Projekter pr. år
Abstract
State/temperature monitoring is one of the key requirements of battery management systems that facilitates efficient and intelligent management to ensure the safe operation of batteries in electrified transportation. This paper proposes an online end-to-end state monitoring method based on transferred multi-task learning. Measurement data is directly used for sharing information generation with the convolutional neural network. Then, the multiple task-specific layers are added for state/temperature monitoring. The transfer learning strategy is designed to improve accuracy further under various application scenarios. Experiments under different working profiles, temperatures, and aging conditions are conducted to evaluate the method, which covers the wide usage ranges in electric vehicles. Comparisons with several benchmarks illustrate the superiority of the proposed method with better accuracy and computational efficiency. The monitoring results under extremely current working profiles and variable internal and external conditions are evaluated. Results show that the mean absolute error and root mean square error of state of charge and state of energy estimation are less than 2.31% and 3.31%, respectively. The above errors in the prediction of future temperature five steps ahead are less than 0.89 °C and 1.29 °C, respectively. The framework is also suitable for monitoring second-life batteries retired from electric vehicles. This paper illustrates the potential application of data-driven multi-state monitoring throughout the entire battery life.
Originalsprog | Engelsk |
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Tidsskrift | IEEE Transactions on Vehicular Technology |
Vol/bind | 72 |
Udgave nummer | 8 |
Sider (fra-til) | 10037-10047 |
Antal sider | 11 |
ISSN | 0018-9545 |
DOI | |
Status | Udgivet - 1 aug. 2023 |
Bibliografisk note
Publisher Copyright:IEEE
Fingeraftryk
Dyk ned i forskningsemnerne om 'Battery States Monitoring for Electric Vehicles Based on Transferred Multi-Task Learning'. Sammen danner de et unikt fingeraftryk.-
CROSBAT: SMART BATTERY
Teodorescu, R. (PI (principal investigator)), Stroe, D.-I. (CoPI), Kulkarni, A. (Projektdeltager), Che, Y. (Projektdeltager), Zheng, Y. (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
- 14 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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