Charging Station Siting Using Real-Time Traffic Flow Optimization
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Solution Overview
Problem
Existing methods for identifying charging station locations are based on static optimization techniques, which do not 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 map into grids, estimate traffic density, cluster grids, and compute a total score based on distance and traffic penalties, iteratively selecting candidate locations using an optimization technique.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pure optimization techniques are used to identify charging station locations, then the method is simple to implement, but it cannot effectively output the global optimal solution for the city
Solution Approach 1:
The patent transitions from static optimization techniques to dynamic data-driven optimization. The system continuously updates charging station location recommendations based on real-time traffic data, vehicle location data, and charging demand patterns, allowing the optimization model to adapt and improve accuracy over time while maintaining implementation feasibility through modular architecture.
Solution Approach 2:
The patent changes the parameters of the optimization system by incorporating multiple dynamic variables including traffic flow data, vehicle battery status, charging station capacity, and temporal patterns. This multi-parameter approach enables global optimal solution identification while the system maintains ease of implementation through standardized data processing pipelines and configurable optimization algorithms.
2Adaptability or versatility
If data-based techniques are implemented with objective functions selected based on data nature, then the solution adapts to data characteristics, but gaps remain in the solution coverage
Solution Approach 1:
The patent creates a universal optimization framework that handles multiple data types and objectives simultaneously. The system processes diverse inputs including traffic data, vehicle telemetry, charging patterns, and urban planning data through a unified objective function that adapts to different data characteristics while ensuring comprehensive solution coverage through multi-objective optimization that simultaneously considers technical, economic, and social factors.
Solution Approach 2:
The patent segments the optimization problem into multiple independent modules: data preprocessing module, feature extraction module, objective function formulation module, optimization execution module, and validation module. Each module handles specific data characteristics independently, allowing the system to adapt to different data types while ensuring complete solution coverage through the coordinated operation of all segments.
3Loss of energy
If existing techniques are not performed at real-time, then computational resources are conserved, but the identification of charging infrastructure locations is not timely
Solution Approach 1:
The patent implements periodic real-time optimization at strategic decision points such as daily charging infrastructure planning updates, weekly route recommendations, and on-demand adjustments during peak charging demand periods. Between these periodic optimization cycles, the system uses pre-computed solutions and lightweight adjustments, maintaining timely identification while conserving computational resources through intelligent scheduling of intensive processing operations.
Solution Approach 2:
The patent performs preliminary optimization computations in advance during low-demand periods, pre-calculating charging station location recommendations and storing them in a recommendation database. During real-time operations, the system quickly retrieves and adjusts these pre-computed solutions based on current conditions, achieving timely identification without requiring continuous intensive computational resources.
Data Source
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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.