Online tire model iterative learning considering tire relaxation behavior

Online tire model learning is of great importance in the application of vehicle dynamics related automated driving functions. Previous research mainly focuses on static tire model identification, for example, based on brush model. However, tire transient behavior, which is difficult to measure directly, plays a critical role on vehicle lateral dynamics, especially on safety-critical handling conditions. In this work, we propose a framework for online tire model iterative learning considering tire relaxation. In each steering scenario, we utilize the learning results of tire model from previous ones as initial condition. Subsequently, we implement singular value decomposition to detect whether there is enough excitation for model learning update with different simplified tire relaxation models. Meanwhile, based on these models, we also recursively calculate and compare the least squares cost function, such that the tire parameters can be robustly estimated and optimized. Furthermore, the estimated tire parameters are then fused with those from previous scenarios based on recursive average for better control application in the next steering maneuver. Experiments demonstrate the proposed online tire model iterative learning framework has similar performance with large-scale data-based offline fitting and can be applied for better predicting tire forces than those with purely recursive least squares methods.