Train Driver Advisory Velocity Profiles Under Time And Speed Limits
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Solution Overview
Problem
Existing train driver systems fail to optimize energy consumption during operation, leading to inefficiencies in both fuel-powered and electric trains, as previous methods do not effectively determine energy-optimized operating regimes in real-time.
Innovation Solution
A method using a graph-based optimization algorithm to determine an energy-optimized velocity profile for train operation, constrained by travel time and velocity limits, which provides real-time driving recommendations to the driver, allowing for minimized energy consumption while adhering to operational constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional train operation methods are used, then operational simplicity is maintained, but energy consumption is not optimized
Solution Approach 1:
The system pre-calculates energy-optimized velocity profiles for trip segments before the train actually travels them. By determining optimal velocity profiles in advance based on stored distance and altitude profile data, the system prepares optimization solutions that can be quickly applied during operation without real-time computational delays, thus reducing energy consumption while maintaining operational simplicity.
Solution Approach 2:
The system dynamically adapts velocity profiles to changing operational conditions by allowing drivers to input actual velocity data and by recalculating recommendations for subsequent trip segments. The graph-based optimization algorithm adjusts velocity profiles based on actual train performance and changing conditions, enabling the system to maintain optimization effectiveness while adapting to dynamic operational requirements.
2Use of energy by moving object
If real-time optimization calculations are performed, then energy-optimized velocity profiles are achieved, but computational time and complexity increase
Solution Approach 1:
The journey is divided into discrete trip segments with defined start and end points. The system calculates energy-optimized velocity profiles for each segment independently using graph-based optimization algorithms. This segmentation allows the complex optimization problem to be broken down into manageable sub-problems that can be solved efficiently for each segment rather than attempting to optimize the entire journey at once.
Solution Approach 2:
Distance and altitude profile data for trip segments are stored in advance before the train journey begins. This preliminary preparation of geographic and operational data enables the optimization algorithm to quickly retrieve and process pre-prepared information during actual operation, significantly reducing real-time computational requirements while still achieving energy-optimized velocity profiles.
3Reliability
If constrained optimization is applied, then operational constraints are satisfied, but solution flexibility is reduced
Solution Approach 1:
The system maintains flexibility by allowing constraints to be dynamically adjusted based on actual operational conditions. Drivers can input actual velocity data that reflects real-world conditions, and the system recalculates velocity profiles for subsequent segments based on these updated conditions. This dynamic approach ensures that constraints are satisfied while maintaining adaptability to changing operational requirements.
Solution Approach 2:
The system incorporates feedback mechanisms where actual train velocity data is fed back into the optimization process. This feedback allows the system to adjust and refine velocity profile recommendations for subsequent trip segments based on actual performance, ensuring that operational constraints are met while adapting to real-world conditions and maintaining solution flexibility.
Data Source
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AI summary
A method of providing a driving recommendation to a driver of a train (1) during operation of the train is disclosed. The driving recommendation is based on a distance (Dseg) and an altitude profile (32) of a trip segment to be covered by the train, a travel time limit for the trip segment, and velocity limits (20) along the trip segment. The method includes the steps of defining an initial state (10) of the train on the basis of an actual position of the train and an actual velocity of the train; defining a goal state (12) of the train on the basis of a goal position and a goal velocity at the end of the trip segment to be covered by the train; determining an energy-optimized velocity profile (18) between the initial state of the train and the goal state of the train, with the energy-optimized velocity profile being determined by a graph based optimization algorithm, constrained by the travel time limit for the trip segment and the velocity limits along the trip segment; and providing the driving recommendation to the driver on the basis of the energy-optimized velocity profile.