EV Charging Point Reservation Using Predicted Availability
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Solution Overview
Problem
Existing vehicle charging systems often result in inefficient use of charging stations and prolonged waiting times for electric vehicles, as they lack a proactive method to reserve charging points based on real-time data and predicted availability.
Innovation Solution
A system that utilizes sensors and predictive analytics to reserve an available charging point at a charging station for a vehicle when another point is expected to become available within a threshold time after the vehicle's arrival, minimizing waiting times and optimizing station efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a vehicle reserves a charging point at arrival time when another charging point will be available within threshold time, then waiting time is reduced, but charging station resource allocation complexity increases
Solution Approach 1:
The system performs preliminary actions by predicting charging point availability in advance and proactively allocating charging points to vehicles before they arrive. The server determines predicted availability times of charging points and makes allocations ahead of time, reducing waiting time when vehicles actually arrive at the charging station.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring the actual status of charging points and comparing it with predicted availability. The server receives indications of actual status and uses this feedback to refine future predictions and adjustments, creating a closed-loop system that improves resource allocation over time.
2Productivity
If charging points are allocated based on predicted availability, then station efficiency is improved, but system complexity increases
Solution Approach 1:
The system applies dynamics by making charging point allocations flexible and adaptable rather than static. The server continuously adjusts allocations based on predicted availability times and actual status feedback, allowing the system to dynamically respond to changing conditions at the charging station and improve overall efficiency.
Solution Approach 2:
The server acts as an intermediary between vehicles and charging points, managing the complex predictive allocation logic centrally. This intermediary coordinates allocations based on predicted availability, simplifies the interface for vehicles, and handles the computational complexity of forecasting and optimization in a centralized manner.
3Measurement precision
If the system adjusts allocations based on actual status feedback, then allocation accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system applies partial action by processing and responding to only the most critical feedback information rather than all possible data. The server focuses on determining whether actual status deviates significantly from predicted status, adjusting allocations only when necessary to maintain accuracy, thereby reducing unnecessary data processing while preserving allocation precision.
Data Source
AI summary
An example operation includes one or more of reserving an available charging point at a charging station for a vehicle at an arrival time at the charging station when one other charging point at the charging station will be available for one other vehicle in a time less than a threshold time after the arrival time.