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Key Materials and System Integration Technology for Hydrogen Fuel Cell Stack

As a zero emission power source, the performance breakthrough of hydrogen fuel cells relies on innovation in core materials such as membrane electrodes and bipolar plates. This article analyzes technological progress from three levels: materials, structure, and control.


1. Membrane electrode assembly (MEA) performance leap

Proton exchange membrane: The thickness of perfluorosulfonic acid membrane (Nafion) has been reduced from 50 μ m to 8 μ m, and the proton conductivity has been increased to 0.2 S/cm, but the mechanical strength has decreased and requires support from carbon paper.

Catalyst layer:

Platinum loading optimization: Pt/C catalyst was prepared by atomic layer deposition, with anode loading reduced to 0.05mg/cm 2, cathode 0.2mg/cm 2, and power density reaching 1.5W/cm 2

Non platinum catalyst: Fe-N-C catalyst has an ORR activity of 0.1A/mgPt, but its durability needs to be improved to 8000 hours

Gas diffusion layer: made of carbon fiber paper (Toray TGP-H-060) with a thickness of 190 μ m, air permeability>2000 (mL · cm)/(cm 2 · s · kPa), and contact resistance<5m Ω· cm 2

2. Bipolar plate material revolution

Metal bipolar plate: 316L stainless steel surface coated with CrN, contact resistance<10m Ω· cm 2, corrosion resistance current density<1 μ A/cm 2, but stamping cost accounts for 25% of the stack

Graphite composite bipolar plate: Phenolic resin/graphite composite material with bending strength>100MPa, reduces flow field processing cost by 60% through injection molding

3. System integration innovation

Injector circulation system: utilizing the Venturi effect to achieve hydrogen circulation, parasitic power<0.5kW, system efficiency increased by 2%

Adaptive hydrothermal management: By coupling phase change materials with microchannel cold plates, the temperature fluctuation of the fuel cell stack is controlled within ± 2 ℃, and the humidity uniformity is greater than 90%

AI control strategy: Based on reinforcement learning algorithm, optimize the air metering ratio and hydrogen pressure to increase the net output power of the 150kW fuel cell by 8%


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