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...
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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 |
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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 |
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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 |
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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. |
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59527815400 |
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59527815400 Karna S. Satpathy P.R. Bhowmik P. |
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Conference paper |
author |
Karna S. Satpathy P.R. Bhowmik P. |
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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 |
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Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2025 |
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1825816286654889984 |
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13.244109 |