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Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model

[Image: see text] The analysis of performance degradation in lithium-ion batteries plays a crucial role in achieving accurate and efficient fault diagnosis as well as safety management. This paper proposes a method for studying the degradation pattern of lithium-ion batteries and establishing the st...

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Autores principales: Fang, Pengya, Zhang, Anhao, Sui, Xiaoxiao, Wang, Di, Yin, Liping, Wen, Zhenhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500667/
https://www.ncbi.nlm.nih.gov/pubmed/37720804
http://dx.doi.org/10.1021/acsomega.3c04222
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author Fang, Pengya
Zhang, Anhao
Sui, Xiaoxiao
Wang, Di
Yin, Liping
Wen, Zhenhua
author_facet Fang, Pengya
Zhang, Anhao
Sui, Xiaoxiao
Wang, Di
Yin, Liping
Wen, Zhenhua
author_sort Fang, Pengya
collection PubMed
description [Image: see text] The analysis of performance degradation in lithium-ion batteries plays a crucial role in achieving accurate and efficient fault diagnosis as well as safety management. This paper proposes a method for studying the degradation pattern of lithium-ion batteries and establishing the structure–activity relationship between internal and external parameters by employing a lumped particle diffusion model. To simulate real-world operating conditions, a cycle life test was conducted with the constant current–constant voltage (CC–CV) charge mode and the discharge mode under New European Driving Cycle (NEDC) working condition. The test aimed to analyze the variations in the external macroscopic characteristic parameters of the battery. Building upon this analysis, a lumped particle diffusion model was constructed, and the model parameters were identified using the Levenberg–Marquardt (L–M) algorithm. Subsequently, the ohmic, activation, and concentration losses of the battery under different aging conditions were determined, revealing the internal state evolution during the degradation process of lithium-ion batteries. The findings indicate that the lumped particle diffusion model provides a comprehensive explanation of the internal mechanisms contributing to the performance degradation of lithium-ion batteries. Moreover, the proposed method offers a novel perspective for the real-time quantitative analysis of lithium-ion battery performance degradation.
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spelling pubmed-105006672023-09-15 Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model Fang, Pengya Zhang, Anhao Sui, Xiaoxiao Wang, Di Yin, Liping Wen, Zhenhua ACS Omega [Image: see text] The analysis of performance degradation in lithium-ion batteries plays a crucial role in achieving accurate and efficient fault diagnosis as well as safety management. This paper proposes a method for studying the degradation pattern of lithium-ion batteries and establishing the structure–activity relationship between internal and external parameters by employing a lumped particle diffusion model. To simulate real-world operating conditions, a cycle life test was conducted with the constant current–constant voltage (CC–CV) charge mode and the discharge mode under New European Driving Cycle (NEDC) working condition. The test aimed to analyze the variations in the external macroscopic characteristic parameters of the battery. Building upon this analysis, a lumped particle diffusion model was constructed, and the model parameters were identified using the Levenberg–Marquardt (L–M) algorithm. Subsequently, the ohmic, activation, and concentration losses of the battery under different aging conditions were determined, revealing the internal state evolution during the degradation process of lithium-ion batteries. The findings indicate that the lumped particle diffusion model provides a comprehensive explanation of the internal mechanisms contributing to the performance degradation of lithium-ion batteries. Moreover, the proposed method offers a novel perspective for the real-time quantitative analysis of lithium-ion battery performance degradation. American Chemical Society 2023-08-30 /pmc/articles/PMC10500667/ /pubmed/37720804 http://dx.doi.org/10.1021/acsomega.3c04222 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Fang, Pengya
Zhang, Anhao
Sui, Xiaoxiao
Wang, Di
Yin, Liping
Wen, Zhenhua
Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model
title Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model
title_full Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model
title_fullStr Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model
title_full_unstemmed Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model
title_short Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model
title_sort analysis of performance degradation in lithium-ion batteries based on a lumped particle diffusion model
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500667/
https://www.ncbi.nlm.nih.gov/pubmed/37720804
http://dx.doi.org/10.1021/acsomega.3c04222
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