摘要
为实现电池剩余使用寿命(RUL)在线预测和降低数据离群值对预测精度影响,提出基于改进轻量型梯度提升机(LightGBM)的RUL在线预测方法。首先,为实现RUL在线预测,通过等压降时间与容量衰减的关系,选取等压降时间为健康因子;然后,为降低数据离群值对预测精度的影响,构建基于LightGBM的预测模型,采用Bagging的学习方式,忽略离群值权重;接着,为进一步降低离群值影响,基于一种兼具自适应性和鲁棒性的损失函数(ARLF)对LightGBM进行改进,通过超参数α限制损失函数一阶导数幅值的饱和值,在残差增长时,限制离群值对梯度的影响;最后,通过行驶工况下电池全生命周期容量测试实验数据,对比基于不同损失函数的RUL在线预测效果,验证所构建健康因子和所提预测方法的有效性。
In order to achieve the remaining useful life(RUL)on-line prediction and reduce the impact of outlier value on prediction accuracy,this paper proposes an on-line prediction method based on the improved light gradient boosting machine(LightGBM).Firstly,in order to accomplish RUL on-line prediction,the health indicator is selected according to the relationship between isobaric time series and capacity.Then,in order to reduce the impact of outliers on the prediction accuracy,the prediction model based on LightGBM is built,and Bagging learning method is adopted,which ignores the weights of outliers.The improved LigthGBM based on adaptive robust loss function is established to reduce the impact further.Parameterαis utilized to limit the saturation value for first-order derivative of loss function,so that the influence of residual error on the gradient is reduced.Finally,the effectiveness of the established health indicator and the proposed RUL prediction method is verified by experimental data,and the RUL prediction performance based on different loss functions are compared.The results demonstrate that the proposed method has higher prediction accuracy and better robustness.
作者
肖迁
穆云飞
焦志鹏
孟锦豪
贾宏杰
Xiao Qian;Mu Yunfei;Jiao Zhipeng;Meng Jinhao;Jia Hongjie(Key Laboratory of Smart Grid of Ministry of Education Tianjin University,Tianjin 300072,China;State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology,Tianjin 300130,China;College of Electrical Engineering Sichuan University,Chengdu 610065,China)
出处
《电工技术学报》
EI
CSCD
北大核心
2022年第17期4517-4527,共11页
Transactions of China Electrotechnical Society
基金
国家自然科学基金(52107121,U2066213)
中国博士后科学基金(2020M680880)资助项目。
关键词
锂离子电池
剩余使用寿命
在线预测
离群值
改进轻量型梯度提升机
Lithium-ion battery
remaining useful life
on-line prediction
outlier value
improved light gradient boosting machine
