Vehicle Replenishment Station Assignment for Long-Trip Readiness
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Solution Overview
Problem
Vehicles often experience delays when their energy reserve is low before a long trip, as they need to replenish at a nearby station, which can lead to inefficiencies in transportation, especially for public or shared transport systems.
Innovation Solution
Assigning a specific replenishing station to a vehicle based on statistical data, such as trip length, frequency, and energy reserve levels, to optimize the timing and location of energy replenishment, reducing waiting times and improving fleet efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If vehicles replenish energy at nearby stations when energy reserve is low, then vehicles can maintain operation with simple reactive logic, but vehicles may experience delays before long trips due to unnecessary trips to replenishing stations
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 energy. The central server analyzes upcoming trip requests and assigns vehicles to specific replenishing stations in advance, so that when a trip is requested, the vehicle is already positioned at an appropriate station rather than needing to travel to a nearby station when energy is low.
2Productivity
If vehicles proactively assign replenishing stations based on statistical data, then waiting times are reduced and trip readiness is improved, but system complexity increases due to data collection and analysis requirements
Solution Approach 1:
The patent introduces a central server as an intermediary that manages the complexity of data collection, statistical analysis, and replenishing station assignment. Instead of each vehicle independently analyzing complex datasets, the central server aggregates trip data from multiple vehicles, performs statistical analysis to identify patterns, and generates assignment recommendations. This mediator approach centralizes the computational complexity while keeping individual vehicle systems relatively simple.
3Ease of manufacture
If reactive replenishment is used, then system implementation is straightforward, but energy reserve management is inefficient leading to increased waiting times
Solution Approach 1:
The system implements feedback mechanisms where the central server continuously monitors trip data, energy consumption patterns, and vehicle locations. This feedback loop allows the system to learn from actual usage patterns and refine its statistical models over time. The server adjusts replenishing station assignments based on accumulated data, improving energy reserve management efficiency while maintaining a manageable implementation complexity through iterative optimization.
Data Source
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.