- Proceeding Paper
10 Pages
This work presents a data-driven approach for state-of-charge estimation of rechargeable zinc-air batteries based on electrochemical impedance spectroscopy. Due to the nonlinear electrochemical behavior and flat discharge voltage profile of zinc-air batteries, accurate state-of-charge estimation remains challenging. The investigated cells employ a three-electrode configuration with a dedicated gas diffusion electrode for discharge and a separate electrode for charging. This work focuses exclusively on discharge operation, as the two current paths involve physically distinct electrodes with fundamentally different impedance characteristics. High-dimensional impedance spectra are combined with physically interpretable features derived from a simplified equivalent circuit model and compressed via principal component analysis. A long short-term memory network models the relationship between the resulting feature representation and state-of-charge, with Bayesian hyperparameter tuning applied to optimize architecture and training configuration. Performance is compared against baseline models including multilayer perceptrons. The model is trained on multiple battery cells and evaluated on a completely held-out cell to assess cross-cell generalization. The results show that principal component analysis compression of the combined impedance spectrum and equivalent circuit feature vector is the decisive optimization step, achieving a mean absolute error of 1.04% on an unseen test cell. In contrast, the choice of model architecture has a smaller impact on performance.
Eng. Proc.
24 September 2026
