基于VMD和优化的LSTM锂离子电池寿命预测方法  

Remaining useful life prediction method of lithium-ion battery based on variational mode decomposition and optimized LSTM

作　　者：叶鑫 王海瑞[1] 李远博 朱贵富[2] Ye Xin;Wang Hairui;Li Yuanbo;Zhu Guifu(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Information Technology Construction Management Center,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650500 [2]昆明理工大学信息化建设管理中心,昆明650500

出　　处：《电子测量技术》2022年第23期153-158,共6页Electronic Measurement Technology

基　　金：国家自然科学基金(61263023,61863016)项目资助。

摘　　要：针对锂电池使用过程中存在容量回升造成非平稳的容量退化趋势,造成模型的预测精度容易受到干扰的问题,提出一种基于变分模态分解(VMD)与贝叶斯优化(BO)的长短期记忆神经网络(LSTM)的锂电池剩余寿命预测方法。首先,通过变分模态分解将原始容量退化序列进行分解,得到有限个模态分量;然后对分解之后的分量进行降噪、重构;最后,使用贝叶斯优化的长短期记忆神经网络算法对处理之后的数据进行寿命预测,获得最终的锂电池剩余寿命(RUL)预测结果。通过CALCE中心的锂离子电池数据集进行实验,所提出的VMD-BO-LSTM锂电池组合预测模型具有较高的预测精度与稳定性,实验采用的电池均方根误差的平均值小于7%,且优于其他预测模型。Aiming at the non-stationary capacity degradation trend caused by the capacity recovery during the use of lithium batteries, which makes the prediction accuracy of the model vulnerable to interference, a long short-term memory network(LSTM) prediction method of lithium battery remaining useful life based on variational mode decomposition(VMD) and bayesian optimization(Bo) is proposed. Firstly, the capacity data of lithium battery is decomposed by variational modal decomposition, and a finite number of modal components are obtained;Then the decomposed components are denoised and reconstructed;Finally, the Bayesian optimized long and short-term memory neural network algorithm is used to predict the service life of the processed data, and the final prediction result of remaining useful life(RUL) of lithium battery is obtained. Through the experiment on the lithium-ion battery data set of CALCE center, the proposed VMD-BO-LSTM lithium battery combination prediction model has high prediction accuracy and stability, and the average value of the root mean square error of the battery used in the experiment is less than 7%, and is better than other prediction models.

关 键 词：锂离子电池 剩余使用寿命 变分模态分解 贝叶斯优化 长短期记忆神经网络 

分 类 号：TM912[电气工程—电力电子与电力传动]