基于CEEMDAN和GA-BILSTM的锂离子电池剩余使用寿命预测

徐鹏; 冉文文; 黄媛; 李会娟; 肖科林; 万世斌

doi:10.19912/j.0254-0096.tynxb.2024-1019

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (11) : 35-43. DOI: 10.19912/j.0254-0096.tynxb.2024-1019

基于CEEMDAN和GA-BILSTM的锂离子电池剩余使用寿命预测

徐鹏, 冉文文, 黄媛, 李会娟, 肖科林, 万世斌

作者信息 +

REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES BASED ON CEEMDAN AND GA-BILSTM

Xu Peng, Ran Wenwen, Huang Yuan, Li Huijuan, Xiao Kelin, Wan Shibin

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文章历史 +

摘要

该文提出一种自适应白噪声完备集成经验模态分解（CEEMDAN）和遗传算法（GA）优化双向长短期记忆神经网络（BILSTM）的剩余使用寿命（RUL）预测方法。在该方法中,CEEMDAN对电池容量数据进行分解,同时对电池数据进行差分分析来获得特征输入,随后,采用GA优化BILSTM的超参数,建立GA-BILSTM的RUL预测模型,最后,在NASA数据集上进行验证。结果表明该方法能准确有效地实现RUL预测。

Abstract

In this paper, an RUL prediction method with complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） and a genetic algorithms （GA） optimized bi-directional long and short-term memory （BILSTM） neural network is proposed. In this method, CEEMDAN decomposes the battery capacity data and also performs differential analysis on the battery data to obtain the feature inputs, and subsequently, the GA is used to optimize the hyperparameters of BILSTM to build the RUL prediction model of GA-BILSTM. Finally, validation is performed on the NASA dataset, and the results show that the method can realize RUL prediction accurately and effectively.

导出引用

徐鹏, 冉文文, 黄媛, 李会娟, 肖科林, 万世斌. 基于CEEMDAN和GA-BILSTM的锂离子电池剩余使用寿命预测[J]. 太阳能学报. 2025, 46(11): 35-43 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1019

Xu Peng, Ran Wenwen, Huang Yuan, Li Huijuan, Xiao Kelin, Wan Shibin. REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES BASED ON CEEMDAN AND GA-BILSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 35-43 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1019

中图分类号： TM912

参考文献

[1] REZA M S, MANNAN M, MANSOR M, et al.Recent advancement of remaining useful life prediction of lithium-ion battery in electric vehicle applications: a review of modelling mechanisms, network configurations, factors, and outstanding issues[J]. Energy reports, 2024, 11: 4824-4848.
[2] 李炳金, 韩晓霞, 张文杰, 等. 锂离子电池剩余使用寿命预测方法综述[J]. 储能科学与技术, 2024, 13(4): 1266-1276.
LI B J, HAN X X, ZHANG W J, et al.Review of the remaining useful life prediction methods for lithium-ion batteries[J]. Energy storage science and technology, 2024, 13(4): 1266-1276.
[3] RAUF H, KHALID M, ARSHAD N.Machine learning in state of health and remaining useful life estimation: theoretical and technological development in battery degradation modelling[J]. Renewable and sustainable energy reviews, 2022, 156: 111903.
[4] GAO D, HUANG M H.Prediction of remaining useful life of lithium-ion battery based on multi-kernel support vector machine with particle swarm optimization[J]. Journal of power electronics, 2017, 17(5): 1288-1297.
[5] LIU D T, PANG J Y, ZHOU J B, et al.Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression[C]//Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing). Beijing, China, 2012: 1-5.
[6] ZHANG Y Z, XIONG R, HE H W, et al.Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries[J]. IEEE transactions on vehicular technology, 2018, 67(7): 5695-5705.
[7] CHEN Z, XUE Q, XIAO R X, et al.State of health estimation for lithium-ion batteries based on fusion of autoregressive moving average model and Elman neural network[J]. IEEE access, 2019, 7: 102662-102678.
[8] 朱博, 刘雪芹, 王宇珲, 等. 基于EMD-LSTM-Attention的锂离子电池SOC预测[J/OL]. 电源学报, 2024-04-20. https://link.cnki.net/urlid/12.1420.TM.20240419.1720.008.
ZHU B, LIU X Q, WANG Y H, et al. SOC Prediction for lithium battery based on the model of EMD-LSTM-Attention[J/OL]. Journal of power supply, 2024-04-20. https://link.cnki.net/urlid/12.1420.TM.20240419.1720.008.
[9] ZHOU Y P, HUANG M H.Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model[J]. Microelectronics reliability, 2016, 65: 265-273.
[10] YANG Y, LI S S, LI C, et al.Research on ultrasonic signal processing algorithm based on CEEMDAN joint wavelet packet thresholding[J]. Measurement, 2022, 201: 111751.
[11] 刘凡, 李捍东, 覃涛. 基于CEEMDAN-AsyHyperBand-MultiTCN的短期风电功率预测[J]. 太阳能学报, 2024, 45(1): 151-158.
LIU F, LI H D, QIN T.Short-term wind power prediction based on CEEMDAN-AsyHyperBand-MultiTCN[J]. Acta energiae solaris sinica, 2024, 45(1): 151-158.
[12] 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.
[13] 何山, 郝雄博, 赵宇明, 等. 基于遗传算法优化支持向量回归的电池SOH预测[J]. 汽车技术, 2024(5): 31-36.
HE S, HAO X B, ZHAO Y M, et al.Battery SOH prediction based on support vector regression optimized by genetic algorithm[J]. Automobile technology, 2024(5): 31-36.
[14] 陈则王, 李福胜, 林娅, 等. 基于GA-ELM的锂离子电池RUL间接预测方法[J]. 计量学报, 2020, 41(6): 735-742.
CHEN Z W, LI F S, LIN Y, et al.Indirect prediction method of RUL for lithium-ion battery based on GA-ELM[J]. Acta metrologica sinica, 2020, 41(6): 735-742.
[15] 王英立, 陶帅, 候晓晓, 等. 基于MIV分析的GA-BP神经网络光伏短期发电预测[J]. 太阳能学报, 2020, 41(8): 236-242.
WANG Y L, TAO S, HOU X X, et al.GA-BP neural network photovoltaic power generation short-term forecast based on MIV analysis[J]. Acta energiae solaris sinica, 2020, 41(8): 236-242.
[16] MA Z Q, CAI H Y.Research on electricity load forecasting based on EEMD-GA-LSTM[C]//2024 36th Chinese Control and Decision Conference (CCDC). Xi’an, China, 2024: 4434-4439.
[17] OUYANG Z, SUN J Y, ZHAO X, et al.Short-term wind power prediction model based on PCA-GA-LSTM neural network[C]//2023 IEEE 6th International Electrical and Energy Conference (CIEEC). Hefei, China, 2023: 148-153.
[18] ALI ALHUSSAN A, EL-KENAWY E M, ABDELHAMID A A, et al. Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms[J]. Frontiers in energy research, 2023, 11: 1172176.
[19] ZHEN H, NIU D X, WANG K K, et al.Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information[J]. Energy, 2021, 231: 120908.
[20] SAHA B, GOEBEL K, (2007) Battery Data Set[R]. NASA Ames Research Center.
[21] 王睿茜, 周星, 王羽, 等. 基于微分曲线的锂离子电池老化诊断研究综述[J]. 中国电机工程学报, 2024, 44(22): 8920-8936.
WANG R X, ZHOU X, WANG Y, et al.A review of degradation diagnosis of lithium-ion batteries based on differential curves[J]. Proceedings of the CSEE, 2024, 44(22): 8920-8936.
[22] DUBARRY M, LIAW B Y.Identify capacity fading mechanism in a commercial LiFePO₄ cell[J]. Journal of power sources, 2009, 194(1): 541-549.
[23] MAHER K, YAZAMI R.A study of lithium ion batteries cycle aging by thermodynamics techniques[J]. Journal of power sources, 2014, 247: 527-533.
[24] MENG H X, GENG M Y, HAN T.Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis[J]. Reliability engineering & system safety, 2023, 236: 109288.
[25] QU J T, LIU F, MA Y X, et al.A neural-network-based method for RUL prediction and SOH monitoring of lithiumion battery[J]. IEEE access, 2019, 7: 87178-87191.