This proposal addresses the online data-driven modelling of batteries. In Electrochemical Impedance Spectroscopy (EIS), one models electrochemical processes by a linear time-varying model: the time-varying impedance. Such models are then used for tracking the State-of-Charge (SoC) and State-of-Health (SoH) of a battery. Techniques exist for estimating the time-varying impedance (see figure) from current and voltage measurements in operando, i.e. while the battery is charging . However, these techniques allow only to model the battery after and not during the measurement. In this thesis you will implement an online algorithm for estimating the time-varying impedance while time domain data is being acquired, by using recursive techniques and regularisation. The algorithm will be tested on real-life battery measurements at the electrochemical department of the VUB (SURF).
 Hallemans, N., Pintelon, R., Zhu, X., Collet, T., Claessens, R., Wouters, B., Hubin, A. and Lataire, J., 2020. Detection, Classification, and Quantification of Nonlinear Distortions in Time-Varying Frequency Response Function Measurements. IEEE Transactions on Instrumentation and Measurement, 70, pp.1-14.
Prerequisites: good knowledge in system theory, signal processing, measurement, data-based modelling and Matlab.
Xinhua Zhu (SURF-VUB)