Graph-Based Network Analysis for EV Charging Station Placement
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Solution Overview
Problem
Current PEV charging infrastructure planning fails to consider the unique arrangement of roads and socio-economic factors, leading to inequitable access and increased traffic congestion, and is computationally inefficient.
Innovation Solution
An intelligent system using machine learning to simulate charging station locations by converting networks into graphs, identifying high-impact edges through entropy reduction and centrality measures, reducing computational load and promoting equitable access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional planning methods focusing on socio-economic factors and business centers are used, then charging stations are concentrated in cities, but low income areas remain remote from charging stations, resulting in inequitable access
Solution Approach 1:
The patent segments the road network into a graph structure where nodes represent intersections or key locations and edges represent road segments. This segmentation allows the system to analyze and evaluate individual road segments (edges) for charging station placement, enabling equitable distribution across different areas including low-income regions rather than concentrating stations only in business centers.
Solution Approach 2:
The patent changes the evaluation parameters from traditional socio-economic factors to graph-theoretic parameters such as edge betweenness centrality and network entropy. This parameter transformation enables the system to identify high-impact road segments based on their structural importance in the network, leading to more equitable charging station distribution that considers accessibility for all communities.
2Measurement precision
If enumeration models are used to determine charging station locations, then comprehensive coverage can be achieved, but computational speed becomes slow
Solution Approach 1:
The patent extracts only the most critical road segments from the complete road network by identifying edges with high betweenness centrality scores. Instead of evaluating all possible locations comprehensively, the system focuses computation on these high-impact edges, significantly reducing computational load while maintaining location optimization quality.
Solution Approach 2:
The patent applies partial action by evaluating only a subset of road segments (those categorized as high-impact edges) rather than performing exhaustive enumeration of all possible charging station locations. This partial evaluation approach achieves sufficient optimization for practical deployment while dramatically improving computational speed.
3Ease of manufacture
If charging stations are placed without considering road network arrangement, then placement can be simplified, but traffic congestion problems are exacerbated
Solution Approach 1:
The patent performs preliminary analysis of the road network structure by converting it to a graph and calculating betweenness centrality for all edges before determining charging station locations. This preliminary identification of high-impact road segments ensures that stations are placed strategically to maximize accessibility and minimize traffic congestion, rather than using simplified placement methods.
Data Source
AI summary
Systems and methods to facilitate the identification of connections or relationships in a network that are high impact in order to generate recommendations for future network growth are disclosed. The embodiments convert network maps into graphs comprising nodes and edges. The system identifies the edge that, when removed, causes the greatest impact on the network as a whole. In one embodiment, the system can be used to identify locations for installation of electric vehicle charging stations.