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A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks
Li-ion batteries are the main power source used in electric propulsion applications (e.g., electric cars, unmanned aerial vehicles, and advanced air mobility aircraft). Analytics-based monitoring and forecasting for metrics such as state of charge and state of health based on battery-specific usage...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449926/ https://www.ncbi.nlm.nih.gov/pubmed/37620364 http://dx.doi.org/10.1038/s41598-023-33018-0 |
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author | Nascimento, Renato G. Viana, Felipe A. C. Corbetta, Matteo Kulkarni, Chetan S. |
author_facet | Nascimento, Renato G. Viana, Felipe A. C. Corbetta, Matteo Kulkarni, Chetan S. |
author_sort | Nascimento, Renato G. |
collection | PubMed |
description | Li-ion batteries are the main power source used in electric propulsion applications (e.g., electric cars, unmanned aerial vehicles, and advanced air mobility aircraft). Analytics-based monitoring and forecasting for metrics such as state of charge and state of health based on battery-specific usage data are critical to ensure high reliability levels. However, the complex electrochemistry that governs battery operation leads to computationally expensive physics-based models; which become unsuitable for prognosis and health management applications. We propose a hybrid physics-informed machine learning approach that simulates dynamical responses by directly implementing numerical integration of principle-based governing equations through recurrent neural networks. While reduced-order models describe part of the voltage discharge under constant or variable loading conditions, model-form uncertainty is captured through multi-layer perceptrons and battery-to-battery aleatory uncertainty is modeled through variational multi-layer perceptrons. In addition, we use a Bayesian approach to merge fleet-wide data in the form of priors with battery-specific discharge cycles, where the battery capacity is fully available or only partially available. We illustrate the effectiveness of our proposed framework using the NASA Prognostics Data Repository Battery dataset, which contains experimental discharge data on Li-ion batteries obtained in a controlled environment. |
format | Online Article Text |
id | pubmed-10449926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104499262023-08-26 A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks Nascimento, Renato G. Viana, Felipe A. C. Corbetta, Matteo Kulkarni, Chetan S. Sci Rep Article Li-ion batteries are the main power source used in electric propulsion applications (e.g., electric cars, unmanned aerial vehicles, and advanced air mobility aircraft). Analytics-based monitoring and forecasting for metrics such as state of charge and state of health based on battery-specific usage data are critical to ensure high reliability levels. However, the complex electrochemistry that governs battery operation leads to computationally expensive physics-based models; which become unsuitable for prognosis and health management applications. We propose a hybrid physics-informed machine learning approach that simulates dynamical responses by directly implementing numerical integration of principle-based governing equations through recurrent neural networks. While reduced-order models describe part of the voltage discharge under constant or variable loading conditions, model-form uncertainty is captured through multi-layer perceptrons and battery-to-battery aleatory uncertainty is modeled through variational multi-layer perceptrons. In addition, we use a Bayesian approach to merge fleet-wide data in the form of priors with battery-specific discharge cycles, where the battery capacity is fully available or only partially available. We illustrate the effectiveness of our proposed framework using the NASA Prognostics Data Repository Battery dataset, which contains experimental discharge data on Li-ion batteries obtained in a controlled environment. Nature Publishing Group UK 2023-08-24 /pmc/articles/PMC10449926/ /pubmed/37620364 http://dx.doi.org/10.1038/s41598-023-33018-0 Text en © © KBR, Inc. 2023 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Nascimento, Renato G. Viana, Felipe A. C. Corbetta, Matteo Kulkarni, Chetan S. A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks |
title | A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks |
title_full | A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks |
title_fullStr | A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks |
title_full_unstemmed | A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks |
title_short | A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks |
title_sort | framework for li-ion battery prognosis based on hybrid bayesian physics-informed neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449926/ https://www.ncbi.nlm.nih.gov/pubmed/37620364 http://dx.doi.org/10.1038/s41598-023-33018-0 |
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