CNN Battery State Estimation via Impedance Spectra

The patent transforms the input impedance data from a simple scalar value into a two-dimensional frequency spectrum representation. By adding the frequency dimension to the impedance measurements, the CNN can analyze impedance characteristics across multiple frequencies simultaneously, significantly improving battery state estimation accuracy while maintaining manageable system complexity through automated feature extraction.

The patent introduces an intermediary Fourier transform step that converts time-domain impedance measurements into frequency-domain spectra. This intermediary representation serves as a bridge between the raw measurement data and the CNN processing, enabling the network to automatically extract relevant features without manual feature engineering, thus improving accuracy without proportionally increasing complexity.

The CNN architecture performs self-service by automatically extracting relevant features from the multi-frequency impedance spectra without requiring manual feature selection or engineering. The network learns and extracts the most informative characteristics directly from the raw spectral data, maximizing information utilization while keeping the processing pipeline relatively simple through end-to-end learning.

The patent applies preliminary Fourier transform to convert impedance measurements into frequency spectra before CNN processing. This preliminary action organizes the multi-frequency data into a structured format that the CNN can efficiently process, extracting maximum information from the measurements while maintaining computational tractability through pre-organized input data.