Exploring the influence of porosity and thickness on lithium-ion battery electrodes using an image-based model

Boyce, Adam M.; Lu, Xuekun; Brett, Dan J. L.; Shearing, Paul R.

doi:10.1016/j.jpowsour.2022.231779

Cited by 26 publications

(21 citation statements)

References 39 publications

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“…Parametrisation is a key step in the accurate use of image-based models. It becomes difficult to determine experimental parameters at the micro-scale, for example, there is limited mechanical characterisation of the [184] for details on the associated equations and methodology. Adapted from [184], Copyright (2022), with permission from Elsevier.…”

Section: Current and Future Challengesmentioning

confidence: 99%

“…The NMC622 electrodes were discharged at a rate of 5 C within a voltage window of 3-4.2 V. The profiles shown are at 100% degree of discharge (3 V). See Boyce et al[184] for details on the associated equations and methodology. Adapted from[184], Copyright (2022), with permission from Elsevier.…”

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confidence: 99%

“…See Boyce et al[184] for details on the associated equations and methodology. Adapted from[184], Copyright (2022), with permission from Elsevier.…”

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confidence: 99%

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Roadmap on Li-ion battery manufacturing research

Grant¹,

Greenwood²,

Pardikar³

et al. 2022

J. Phys. Energy

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Growth in the Li-ion battery market continues to accelerate, driven by increasing need for economic energy storage in the electric vehicle market. Electrode manufacture is the first main step in production and in an industry dominated by slurry casting, much of the manufacturing process is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding value to the electrode manufacturing value chain. Overcome the current barriers in the electrode manufacturing requires advances in material innovation, manufacturing technology, in-line process metrology and data analytics to improve cell performance, quality, safety and process sustainability. In this roadmap we present where fundamental research can impact advances in each stage of the electrode manufacturing process from materials synthesis to electrode calendering. We also highlight the role of new process technology such as dry processing and advanced electrode design supported through electrode level, physics-based modelling. To compliment this, the progresses in data driven models of full manufacturing processes is reviewed. For all the processes we describe, there is a growing need process metrology, not only to aid fundamental understanding but also to enable true feedback control of the manufacturing process. It is our hope this roadmap will contribute to this rapidly growing space and provide guidance and inspiration to academia and industry.

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Section: Current and Future Challengesmentioning

confidence: 99%

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confidence: 99%

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Roadmap on Li-ion battery manufacturing research

Grant¹,

Greenwood²,

Pardikar³

et al. 2022

J. Phys. Energy

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“…The greatest effect is probably for the 30 s pulses at 20% state of charge. According to the standard ASI theory, the electrode resistance is inversely proportional to the coating thickness, [ 64 ] but the difference is greatest when comparing thin and very thin coatings. As the coating weight increases, it becomes increasingly difficult to achieve uniform utilization of the active material throughout the electrode.…”

Section: Explanation By Xmlmentioning

confidence: 99%

“…As the coating weight increases, it becomes increasingly difficult to achieve uniform utilization of the active material throughout the electrode. [ 64 ] This leads to variations in the local current density and an increased resistance.…”

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confidence: 99%

The Impact of Calendering Process Variables on the Impedance and Capacity Fade of Lithium‐Ion Cells: An Explainable Machine Learning Approach

et al. 2022

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Determining the calendering process variables during electrode manufacturing is critical to guarantee lithium‐ion battery cell's performance; however, it is challenging due to the strong and unknown interdependencies. Herein, explainable machine learning (ML) techniques are used to uncover the impact of calendering process variables on the cells’ performance in terms of impedance and capacity fade. The study is based on experimental data from pilot‐scale manufacturing line considering critical factors of calendering gap, calendering temperature, electrodes’ coating weight, and target porosity. It offers a hierarchical methodology based on designed experiment, data‐oriented modeling via ML techniques, and model explainability technologies. The study reveals the relative importance of calendering control variables for cell impedance and capacity fade and quantifies the contribution of factors and the predictability of the cell's characteristics. The results show that the calendering factors affect cell's performance differently and are dominated by electrode features.

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Enhancing Volumetric Energy Density of LiFePO₄ Battery Using Liquid Metal as Conductive Agent

Zhu,

Liu,

et al. 2024

Adv Funct Materials

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Lithium iron phosphate (LiFePO4) is a widely utilized cathode material in lithium‐ion batteries, prized for its safety, low cost, and extensive cycling lifespan. However, its low compaction density limits its application in batteries requiring high volumetric energy density. The inclusion of conductive carbon black in electrodes, while increasing porosity, also exacerbates side reactions due to its high specific surface area. In this study, the use of liquid metal (GaIn) nanoparticles is explored as a substitute for carbon black in electrode formulations. GaIn nanoparticles' stable oxide film prevents oxidation at cathode potential. Additionally, liquid metal improves the particle interface to a solid‐liquid interface, reducing particle friction and providing a lubricating effect, resulting in a denser electrode structure. As a result, the porosity of LiFePO4 electrodes prepared with liquid metal is reduced, leading to a 20.7% increase in volumetric energy density. Moreover, GaIn‐containing batteries exhibit fewer side reactions, especially at elevated temperatures in both liquid and solid battery configurations. Solid‐state batteries employing GaIn nanoparticles demonstrate a specific capacity of 144 mAh g−1 at 0.5C, showcasing superior cycling performance compared to traditional formulations. This underscores the potential of GaIn to elevate the performance of LiFePO4 batteries.

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Exploring the influence of porosity and thickness on lithium-ion battery electrodes using an image-based model

Cited by 26 publications

References 39 publications

Roadmap on Li-ion battery manufacturing research

Roadmap on Li-ion battery manufacturing research

The Impact of Calendering Process Variables on the Impedance and Capacity Fade of Lithium‐Ion Cells: An Explainable Machine Learning Approach

Enhancing Volumetric Energy Density of LiFePO₄ Battery Using Liquid Metal as Conductive Agent

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Exploring the influence of porosity and thickness on lithium-ion battery electrodes using an image-based model

Cited by 26 publications

References 39 publications

Roadmap on Li-ion battery manufacturing research

Roadmap on Li-ion battery manufacturing research

The Impact of Calendering Process Variables on the Impedance and Capacity Fade of Lithium‐Ion Cells: An Explainable Machine Learning Approach

Enhancing Volumetric Energy Density of LiFePO4 Battery Using Liquid Metal as Conductive Agent

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Enhancing Volumetric Energy Density of LiFePO₄ Battery Using Liquid Metal as Conductive Agent