Simulation for All-Solid State Batteries with Multi-Element Network Model

Hirate, Ryusei; Mashioka, Hiroki; Yano, Shinichiro; Tsuge, Yoshifumi; Inoue, Gen

doi:10.1051/matecconf/202133317002

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…When it comes to the study of LIB electrodes in three dimensions, the most intuitive approach to model their mesostructure is to assume particles as having polyhedral or spherical shape, and to artificially build an electrode by randomly (stochastically) packing them into a given volume, until reaching an experimental porosity or volume fraction (see Figure 6). [104][105][106] Usually, this process is repeated several times for different random seeds and the average mesostructure is retained.…”

Section: Stochastic Models Of Electrode Mesostructures Generationmentioning

confidence: 99%

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

Ayerbe

Berecibar

Clark

et al. 2021

Advanced Energy Materials

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As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and nearterm developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aenm.202102696.

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Section: Stochastic Models Of Electrode Mesostructures Generationmentioning

confidence: 99%