A Novel Approach for State of Health Estimation and Remaining Useful Life Prediction of Supercapacitors Using an Improved Honey Badger Algorithm Assisted Hybrid Neural Network  

作　　者：Zhenxiao Yi Shi Wang Zhaoting Li Licheng Wang Kai Wang 

机构地区：[1]the School of Electrical Engineering,Qingdao University,Qingdao 266071,China,and the State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,China [2]the Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China [3]the School of Engineering,Brown University,Providence,Rhode Island 02912,USA [4]the School of Information Engineering,Zhejiang University of Technology,Hangzhou 310014,China [5]the School of Electrical Engineering,Qingdao University,Qingdao 266071,China

出　　处：《Protection and Control of Modern Power Systems》2024年第6期1-18,共18页现代电力系统保护与控制(英文)

基　　金：supported by the Zhejiang Province Natural Science Foundation(No.LY22E070007);the National Natural Science Foundation of China(No.52007170);supported by Youth Innovation Technology Project of Higher School in Shandong Province(No.2022KJ139).

摘　　要：Supercapacitors(SCs)are widely recognized as excellent clean energy storage devices.Accurate state of health(SOH)estimation and remaining useful life(RUL)prediction are essential for ensuring their safe and reliable operation.This paper introduces a novel method for SOH estimation and RUL prediction,based on a hybrid neural network optimized by an improved honey badger algorithm(HBA).The method combines the advantages of convolutional neural network(CNN)and bidirectional long-short-term memory(BiLSTM)neural network.The HBA optimizes the hyperparameters of the hybrid neural network.The CNN automatically extracts deep features from time series data and reduces dimensionality,which are then used as input for the BiLSTM.Additionally,recurrent dropout is introduced in the recurrent layer to reduce overfitting and facilitate the learning process.This approach not only improves the accuracy of estimates and forecasts but also significantly reduces data processing time.SCs under different working conditions are used to validate the proposed method.The results show that the proposed hybrid model effectively extracts features,enriches local details,and enhances global perception capabilities.The proposed hybrid model outperforms single models,reducing the root mean square error to below 1%,and offers higher prediction accuracy and robustness compared to other methods.

关 键 词：SUPERCAPACITORS state of health remaining useful life honey badger algorithm recurrent dropout. 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]