EXTENDED KALMAN FILTER DESIGN FOR RAILWAY TRACTION MOTOR

Abstract

Monitoring the adhesion force between a railway wheel and a rail surface is very essential in maintaining the high acceleration and braking performance of railway vehicles. Due to the difficulties encountered in direct measurement of friction coefficient, creepage and adhesion force, state observers are used as indirect estimation methods. This paper proposes an effective estimation method, which exploits railway traction motor behaviour to give an assistance for realizing wheel slip and adhesion control in order to be used in railway applications. This method plays an active role in optimizing the use of the existing adhesion and reducing wheel wear by decreasing high creep values. With this method, adhesion force can be indirectly estimated by measuring stator currents, and angular speed of the AC traction motor and using dynamic relationships based on the extended Kalman filter (EKF) simulation model. The re-adhesion controller can be designed to regulate the motor torque command according to the maximum available adhesion depending on the estimated results. To test the proposed method, simulations were performed under different friction coefficients.