Cited by 224Advanced Battery Technologies ResearchAdvancements in Battery MaterialsModel Reduction and Neural Networks

Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis

Open access
Nascimento, Renato G., Corbetta, Matteo, Kulkarni, Chetan S., Viana, Felipe A.C.·2021·Journal of Power Sources

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Crossref · 10.1016/j.jpowsour.2021.230526

Cites

49 of 49 references verified0 citation errors · 0 not checkable

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  • Nascimento et al. introduced a hybrid physical-NN method for LIBs, integrating known discharge physics, such as the Nernst and Butler-Volmer equations, with flexible ML to enhance uncertainty quantification and improve consistency with experimental data.

    “For example, Nascimento et al. introduced a hybrid physical-NN method for LIBs, integrating known discharge physics, such as the Nernst and Butler-Volmer equations, with flexible ML to enhance uncertainty quantification and improve consistency with experimental data[129].”

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Authors
Nascimento, R. G., Corbetta, M., Kulkarni, C. S., & Viana, F. A. C.
Affiliations
  • University of Central Florida (US)
  • Ames Research Center (US)
Corresponding author
Nascimento, Renato G.
Venue
Journal of Power Sources
Publisher
Elsevier BV
Published
2021-11
Volume
513
Pages
230526
ISSN
0378-7753 (ISSN-L)
OpenAlex
W3201108028
Cited by
224
References
49
Citations by year
2026: 62 · 2025: 51 · 2024: 51 · 2023: 42 · 2022: 18
FWCI
9.01
Type
journal article
Language
en
Open access
Locations
Journal of Power Sources (acceptedVersion)
Funders
Ames Research Center (80ARC020D0010)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
Keywords
lithium-ion batteries, physics-informed neural networks, battery degradation, model reduction, Nernst equation, Butler-Volmer equation, multilayer perceptron, variational inference, ensemble learning, internal resistance, unmanned aerial vehicles

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