Determination of time constants of diffusion and electrochemical processes in Polymer Electrolyte Membrane Fuel Cells

Iranzo, Alfredo; Navas, Sergio J.; Rosa, Felipe; Berber, Mohamed R.

doi:10.1016/j.energy.2021.119833

Cited by 14 publications

(3 citation statements)

References 74 publications

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“…with respect to 𝜽 where 𝜽 is taken as the starting point of the minimization (see Figure 1). We must stress that 𝜸 (log 𝝉 , 𝜽) in (7) must be discretized at the same collocation points as 𝜸 (log 𝝉 , 𝜆 ). Nonetheless, the log-timescale vectors log 𝝉 in (7) and in (8) are not necessarily identical since a denser grid for 𝜸 (log 𝝉 , 𝜽) can be used during training (see Section 2.5.3 for more details).…”

Section: Dnn Deconvolutionmentioning

confidence: 99%

“…To deconvolve the DRT, the problem in ( 7) was first normalized with respect to the RR polarization resistance, 𝑅 , obtained by integrating 𝛾 (log 𝜏 , 𝜆 ) from log 𝜏 to log 𝜏 with the trapezoidal rule. Then, the optimization in (7) was carried out with Adam algorithm for 2,000 steps [65].…”

Section: Dnn Pretrainingmentioning

confidence: 99%

“…Electrochemical impedance spectroscopy (EIS) is an experimental technique widely used for the analysis of electrochemical energy devices (e.g., batteries [1][2][3][4][5], fuel cells [6][7][8][9][10], and supercapacitors [11][12][13][14]), medical [15,16], and biological [17][18][19] systems. The major appeals of this technique are that it can be carried out over a wide range of frequencies (from mHz to MHz) [20] and that modern equipment allows us to measure EIS spectra quickly and efficiently with limited user expertise.…”

Section: Introductionmentioning

confidence: 99%

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Deconvolution of Electrochemical Impedance Spectroscopy Data Using the Deep-Neural-Network-Enhanced Distribution of Relaxation Times

Quattrocchi

Maradesa

et al. 2022

Preprint

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Electrochemical impedance spectroscopy (EIS) is a characterization technique widely used to evaluate the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as a model-free alternative to equivalent circuits and physical models to circumvent the inherent challenges of EIS analysis. Deep neural networks (DNNs) can be used to deconvolve DRTs, but several issues remain, e.g., the long training time, the DNN accuracy, and the deconvolution of DRTs with negative peaks. The DNN-DRT model was developed here to address these fundamental limitations. Specifically, a pretraining step was included to decrease the computation time. A thorough error analysis was also conducted to evaluate the different components of the DRT and impedance errors to ultimately decrease them. Lastly, the training loss function was modified to handle DRTs with negative peaks. These different advances were validated with an array of synthetic EIS spectra and real EIS spectra from a lithium-ion battery, a solid oxide fuel cell, and a proton exchange membrane fuel cell. Moreover, this new model outperformed in most cases the previously developed DRTtools and deep-DRT model. Overall, we envision that this research will open the venue for more DNN-based analyses of EIS data for electrochemical systems.

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Section: Dnn Deconvolutionmentioning

confidence: 99%

Section: Dnn Pretrainingmentioning

confidence: 99%