This study reviews modeling and validation techniques for tire behavior in
various applications, including vehicle dynamics, road safety, and fuel efficiency. It
aims to assess the strengths and limitations of different tire models, particularly in
predicting tire forces and moments under dynamic conditions. The research also
explores emerging trends and technologies that enhance tire performance analysis,
providing insights into future advancements in tire modeling . Research Methods:
The study follows a systematic review approach, analyzing existing literature on tire
modeling and validation techniques. It categorizes models into analytical, empirical,
and finite element methods, comparing their accuracy, computational efficiency, and
applicability in real-world scenarios. Experimental validation methods, including
laboratory testing and field measurements, are examined to assess their effectiveness
in verifying model predictions. The study also evaluates the integration of machine
learning and data-driven approaches in tire modeling. Findings: The review
highlights that while empirical models offer quick predictions, they often lack
accuracy in complex scenarios. Finite element models provide detailed insights but
are computationally expensive. Hybrid approaches combining physics-based and
data-driven methods show promise in improving accuracy and efficiency. Advances
in sensor technology and artificial intelligence are enhancing real-time tire
performance predictions. Conclusion: The study concludes that no single tire
modeling technique is universally superior, and the choice depends on the specific
application requirements. The integration of advanced computational techniques,
such as AI and machine learning, is expected to improve predictive accuracy. Future
research should focus on refining hybrid models and enhancing validation
techniques to bridge the gap between simulation and real-world performance