This paper presents an improved Halpin–Tsai model for predicting the mechanical, thermal, and electrical properties of silicon-carbide-reinforced polypropylene composites. The model incorporates the effects of porosity and silicon-carbide volume fractions, and derives relationships between material property shape factors and aspect ratio, silicon-carbide volume fraction, and porosity. The improved model's predictions show errors of 4.00% for mechanical properties, 2.13% for thermal properties, and 2.24% for electrical properties compared to finite element analysis. The study demonstrates that the improved Halpin–Tsai model can effectively predict the properties of silicon-carbide-reinforced polypropylene composites, aiding in their design and optimization. The model considers the influence of porosity on the mechanical, thermal, and electrical properties of composite materials. It introduces a porosity factor to investigate the effects of different porosity and volume fractions on the properties of silicon-carbide-reinforced polypropylene composites. The model accounts for the volume fractions of the matrix, particles, and voids in three-phase silicon-carbide particle-reinforced composite materials. The improved Halpin–Tsai model is used to predict the mechanical, thermal, and electrical properties of the composites, with the model incorporating the effects of porosity, particle volume fraction, and aspect ratio. The results show that an increase in porosity leads to a decrease in the elastic modulus, thermal conductivity, and electrical conductivity of the composite material. Conversely, an increase in the particle-reinforcement volume fraction results in an increase in the elastic modulus, thermal conductivity, and electrical conductivity of the composite material. The improved Halpin–Tsai model is found to be accurate in predicting the performance of silicon-carbide particle-reinforced polypropylene composite materials, with maximum errors of 4.00%, 2.13%, and 2.24% for elastic modulus, thermal conductivity, and electrical conductivity, respectively. The model provides a reliable method for predicting the performance of such composite materials, simplifying the calculation of performance parameters.This paper presents an improved Halpin–Tsai model for predicting the mechanical, thermal, and electrical properties of silicon-carbide-reinforced polypropylene composites. The model incorporates the effects of porosity and silicon-carbide volume fractions, and derives relationships between material property shape factors and aspect ratio, silicon-carbide volume fraction, and porosity. The improved model's predictions show errors of 4.00% for mechanical properties, 2.13% for thermal properties, and 2.24% for electrical properties compared to finite element analysis. The study demonstrates that the improved Halpin–Tsai model can effectively predict the properties of silicon-carbide-reinforced polypropylene composites, aiding in their design and optimization. The model considers the influence of porosity on the mechanical, thermal, and electrical properties of composite materials. It introduces a porosity factor to investigate the effects of different porosity and volume fractions on the properties of silicon-carbide-reinforced polypropylene composites. The model accounts for the volume fractions of the matrix, particles, and voids in three-phase silicon-carbide particle-reinforced composite materials. The improved Halpin–Tsai model is used to predict the mechanical, thermal, and electrical properties of the composites, with the model incorporating the effects of porosity, particle volume fraction, and aspect ratio. The results show that an increase in porosity leads to a decrease in the elastic modulus, thermal conductivity, and electrical conductivity of the composite material. Conversely, an increase in the particle-reinforcement volume fraction results in an increase in the elastic modulus, thermal conductivity, and electrical conductivity of the composite material. The improved Halpin–Tsai model is found to be accurate in predicting the performance of silicon-carbide particle-reinforced polypropylene composite materials, with maximum errors of 4.00%, 2.13%, and 2.24% for elastic modulus, thermal conductivity, and electrical conductivity, respectively. The model provides a reliable method for predicting the performance of such composite materials, simplifying the calculation of performance parameters.