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Obiekt
Tytuł: Remaining useful life prediction of a lithium-ion battery based on a temporal convolutional network with data extension
Contributor:
Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Group publication title:
Abstract:
Unmanned underwater vehicles are typically deployed in deep sea environments, which present unique working conditions. Lithium-ion power batteries are crucial for powering underwater vehicles, and it is vital to accurately predict their remaining useful life (RUL) to maintain system reliability and safety. ; We propose a residual life prediction model framework based on complete ensemble empirical mode decomposition with an adaptive noise-temporal convolutional net (CEEMDAN-TCN), which utilizes dilated causal convolutions to improve the model`s ability to capture local capacity regeneration and enhance the overall prediction accuracy. CEEMDAN is employed to denoise the data and prevent RUL prediction errors caused by local regeneration, and feature expansion is utilized to extend the temporal dimension of the original data. ; The NASA and CALCE battery capacity datasets are used as input to train the network framework. The output is the current predicted residual capacity, which is compared with the real residual battery capacity. The MAE, RMSE and RE are used as the evaluation indexes of the RUL prediction performance. The proposed network model is verified on the NASA and CACLE datasets. The evaluation results show that our method has better life prediction performance. At the same time, it is proved that both feature expansion and modal decomposition can improve the generalization ability of the model, which is very useful in industrial scenarios.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, volume 34, number 1 (2024) ; kliknij tutaj, żeby przejść
Language:
License CC BY 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Kolekcje, do których przypisany jest obiekt:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2024)
Data ostatniej modyfikacji:
Jul 29, 2025
Data dodania obiektu:
Jul 29, 2025
Liczba wyświetleń treści obiektu:
55
Wszystkie dostępne wersje tego obiektu:
https://zbc.uz.zgora.pl/repozytorium/publication/101658
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 1 (2024)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 2 (2024)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 3 (2024)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 4 (2024)
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| Nazwa wydania | Data |
|---|---|
| Remaining useful life prediction of a lithium-ion battery based on a temporal convolutional network with data extension | Jul 29, 2025 |
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