The bipolar plate geometry design is one of the fuel cell's key features that determines the cell's power. It equally has a direct correlation to the thermal and water management of the cell as it tends to regulate the amount of by-product water that can be expunged from the fuel cell. This study, therefore, explored the development of novel bipolar plate geometry designs, namely the square baffled channel, the rectangular baffled channel, the parallel channel design, and the double serpentine geometry design. This was further compared with the traditional serpentine design to ascertain the design with the optimum fuel cell performance. With the squared baffled channel presenting the best results, varying operating conditions that will influence the performance of the novel fuel cell channel design were also evaluated. It was observed that the hydrogen mass fraction increased by 22.6% for the square baffled channel design compared with the other geometry designs considered in the present study. The square baffle channel showed 12.11% increase in power density and 14.54% increase in current density compared to the rectangular baffle channel. In terms of the parallel channel design, the square baffle showed 18.941% increase in power density and 22.278% increase in current density. The least performing channel geometry design was the double serpentine design. Comparing the double serpentine channel geometry design to the square baffle channel geometry design, there was an increase in current density by 67.72% and 77.88% in terms of power density in favour of the square baffle channel geometry design. The square baffle channel also showed 50% increase in current density and 58.23% increase in power density compared to conventional serpentine channel flow plate geometry design. An adaptive neuro fuzzy inference system (ANFIS) was also adopted to predict the output power of the cell. This was then compared with Feed Forward Back Propagation Neural Network to determine the model with the most accurate results. The adaptive neuro-fuzzy inference model accurately predicted the non-linearities associated with fuel cell performance, hence recommended as ideal for Proton Exchange membrane fuel cell prediction. The main contribution for the study is the development of optimal flow plate geometry design that will ensure maximum fuel cell performance. The current study is aimed at providing technical information to policy makers and the fuel cell industry on how optimization of the flow plate design via the introduction of baffles could increase the cell performance hence accelerates its commercialization and widen their applications in various sectors beyond the automotive industry.
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- Adaptive Neuro-Fuzzy inference system
- Bipolar plate
- Feed forward back propagation neural network
- Multiple linear regression
- Proton Exchange Membrane Fuel cell