Prediction of the Remaining Useful Life of Supercapacitors

Yi, Zhenxiao; Zhao, Kun; Sun, Jianrui; Wang, Licheng; Wang, Kai; Ma, Yongzhi

doi:10.1155/2022/7620382

Cited by 39 publications

(21 citation statements)

References 50 publications

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“…In recent times the data acquisition and calculation have greatly improved, so the data-driven methods are very popular and have been widely used in various classifications and estimates [24][25][26][27][28][29]. Compared with other methods, the data-based methods do not need to deeply understand the internal mechanism of the battery.…”

Section: Open Accessmentioning

confidence: 99%

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Guo

Yang

Zhang

et al. 2022

Prot Control Mod Power Syst

114

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The estimation of state of health (SOH) of a lithium-ion battery (LIB) is of great significance to system safety and economic development. This paper proposes a SOH estimation method based on the SSA-Elman model for the first time. To improve the correlation rates between features and battery capacity, a method combining median absolute deviation filtering and Savitzky–Golay filtering is proposed to process the data. Based on the aging characteristics of the LIB, five features with correlation rates above 0.99 after data processing are then proposed. Addressing the defects of the Elman model, the sparrow search algorithm (SSA) is used to optimize the network parameters. In addition, a data incremental update mechanism is added to improve the generalization of the SSA-Elman model. Finally, the performance of the proposed model is verified based on NASA dataset, and the outputs of the Elman, LSTM and SSA-Elman models are compared. The results show that the proposed method can accurately estimate the SOH, with the root mean square error (RMSE) being as low as 0.0024 and the mean absolute percentage error (MAPE) being as low as 0.25%. In addition, RMSE does not exceed 0.0224 and MAPE does not exceed 2.21% in high temperature and low temperature verifications.

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Section: Open Accessmentioning

confidence: 99%

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Guo

Yang

Zhang

et al. 2022

Prot Control Mod Power Syst

114

View full text Add to dashboard Cite

show abstract

“…Therefore, the development of new computing methods for future materials and equipment based on artificial intelligence (AI) will solve this problem. At present, there are performance predictions for lithium batteries and supercapacitors based on AI calculation methods, but there is less research on performance prediction for air filter [189] , [190] , [191] , [192] .…”

Section: Discussionmentioning

confidence: 99%

High-performance multifunctional electrospun fibrous air filter for personal protection: A review

Shao

Chen²,

Wang³

et al. 2022

Separation and Purification Technology

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“…We denote by 􏽢 σ n any estimation of σ obtained from x 1: n . On the basis of numerous works carried out on the estimation of the standard deviation from the sample range ( [38][39][40][41][42][43]), we can assume that there is a real constant α(X, n) such that…”

Section: E Range-mbr Algorithmmentioning

confidence: 99%

Mathematical Methods for IoT-Based Annotating Object Datasets with Bounding Boxes

Zaïdi

2022

Mathematical Problems in Engineering

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Object datasets used in the construction of object detectors are typically annotated with horizontal or oriented bounding rectangles for IoT-based. The optimality of an annotation is obtained by fulfilling two conditions: (i) the rectangle covers the whole object and (ii) the area of the rectangle is minimal. Building a large-scale object dataset requires annotators with equal manual dexterity to carry out this tedious work. When an object is horizontal for IoT-based, it is easy for the annotator to reach the optimal bounding box within a reasonable time. However, if the object is oriented, the annotator needs additional time to decide whether the object will be annotated with a horizontal rectangle or an oriented rectangle for IoT-based. Moreover, in both cases, the final decision is not based on any objective argument, and the annotation is generally not optimal. In this study, we propose a new method of annotation by rectangles for IoT-based, called robust semi-automatic annotation, which combines speed and robustness. Our method has two phases. The first phase consists in inviting the annotator to click on the most relevant points located on the contour of the object. The outputs of the first phase are used by an algorithm to determine a rectangle enclosing these points. To carry out the second phase, we develop an algorithm called RANGE-MBR, which determines, from the selected points on the contour of the object, a rectangle enclosing these points in a linear time. The rectangle returned by RANGE-MBR always satisfies optimality condition (i). We prove that the optimality condition (ii) is always satisfied for objects with isotropic shapes. For objects with anisotropic shapes, we study the optimality condition (ii) by simulations. We show that the rectangle returned by RANGE-MBR is quasi-optimal for the condition (ii) and that its performance increases with dilated objects, which is the case for most of the objects appearing on images collected by aerial photography.

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Prediction of the Remaining Useful Life of Supercapacitors

Cited by 39 publications

References 50 publications

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

High-performance multifunctional electrospun fibrous air filter for personal protection: A review

Mathematical Methods for IoT-Based Annotating Object Datasets with Bounding Boxes

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