Projekter pr. år
Abstract
Accurate and reliable degradation and lifetime prediction for lithium-ion batteries is the main challenge for smart prognostic and health management. This article proposes a novel semi-supervised self-learning method for battery lifetime prediction. First, three health indicators (HIs) are extracted from the partial capacity-voltage curve. Second, the capacity estimation model and lifetime prediction model are built using data from three randomly selected batteries in the source domain. Then, the HIs are used to reconstruct the historical capacities to provide pseudo values for self-training of the lifetime model. Finally, the self-trained lifetime model is used to predict future degradation. The uncertainty expression is also included to provide the probabilistic prediction of future capacities. Different application scenarios are considered in the verification. The mean lifetime prediction error is less than 23 cycles with only three known checkpoints for batteries aging under different profiles. Predictions for different battery types show that the errors are less than 50 cycles with relative errors less than 4.1% for long lifespan batteries, and less than 20 cycles with relative errors less than 5.21% for short lifespan batteries. This article guides proper solutions for lifetime prediction when the labeled capacities in the real world are limited.
Originalsprog | Engelsk |
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Tidsskrift | IEEE Transactions on Industrial Informatics |
Vol/bind | 19 |
Udgave nummer | 5 |
Sider (fra-til) | 6471-6481 |
Antal sider | 11 |
ISSN | 1551-3203 |
DOI | |
Status | Udgivet - 1 maj 2023 |
Bibliografisk note
Publisher Copyright:IEEE
Fingeraftryk
Dyk ned i forskningsemnerne om 'Semi-supervised self-learning-based lifetime prediction for batteries'. 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
- 41 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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