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Review of adhesion estimation approaches for rail vehicles
The estimation of adhesion conditions between wheels and rails during railway operations is an important task as it helps to characterise the braking and traction control system. Since the adhesion condition is influenced by many factors, its estimation process is complex. This paper reviews the existing approaches to estimate adhesion conditions. These approaches are model-based prediction, inverse dynamic modelling, Kalman filter method, artificial neural network method and particle swarm optimisation method. The classification, methodologies, theories and applications of these approaches are included in this paper. The advantages and limitations of these methods are analysed to provide an application recommendation for adhesion estimation. This review has concluded that all estimation approaches undergo a linearisation stage where error cannot be avoided. The trade-off between accuracy and analysis time must be considered. This review also discusses how to improve existing approaches to achieve a more precise estimation of adhesion conditions.