On-Board Train Control With Convex Optimization for Energy and Timing
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Solution Overview
Problem
Existing train control systems face challenges in achieving optimal energy efficiency, timetable adherence, and reducing wear-and-tear while ensuring safe and robust operation, particularly in automatic train operation (ATO) systems.
Innovation Solution
A vehicle on-board controller (VOBC) implements an optimal control technique using a convex second-order cone optimization problem to generate driving profiles that minimize energy consumption and adhere to time constraints, while ensuring safe vehicle separation and real-time adaptation to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional train control systems are used, then system simplicity is maintained, but energy consumption increases and timetable adherence deteriorates
Solution Approach 1:
The patent replaces traditional mechanical control systems with an optimal control technique based on convex second-order cone optimization. This substitution enables real-time computation of energy-efficient driving profiles while maintaining system responsiveness, directly addressing the contradiction between energy efficiency and system complexity.
Solution Approach 2:
The system dynamically changes control parameters by solving optimization problems that adjust driving profiles based on current vehicle state, track conditions, and timetable requirements. This parameter adaptation allows the system to minimize energy consumption while adhering to time constraints without requiring overly complex hardware modifications.
2Use of energy by moving object
If optimal control techniques are implemented, then energy efficiency improves, but computational complexity increases
Solution Approach 1:
The control problem is segmented into discrete optimization steps executed at different control iterations. By dividing the path into segments and solving optimization problems incrementally based on current vehicle state, the system achieves real-time energy optimization without requiring excessively complex computational resources.
Solution Approach 2:
The system employs dynamic optimization where the driving profile is continuously adjusted based on real-time vehicle state and environmental conditions. This dynamic approach allows the controller to adapt to changing conditions while maintaining computational efficiency through the use of convex optimization techniques that can be solved rapidly.
3Loss of time
If real-time control adjustments are made, then timetable adherence improves, but system response time requirements increase
Solution Approach 1:
The system performs preliminary optimization by pre-calculating driving profiles based on predicted future states and constraints. This allows the controller to prepare optimal control actions in advance, ensuring timetable adherence while maintaining manageable response times through proactive rather than reactive control adjustments.
4Reliability
If safety constraints are enforced, then vehicle separation is ensured, but operational flexibility decreases
Solution Approach 1:
The system resolves the contradiction between safety and flexibility by introducing an additional optimization dimension. Safety constraints are enforced as boundary conditions in the optimization problem, while the objective function optimizes for energy efficiency and timetable adherence. This multi-dimensional approach allows the system to maintain safety guarantees while achieving operational flexibility through mathematical optimization.
Data Source
AI summary
A controller for a vehicle includes a processor configured to repeatedly perform operations, at each control iteration among a plurality of control iterations along a path of the vehicle from a start location to a target location. The operations include, based on a current state of the vehicle, generating a travel plan for a remainder of the path from a current position of the vehicle to the target location, by solving an optimization problem. The operations further include controlling a motoring and braking system of the vehicle to execute the generated travel plan until a next control iteration among the plurality of control iterations.