A one-dimensional, two-phase, transient thermal model of the proton exchange membrane fuel cell (Wang et al., J. Power Sources 488, 2021) reproduced static and dynamic heat-transfer behavior inside the cell but had to neglect or assume three inputs that no experiment has supplied: the magnitude of side-reaction heat, the effective thermal conductivity of the interface regions between the porous layers of the membrane-electrode assembly, and the phase state of product water in the cathode catalyst layer. The open problem is to determine these quantities experimentally and to incorporate them into a multi-dimensional transient thermal model whose predicted temperature transients are validated against spatially resolved in-situ measurements under realistic dynamic operating conditions, including water-thermal coupling regimes such as automotive load cycles, cold start, and dry-out. Measurements of these three inputs and a validation that uses them as inputs rather than fitted parameters are not yet reported.

Background

A proton exchange membrane fuel cell (PEMFC) converts hydrogen and oxygen to electricity in a membrane-electrode assembly (MEA): a proton-conducting polymer electrolyte membrane (PEM) sandwiched between catalyst layers (CLs, porous Pt/C-ionomer films ~10 um thick where the electrochemical reactions occur), microporous layers (MPLs), and gas diffusion layers (GDLs, also called gas diffusion backings) that distribute reactants, all clamped between bipolar plates machined with gas channels separated by lands (ribs). Heat is generated mainly in the cathode catalyst layer (CCL) by the oxygen reduction reaction: irreversible (voltage-loss) heat, reversible (entropic) heat, Joule heating from electron and proton transport, and latent heat of water phase change (evaporation/condensation, sorption/desorption in the membrane and ionomer); a full taxonomy of these sources was given by Ramousse et al. (2009). Heat leaves by conduction through the layered stack to the coolant, and accurate prediction matters because local overheating accelerates membrane and catalyst degradation.

The thermal resistance of the stack is governed not only by the bulk thermal conductivity (TC) of each porous layer but by the thermal contact resistance (TCR) at the interfaces between layers, and both depend strongly on assembly compression and on liquid-water saturation, since liquid water has a much higher TC and volumetric heat capacity than gas-filled pores. Ex-situ measurements exist for the through-plane TC and TCR of diffusion media and their interfaces with bipolar plates under compression (Khandelwal and Mench 2006; Nitta et al. 2008; Burheim et al. 2010; Karimi et al. 2010), for the microporous layer (Unsworth et al. 2012), for partially saturated diffusion media (Xu et al. 2014), and for MEA components (Alhazmi et al. 2014). On the water side, combined water-and-thermal-management research needs, including cold-start conditions, were framed by Kandlikar and Lu (2009); liquid water inside the operating cathode catalyst layer has been visualized in situ by soft X-ray radiography (Sasabe et al. 2010; Deevanhxay et al. 2012), and liquid-water distributions have been measured simultaneously with catalyst-layer surface temperature (Watanabe et al. 2015).

Against this background, Wang et al. (2021) built a one-dimensional (through-plane), two-phase (gas plus liquid water), transient thermal PEMFC model that included most heat sources and energy-transport terms, TCR at the gas-diffusion-backing/bipolar-plate interface, and the dependence of effective TC and specific heat capacity (SHC) on liquid water. It predicted the CCL as the hot spot, a rapid CCL temperature jump of about 3 degrees C within roughly 1 s of a current step, and strong sensitivity of CCL temperature to the dry GDL thermal conductivity and the TCR. However, three physically relevant inputs could not be taken from experiment and were neglected or assumed: the magnitude of side-reaction heat (heat from parasitic electrochemical side reactions, e.g. peroxide formation or combustion of crossover gas), the effective TC of the interface regions between the porous layers themselves, and the phase state (vapor versus liquid) of product water at its generation site in the CCL. The 1D formulation also could not resolve in-plane (channel-versus-land) temperature variations, and its transient predictions had not been validated against spatially resolved in-situ measurements, particularly under realistic automotive load cycles including cold start (startup from below-freezing temperatures) and dry-out (membrane dehydration under hot, low-humidity operation). The unresolved problem is to close these input and validation gaps so that dynamic thermal prediction rests on measured physics rather than assumptions.

Problem Statement

Determine, from experiment, the three thermal-model inputs that Wang et al. (2021) had to neglect or assume in proton exchange membrane fuel cell (PEMFC) dynamic heat-transfer prediction, and demonstrate their consequences in a validated multi-dimensional model:

  1. The magnitude of the heat released by parasitic side reactions (for example peroxide formation or combustion of crossover gas) in an operating PEMFC, experimentally determined.
  2. The effective thermal conductivity - equivalently, the thermal contact resistance - of the interface regions between the porous layers of the membrane-electrode assembly (GDL/MPL, MPL/CL, CL/membrane), including its dependence on assembly compression and liquid-water saturation.
  3. The phase state of product water at its generation site in the cathode catalyst layer during operation (vapor versus liquid, and its evolution during load transients).

Then incorporate the measured values into a transient two-phase thermal model that resolves the in-plane channel-land coupling (going beyond the source's one-dimensional through-plane formulation) and show that the predicted transient temperature distributions agree with spatially resolved in-situ temperature measurements under dynamic operation, including at least one water-thermal coupling regime representative of automotive conditions (load cycles, cold start, or dry-out), with the measured quantities used as inputs rather than fitted to the validation data. The completion boundary is that each of the three quantities is experimentally determined, and the resulting dynamic predictions are tested against in-situ data rather than calibrated against it.

References

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