Development and Application of CAE Methods for Predicting Snow Accumulation on Floor Undercovers During Snowy Road Driving

The automotive industry is increasingly challenged to address environmental concerns, particularly the goal of achieving carbon neutrality (CN). Japan has committed to CN by 2050 and aims for 100% electric vehicle (EV) sales by 2035. A key factor influencing EV adoption is driving range, which is significantly affected by driving resistance—especially aerodynamic drag. Underbody covers (undercovers) are widely used to improve aerodynamic performance, but they are exposed to various environmental stresses, including water intrusion, stone chipping, and snow accumulation during winter driving. While previous studies have effectively simulated water and chipping stresses, research specifically targeting snow stress has been limited. This study proposes a novel simulation approach to evaluate snow stress on undercovers, focusing on slushy road conditions commonly found in Japan’s snowy regions. A particle-based method was employed to model the interaction between slush and underbody components, with key parameters identified through experiments replicating slush dispersion. The simulation results showed strong correlation with actual snow accumulation, validating the accuracy and reliability of the proposed method. This approach was also applied to multiple vehicle models to confirm its versatility and robustness. By enabling precise prediction of slush-related stress and accumulation, the method supports more efficient design processes and allows for year-round evaluation of snow stress, independent of seasonal constraints. Consequently, it contributes to shorter development cycles, reduced design costs, and improved product quality. Accurate prediction of slush accumulation facilitates optimized undercover designs, enhancing vehicle durability, safety, and performance in snowy conditions. This study highlights the importance of advanced simulation techniques in addressing environmental challenges and improving design efficiency in the automotive sector.