利用贝叶斯和遗传算法优化提高锂离子电池正极性能:实验和数值分析

IF 7.3 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Mohamed Atwair, Jihyeon Kang, Ali Cherif, Seung-Kwon Seo, Inho Nam* and Chul-Jin Lee*, 
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引用次数: 0

摘要

在实际应用中,电极参数的设计是决定锂存储速率和数量的关键因素,直接影响锂离子电池的能量密度和整体性能。因此,电极参数的优化设计对于提高锂离子电池的性能至关重要,特别是在高要求的工作条件下。在这项研究中,我们开发了一个混合优化框架,将贝叶斯优化(BO)与遗传算法(GA)相结合,系统地确定LIB阴极的最佳设计条件。与传统方法不同,我们在实际和实验一致的条件下验证我们的调查,以确保结果的可靠性和适用性。在这项研究中,采用伪二维(P2D)模型来检验物理设计参数对不同c -速率下电化学性能的影响。将贝叶斯方法与P2D模型结合到高斯过程中,构建代理模型,优化电极设计参数,使放电容量最大化。总体而言,本文为提高lib性能的电极参数的设计和优化提供了有价值的见解,特别是在高c -速率下工作的NCM622阴极。这种方法不仅弥合了模拟与实际应用之间的差距,而且为未来的电池设计优化展示了一种可扩展的方法。此外,我们还揭示了BO是设计电池组件的有效技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancement of Li-Ion Battery Cathode Performance via Bayesian and Genetic Algorithm Optimization: Experimental and Numerical Analysis

Enhancement of Li-Ion Battery Cathode Performance via Bayesian and Genetic Algorithm Optimization: Experimental and Numerical Analysis

The design of electrode parameters is a crucial determinant of the rate and quantity of lithium storage, which directly impacts the energy density and overall performance of lithium-ion batteries (LIBs) in practical applications. Therefore, the optimal design of electrode parameters is essential for enhancing the performance of LIB cells, especially under high-demand operating conditions. In this study, we develop a hybrid optimization framework that combines Bayesian Optimization (BO) with a Genetic Algorithm (GA) to systematically identify optimal design conditions for LIB cathodes. Unlike conventional approaches, we validate our investigation under practical and experimentally aligned conditions to ensure the reliability and applicability of the results. In the study, a pseudo two-dimensional (P2D) model is employed to examine the impact of physical design parameters on the electrochemical performance at varying C-rates. The Bayesian approach is integrated with the P2D model into the Gaussian process to construct a surrogate model, with the aim of optimizing the electrode design parameters to maximize the discharge capacity. Overall, this paper provides valuable insights into the design and optimization of electrode parameters for improving the performance of LIBs, particularly for NCM622 cathodes operating at high C-rates. This approach not only bridges the gap between simulation and practical application but also demonstrates a scalable methodology for future battery design optimization. Moreover, we reveal that BO is an effective technique for designing battery components.

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来源期刊
ACS Sustainable Chemistry & Engineering
ACS Sustainable Chemistry & Engineering CHEMISTRY, MULTIDISCIPLINARY-ENGINEERING, CHEMICAL
CiteScore
13.80
自引率
4.80%
发文量
1470
审稿时长
1.7 months
期刊介绍: ACS Sustainable Chemistry & Engineering is a prestigious weekly peer-reviewed scientific journal published by the American Chemical Society. Dedicated to advancing the principles of green chemistry and green engineering, it covers a wide array of research topics including green chemistry, green engineering, biomass, alternative energy, and life cycle assessment. The journal welcomes submissions in various formats, including Letters, Articles, Features, and Perspectives (Reviews), that address the challenges of sustainability in the chemical enterprise and contribute to the advancement of sustainable practices. Join us in shaping the future of sustainable chemistry and engineering.
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