Accurate state estimation is a critical factor for the optimal management, safety, and longevity of modern energy storage systems. One promising method for achieving precise state estimation is electrochemical impedance spectroscopy (EIS), a technique that has been widely documented in the literature for its effectiveness in monitoring the internal states of batteries. Traditional methods for online state estimation using EIS typically rely on selecting simple, direct features from impedance spectra, such as the real part, imaginary part, magnitude, or phase at specific frequencies.

In contrast, this methodology, demonstrated for State of Charge (SoC) estimation, introduces a more sophisticated approach to feature generation and evaluation. A custom tool has been developed, with the automated workflow starting with the processing of the measured impedance data to generate a valid impedance data base. The feature generation process is based on curve tracing of the impedance spectra, with the analysis of stationary, inflection and interception points, plotted in various configurations and combinations. This first set of characteristic features are used further for the generation of additional interconnected and nested features within the set. This leads to an exemplary feature, such as the ratio between the frequency of the inflection point and the first maximum from the plot of the imaginary part over frequency, thus moving beyond the limited view of classical and known battery characteristic impedance features. With the feature set being complete, each feature is evaluated based on its effectiveness for SoC estimation, considering to changes in SoC and sensitivity against external influences as e.g. temperature. The tool does not only perform automated assessments but also provides a detailed review of the generated features, enabling users to identify the best feature combinations.

The key innovation lies in the ability to capture hidden patterns and interactions within the impedance data that may not be apparent when using simpler features. This enhances the sensitivity to SoC variations, while also improving robustness due to lower sensitivity to cross-influences such as temperature—a critical external factor known to highly affect the impedance and therefore the SoC estimation accuracy. By accounting for temperature variations, this method aims to minimize the risk of inaccurate SoC predictions caused by uncertainties in the temperature estimation.