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基于集总粒子扩散模型的锂离子电池性能退化分析

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

作者信息

Fang Pengya, Zhang Anhao, Sui Xiaoxiao, Wang Di, Yin Liping, Wen Zhenhua

机构信息

School of Aero Engine, Zhengzhou University of Aeronautics, Zhengzhou 450015, China.

School of Materials Science and Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450015, China.

出版信息

ACS Omega. 2023 Aug 30;8(36):32884-32891. doi: 10.1021/acsomega.3c04222. eCollection 2023 Sep 12.

Abstract

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.

摘要

锂离子电池性能退化分析在实现准确高效的故障诊断以及安全管理方面发挥着关键作用。本文提出了一种通过采用集总颗粒扩散模型来研究锂离子电池退化模式并建立内部与外部参数之间结构 - 活性关系的方法。为模拟实际运行条件,在新欧洲行驶循环(NEDC)工况下采用恒流 - 恒压(CC - CV)充电模式和放电模式进行了循环寿命测试。该测试旨在分析电池外部宏观特征参数的变化。基于此分析,构建了集总颗粒扩散模型,并使用列文伯格 - 马夸尔特(L - M)算法识别模型参数。随后,确定了不同老化条件下电池的欧姆、活化和浓度损失,揭示了锂离子电池退化过程中的内部状态演变。研究结果表明,集总颗粒扩散模型对导致锂离子电池性能退化的内部机制提供了全面解释。此外,所提出的方法为锂离子电池性能退化的实时定量分析提供了新的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/828e/10500667/9eb070df606a/ao3c04222_0001.jpg

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