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 p...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.展开更多
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).
文摘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.
