High‐temperature dielectric properties and impedance spectroscopy of PbHf1−xSnxO3ceramics

Liu, Zhigang; Ge, Peng-Zu; Tang, Hui; Tang, Xin‐Gui; Zeng, Si-Ming; Jin, Yanping; Tang, Zhenhua; Liu, Qiu-Xiang

doi:10.1049/iet-nde.2020.0030

Cited by 10 publications

(2 citation statements)

References 42 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neutron scattering has been observed to extract specific surface area for silicon nanoparticles, which have correlation with the first cycle Coulombic efficiency of Li-ion batteries [2]. Synthesis of PbHf 1-x Sn x O 3 ceramics have been observed to investigate the dielectric properties in the temperature range of 20°C-650°C [3]. Analysis was made by AI-doped ZnO nanoparticles, which reveal the effectiveness of pure ZnO-based nanoparticles in Li-Ion batteries [4].…”

Section: Introductionmentioning

confidence: 99%

“…I G U R E 3 S-parameter of coefficients at different decomposition levels for different ampere-hour of the lead acid battery (a) SA (b) SD F I G U R E 5 S-parameter of coefficients at different decomposition levels for different ampere-hour of lithium-ion battery (a) SA (b) SD F I G U R E 6 Current transients in Nickel-Cadmium battery F I G U R E 4 Current transients in the lithium-ion battery RAY ET AL.-3 …”

mentioning

confidence: 99%

See 1 more Smart Citation

Switching transient‐based state of Ampere‐hour prediction of lithium‐ion, nickel‐cadmium, nickel‐metal‐hydride and lead acid batteries used in vehicles

Ray

Roy

Chattopadhyay³

2021

IET Nanodielectrics

View full text Add to dashboard Cite

The state of the ampere-hour capacity of the battery depends on the condition of materials used in it. Large reduction of capacity ends with maintenance or replacement of the battery. Modern battery materials include application of nanomaterials and nanotechnology in various stages of production. This article attempts to monitor the capacity of battery used for vehicles which are made of different types of materials using switching transients. The analytical part was done using wavelet-based decompositions. Data sets of large number of coefficients have been developed for learning. Their statistical behaviour has been studied, and monitoring was initially carried out by some selective parameters. Then the artificial neural network-based algorithm was developed which includes all features of statistical variation for better monitoring. Case studies have been carried out followed by comparison. The study ends with a satisfactory monitoring.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%