首页|多角度基于CEEMDAN-CNN-BiLSTM模型的锂离子电池RUL预测

多角度基于CEEMDAN-CNN-BiLSTM模型的锂离子电池RUL预测

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

lithium ion batteryremaining useful lifeconvolutional neural networkcomplete ensemble empirical mode decomposition with adaptive noiseBi-directional long short-term memory

郭喜峰、王凯泽、单丹、郑迪、宁一

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沈阳建筑大学电气学院,沈阳 110168

锂离子电池 剩余使用寿命 卷积神经网络 自适应噪声完全集合经验模态分解 双向长短期记忆神经网络

国家自然科学基金辽宁省教育厅基本科研项目辽宁省教育厅基本科研项目

62003225LJKQZ2021062LJKQZ20222276

2024

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

太阳能学报
中国可再生能源学会

太阳能学报

CSTPCD北大核心
影响因子：0.392
ISSN：0254-0096
年,卷(期)：2024.45(7)
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