F, Wang K Z, Shan D, et al.Rul prediction for lithium ion batteries based on ceemdan-cnn-BiLSTM model from multiple perspectives[J]. Acta Energiae Solaris Sinica, 2024, 45(7): 181-189.
[9] Khine M H H, Kim C G, Aunsri N. A review of Bayesian-filtering-based techniques in RUL prediction for Lithium-Ion batteries[J]. Journal of Energy Storage, 2025, 111: 115371.
[10] 范文杰, 徐广昊, 于泊宁, 等. 基于电化学阻抗谱的锂离子电池内部温度在线估计方法研究[J]. 中国电机工程学报, 2021, 41(9): 3283-3293.
Fan W J, Xu G H, Yu B N, et al.On-line estimation method for internal temperature of lithium-ion battery based on electrochemical impedance spectroscopy[J]. Proceedings of the CSEE, 2021, 41(9): 3283-3293.
[11] 夏向阳, 岳家辉, 曾小勇, 等. 基于状态相依的RBF-ARX模型的锂离子电池剩余容量估计方法[J]. 中国电机工程学报, 2025, 45(2): 638-650.
Xia X Y, Yue J H, Zeng X Y, et al.The remaining capacity estimation of battery based on state-dependent RBF-ARX model[J]. Proceedings of the CSEE, 2025, 45(2): 638-650.
[12] Ren L, Dong J B, Wang X K, et al.A data-driven auto-CNN-LSTM prediction model for lithium-ion battery remaining useful life[J]. IEEE Transactions on Industrial Informatics, 2021, 17(5): 3478-3487.
[13] 王凯丰, 谢丽蓉, 乔颖, 等. 基于退役电池阈值设定和分级控制的弃风消纳模式[J]. 电力自动化设备, 2020, 40(10): 92-98.
Wang K F, Xie L R, Qiao Y, et al.Curtailed wind consumption mode based on threshold setting and hierarchical control of retired batteries[J]. Electric Power Automation Equipment, 2020, 40(10): 92-98.
[14] Richman J S, Moorman J R.Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology-Heart and Circulatory Physiology, 2000, 278(6): H2039-H2049.
[15] 郭家梁, 钟宁, 马小萌, 等. 基于振幅-周期二维特征的脑电样本熵分析[J]. 物理学报, 2016, 65(19): 190501.
Guo J L, Zhong N, Ma X M, et al.Sample entropy analysis of electro encephalogram based on the two-dimensional feature of amplitude and period[J]. Acta Physica Sinica, 2016, 65(19): 190501.
[16] Pincus S M.Approximate entropy as a measure of system complexity[J]. Proceedings of the National Academy of Sciences of the United States of America, 1991, 88(6): 2297-2301.
[17] He N, Yang Z Q, Qian C, et al.Remaining useful life prediction of lithium-ion battery based on fusion model considering capacity regeneration phenomenon[J]. Journal of Energy Storage, 2024, 85: 111068.
[18] 夏向阳, 吕崇耿, 吴小忠, 等. 基于混合模型的储能用锂离子电池剩余寿命预测方法研究[J]. 太阳能学报, 2024, 45(10): 726-735.
Xia X Y, Lyu C G, Wu X Z, et al.Research on remaining life prediction method of lithium-ion battery for energy storage based on hybrid model[J]. Acta Energiae Solaris Sinica, 2024, 45(10): 726-735.
[19] Lu G Y, Liu Y L, Wang J, et al.CNN-BiLSTM-Attention: a multi-label neural classifier for short texts with a small set of labels[J]. Information Processing & Management, 2023, 60(3): 103320.