FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4379013179> ?p ?o ?g. }

• W4379013179 endingPage "59" @default.
• W4379013179 startingPage "42" @default.
• W4379013179 abstract "In order to predict the remaining useful life (RUL) of lithium-ion batteries, a capacity degradation model may be developed using either simplified physical laws or machine learning-based methods. It is observed that even though degradation models based on simplified physical laws are easy to implement, they may result in large error in the application of failure prognostics. While data-driven prognostics models can provide more accurate degradation forecasting, they may require a large volume of training data and may invoke predictions inconsistent with physical laws. It is also very challenging for existing methods to predict the RUL at the early stages of battery life. In this paper, we propose a Bayesian physics-constrained neural network for battery RUL prediction by overcoming limitations of the current methods. In the proposed method, a neural differential operator is learned from the first 100 cycles of data. The neural differential operator is modeled with a Bayesian neural network architecture that separates the fixed history dependence from the time dependence to isolate epistemic uncertainty quantification. Using the battery dataset presented in the paper by Severson et al. as an example, we compare our proposed method with a simplified physics-based degradation forecasting model and two data-driven prognostics models. The results show that the proposed physics-constrained neural network can provide more accurate RUL estimation than the other methods with the same group of training data. Most importantly, the proposed method allows for RUL prediction at earlier stages of the battery life cycle." @default.
• W4379013179 created "2023-06-02" @default.
• W4379013179 creator A5038289783 @default.
• W4379013179 creator A5046520383 @default.
• W4379013179 creator A5052874876 @default.
• W4379013179 creator A5075939769 @default.
• W4379013179 date "2023-10-01" @default.
• W4379013179 modified "2023-10-16" @default.
• W4379013179 title "A Physics-Constrained Bayesian neural network for battery remaining useful life prediction" @default.
• W4379013179 cites W1970776328 @default.
• W4379013179 cites W1986907389 @default.
• W4379013179 cites W1992124464 @default.
• W4379013179 cites W2007655149 @default.
• W4379013179 cites W2007672025 @default.
• W4379013179 cites W2008445369 @default.
• W4379013179 cites W2019221735 @default.
• W4379013179 cites W2056207828 @default.
• W4379013179 cites W2103496339 @default.
• W4379013179 cites W2137983211 @default.
• W4379013179 cites W2139923370 @default.
• W4379013179 cites W2155832827 @default.
• W4379013179 cites W2158788069 @default.
• W4379013179 cites W2184265935 @default.
• W4379013179 cites W2546933409 @default.
• W4379013179 cites W2772066491 @default.
• W4379013179 cites W2790625295 @default.
• W4379013179 cites W2883525675 @default.
• W4379013179 cites W2895905230 @default.
• W4379013179 cites W2899283552 @default.
• W4379013179 cites W2901287750 @default.
• W4379013179 cites W2920879126 @default.
• W4379013179 cites W2924382816 @default.
• W4379013179 cites W2960071712 @default.
• W4379013179 cites W2964280852 @default.
• W4379013179 cites W2981984647 @default.
• W4379013179 cites W2987934294 @default.
• W4379013179 cites W2996610370 @default.
• W4379013179 cites W3012264837 @default.
• W4379013179 cites W3013372615 @default.
• W4379013179 cites W3036910660 @default.
• W4379013179 cites W3049495830 @default.
• W4379013179 cites W3084614174 @default.
• W4379013179 cites W3102763196 @default.
• W4379013179 cites W3111531074 @default.
• W4379013179 cites W3137474564 @default.
• W4379013179 cites W3171004415 @default.
• W4379013179 cites W3181227736 @default.
• W4379013179 cites W3194090521 @default.
• W4379013179 cites W3196730644 @default.
• W4379013179 cites W3196731055 @default.
• W4379013179 cites W3199215188 @default.
• W4379013179 cites W3212635734 @default.
• W4379013179 cites W4220936876 @default.
• W4379013179 cites W4221107806 @default.
• W4379013179 cites W4281695775 @default.
• W4379013179 cites W4281723587 @default.
• W4379013179 cites W4285982395 @default.
• W4379013179 cites W4308141542 @default.
• W4379013179 cites W4309333726 @default.
• W4379013179 cites W4310155374 @default.
• W4379013179 cites W4311634026 @default.
• W4379013179 cites W4323048778 @default.
• W4379013179 doi "https://doi.org/10.1016/j.apm.2023.05.038" @default.
• W4379013179 hasPublicationYear "2023" @default.
• W4379013179 type Work @default.
• W4379013179 citedByCount "0" @default.
• W4379013179 crossrefType "journal-article" @default.
• W4379013179 hasAuthorship W4379013179A5038289783 @default.
• W4379013179 hasAuthorship W4379013179A5046520383 @default.
• W4379013179 hasAuthorship W4379013179A5052874876 @default.
• W4379013179 hasAuthorship W4379013179A5075939769 @default.
• W4379013179 hasConcept C107673813 @default.
• W4379013179 hasConcept C119857082 @default.
• W4379013179 hasConcept C121332964 @default.
• W4379013179 hasConcept C124101348 @default.
• W4379013179 hasConcept C127413603 @default.
• W4379013179 hasConcept C129364497 @default.
• W4379013179 hasConcept C154945302 @default.
• W4379013179 hasConcept C163258240 @default.
• W4379013179 hasConcept C41008148 @default.
• W4379013179 hasConcept C50644808 @default.
• W4379013179 hasConcept C555008776 @default.
• W4379013179 hasConcept C62520636 @default.
• W4379013179 hasConceptScore W4379013179C107673813 @default.
• W4379013179 hasConceptScore W4379013179C119857082 @default.
• W4379013179 hasConceptScore W4379013179C121332964 @default.
• W4379013179 hasConceptScore W4379013179C124101348 @default.
• W4379013179 hasConceptScore W4379013179C127413603 @default.
• W4379013179 hasConceptScore W4379013179C129364497 @default.
• W4379013179 hasConceptScore W4379013179C154945302 @default.
• W4379013179 hasConceptScore W4379013179C163258240 @default.
• W4379013179 hasConceptScore W4379013179C41008148 @default.
• W4379013179 hasConceptScore W4379013179C50644808 @default.
• W4379013179 hasConceptScore W4379013179C555008776 @default.
• W4379013179 hasConceptScore W4379013179C62520636 @default.
• W4379013179 hasFunder F4320306084 @default.
• W4379013179 hasFunder F4320332369 @default.
• W4379013179 hasFunder F4320338291 @default.

• next