Process insights with physics-inspired data-driven modeling- example of battery electrode processing
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- @Article{HOSSEINHASHEMI:2023:est,
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author = "Somayeh Hosseinhashemi and Yibo Zhang and
Christoph Thon and Carsten Schilde",
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title = "Process insights with physics-inspired data-driven
modeling- example of battery electrode processing",
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journal = "Journal of Energy Storage",
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volume = "73",
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pages = "109046",
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year = "2023",
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ISSN = "2352-152X",
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DOI = "doi:10.1016/j.est.2023.109046",
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URL = "https://www.sciencedirect.com/science/article/pii/S2352152X23024441",
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keywords = "genetic algorithms, genetic programming,
Physics-inspired data-driven modeling, Dimensional
analysis, ANN, Deep neural networks, Hybrid modeling,
Gray-box technique",
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abstract = "Lithium-ion batteries are essential for a wide range
of applications due to their high energy density and
rechargeability. However, their production and
performance improvement often rely on time-consuming
and expensive experiments. Typically, simulation
analyzes is used to build process models to optimize
battery performance and lifetime. However, simulations
cannot always take into account the limitations of the
manufacturing process. As a result, the process
parameters determined by such a model remain largely
theoretical. For the efficient production of
lithium-ion batteries, an understanding of the
relationships between physical quantities is essential
to meet optimized performance and safety standards.
However, these relationships are quite complex, making
it difficult for traditional methods to determine the
physical model. Alternatively, artificial intelligence
methods can be beneficial. This work uses a novel
gray-box modeling technique that incorporates physical
knowledge and empirical data. To achieve this goal, we
combine a deep neural network with a genetic algorithm
to determine the existing physical relationships within
the data and then estimate the final model for the
system",
- }
Genetic Programming entries for
Somayeh Hosseinhashemi
Yibo Zhang
Christoph Thon
Carsten Schilde
Citations