A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction  

作　　者：Yuan Chen Wenxian Duan Yigang He Shunli Wang Carlos Fernandez 

机构地区：[1]School of Artificial Intelligence,Anhui University,Hefei 230009,China [2]State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China [3]School of Electrical Engineering and Automation,Wuhan University,Wuhan 430000,China [4]School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,China [5]School of Pharmacy and Life Sciences,Robert Gordon University,Aberdeen,AB10-7GJ,UK

出　　处：《Green Energy and Intelligent Transportation》2024年第2期50-59,共10页新能源与智能载运（英文）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant number 51577046);the State Key Program of the National Natural Science Foundation of China(Grant number 51637004);the National Key Research and Development Plan“Important Scientific Instruments and Equipment Development”(Grant number 2016YFF0102200).

摘　　要：Battery life prediction is of great significance to the safe operation,and reduces the maintenance costs.This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the battery.By feature extraction,eight features are obtained to fed into the life prediction model.The hybrid framework combines variational mode decomposition,the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward,uneven distribution of high-dimensional feature space and the local escape ability,respectively.Better parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm.The algorithm can improve the local escape ability and convergence performance and find the global optimum.The comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction performance.Compared with other algorithms,the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%.With the advance of the start point,the RUL prediction accuracy of the proposed algorithm does not change much.

关 键 词：State of heath Improved sparrow search algorithm Remaining useful life Variational mode decomposition Multi-kernel support vector regression Feature extraction 

分 类 号：TM91[电气工程—电力电子与电力传动]