摘要
锂离子电池剩余使用寿命(RUL)的估算是锂离子电池健康管理的关键,准确可靠地预测锂离子电池的剩余使用寿命对系统的安全正常运行至关重要。提出了一种结合完备集合经验模态分解(CEEMDAN)和支持向量回归(SVR)的锂离子电池剩余使用寿命预测方法。首先,在放电过程中提取了一个可测量的健康因子,并使用Pearson和Spearman法分析健康因子与容量之间的相关性,然后利用CEEMDAN将健康因子进行分解,获得一系列相对平稳的分量,最后采用CEEMDAN分解后的健康因子作为SVR预测模型输入,容量作为输出,实现锂离子电池RUL预测。利用NASA PCoE提供的锂离子电池退化数据集进行试验,与标准SVR模型相比,实验结果表明利用该方法能够有效验证所提出的RUL预测模型的有效性,并且使预测误差控制在2%以下。
Estimation of lithium-ion battery remaining useful life(RUL)is the key to lithium-ion battery health.Achieving accurate and reliable remaining useful life prediction of lithium-ion batteries is very vital for the normal operation of the battery system.Proposes a lithium-ion battery RUL prediction method based on the combination of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and support vector machine-regression(SVR).First,a measurable health factor is extracted during the discharge process,and the correlation between health factor and capacity is analyzed by Pearson and Spearman methods.Then,the health factor is decomposed by CEEMDAN to obtain a series of the relatively stable components.Finally,the health factor decomposed by CEEMDAN is used as the input of SVR prediction model,and the capacity is used as the output,so as to realize lithium-ion RUL prediction.The lithium-ion battery data published by NASA PcoE is used to carry out simulation experiments,and compare it with the standard SVR model,the experimental results show that the proposed method can effectively verify the effectiveness of the proposed RUL prediction model,and the prediction error is controlled below 2%.
作者
杨彦茹
温杰
史元浩
张泽慧
刘文海
Yang Yanru;Wen Jie;Shi Yuanhao;Zhang Zehui;Liu Wenhai(School of electrical and control engineering,North University of China,Taiyuan 030051,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2020年第12期197-205,共9页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(61533013)
山西省重点研发计划(201703D111011)
山西省自然科学基金(201801D121159)
山西省青年自然科学基金(201801D221208)
山西省高等学校科技创新项目(2019L0583)
山西省研究生教育创新项目(2020SY408,2020SY405)资助。
关键词
锂离子电池
剩余使用寿命
支持向量机回归
完备集合经验模态分解
lithium-ion battery
remaining useful life
support vector regression
complete ensemble empirical mode decomposition with adaptive noise
