a dynamic model for estimating tire contact patch using machine vision techniques

Tire is the main connection between a vehicle and the road that significantly affects the dynamic behavior and the performance of it.  tires also influence other characteristics of vehicles such comprising fuel consumption, handling, ride quality, traction, braking performance and stability. the importance of investigating the contact patch is valuable from perspective of increasing traction efficiency to reducing fuel consumption. in this research, the contact patch of the wheel was investigated based on experimental-analytical methods. the dynamically contact tire patch was utilized using an image processing technique. then, contact patch was modeled by the bat algorithm considering the load on the tire and inflation pressure. evaluation of the model revealed that error rate compared with observed data (extracted from image processing) is equal to 4%. the calculated coefficient of determination (r2) for this model was 95% which indicates the high credibility of the model for dynamic conditions.