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Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications

Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and p...

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Autores principales: El Marghichi, Mouncef, Dangoury, Soufiane, zahrou, Younes, Loulijat, Azeddine, Chojaa, Hamid, Banakhr, Fahd A., Mosaad, Mohamed I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621954/
https://www.ncbi.nlm.nih.gov/pubmed/37917753
http://dx.doi.org/10.1371/journal.pone.0293753
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author El Marghichi, Mouncef
Dangoury, Soufiane
zahrou, Younes
Loulijat, Azeddine
Chojaa, Hamid
Banakhr, Fahd A.
Mosaad, Mohamed I.
author_facet El Marghichi, Mouncef
Dangoury, Soufiane
zahrou, Younes
Loulijat, Azeddine
Chojaa, Hamid
Banakhr, Fahd A.
Mosaad, Mohamed I.
author_sort El Marghichi, Mouncef
collection PubMed
description Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we propose a novel method that utilizes the gradient-based optimizer (GBO) to evaluate the SOH of lithium batteries. The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. We evaluated our method against four robust algorithms, namely particle swarm optimization-least square support vector regression (PSO-LSSV), BCRLS-multiple weighted dual extended Kalman filtering (BCRLS-MWDEKF), Total least square (TLS), and approximate weighted total least squares (AWTLS) in hybrid electric vehicle (HEV) and electric vehicle (EV) applications. Our method consistently outperformed the alternatives, with the GBO achieving the lowest maximum error. In EV scenarios, GBO exhibited maximum errors ranging from 0.65% to 1.57% and mean errors ranging from 0.21% to 0.57%. Similarly, in HEV scenarios, GBO demonstrated maximum errors ranging from 0.81% to 3.21% and mean errors ranging from 0.39% to 1.03%. Furthermore, our method showcased superior predictive performance, with low values for mean squared error (MSE) (<1.8130e-04), root mean squared error (RMSE) (<1.35%), and mean absolute percentage error (MAPE) (<1.4).
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spelling pubmed-106219542023-11-03 Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications El Marghichi, Mouncef Dangoury, Soufiane zahrou, Younes Loulijat, Azeddine Chojaa, Hamid Banakhr, Fahd A. Mosaad, Mohamed I. PLoS One Research Article Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we propose a novel method that utilizes the gradient-based optimizer (GBO) to evaluate the SOH of lithium batteries. The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. We evaluated our method against four robust algorithms, namely particle swarm optimization-least square support vector regression (PSO-LSSV), BCRLS-multiple weighted dual extended Kalman filtering (BCRLS-MWDEKF), Total least square (TLS), and approximate weighted total least squares (AWTLS) in hybrid electric vehicle (HEV) and electric vehicle (EV) applications. Our method consistently outperformed the alternatives, with the GBO achieving the lowest maximum error. In EV scenarios, GBO exhibited maximum errors ranging from 0.65% to 1.57% and mean errors ranging from 0.21% to 0.57%. Similarly, in HEV scenarios, GBO demonstrated maximum errors ranging from 0.81% to 3.21% and mean errors ranging from 0.39% to 1.03%. Furthermore, our method showcased superior predictive performance, with low values for mean squared error (MSE) (<1.8130e-04), root mean squared error (RMSE) (<1.35%), and mean absolute percentage error (MAPE) (<1.4). Public Library of Science 2023-11-02 /pmc/articles/PMC10621954/ /pubmed/37917753 http://dx.doi.org/10.1371/journal.pone.0293753 Text en © 2023 Marghichi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
El Marghichi, Mouncef
Dangoury, Soufiane
zahrou, Younes
Loulijat, Azeddine
Chojaa, Hamid
Banakhr, Fahd A.
Mosaad, Mohamed I.
Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications
title Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications
title_full Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications
title_fullStr Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications
title_full_unstemmed Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications
title_short Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications
title_sort improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: case study in electric vehicle applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621954/
https://www.ncbi.nlm.nih.gov/pubmed/37917753
http://dx.doi.org/10.1371/journal.pone.0293753
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