Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis
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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].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [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
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 325 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- 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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