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Real-time battery temperature monitoring using FBG sensors: a data-driven calibration method

Zhang, L., Liu, X., Li, K. , Du, D., Zheng, M., Niu, Q., Yang, Y., Zhou, Q., Sun, T. & Grattan, K. T. V. ORCID: 0000-0003-2250-3832 (2022). Real-time battery temperature monitoring using FBG sensors: a data-driven calibration method. IEEE Sensors Journal, doi: 10.1109/JSEN.2022.3200589


Battery storage has an important role to play in integrating large scale renewable power generations and in transport decarbonization. Realtime monitoring of battery temperature profile is indispensable for battery safety management. Due to the advantages of small size, resistance to corrosion, immunity to electromagnetic interference, and multiplexing, fiber Bragg-grating (FBG) sensing has received substantial interests in recent years for battery temperature measurement. However, traditional temperature calibration for FBG sensors often requires a high-standard reference, and cause the sensors fail to be consistent during the calibration or re-calibration processes. To tackle the challenges, an ensemble datadriven calibration method is developed in this paper for FBG sensors. The calibration model consists of a linear part and a nonlinear part. First, the fuzzy Cmeans (FCM) algorithm is used to extract the linear relationship between the measured FBG wavelength shift and temperature variation. Then, the empirical mode decomposition (EMD) technique is used to classify the intrinsic mode functions (IMFs) and the remainder for the unmodeled nonlinear information. The unmodeled nonlinear information is further compensated using battery state of charge (SOC) and cycle number information. The experimental results confirm that the proposed temperature calibration method achieves desirable accuracy and reliability, with both the mean absolute error and root mean square error being around 0.2 ℃ respectively. Compared with the traditional temperature calibration method, the proposed approach can be used online in real-life applications.

Publication Type: Article
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Lithium-ion battery, Data-driven method, Temperature calibration, FBG, EMD
Subjects: Q Science > QC Physics
T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Science & Technology > Engineering
Text - Accepted Version
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