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

Abstract 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 produ...

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Main Authors: Debopoma Kar Ray, Tamal Roy, Surajit Chattopadhyay
Format: Article
Language:English
Published: Wiley 2021-09-01
Series:IET Nanodielectrics
Online Access:https://doi.org/10.1049/nde2.12017
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spelling doaj-d5c35ac6bed14c368f53732e027ef4312021-09-08T07:31:08ZengWileyIET Nanodielectrics2514-32552021-09-014312112910.1049/nde2.12017Switching transient‐based state of Ampere‐hour prediction of lithium‐ion, nickel‐cadmium, nickel‐metal‐hydride and lead acid batteries used in vehiclesDebopoma Kar Ray0Tamal Roy1Surajit Chattopadhyay2Electrical Engineering Department MCKV Institute of Engineering Howrah West Bengal IndiaElectrical Engineering Department MCKV Institute of Engineering Howrah West Bengal IndiaElectrical Engineering Department GKCIET West Bengal IndiaAbstract 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.https://doi.org/10.1049/nde2.12017
collection DOAJ
language English
format Article
sources DOAJ
author Debopoma Kar Ray
Tamal Roy
Surajit Chattopadhyay
spellingShingle Debopoma Kar Ray
Tamal Roy
Surajit Chattopadhyay
Switching transient‐based state of Ampere‐hour prediction of lithium‐ion, nickel‐cadmium, nickel‐metal‐hydride and lead acid batteries used in vehicles
IET Nanodielectrics
author_facet Debopoma Kar Ray
Tamal Roy
Surajit Chattopadhyay
author_sort Debopoma Kar Ray
title Switching transient‐based state of Ampere‐hour prediction of lithium‐ion, nickel‐cadmium, nickel‐metal‐hydride and lead acid batteries used in vehicles
title_short Switching transient‐based state of Ampere‐hour prediction of lithium‐ion, nickel‐cadmium, nickel‐metal‐hydride and lead acid batteries used in vehicles
title_full Switching transient‐based state of Ampere‐hour prediction of lithium‐ion, nickel‐cadmium, nickel‐metal‐hydride and lead acid batteries used in vehicles
title_fullStr Switching transient‐based state of Ampere‐hour prediction of lithium‐ion, nickel‐cadmium, nickel‐metal‐hydride and lead acid batteries used in vehicles
title_full_unstemmed Switching transient‐based state of Ampere‐hour prediction of lithium‐ion, nickel‐cadmium, nickel‐metal‐hydride and lead acid batteries used in vehicles
title_sort switching transient‐based state of ampere‐hour prediction of lithium‐ion, nickel‐cadmium, nickel‐metal‐hydride and lead acid batteries used in vehicles
publisher Wiley
series IET Nanodielectrics
issn 2514-3255
publishDate 2021-09-01
description Abstract 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.
url https://doi.org/10.1049/nde2.12017
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AT tamalroy switchingtransientbasedstateofamperehourpredictionoflithiumionnickelcadmiumnickelmetalhydrideandleadacidbatteriesusedinvehicles
AT surajitchattopadhyay switchingtransientbasedstateofamperehourpredictionoflithiumionnickelcadmiumnickelmetalhydrideandleadacidbatteriesusedinvehicles
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