PERFORMANCE TESTING OF A LIQUID COOLED 5KW

HIGH TEMPERATURE PEM FUEL CELL STACK

SØREN JUHL ANDREASEN, ASSOCIATE PROFESSOR

DEPARTMENT OF ENERGY TECHNOLOGY

AALBORG UNIVERSITY

DENMARK

sja@et.aau.dk

Presentation Outline

• Department of Energy Technology, Aalborg University• High temperature PEM fuel cells• Reformer system topologies• Fuel cell stack testing methodology• Experimental results• Conclusions• Future work

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High Temperature PEM Fuel Cells

Advantages• PBI-based MEAs have a high tolerance to CO• A liquid fuel, such as methanol is accessible and storable• Heat can be utilized in fuel conversion• System energy density increase is ”cheap”

Challenges• System size and complexity increases• Impurities are introduced• System heat-up

Different reformer system topologies using methanol reformers

Reformer system topologies using liquid cooling

• Parallell thermal connection

• Low temperature fuel cell stack cooling/heating system

(160-180oC)

• High temperature reformer cooling/heating system

(230-300oC)

• Burner running on anode waste gas adds heat to reforming process

• Fuel cell stack cathode air exhaust utilized for fuel evaporation

Reformer system topologies using liquid cooling

• Seriel thermal connection

• Cooler inserted to reduce temperature to stack level

• Burner increases temperature from fuel cell stack level to reformer level.

• Special fuel evaporator needed in order to properly exchange heat between various incoming flows.

Fuel cell stack

• 5kW Serenergy fuel cell stack• 120 cells, 165 cm2

• Trial using Danish Power Systems MEAs

• Challenges:• New MEA type• Gaskets• Coolant leaks• Customized assembly

Experimental setup

• Greenlight G400 fuel cell test station• 12 kW electronic load 500V,1000A• Gas concentration, pressure,

temperature, flow, dew point control of all inlets

• Water balance measurement• External cooling cart for thermal oil

temperature heating/cooling• Integrated CVM (3rd party)• Electrochemical characterization using

EIS (under development for high voltage stack)

• Automation of reference performance test under various operating conditions. verified on 18 cell short stack.

Initial CVM

• Pure Hydrogen, ~165oC• Stack performance

81,6V - 16,3A – 1335W75,5V – 33,0A – 2493W70,2V – 49,5A – 3473W65,8V – 66,8A – 4347W

• Slightly high variance in cell voltage:

Max spread 56mV@49,5A

Several tests, storage time later

3 cells, critical cell reversal -1000mV

75,4V - 18A – 1358W69,3V - 36A – 2496W

Several tests, storage time later

3 cells removed, thus 0V

76,8V - 18A – 1382W Increased performance even with 3 cells missing70,5V - 36A – 2539W

Stack start-up

Start-up procedure:

• Avoid too high inlet collant pressure (viscosity of oil)

• Avoid too high temperature difference on stack

• Avoid too much liquid water in stack

• Avoid high potentials on stack to reduced CC

Stack operation

• Limited operation untill 160 degrees

• Enables additional ”electrochemical heating”

• Very stable stack voltage (only excursions are due to dynamic thermal test)

• Fast response to load changes

• Current limited by cooling system

Conclusions

• Successful trial of new MEA type in 5kW stack in fuel cell test station

• Load changes and thermal dynamics examined

• Many initial challenges overcome regarding implementation of new MEAs• Stack assembly with new MEA thickness• Leak proofing, gasket choice

• Cell reversal identified, cell removed and processed to further analysis in order to evaluate cause of failure: collant leak, membrane crack, gasket failure, stack assembly, internal short

• Automated testing procedure verified on short stack and ready for trial with full stack

Future work

• Rapid start-up

• Evaluation of shutdown proceedures, evaluating carbon corrosion on stack level

• System control strategy development

• Model based prediction of anode hydrogen availability

• Fuel cell stack and system diagnostics

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International Journal of Hydrogen Energy, Vol. 38, No. 3, 06.02.2013, p. 1676-1684.

Dynamic Modeling of a Reformed Methanol Fuel Cell System using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models

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In: International Journal of Hydrogen Energy, Vol. 38, No. 25, 21.08.2013, p. 10577-10584

Acknowledgements

The authours would like to acknowledge the financial support from the EUDP program and the Danish Energy Agency

Thank you for your attention!