Real-Time Battery Temperature Monitoring Using FBG Sensors: A Data-Driven Calibration Method

Abstract

Battery storage has an important role to play in integrating large-scale renewable power generations and in transport decarbonization. Real-time monitoring of battery temperature profiles 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 interest in recent years for battery temperature measurement. However, traditional temperature calibration for FBG sensors often requires a high-standard reference and causes the sensors to fail to be consistent during the calibration or recalibration processes. To tackle the challenges, an ensemble data-driven calibration method is developed in this article for FBG sensors. The calibration model consists of a linear part and a nonlinear part. First, the fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> -means (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 of 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 (MAE) and root mean square error (RMSE) being around 0.2 °C, respectively. Compared with the traditional temperature calibration method, the proposed approach can be used online in real-life applications.