This paper presents the implementation of the dual extended Kalman filter (DEKF) to estimate wheelset equivalent conicity, an accurate understanding of which can facilitate the implementation of an effective model-based estimator. The estimator is developed to identify the wheelset equivalent conicity of high-speed railway vehicles while negotiating a curve. The designed DEKF estimator employs two discrete-time extended Kalman filters combining state and parameter estimators in parallel. This estimator uses easily available measurements from acceleration sensors measuring at axle boxes and a rate gyroscope measuring bogie frame yaw velocity. Two tests, including linearized and actual wheel-rail geometry, are carried out at a speed of 250 km/h with stochastic and deterministic track features using multibody simulations, SIMPACK. The results with acceptable estimation errors for both track conditions indicate adequate performance and reliability of the designed DEKF estimator. They demonstrate the feasibility of utilizing this DEKF method in rail vehicle applications as the knowledge of time-varying parameters is not only important in achieving an effective estimator for vehicle control but also useful for vehicle condition monitoring.

本文介绍了使用双扩展卡尔曼滤波器（DEKF）来估计轮对等效锥度的实现，对其的准确理解可以促进有效的基于模型的估计器的实现。该估算器旨在识别高速铁路车辆在转弯时的轮对等效锥度。设计的 DEKF 估计器采用两个并行组合状态估计器和参数估计器的离散时间扩展卡尔曼滤波器。该估算器使用在轴箱处测量的加速度传感器和测量转向架框架偏航速度的速率陀螺仪轻松获得的测量结果。使用多体仿真 SIMPACK，以 250 km/h 的速度进行了两项测试，包括线性化和实际轮轨几何形状，具有随机和确定性轨道特征。两种轨道条件下的估计误差均可接受的结果表明所设计的 DEKF 估计器具有足够的性能和可靠性。他们证明了在轨道车辆应用中利用这种 DEKF 方法的可行性，因为时变参数的知识不仅对于实现车辆控制的有效估计器很重要，而且对于车辆状态监测也很有用。