Rail Vehicle Driving Graphs for Fast Energy-Efficient Rescheduling
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Solution Overview
Problem
Existing rail vehicle driving optimization systems face high computational effort and delayed recalculation due to robust but slow computer hardware, failing to efficiently adapt to deviations during journeys, and require extensive simulations for energy-efficient driving.
Innovation Solution
A method using initial and augmented graphs based on mathematical graph theory to determine and adapt energy-saving driving styles, allowing quick implementation and real-time adjustments through heuristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If complete recalculation is performed to optimize driving style after deviations occur, then energy efficiency is improved, but calculation time increases significantly
Solution Approach 1:
The route is divided into discrete sections with nodes representing specific locations and edges representing possible driving strategies between them. This segmentation allows the system to process only relevant portions of the route when deviations occur, rather than recalculating the entire journey, thus reducing calculation time while maintaining energy optimization.
Solution Approach 2:
The initial graph and all possible driving strategies are pre-calculated before the journey begins. When deviations occur during the journey, the system only needs to evaluate alternative paths from the current node using the pre-computed graph structure, avoiding complete recalculation and enabling rapid adaptation while preserving energy efficiency.
2Use of energy by moving object
If many simulations with different driving styles are conducted to identify energy-efficient driving styles, then energy efficiency is improved, but computational effort increases
Solution Approach 1:
Instead of conducting multiple simulations with different driving styles, the system creates a single comprehensive graph that incorporates all possible driving strategies (acceleration, coasting, braking, maintaining speed) as edges. This graph structure allows the system to evaluate all alternatives simultaneously during route planning without requiring repeated simulations, significantly reducing computational effort while identifying energy-efficient paths.
3Reliability
If robust computer hardware is used in rail vehicles, then system reliability is improved, but processing speed decreases
Solution Approach 1:
The graph structure and all driving strategy evaluations are prepared in advance before the journey begins. During the journey, when deviations occur, the system only needs to query the pre-computed graph for alternative paths from the current node, which is a much faster operation than performing complete recalculations. This approach enables robust hardware to deliver responsive performance without sacrificing reliability.
Solution Approach 2:
The route is segmented into discrete nodes and edges, allowing the system to process only the relevant segment from the current node to the destination when deviations occur. This segmentation reduces the computational scope from the entire route to a manageable subset, enabling faster processing on robust but not high-speed hardware while maintaining system reliability.
Data Source
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AI summary
The invention relates to a method for determining an energy-saving driving style for a vehicle, in particular a rail vehicle, traveling on a route between a starting point and a destination point while adhering to a predetermined maximum travel time. An initial driving style is determined in the form of speed values over a given location according to a specification, wherein the initial driving style is described by output nodes and output edges forming an output graph. Based on the output graph, an augmented graph is created by adding further edges to the output edges, the augmented graph defining alternative driving styles to the initial driving style. Subsequently, it is checked which of the alternative driving styles comply with the predetermined maximum travel time, and a tree is generated based on the augmented graph that describes the possible driving styles while adhering to the maximum travel time.Based on the tree, the driving style that requires the least energy consumption for the journey is selected.