Tire Cornering Stiffness Estimation via Sensor Fusion

The system segments the cornering stiffness estimation problem into multiple independent measurement components: tire-specific parameters (inflation pressure, temperature, wear state) are measured separately by dedicated sensors, while hub-mounted accelerometers capture vibration signals. These segmented measurements are then integrated through a model-based estimator to produce the final cornering stiffness value, thereby achieving high measurement precision without requiring a single overly complex measurement system.

A model-based tire cornering stiffness estimator serves as an intermediary component that processes raw sensor data from multiple sources (accelerometers, pressure sensors, temperature sensors) and transforms them into accurate cornering stiffness estimates. This intermediary model layer reconciles the complex multi-sensor inputs with the desired output parameter, enabling accurate estimation while maintaining a modular system architecture that manages complexity.

The hub-mounted accelerometer serves multiple functions simultaneously: it captures vibration signals for cornering stiffness estimation, monitors tire load variations, and detects wheel rotation characteristics. By making this single sensor multi-functional, the system achieves comprehensive tire parameter measurement capability without proportionally increasing the number of sensors, thus improving measurement precision while controlling device complexity.

The system merges multiple measurement functions into a unified estimation framework. Instead of using separate dedicated sensors for each tire parameter, the system combines data from tire pressure sensors, temperature sensors, and hub accelerometers into a single model-based estimation process that outputs cornering stiffness. This merging approach reduces the overall sensor count while maintaining measurement precision through complementary sensor fusion.

The cornering stiffness estimation system is designed to be dynamic rather than static. The model-based estimator continuously updates cornering stiffness values in real-time as the vehicle operates, adapting to changing tire conditions such as temperature variations, pressure changes, and wear progression. This dynamic adaptation is achieved through continuous processing of sensor data streams without requiring complex reconfiguration of the system architecture.

The system implements feedback through continuous monitoring of tire-specific parameters by dedicated sensors. The measured parameters (inflation pressure, temperature, wear state) are fed back to the model-based estimator, which adjusts the cornering stiffness calculation accordingly. This feedback mechanism enables automatic adaptation to changing tire conditions while maintaining a relatively simple system structure, as the feedback loop uses established sensor-processor architecture.