Nonlinear Frequency Response Analysis for process identification and characterization of Li-ion batteries

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Li-ion batteries are crucial (future) components for electromobility, and for buffering intermittent renewables. To increase their energy density and save costs, improved electrode and cell designs are developed. Furthermore, existing cells are preferably operated at their performance limits, which leads to aging and safety issues. In both cases, there is urgent need for non-destructive operando analysis that provides in-depth and especially quantitative understanding of the physical and chemical processes in the batteries, and how cell design and operating mode impact them. Only voltage and current can be readily measured, and electrochemical impedance spectroscopy, a linear analysis method, is frequently used for this purpose. Yet, various states and effects can cause similar spectra. To address these limitations, nonlinear methods, in particular nonlinear frequency response analysis (NFRA), is used in this project. The analysed higher harmonic responses contain information on nonlinear processes, especially the highly nonlinear reaction kinetics, and on surface states. NFRA is thus an extremely powerful and promising tool to diagnose batteries, but currently barely understood. Presently, there is no common understanding how NFRA spectra should be measured and - more importantly - analysed. These are main obstacles hindering broader acceptance of this method in the community. In this project different NFR approaches, developed at KIT and MPI will be employed for battery analyses. For the first time, NFR spectra of anode and cathode will be studied to discriminate between their contributions in the cell signal; further, the previously not analysed phase information and DC component is evaluated regarding information content. Higher order frequency response functions, which analogously to impedance are not amplitude-dependent but contain information on the nonlinear response will be determined. Analytical and numerical solutions of models will be used to reproduce and interpret the experimental spectra. Resultingly, the project establishes a systematic methodology for optimal extraction of battery (state) information by nonlinear frequency response analysis, and paves the way for a wide-spread analysis of this new technique.