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Advanced Maximum Adhesion Tracking Strategies in Railway Traction Drives

Ahmed Fathy Abouzeid, Juan Manuel Guerrero, Lander Lejarza, Iker Muniategui, Aitor Endemaño, Fernando Briz

Abstract

Modern railway traction systems are often equipped with anti-slip control strategies to comply with performance and safety requirements. A certain amount of slip is needed to increase the torque transferred by the traction motors onto the rail. Commonly, constant slip control is used to limit the slip velocity between the wheel and rail avoiding excessive slippage and vehicle derailment. This is at the price of not fully utilizing the train's traction and braking capabilities. Finding the slip at which maximum traction force occurs is challenging due to the non-linear relationship between slip and wheel-rail adhesion coefficient, as well as to its dependence on rail and wheel conditions. Perturb and observe (P\&O) and steepest gradient (SG) methods have been reported for the Maximum Adhesion Tracking (MAT) search. However, both methods exhibit weaknesses. Two new MAT strategies are proposed in this paper which overcome the limitations of existing methods, using Fuzzy Logic Controller (FLC) and Particle Swarm Optimization (PSO) respectively. Existing and proposed methods are first simulated and further validated experimentally using a scaled roller rig under identical conditions. The results show that the proposed methods improve the traction capability with lower searching time and oscillations compared to existing solutions. Tuning complexity and computational requirements will also be shown to be favorable to the proposed methods.

Advanced Maximum Adhesion Tracking Strategies in Railway Traction Drives

Abstract

Modern railway traction systems are often equipped with anti-slip control strategies to comply with performance and safety requirements. A certain amount of slip is needed to increase the torque transferred by the traction motors onto the rail. Commonly, constant slip control is used to limit the slip velocity between the wheel and rail avoiding excessive slippage and vehicle derailment. This is at the price of not fully utilizing the train's traction and braking capabilities. Finding the slip at which maximum traction force occurs is challenging due to the non-linear relationship between slip and wheel-rail adhesion coefficient, as well as to its dependence on rail and wheel conditions. Perturb and observe (P\&O) and steepest gradient (SG) methods have been reported for the Maximum Adhesion Tracking (MAT) search. However, both methods exhibit weaknesses. Two new MAT strategies are proposed in this paper which overcome the limitations of existing methods, using Fuzzy Logic Controller (FLC) and Particle Swarm Optimization (PSO) respectively. Existing and proposed methods are first simulated and further validated experimentally using a scaled roller rig under identical conditions. The results show that the proposed methods improve the traction capability with lower searching time and oscillations compared to existing solutions. Tuning complexity and computational requirements will also be shown to be favorable to the proposed methods.
Paper Structure (17 sections, 10 equations, 23 figures, 1 table)

This paper contains 17 sections, 10 equations, 23 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model and Test bench Description
  3. Proposed wheel-rail contact emulator
  4. Proposed scaled roller rig overall control scheme
  5. Overview of Wheel-Rail Slip Velocity Control Methods
  6. Constant slip velocity control
  7. Variable slip velocity control with maximum adhesion estimation
  8. Perturb and Observe (P&O)
  9. Steepest Gradient
  10. Proposed MAT Techniques for Railways
  11. Proposed MAT Using Fuzzy Logic Control
  12. Proposed MAT Using Particle Swarm Optimization
  13. Experimental Validation
  14. Test Bench Setup
  15. Methodology
  16. ...and 2 more sections

Figures (23)

  • Figure 1: Schematic representation of the proposed scaled roller rig.
  • Figure 2: Overall control scheme of proposed scaled roller rig for slip velocity control.
  • Figure 3: Detailed slip velocity control block diagram. Slip Control Mode Selection block is explained in Sections \ref{['sec:overview_slip_control']} and \ref{['sec:proposed']}.
  • Figure 4: Classification of slip velocity control mode.
  • Figure 5: Constant slip velocity control command generation.
  • ...and 18 more figures