基于迁移学习和DAE-LSTM的锂离子电池剩余寿命预测

doi:10.19595/j.cnki.1000-6753.tces.221890

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摘要

针对锂离子电池退化数据噪声大、数据量少以及不同生命时期的退化趋势不同而导致的模型预测精度低、泛化能力差等问题,本文从数据预处理、预测模型的构建与训练三方面展开研究：首先结合变分自编码器（VAE）和生成对抗网络模型（GAN）构建VAE-GAN模型生成多组数据,实现电池的退化数据增强;然后结合降噪自编码器（DAE）和长短时记忆（LSTM）神经网络构建DAE-LSTM模型进行数据降噪和容量预测,为了降低模型参数,此过程中的数据降噪和预测共享同一个损失函数;最后先利用生成数据对DAE-LSTM模型进行预训练,再利用真实数据对其进行迁移训练。在CACLE和NASA公开数据集进行性能测试,实验结果表明本文所提方法精度高、鲁棒性强,能够有效提高锂离子电池剩余寿命的预测效果。

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	尹杰
	刘博
	孙国兵
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关键词 ：锂离子电池, 剩余寿命预测, 降噪, 自编码器, 长短期记忆神经网络, 迁移学习

Abstract：

Degradation data of battery capacity can be used to predict the battery remaining using life(RUL), but there exist numerous noise data in the battery degradation process caused by factors such as ambient temperature, charge/discharge process and capacity recovery phenomenon. It makes prediction of data-driven life lithium-ion battery challenging. To improve the prediction accuracy and generalization ability of batteries RUL, we proposed a method based on Transfer Learning and Denoising AutoEncoder - Long Short Term Memory (DAE-LSTM).
Firstly, the variational autoEncoder - generative adversarial network (VAE-GAN) method was constructed designed. Encoding network was used to estimate the distribution of input data, and generating network and discriminant network were used for data regeneration. It improved the reliability of generated data by VAE method, and solved the problem that GAN method had been difficult to train. Secondly, the DAE-LSTM method was constructed for data denoising and capacity prediction. The DAE can reconstruct the input data and its encoder improved the robustness of the method by adding Gaussian noise. LSTM layer can analyze the temporal characteristics of data for capacity prediction. Due to the small amount of data, the network layer of the overall method was less to avoid overfitting. To reduce the parameters, the same loss function was used in both data denoising and capacity prediction. Finally, the optimal training scheme was determined through the different experiment: The data generated by VAE-GAN was used for method pre-training, then all network layers of the basic method were fine-tuned by actual data. This would effectively improve the prediction accuracy of the method, and ensure the reliability of the prediction results.
Experimental results showed that the proposed method has better predictive performance, and degradation trend of most batteries can be well predicted. MAE and RMSE were controlled within 2.46% and 3.76% respectively, and the lowest was 0.95% and 1.06%. Experimental results with different prediction starting points showed that the prediction were more accurate when the prediction starting point was closer to the failure threshold. This indicates that the method can accurately predict the degradation trend in later stages of battery life. Experimental results with other datasets showed that the proposed method has strong adaptability and generalization ability. It can effectively predict the lithium-ion battery RUL in small data samples. The 90% confidence interval of the prediction results with NASA dataset is narrow, indicating that the method has strong robustness. In addition, we counted the time taken to complete the RUL prediction for different datasets. As the RUL prediction of batteries is offline prediction with low real-time requirement, the training and testing time of the method meets the offline prediction requirement.
The following conclusions can be drawn from the simulation results: 1) The DAE-LSTM method can effectively denoising the degradation data of lithium-ion batteries, and making the prediction result more accurate. 2) VAE-GAN method can generate multiple groups of degradation data conforming to the real degradation to achieve the purpose of data enhancement. 3) Transfer Learning can ensure that the effective information of generated data and real data is fully utilized, so that the prediction model has higher accuracy and better generalization ability. By comparing the prediction results of other literatures, it is proved the proposed method has higher P_re and can be used to predict the RUL of lithium-ion batteries.

Key words： Li-ion battery remaining useful life denoising auto-encoder long short-term memory transfer learning

收稿日期: 2022-10-05

PACS:	TM912
	TP206+.3

通讯作者: 刘博, 男,1979年生,副教授,硕士生导师,研究方向为复杂系统可靠性与机器学习。E-mail：liubo1900@hrbust.edu.cn

作者简介: 尹杰, 男,1999年生,硕士研究生,研究方向为复杂系统可靠性与机器学习。E-mail：yin_jie1212@163.com

引用本文:

尹杰, 刘博, 孙国兵, 钱湘伟. 基于迁移学习和DAE-LSTM的锂离子电池剩余寿命预测[J]. 电工技术学报, 0, (): 8905-. Yin Jie, Liu Bo, Sun guobing, Qian xiangwei. Transfer Learning DAE-LSTM for Remaining Useful Life Prediction of Li-Ion Batteries. Transactions of China Electrotechnical Society, 0, (): 8905-.

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