Projekter pr. år
Abstract
Capacity degradation of lithium-ion batteries influences their service abilities as energy storage systems. Lifetime prediction, historical degradation curve trajectory, and capacity estimation help prognose the long-term serviceability and timely health status of batteries, which guides early maintenance and intelligent management. This paper proposed a novel method to predict the lifetime, reconstruct the historical degradation curve and estimate the capacity via excavating the information hindered under partially charged capacity and temperature curves. Only partial raw data of temperature and charged capacity are needed for modeling without manual feature engineering. The convolutional neural network is used to build the lifetime prediction and capacity estimation models. In addition, the probabilistic regression is added to the establishment of the capacity estimation model, which could provide the probabilistic estimation results. Finally, transfer learning is adopted to update the model with a few available data of testing batteries. Results show that the predictions are accurate and reliable.
Originalsprog | Engelsk |
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Titel | IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG) |
Forlag | IEEE (Institute of Electrical and Electronics Engineers) |
Publikationsdato | 2022 |
ISBN (Elektronisk) | 978-1-6654-6618-9 |
DOI | |
Status | Udgivet - 2022 |
Begivenhed | 13th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2022 - Kiel, Tyskland Varighed: 26 jun. 2022 → 29 jun. 2022 |
Konference
Konference | 13th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2022 |
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Land/Område | Tyskland |
By | Kiel |
Periode | 26/06/2022 → 29/06/2022 |
Navn | IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG) |
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ISSN | 2329-5767 |
Bibliografisk note
Publisher Copyright:© 2022 IEEE.
Fingeraftryk
Dyk ned i forskningsemnerne om 'Battery Lifetime Prediction and Degradation Reconstruction based on Probabilistic Convolutional Neural Network'. Sammen danner de et unikt fingeraftryk.-
CROSBAT: SMART BATTERY
Teodorescu, R. (PI (principal investigator)), Stroe, D.-I. (CoPI), Che, Y. (Projektdeltager), Zheng, Y. (Projektdeltager), Kulkarni, A. (Projektdeltager), Sui, X. (Projektdeltager), Vilsen, S. B. (Projektdeltager), Bharadwaj, P. (Projektdeltager), Weinreich, N. A. (Projektdeltager), Christensen, M. D. (Projektkoordinator) & Steffensen, B. (Projektkoordinator)
01/09/2021 → 31/08/2027
Projekter: Projekt › Forskning
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State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning
Che, Y. (PI (principal investigator)), Teodorescu, R. (Supervisor) & Sui, X. (Supervisor)
01/12/2021 → 31/12/2023
Projekter: Projekt › Ph.d.-projekt
Publikation
- 1 Citationer
- 1 Ph.d.-afhandling
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State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning
Che, Y., 2023, Aalborg Universitetsforlag.Publikation: Ph.d.-afhandling
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