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A statistical-thermodynamic formula based on a new approach has been developed to predict electrical conductivity of polymer-based carbon composites, used for bipolar plate of proton exchange membrane fuel cells. In the model, based on the percolation threshold phenomenon, the relationship between electrical conductivity of composite versus the filler volume fraction is represented as sigmoidal distribution. Moreover, four variables, including filler electrical conductivity, filler aspect ratio, filler roundness, and wettability are included in the sigmoidal equation. Some composites containing graphite, expanded graphite, and carbon fiber as fillers and phenolic resin as polymer are manufactured. These composites plus several other composites derived from the literature are used to validate the model. These composites are divided into two main categories: the first, nanofiller composites including graphene, carbon naotube, expanded graphite, and carbon black; the second, microfiller composites including graphite and carbon fiber. In the paper, the effective factors on composite conductivity including the mixing methods, filler conductivity, filler aspect ratio, filler alignment, and surface energy between filler and matrix, are comprehensively discussed. The curve fitting is performed by MATLAB software. The results show there is good agreement between the model and experimental data on both nanofiller and microfiber composites.

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