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A Novel Approach for Tire-Road Friction Coefficient Estimation Using Adaptive Cubature Kalman Filter

Advanced chassis control systems are essential to improve vehicle handling characteristics. Tire-road friction coefficient (TRFC) is a key parameter for these systems. Instead of directly measuring this parameter, the indirect estimation of TRFC will provide a cost-effective way for the implementation of vehicle chassis control systems. In this article, a novel adaptive cubature Kalman filter (ACKF) is proposed to estimate TRFC. First, a nonlinear vehicle dynamics model is established. Then, an improved cubature Kalman filter is used to estimate TRFC, which process noise can be updated dynamically based on lateral acceleration information. To verify the effectiveness of ACKF, some virtual validation tests are carried out. The test results indicate that the estimation performance of ACKF is better than the cubature Kalman filter.