Rail Axle Speed Estimation Under Slip Using Adaptive Torque Control
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Solution Overview
Problem
Modern railway vehicles face a challenge in maintaining accurate speed measurement during slipping phases due to degraded adhesion conditions, as traditional methods require a 'dead' axle, which reduces traction and braking capacity, especially in limited compositions like subway vehicles.
Innovation Solution
A method using a control system with processors to estimate wheel adhesion and slip, generating a driving signal via an adaptive Least Mean Square (LMS) algorithm to control torque, allowing the axle to track speed while maintaining maximum adhesion and traction capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 'dead' axle is used to accurately determine vehicle speed during slipping phases, then speed measurement precision is improved, but traction and braking capacity is reduced
Solution Approach 1:
The system dynamically adjusts the torque parameter applied to the axle based on real-time adhesion conditions. By changing the torque parameter from zero (dead axle) to controlled values, the system maintains speed measurement accuracy while recovering traction and braking capacity when adhesion improves or during non-slipping phases.
Solution Approach 2:
The system continuously monitors wheel slip, adhesion conditions, and speed measurements to provide feedback to the torque control module. This feedback loop enables the system to adjust torque application dynamically, ensuring the axle remains in an optimal state for both measurement accuracy and power transmission based on current operating conditions.
2Reliability
If a 'dead' axle is maintained to track vehicle speed accurately, then reliability of speed indication is improved, but the axle cannot contribute to traction and braking
Solution Approach 1:
The system transitions the axle from a static 'dead' state to a dynamic state where torque is continuously adjusted based on real-time conditions. This dynamic approach allows the axle to adapt its function between pure measurement (when slipping occurs) and power transmission (when adhesion is sufficient), maximizing both reliability and productivity across different operating phases.
Solution Approach 2:
The axle is designed to perform multiple functions: speed measurement during slipping phases and power transmission during normal adhesion conditions. The torque control module enables the same axle to universally serve both measurement and propulsion/braking roles, eliminating the need for dedicated dead axles in limited-composition vehicles.
3Device complexity
If limited composition (e.g., two carriages) is used for modern railway vehicles, then device complexity is reduced, but the impact of losing a 'dead' axle on traction and braking capacity increases
Solution Approach 1:
The system enables each axle to self-adjust its torque application based on its own wheel slip and adhesion conditions. This self-service capability allows limited-composition vehicles to maintain optimal performance without requiring complex multi-axle configurations or dedicated dead axles, as each axle independently optimizes its contribution to both measurement and power transmission.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate speed estimation with minimal error (less than 2%) and maximizes traction and braking capacity by dynamically controlling torque, even in degraded adhesion conditions.
Implementation Method 1
generating, via an adaptive filter that implements a Least Mean Square (LMS) algorithm, a driving signal (C(Tj+1)) based on said derivative (dμ/dδ). The LMS algorithm is continuously adapted based on the error signal to reduce and keep the error signal substantially at zero
Implementation Method 2
estimating, via a control system that includes one or more processors, as a function of said angular speed (ω), a value of adhesion (μ) of a contact area of the wheels of said axle to a route
Implementation Method 3
applying said driving signal (C(Tj+1)) to a torque control module to control a torque exerted on said axle or the wheels of said axle
Data Source
AI summary
A method includes estimating, as a function of an angular speed of wheels of an axle of a vehicle, a value of adhesion of a contact area of the wheels of said axle to a route, and calculating a value of slip of the wheels of said axle. The method also includes generating signals representative of a derivative of said adhesion as a function of the slip of the wheels of said axle, and calculating an error signal as a difference between a value of said derivative and a predetermined reference value. The method includes generating, via an adaptive filter that implements a Least Mean Square (LMS) algorithm, a driving signal based on said derivative. The LMS algorithm is continuously adapted based on the error signal to reduce and keep the error signal substantially at zero. The method includes applying said driving signal to a torque control module.