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

doi:10.23919/PCMP.2023.000187

	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
	Yi, Zhenxiao 1,2; Wang, Shi 3; Li, Zhaoting 4; Wang, Licheng 5; Wang, Kai 1
	2024-11-01
发表期刊	PROTECTION AND CONTROL OF MODERN POWER SYSTEMS
ISSN	2367-2617
卷号	9 期号:6 页码:1-18
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	Accuracy Recurrent neural networks Computational modeling Time series analysis Predictive models Supercapacitors Feature extraction Prediction algorithms Convolutional neural networks Long short term memory state of health re-maining useful life honey badger algorithm recurrent dropout
DOI	10.23919/PCMP.2023.000187
收录类别	SCI
语种	英语
资助项目	Zhejiang Province Natural Science Foundation[LY22E070007] ; National Natural Science Foundation of China[52007170] ; Youth Innovation Technology Project of Higher School in Shandong Province[2022KJ139]
WOS研究方向	Energy & Fuels ; Engineering
WOS类目	Energy & Fuels ; Engineering, Electrical & Electronic
WOS记录号	WOS:001349844600008
出版者	Power System Protection & Control Press
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39478
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Shi; Wang, Kai
作者单位	1.Qingdao Univ, Sch Elect Engn, Qingdao 266071, Peoples R China 2.Hebei Univ Technol, State Key Lab Reliabil & Intelligence Elect Equipm, Tianjin 300130, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Brown Univ, Sch Engn, Providence, RI 02912 USA 5.Zhejiang Univ Technol, Sch Informat Engn, Hangzhou 310014, Peoples R China
推荐引用方式 GB/T 7714	Yi, Zhenxiao,Wang, Shi,Li, Zhaoting,et al. 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[J]. PROTECTION AND CONTROL OF MODERN POWER SYSTEMS,2024,9(6):1-18.
APA	Yi, Zhenxiao,Wang, Shi,Li, Zhaoting,Wang, Licheng,&Wang, Kai.(2024).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.PROTECTION AND CONTROL OF MODERN POWER SYSTEMS,9(6),1-18.
MLA	Yi, Zhenxiao,et al."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".PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 9.6(2024):1-18.