This work presents a novel application of model-based online estimators for the simultaneous identification of wheel-rail adhesion and brake pad-disc friction for railway vehicles. The detailed knowledge of adhesion and friction allows for a significant improvement of capacity and reliability of the existing rail network, e.g. by contributing to continuous track and vehicle monitoring. In order to exploit this potential in daily operation, a ready to use design is required, which is challenging in two ways. Firstly, it demands for a minimum set of mostly in-service sensors and, secondly, the estimator framework has to suit the limited computational resources of regular train control units. Further challenges arise from the simultaneous estimation of two partly interconnected parameters and their identification for every single wheelset.
Based on a detailed analysis of railway vehicles, two nonlinear systems are generated and their observability is verified. To complement the qualitative observability criterion, a quantitative and signal-based correlation measure is developed and evaluated. The two nonlinear system variants are combined with two estimator concepts, Extended and Unscented Kalman Filter, so that four configurations are applied to test rig data. The 50 tested scenarios comprise different variations, which ensure robustness, reliability, and accuracy of the estimators for a broad spectrum of operating conditions. To confirm the test rig results, the estimation environment is adapted to field test data. With the given measurement equipment in the test vehicle the plausibility of estimation results could be verified. Finally, two adhesion-based control concepts are introduced that address repeatability of braking distances and their reduction, respectively.