Enhanced State of Charge Estimation for Lithiumion Batteries using Polynomial Voltage Approximation

The escalating adoption of electric machinery as a replacement for the fossil fuel-powered counterparts has underscored the critical need for robust energy storage solutions, with lithium-ion (Li-ion) batteries emerging as a cornerstone technology, particularly in electric vehicles (EVs). However, t...

Full description

Saved in:
Bibliographic Details
Main Authors: Karna S., Satpathy P.R., Bhowmik P.
Other Authors: 59527815400
Format: Conference paper
Published: Institute of Electrical and Electronics Engineers Inc. 2025
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-36846
record_format dspace
spelling my.uniten.dspace-368462025-03-03T15:45:09Z Enhanced State of Charge Estimation for Lithiumion Batteries using Polynomial Voltage Approximation Karna S. Satpathy P.R. Bhowmik P. 59527815400 57195339278 57196457126 Battery management systems Battery storage Benchmarking Polynomial approximation Energy Ion batteries Lithium ions Mismatch Multiple-peak Partial shading Photovoltaics Robust energy State-of-charge estimation States of charges State of charge The escalating adoption of electric machinery as a replacement for the fossil fuel-powered counterparts has underscored the critical need for robust energy storage solutions, with lithium-ion (Li-ion) batteries emerging as a cornerstone technology, particularly in electric vehicles (EVs). However, the intrinsic vulnerability of Li-ion batteries to degradation, caused by cyclic charge-discharge operations, poses significant challenges to accurate state of charge (SOC) estimation and capacity assessment, thereby impeding optimal EV performance [12] [16]. This study presents a novel approach to address these challenges by elucidating a direct correlation between battery voltage and SOC. Through rigorous empirical experimentation and advanced mathematical modelling, a polynomial equation is derived to precisely quantify SOC dynamics in response to voltage fluctuations. This framework facilitates real-time capacity estimation, empowering proactive management of EV energy systems [18]. By integrating empirical data with sophisticated mathematical analysis, this research contributes to deeper understanding of Li-ion battery behavior, paving the way for enhanced energy storage management strategies. The findings hold promise for optimizing EV efficiency, reliability, and longevity in the evolving landscape of electric machinery. ? 2024 IEEE. Final 2025-03-03T07:45:09Z 2025-03-03T07:45:09Z 2024 Conference paper 10.1109/ODICON62106.2024.10797595 2-s2.0-85216028808 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216028808&doi=10.1109%2fODICON62106.2024.10797595&partnerID=40&md5=ddf4d5531b99f31a321de7d6ad71a69e https://irepository.uniten.edu.my/handle/123456789/36846 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Battery management systems
Battery storage
Benchmarking
Polynomial approximation
Energy
Ion batteries
Lithium ions
Mismatch
Multiple-peak
Partial shading
Photovoltaics
Robust energy
State-of-charge estimation
States of charges
State of charge
spellingShingle Battery management systems
Battery storage
Benchmarking
Polynomial approximation
Energy
Ion batteries
Lithium ions
Mismatch
Multiple-peak
Partial shading
Photovoltaics
Robust energy
State-of-charge estimation
States of charges
State of charge
Karna S.
Satpathy P.R.
Bhowmik P.
Enhanced State of Charge Estimation for Lithiumion Batteries using Polynomial Voltage Approximation
description The escalating adoption of electric machinery as a replacement for the fossil fuel-powered counterparts has underscored the critical need for robust energy storage solutions, with lithium-ion (Li-ion) batteries emerging as a cornerstone technology, particularly in electric vehicles (EVs). However, the intrinsic vulnerability of Li-ion batteries to degradation, caused by cyclic charge-discharge operations, poses significant challenges to accurate state of charge (SOC) estimation and capacity assessment, thereby impeding optimal EV performance [12] [16]. This study presents a novel approach to address these challenges by elucidating a direct correlation between battery voltage and SOC. Through rigorous empirical experimentation and advanced mathematical modelling, a polynomial equation is derived to precisely quantify SOC dynamics in response to voltage fluctuations. This framework facilitates real-time capacity estimation, empowering proactive management of EV energy systems [18]. By integrating empirical data with sophisticated mathematical analysis, this research contributes to deeper understanding of Li-ion battery behavior, paving the way for enhanced energy storage management strategies. The findings hold promise for optimizing EV efficiency, reliability, and longevity in the evolving landscape of electric machinery. ? 2024 IEEE.
author2 59527815400
author_facet 59527815400
Karna S.
Satpathy P.R.
Bhowmik P.
format Conference paper
author Karna S.
Satpathy P.R.
Bhowmik P.
author_sort Karna S.
title Enhanced State of Charge Estimation for Lithiumion Batteries using Polynomial Voltage Approximation
title_short Enhanced State of Charge Estimation for Lithiumion Batteries using Polynomial Voltage Approximation
title_full Enhanced State of Charge Estimation for Lithiumion Batteries using Polynomial Voltage Approximation
title_fullStr Enhanced State of Charge Estimation for Lithiumion Batteries using Polynomial Voltage Approximation
title_full_unstemmed Enhanced State of Charge Estimation for Lithiumion Batteries using Polynomial Voltage Approximation
title_sort enhanced state of charge estimation for lithiumion batteries using polynomial voltage approximation
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2025
_version_ 1825816286654889984
score 13.244109