EV Charging Station Siting Using Traffic Grid Optimization
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Solution Overview
Problem
Existing methods for identifying charging station locations are based on static optimization techniques, which fail to provide a global optimal solution and are not performed in real-time, leaving gaps in the identification of charging infrastructure.
Innovation Solution
A data-driven optimization method that utilizes daily traffic count data to divide a city into grids, estimate traffic density, cluster grids, and compute a total score based on distance and traffic penalties to identify optimal candidate locations using an iterative optimization technique.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pure optimization techniques with a static number of charging stations are used, then the planning process is simplified, but the solution fails to provide the global optimal solution for the city
Solution Approach 1:
The patent transitions from static optimization to dynamic data-driven optimization. The system continuously updates charging station locations based on real-time traffic data, grid clustering, and iterative optimization processes, allowing the solution to adapt and improve as new data becomes available, thereby achieving global optimality without excessive complexity
Solution Approach 2:
The patent implements feedback mechanisms by using traffic count data to evaluate and refine charging station locations. The iterative optimization process continuously adjusts the solution based on performance metrics and traffic patterns, ensuring the system converges to the global optimal solution while maintaining manageable planning complexity
2Adaptability or versatility
If data-based techniques with objective functions selected based on data nature are used, then the solution adapts to traffic patterns, but gaps remain in the identification of charging infrastructure
Solution Approach 1:
The patent divides the city into multiple grids and further segments them into sub-clusters, allowing detailed local analysis while maintaining overall city coverage. This segmentation enables the system to identify charging station locations across all areas, eliminating gaps in infrastructure identification while adapting to local traffic patterns
Solution Approach 2:
The patent adds spatial dimensionality by dividing the city into grids and sub-clusters, transforming the problem from a one-dimensional optimization to a multi-dimensional spatial analysis. This approach ensures comprehensive coverage of all city areas while adapting to varying traffic patterns in different regions
3Loss of energy
If traditional optimization techniques are used, then computational resources are conserved, but the identification of charging station locations is not performed in real-time
Solution Approach 1:
The patent segments the city into grids and sub-clusters, allowing parallel processing of multiple regions simultaneously. This segmentation enables real-time identification of charging station locations by distributing computational tasks across different grid areas, achieving real-time performance without excessive resource consumption
Solution Approach 2:
The patent focuses computational resources on identifying candidate locations within each grid and sub-cluster rather than optimizing the entire city at once. This partial action approach enables real-time identification by processing manageable portions of the city concurrently, achieving real-time capability while controlling resource usage
Data Source
AI summary
This disclosure relates generally to identification of charging station location based on data driven optimization. Despite the rapid growth seen in adoption of EVs, several challenges have remained, with the major concern of choosing the location of the charging infrastructure is crucial. The state-of-art techniques to identify the location of charging infrastructure/station is based on pure optimization techniques using static number of charging stations, which may not effectively output the global optimal solution for the city as it is not performed at real-time. The disclosed technique is a flow-based solution approach that is based on daily traffic count data. The disclosed technique analyses traffic condition from vehicle count data, geography of the city or area of interest, road networks, charging station operation constraints to identify a set of candidate locations required to cover entire city or area of the interest based on a score-based ranking of each candidate location.