Charging Station Siting Using Vehicle Density and State of Charge
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Solution Overview
Problem
Existing systems fail to efficiently determine the need for and location of new charging stations based on the number and state-of-charge of vehicles in a given area, leading to potential delays and inefficiencies in charging operations.
Innovation Solution
A method and system that utilize vehicle processors to determine the need for a new charging station when the number of vehicles in an area exceeds a threshold and their average state-of-charge is below a certain level, allowing for the optimization of charging infrastructure deployment and prioritization of vehicles based on their charge levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If charging station locations are determined using existing systems, then charging infrastructure is provided, but the determination is inefficient and does not account for real-time vehicle density and charge levels
Solution Approach 1:
The system continuously monitors vehicle density and average charge levels in real-time, using this feedback to dynamically determine when and where to deploy charging stations. This closed-loop approach ensures charging infrastructure is provided efficiently based on actual demand conditions
Solution Approach 2:
The system proactively identifies areas where charging stations should be deployed before vehicles experience significant wait times or depletion of charge levels. By monitoring thresholds in advance and triggering deployment preemptively, the system prevents charging bottlenecks before they occur
2Adaptability or versatility
If charging stations are deployed without considering vehicle state-of-charge distribution, then infrastructure coverage is provided, but resource allocation is suboptimal and vehicles with lower charges are not prioritized
Solution Approach 1:
The system applies different deployment criteria to different geographic areas based on local vehicle density and charge level characteristics. Rather than uniform deployment, each area receives charging infrastructure tailored to its specific demand profile, optimizing resource allocation efficiency
Solution Approach 2:
The system uses vehicle state-of-charge levels as a key parameter to trigger charging station deployment. When the average charge level falls below a threshold, the system activates deployment procedures, making the infrastructure adaptive to changing energy demand conditions
Data Source
AI summary
An example operation includes determining a new charging station location in an area when a number of vehicles in the area is greater than a first threshold and the number of vehicles has an average state-of-charge less than a second threshold.