Axle Lateral Force Estimation Using Adaptive Mass Property Updates

The patent uses an intermediary estimation system based on vehicle dynamics models and sensor data to indirectly determine tire lateral forces, avoiding the need for direct measurement devices. The controller estimates lateral forces by processing data from accelerometers, gyroscopes, and vehicle parameter sensors through mathematical models, serving as a mediator between available sensors and the desired force information.

The patent replaces mechanical measurement systems (such as force sensors or load cells) with a computational estimation system using onboard vehicle sensors and algorithms. The mechanical approach of directly measuring forces is substituted with an information-processing approach using accelerometer data, vehicle dynamics models, and recursive estimation techniques.

The patent implements dynamic adaptation by continuously updating vehicle mass properties using recursive least squares estimation based on real-time sensor data. The system dynamically adjusts its estimation parameters and mass properties according to current operating conditions, allowing it to adapt to varying road conditions, passenger loads, and fuel consumption without requiring a complex reconfiguration of the system.

The patent changes estimation parameters and mass properties dynamically based on operating conditions. The system modifies its internal model parameters, such as vehicle mass and center of gravity location, based on sensor feedback and recursive estimation, allowing the estimation algorithm to remain accurate across different road surfaces, vehicle loads, and driving conditions without increasing hardware complexity.