Uncovering the impact of battery design parameters on health and lifetime using short charging segments

IF 30.8 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Wendi Guo, Søren Byg Vilsen, Yaqi Li, Ashima Verma, Daniel Ioan Stroe and Daniel Brandell
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引用次数: 0

Abstract

Frequent fast charging of lithium-ion batteries (LiBs) demands robust health monitoring, not only to ensure long-term performance and user confidence, but also to support emerging applications such as vehicle-to-grid (V2G), where energy flows bidirectionally between EVs and the grid. Without clear insight into how upstream design parameters such as solid-state diffusion coefficient, electrode thickness, particle radius, lithium-ion concentration, and porosity impact battery health in real-world use, however, valuable opportunities to optimize early-stage designs and develop tailored usage strategies to mitigate degradation may be lost. This work proposes a machine learning (ML) framework built on a digital twin model that links key design parameters to real-world behaviors of graphite/nickel–manganese–cobalt–oxide LiBs under a diverse range of fast charging protocols, depths of discharge, and dynamic discharge profiles representative of applications in Nordic climates. The framework infers six key design parameters directly from short charging segments, enabling rapid health prediction within seconds. Notably, this approach improves the robustness of health and lifetime predictions by up to 65% and 69%, respectively, compared to baseline multi-layer perceptron and linear regression models, while also outperforming the baseline random forest model, with a training time of 1 second. The strong physical correlation between capacity variability and three design parameters—solid-state diffusion coefficient, particle radius, and electrode thickness—during fast charging highlights their vital role in determining the degradation pathways. The framework can be readily integrated into upstream workflows and battery management systems, enabling end users to tailor usage patterns and guiding developers toward improved design strategies.

Abstract Image

利用短充电段揭示电池设计参数对健康和寿命的影响
锂离子电池(lib)的频繁快速充电需要强大的健康监测,这不仅是为了确保长期性能和用户信心,也是为了支持汽车到电网(V2G)等新兴应用,在这些应用中,能量在电动汽车和电网之间双向流动。然而,如果不清楚固态扩散系数、电极厚度、颗粒半径、锂离子浓度和孔隙度等上游设计参数在实际使用中如何影响电池的健康,就可能失去优化早期设计和制定量身定制的使用策略以减轻电池退化的宝贵机会。这项工作提出了一个基于数字孪生模型的机器学习(ML)框架,该框架将关键设计参数与石墨/镍锰钴氧化物锂在各种快速充电协议、放电深度和代表北欧气候应用的动态放电剖面下的真实行为联系起来。该框架直接从短充电段推断出六个关键设计参数,从而在几秒钟内实现快速健康预测。值得注意的是,与基线多层感知器和线性回归模型相比,该方法将健康和寿命预测的稳健性分别提高了65%和69%,同时也优于基线随机森林模型,训练时间为1秒。在快速充电过程中,容量变异性与三个设计参数(固态扩散系数、颗粒半径和电极厚度)之间的强物理相关性突出了它们在决定降解途径中的重要作用。该框架可以很容易地集成到上游工作流和电池管理系统中,使最终用户能够定制使用模式,并指导开发人员改进设计策略。
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来源期刊
Energy & Environmental Science
Energy & Environmental Science 化学-工程:化工
CiteScore
50.50
自引率
2.20%
发文量
349
审稿时长
2.2 months
期刊介绍: Energy & Environmental Science, a peer-reviewed scientific journal, publishes original research and review articles covering interdisciplinary topics in the (bio)chemical and (bio)physical sciences, as well as chemical engineering disciplines. Published monthly by the Royal Society of Chemistry (RSC), a not-for-profit publisher, Energy & Environmental Science is recognized as a leading journal. It boasts an impressive impact factor of 8.500 as of 2009, ranking 8th among 140 journals in the category "Chemistry, Multidisciplinary," second among 71 journals in "Energy & Fuels," second among 128 journals in "Engineering, Chemical," and first among 181 scientific journals in "Environmental Sciences." Energy & Environmental Science publishes various types of articles, including Research Papers (original scientific work), Review Articles, Perspectives, and Minireviews (feature review-type articles of broad interest), Communications (original scientific work of an urgent nature), Opinions (personal, often speculative viewpoints or hypotheses on current topics), and Analysis Articles (in-depth examination of energy-related issues).
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