多角度基于CEEMDAN-CNN-BiLSTM模型的锂离子电池RUL预测

郭喜峰; 王凯泽; 单丹; 郑迪; 宁一

doi:10.19912/j.0254-0096.tynxb.2023-0403

PDF(1972 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 181-189. DOI: 10.19912/j.0254-0096.tynxb.2023-0403

多角度基于CEEMDAN-CNN-BiLSTM模型的锂离子电池RUL预测

郭喜峰, 王凯泽, 单丹, 郑迪, 宁一

作者信息 +

RUL PREDICTION FOR LITHIUM ION BATTERIES BASED ON CEEMDAN-CNN-BiLSTM MODEL FROM MULTIPLE PERSPECTIVES

Guo Xifeng, Wang Kaize, Shan Dan, Zheng Di, Ning Yi

Author information +

文章历史 +

摘要

通过构建模型对锂离子电池剩余使用寿命进行预测,并探究温度及网络参数对所构建模型预测精准度的影响,进而提高模型的预测精准度。提出自适应噪声完全集合经验模态分解（CEEMDAN）和一维卷积神经网络（1D CNN）与双向长短期记忆（BiLSTM）神经网络相结合的锂离子电池剩余寿命预测方法。选取容量作为健康因子,然后利用CEEMDAN对复杂不平稳数据进行分解,得到稳定的分量。利用1D CNN对锂离子电池容量数据进行深度挖掘,最后利用双BiLSTM神经网络建模对锂离子电池剩余使用寿命（RUL）进行预测。采用NASA数据集和CALCE数据集进行测试,在不同温度与网络参数下进行预测效果对比,并与BiLSTM模型、SVR模型、CNN-BiLSTM模型进行预测对比。

Abstract

This article predicts the remaining service life of lithium ion batteries by constructing a model, and explores the effects of temperature and network parameters on the prediction accuracy of the constructed model, thereby improving the prediction accuracy of the model. A prediction method for the residual life of lithium ion batteries based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise（CEEMDAN）, One-dimensional Convolutional Neural Network（1D CNN） and Bi-directional Long Short-Term Memory（BiLSTM） neural network was proposed. Select capacity as the health factor, and then use CEEMDAN to decompose complex and non-stationary data to obtain stable components. 1D CNN is used to deeply mine the capacity data of lithium ion batteries. Finally, BiLSTM neural network modeling is used to predict the Remaining Useful Life（RUL） of lithium ion batteries. Using NASA and CALCE datasets for testing, the prediction performance was compared under different temperatures and network parameters, and compared with the BiLSTM model, SVR model, and CNN-BiLSTM model for prediction.

导出引用

郭喜峰, 王凯泽, 单丹, 郑迪, 宁一. 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 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0403

Guo Xifeng, Wang Kaize, Shan Dan, Zheng Di, Ning Yi. 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 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0403

中图分类号： TK02

参考文献

[1] SARMAH S B, KALITA P, GARG A, et al.A review of state of health estimation of energy storage systems: challenges and possible solutions for futuristic applications of Li-ion battery packs in electric vehicles[J]. Journal of electrochemical energy conversion and storage, 2019, 16(4): 040801.
[2] 吴皓文, 王军, 龚迎莉, 等. 储能技术发展现状及应用前景分析[J]. 电力学报, 2021, 36(5): 434-443.
WU H W, WANG J, GONG Y L, et al.Development status and application prospect analysis of energy storage technology[J]. Journal of electric power, 2021, 36(5): 434-443.
[3] 徐亮. 锂电池储能电站防消一体化系统设计及控制策略[J]. 太阳能学报, 2022, 43(5): 478-483.
XU L.Design and control strategy of integrated system of early warming and fire protection for lithium-ion batteries energy storage power station[J]. Acta energiae solaris sinica, 2022, 43(5): 478-483.
[4] 孙丙香, 任鹏博, 陈育哲, 等. 锂离子电池在不同区间下的衰退影响因素分析及任意区间的老化趋势预测[J]. 电工技术学报, 2021, 36(3): 666-674.
SUN B X, REN P B, CHEN Y Z, et al.Analysis of influencing factors of degradation under different interval stress and prediction of aging trend in any interval for lithium-ion battery[J]. Transactions of China Electro technical Society, 2021, 36(3): 666-674.
[5] 刘大同, 宋宇晨, 武巍, 等. 锂离子电池组健康状态估计综述[J]. 仪器仪表学报, 2020, 41(11): 1-18.
LIU D T, SONG Y C, WU W, et al.Review of state of health estimation for lithium-ion battery pack[J]. Chinese journal of scientific instrument, 2020, 41(11): 1-18.
[6] KHODADADI SADABADI K, JIN X, RIZZONI G.Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health[J]. Journal of power sources, 2021, 481: 228861.
[7] 刘月峰, 张公, 张晨荣, 等. 锂离子电池RUL预测方法综述[J]. 计算机工程, 2020, 46(4): 11-18.
LIU Y F, ZHANG G, ZHANG C R, et al.Review of RUL prediction method for lithium-ion batteries[J]. Computer engineering, 2020, 46(4): 11-18.
[8] 陈琳, 陈静, 王惠民, 等. 基于小波包能量熵的电池剩余寿命预测[J]. 电工技术学报, 2020, 35(8): 1827-1835.
CHEN L, CHEN J, WANG H M, et al.Prediction of battery remaining useful life based on wavelet packet energy entropy[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1827-1835.
[9] XIA Z Y, ABU QAHOUQ J A. Adaptive and fast state of health estimation method for lithium-ion batteries using online complex impedance and artificial neural network[C]//2019 IEEE Applied Power Electronics Conference and Exposition(APEC), Anaheim, CA, USA, 2019: 3361-3365.
[10] 张婷婷, 于明, 李宾, 等. 基于Wavelet降噪和支持向量机的锂离子电池容量预测研究[J]. 电工技术学报, 2020, 35(14): 3126-3136.
ZHANG T T, YU M, LI B, et al.Capacity prediction of lithium-ion batteries based on Wavelet noise reduction and support vector machine[J]. Transactions of China Electrotechnical Society, 2020, 35(14): 3126-3136.
[11] 成文晶, 潘庭龙. 基于分布估计算法LSSVM的锂电池SOC预测[J]. 储能科学与技术, 2020, 9(6): 1948-1953.
CHENG W J, PAN T L.Prediction for SOC of lithium-ion batteries by estimating the distribution algorithm with LSSVM[J]. Energy storage science and technology, 2020, 9(6): 1948-1953.
[12] HOCHREITER S, SCHMIDHUBER J.Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.
[13] 吴晓欣, 何怡刚, 段嘉珺, 等. 考虑复杂时序关联特性的Bi-LSTM变压器DGA故障诊断方法[J]. 电力自动化设备, 2020, 40(8): 184-193.
WU X X, HE Y G, DUAN J J, et al.Bi-LSTM-based transformer fault diagnosis method based on DGA considering complex correlation characteristics of time sequence[J]. Electric power automation equipment, 2020, 40(8): 184-193.
[14] 李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119.
LI C R, XIAO F, FAN Y X, et al.An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.
[15] 高德欣, 刘欣, 杨清. 基于卷积神经网络与双向长短时融合的锂离子电池剩余使用寿命预测[J]. 信息与控制, 2022, 51(3): 318-329, 360.
GAO D X, LIU X, YANG Q.Remaining useful life prediction of lithium-ion battery based on CNN and BiLSTM fusion[J]. Information and control, 2022, 51(3): 318-329, 360.
[16] 蔡亮, 周泓岑, 白恒, 等. 基于多层BiLSTM和改进粒子群算法的应用负载预测方法[J]. 浙江大学学报(工学版), 2020, 54(12): 2414-2422.
CAI L, ZHOU H C, BAI H, et al.Application load forecasting method based on multi-layer bidirectional LSTM and improved PSO algorithm[J]. Journal of Zhejiang University(engineering science), 2020, 54(12): 2414-2422.
[17] 何冰琛, 杨薛明, 王劲松, 等. 基于PCA-GPR的锂离子电池剩余使用寿命预测[J]. 太阳能学报, 2022, 43(5): 484-491.
HE B C, YANG X M, WANG J S, et al.Prediction of remaining useful life of lithium-ion batteries based on PCA-GPR[J]. Acta energiae solaris sinica, 2022, 43(5): 484-491.
[18] 陈彦余, 夏向阳, 周文钊, 等. 基于EMD-ARMA的锂离子电池剩余寿命预测[J]. 电力学报, 2021, 36(1): 43-50, 59.
CHEN Y Y, XIA X Y, ZHOU W Z, et al.Prediction of the lithium-ion battery remaining useful life based on EMD-ARMA[J]. Journal of electric power, 2021, 36(1): 43-50, 59.
[19] 史永胜, 施梦琢, 丁恩松, 等. 基于CEEMDAN-LSTM 组合的锂离子电池寿命预测方法[J]. 工程科学学报, 2021, 43(7): 985-994.
SHI Y S, SHI M Z, DING E S, et al.Life prediction method of lithium ion battery based on CEEMDAN-LSTM combination[J]. Chinese journal of engineering, 2021, 43(7): 985-994.
[20] 黄世超, 郭永强, 龙本锦, 等. 基于CEEMDAN的综合能源系统负荷预测研究[J]. 智能计算机与应用, 2023, 13(1): 123-128, 135.
HUANG S C, GUO Y Q, LONG B J, et al.Research on load forecasting of integrated energy system based on CEEMDAN[J]. Intelligent computer and applications, 2023, 13(1): 123-128, 135.
[21] 刘洋, 程强, 史曜炜, 等. 基于注意力模块及1D-CNN的滚动轴承故障诊断[J]. 太阳能学报, 2022, 43(3): 462-468.
LIU Y, CHENG Q, SHI Y W, et al.Fault diagnosis of rolling bearings based on attention module and 1D-CNN[J]. Acta energiae solaris sinica, 2022, 43(3): 462-468.
[22] 张加劲. 基于注意力机制和CNN-BiLSTM模型的航空发动机剩余寿命预测[J]. 电子测量与仪器学报, 2022, 36(8): 231-237.
ZHANG J J.Remaining useful life estimation of aeroengine based on CNN-BiLSTM and attention mechanism[J]. Journal of electronic measurement and instrumentation, 2022, 36(8): 231-237.