Vehicle Replenishing Station Assignment for Low-Wait Fleet Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Vehicles often experience delays when their energy reserve is low prior to long trips, as they need to replenish at nearby stations, leading to inefficiencies in transportation and potential disruptions.
Innovation Solution
Assigning specific replenishing stations based on statistical data analysis, such as trip length, frequency, and energy reserve levels, to optimize energy replenishment timing and location, reducing waiting times and improving fleet efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles replenish energy reserve reactively at nearby stations when energy level is low, then vehicles can maintain operation with simple control logic, but vehicles experience delays and disruptions especially before long trips
Solution Approach 1:
The system performs preliminary actions by proactively assigning replenishing stations to vehicles based on predicted trip requirements before the vehicles actually need replenishment. The central server analyzes trip data, vehicle energy consumption patterns, and station availability to schedule replenishment in advance, preventing delays and ensuring vehicles are ready for long trips without reactive waiting.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring vehicle energy levels, trip completion data, and replenishing station status. This feedback loop allows the central server to adjust future station assignments and optimize the proactive replenishment schedule, improving reliability while minimizing waiting time through data-driven decision making.
2Loss of time
If vehicles proactively replenish energy reserve before trips, then vehicles can avoid delays and disruptions, but requires complex prediction and coordination systems
Solution Approach 1:
The patent introduces a central server as an intermediary that handles the complex prediction and coordination tasks. Instead of requiring complex systems in each vehicle, the central server consolidates the intelligence by collecting trip data, analyzing energy consumption patterns, and making centralized assignment decisions, simplifying individual vehicle systems while achieving proactive replenishment.
Solution Approach 2:
The central server performs multiple functions including trip prediction, energy consumption analysis, replenishing station assignment, and real-time monitoring. This multi-functional approach consolidates complexity into a single system that can handle various aspects of proactive replenishment, reducing the need for separate complex systems in each vehicle.
3Productivity
If reactive replenishment is used, then replenishing stations can be used with minimal coordination, but station utilization is inefficient and traffic flow is disrupted
Solution Approach 1:
The system uses feedback from trip data, energy consumption patterns, and station status to continuously optimize station assignments. This feedback mechanism enables efficient coordination between multiple vehicles and stations, improving overall transportation productivity by preventing conflicts and optimizing resource utilization without requiring excessive complexity.
Solution Approach 2:
The central server acts as an intermediary coordinator that manages station assignments for multiple vehicles. It balances the load across replenishing stations, optimizes timing to minimize traffic disruption, and coordinates assignments based on predicted needs, improving productivity while centralizing the coordination complexity.
Data Source
Figure 1
Figure 2a~2b
Figure 3a~3b
AI summary
Example implementations relate to replenishing an energy reserve of a vehicle of interest located in a zone of interest. Such examples comprise emitting, from a computer system and towards a vehicle controller of the vehicle of interest, instructions to direct the vehicle of interest towards a specific replenishing station located in the zone of interest. The specific replenishing station is assigned for replenishing the vehicle of interest based on a set of statistical data