Timetable Optimization for Railway Energy Synchronization
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Solution Overview
Problem
Current solutions for optimizing energy consumption in railway systems, such as fly-wheels and super batteries, are costly and require routine maintenance, while existing methods for synchronizing railway assets to reduce energy consumption are limited in effectiveness.
Innovation Solution
A system and method that modifies timetables to overlap brake times and acceleration times of vehicles, allowing energy transfer between them without additional hardware, using a genetic algorithm to minimize energy consumption by adjusting dwell times and speed profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If hardware solutions such as fly-wheels or super batteries are used to alleviate sustainable energy and energy conservation, then energy management capability is improved, but system cost and maintenance complexity increase
Solution Approach 1:
The patent extracts the energy management function from physical hardware components (fly-wheels, super batteries) and implements it through software-based timetable optimization. The genetic algorithm modifies departure times and dwell times to enable energy coordination between trains, eliminating the need for expensive energy storage hardware while achieving sustainable energy management.
Solution Approach 2:
The patent replaces the mechanical/electrical hardware system (fly-wheels, super batteries) with an information processing system. The controller uses genetic algorithms to process timetable data and generate optimized schedules that coordinate braking and acceleration phases, substituting physical energy storage mechanisms with computational optimization.
2Loss of energy
If timetable modification is used to synchronize braking and acceleration phases, then energy consumption is reduced, but schedule flexibility decreases
Solution Approach 1:
The patent applies partial modification to the timetable by adjusting only specific parameters (departure times and dwell times) rather than completely redesigning the schedule. The genetic algorithm makes targeted adjustments to synchronize braking and acceleration phases while keeping the overall timetable structure intact, achieving energy savings without completely sacrificing schedule flexibility.
Solution Approach 2:
The patent changes specific timetable parameters (departure times, dwell times) to achieve energy optimization. The genetic algorithm iteratively adjusts these parameters to find the optimal balance between energy consumption and schedule requirements, allowing flexible parameter modification while maintaining service quality standards.
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 reduces energy consumption by synchronizing braking and acceleration phases, preventing the loss of regenerative energy and minimizing the need for costly hardware solutions, achieving a 6.6% energy savings in metro lines while maintaining quality of service.
Implementation Method 1
the second vehicle can transfer energy from the first vehicle based upon at least one of the modified timetable and the brake time overlapping with the acceleration time
Data Source
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AI summary
Systems and methods for synchronizing two or more vehicles operating on an electric transportation line to optimize energy consumption. A controller is provided having a computer memory component storing a set of computer-executable instructions, a list of braking intervals, and a list of acceleration intervals for the vehicles. The controller also has a processing component configured to execute the set of computer- executable instructions to operate on the list of braking intervals and the list of acceleration intervals to minimize an energy consumption of the electric transportation line over a determined period of time by shifting acceleration intervals to synchronize with braking intervals. A dedicated heuristic greedy algorithm and an energy model are implemented in the controller as part of the computer-executable instructions to achieve the improved energy consumption.