Predictive Tires for Electric and Autonomous Vehicles: A Comprehensive Review

The role of tires in vehicle dynamics has evolved from passive support elements to active sources of information. This paper provides a comprehensive synthesis of recent advances in wheel-road interaction, structural optimization, and intelligent sensing, toward the emerging concept of the predictive tire. Fundamental phenomena, including slip-induced energy losses, aquaplaning thresholds, and radial stiffness variation, are analyzed for their impact on safety, ride comfort, and energy efficiency. Structural developments, including multi-objective optimizations and advanced composite materials targeting rolling-resistance reduction and NVH (Noise, Vibration, Harshness) improvement, are important for electric-vehicle applications. At the same time, progress is being made in the integration of sensors, such as polyvinylidene fluoride piezoelectric films (PVDF), distributed strain sensors, embedded accelerometers, and hybrid tire pressure monitoring systems (h-TPMSs), along with advanced data-driven methods such as fuzzy logic, hybrid Kalman filters, and Transformer neural networks for estimating friction coefficients and contact forces. The integration of these directions supports the tire's transition toward a predictive multifunctional platform supplying real-time variables for the anti-lock braking system (ABS), electronic stability control (ESC), torque vectoring, and advanced driver assistance systems (ADASs), reinforcing its role in the safe and intelligent mobility of electric and autonomous vehicles.