Rail Vehicle Longitudinal Dynamics Estimation With Fewer Sensors
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Solution Overview
Problem
Existing methods for determining changes in the longitudinal dynamic behavior of rail vehicle chassis are complex, costly, and require numerous sensors, making them expensive and maintenance-intensive, and often result in conservative braking capacity calculations that hinder economic operation due to assumptions about environmental conditions.
Innovation Solution
A method using a control observer to reconstruct non-measurable variables from input and measurement signals, allowing for the characterization of chassis dynamics in all drive and braking scenarios with a minimal number of sensors, and enabling flexible sensor placement for reduced maintenance and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerous sensors are installed to detect forces and torques during braking or acceleration, then measurement precision is improved, but device complexity and maintenance requirements increase significantly
Solution Approach 1:
The patent extracts the essential measurement information from a complex multi-sensor system by identifying that only acceleration and wheel speed measurements are fundamentally necessary. The control observer then reconstructs the remaining dynamic variables (brake forces, traction forces, friction coefficients) through mathematical modeling, thereby eliminating the need for numerous force and torque sensors while maintaining measurement precision.
Solution Approach 2:
The patent creates a virtual model (control observer) that copies and simulates the complex mechanical system dynamics. Instead of physically measuring all forces and torques with sensors, the system creates a mathematical replica of the vehicle's longitudinal dynamics that can infer unmeasured variables from limited sensor data, effectively replacing physical sensors with a virtual sensing model.
2Reliability
If conservative braking performance calculations based on worst-case scenarios are used, then safety is improved, but productivity decreases due to unnecessary speed restrictions
Solution Approach 1:
The patent transitions from static, conservative braking performance calculations to a dynamic assessment method. The control observer continuously adapts the braking performance evaluation based on real-time measurements of actual friction conditions, wear states, and environmental factors. This allows the system to optimize braking performance dynamically rather than relying on fixed worst-case parameters, thereby improving productivity while maintaining safety.
Solution Approach 2:
The patent implements a feedback mechanism where the control observer continuously monitors actual braking behavior and friction conditions, then uses this information to update and refine braking performance calculations. This closed-loop approach allows the system to learn from actual operating conditions and adjust braking performance assessments accordingly, avoiding unnecessary speed restrictions while ensuring safety through continuous verification.
3Measurement precision
If manual control of braking and driving forces is used to maintain braking distances and minimize wear, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent enables the braking system to self-regulate and self-optimize through the control observer, which automatically adjusts braking and driving forces based on real-time system state estimation. The system serves itself by continuously monitoring its own performance, detecting friction condition changes, and autonomously optimizing control parameters without requiring manual intervention, thereby improving ease of operation while maintaining precise braking distance control.
Solution Approach 2:
The patent replaces manual mechanical control with an automated electronic control system based on the control observer. The observer-based controller substitutes human operators by using mathematical models and sensor data to automatically determine optimal braking and driving forces, eliminating the need for manual judgment and control while achieving superior measurement precision and consistency in braking distance control.
Data Source
Figure 1~3
AI summary
The invention relates to a method for determining changes in the longitudinal dynamic behaviour, in particular of a chassis, of a rail vehicle for identification of a current driving state of the rail vehicle, in which, by means of a monitor (1) using control technology, quantities which are not measurable by a system model (20) of the rail vehicle and which characterise the longitudinal dynamic behaviour are reconstructed and evaluated as a monitored real reference system (10) from a known or metrologically determined input signal (u) and at least one measurement signal (y) from the monitored rail vehicle. The at least one measurement signal (y) of the monitored rail vehicle and a corresponding reconstructed measurement signal (ŷ) of the system model (20) are compared and the deviation determined by means of comparison is tracked recursively by a controller, such that the determined deviation is minimised.