基于Wavelet降噪和支持向量机的锂离子电池容量预测研究

doi:10.19595/j.cnki.1000-6753.tces.190620

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摘要随着电池使用次数的增加,电池会出现老化问题。通过对电池的剩余容量进行预测,可以为设备系统中电池管理系统提供可靠的数据支撑。该文采用支持向量机（SVM）对锂离子电池剩余容量进行预测,并采用改进鸡群算法（ICSO）对SVM参数进行优化,从而建立了ICSO-SVM预测模型。为验证预测模型的可行性,首先,采用db5小波对B5和B6电池容量衰减数据进行多尺度分解,进而重构去噪后的信号;其次,对鸡群优化算法（CSO）进行了改进,提出了ICSO优化算法,经测试ICSO算法的收敛精度明显高于粒子群优化算法（PSO）和传统CSO算法;最后,采用两组实验对CSO-SVM模型和ICSO-SVM模型进行验证。通过分析发现,ICSO-SVM模型的平均偏差（AAD）值在1.5%以下,RMSE值在2%以下,R2均值为0.972 6。

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关键词 ：锂离子电池, 支持向量机, 优化算法, 小波去噪, 容量预测

Abstract：As battery usage increases, the battery will age. By predicting the remaining capacity of the battery, reliable data support can be improved for the battery management system in the equipment system. In this study, support vector machine (SVM) is used to predict the remaining capacity of lithium-ion batteries. The improved chicken swarm algorithm (ICSO) is used to optimize the parameters of SVM. The ICSO-SVM model is established. In order to verify the feasibility of the prediction model, the following work has been done. Firstly, the capacity degradation data of B5 and B6 batteries were decomposed by db5 wavelet. And then the denoised signal was reconstructed. Secondly, the chicken swarm optimization algorithm (CSO) was improved and ICSO optimization algorithm was proposed. The convergence accuracy of ICSO is higher than that of PSO and CSO algorithms. Finally, two groups of experiments were used to verify the validity of the CSO-SVM model and the ICSO-SVM model. It is found that the average absolute deviation (AAD) value of ICSO-SVM model is less than 1.5%, RMSE value is less than 2% and average of R2 value is 0.972 6.

Key words： Lithium-ion battery support vector machine optimization algorithm wavelet denoising capacity prediction

收稿日期: 2019-05-22

PACS:

TM572.2

基金资助:中国科学院自动化研究所模式识别国家重点实验室开放课题（201900043）、河北省教育厅青年基金（QN2019207）、天津科技发展战略研究计划（18ZLZXZF00660）和河北省自然科学基金面上项目（F2019202381, F2019202464）资助项目

通讯作者: 于明男,1964年生,教授,博士生导师,研究方向为数字信号处理,图像视频理解,智能视频处理和模式识别。E-mail: yuming@hebut.edu.cn

作者简介: 张婷婷女,1984年生,硕士,讲师,研究方向为数字信号处理,数字图像处理和计算机视觉。E-mail: zhangtingting@hebut.edu.cn

引用本文:

张婷婷, 于明, 李宾, 刘哲. 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 Tingting, Yu Ming, Li Bin, Liu Zhe. Capacity Prediction of Lithium-Ion Batteries Based on Wavelet Noise Reduction and Support Vector Machine. Transactions of China Electrotechnical Society, 2020, 35(14): 3126-3136.

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