A Review of Modeling Electrical Conductivity in Carbon-Filled Polymer Composites

Electrically conductive polymer composites (ECPCs) have attracted growing interest in applications requiring lightweight, processable, and electrically functional materials. Their increasing use has created a strong need for reliable models capable of predicting electrical conductivity from component properties, composite composition, and microstructural features. Although classical percolation theory can describe the sharp increase in conductivity near the percolation threshold, it is often insufficient for predicting conductivity over a wider range of filler concentrations or for distinguishing the underlying conduction mechanisms. This review examines the main modeling approaches used for carbon-filled polymer composites, including percolation-centered, homogenization, network-based, and data-driven models. These approaches are compared in terms of their assumptions, required inputs, strengths, and limitations, with emphasis on how they account for filler morphology, orientation, dispersion, tunneling effects, and conductive-network formation. The review also identifies key challenges and future needs, particularly the development of integrated, orientation-sensitive, and physically informed models for predicting anisotropic electrical conductivity in processed ECPCs.