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 [1]. 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).
[1] 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.
2021
Xinhua Zhu (SURF-VUB)