Towards Robust Autonomous Impedance Spectroscopy Analysis: A Calibrated Hierarchical Bayesian Approach for Electrochemical Impedance Spectroscopy (EIS) Inversion: Article No. 137493

Jake Huang, Meagan Papac, Ryan O'Hayre

Research output: Contribution to journalArticlepeer-review

40 Scopus Citations

Abstract

Distribution-based analyses, such as the distribution of relaxation times (DRT) and the distribution of diffusion times (DDT), present model-free alternatives to equivalent circuit modeling for analysis of electrochemical impedance spectroscopy (EIS) data. However, reconstructing such distributions from noisy impedance data is an ill-posed problem that must be solved with specialized inversion algorithms, requiring careful control and tuning. Furthermore, most inversion algorithms developed to date can only solve problems of limited complexity. In this work, we present a new hierarchical Bayesian method for EIS inversion, leveraging efficient algorithms for optimization and Hamiltonian Monte Carlo (HMC) sampling to solve models of arbitrary complexity. We overcome the challenge of ad-hoc parameter tuning by encoding intrinsic characteristics of the DRT and DDT into flexible prior distributions and “pre-calibrating” the model to simulated data. This approach is versatile, highly robust to noise, and provides quantitative estimates of both the error structure of the data and the uncertainty in the recovered distributions. The model is validated with simulated data to demonstrate accurate recovery of the DRT and the DDT. The method also shows promise for simultaneous recovery of multiple distributions, raising the intriguing possibility of semi-autonomous EIS analysis and ad-hoc model construction. Finally, the practical utility of the method is illustrated with experimental data. Throughout, we draw comparisons to several recently published EIS inversion methodologies.
Original languageAmerican English
Number of pages26
JournalElectrochimica Acta
Volume367
DOIs
StatePublished - 2021

NREL Publication Number

  • NREL/JA-5K00-78699

Keywords

  • batteries
  • Bayesian inference
  • distribution of diffusion times
  • distribution of relaxation times
  • electrochemical impedance spectroscopy
  • fuel cells
  • Hamiltonian Monte Carlo

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