Vehicle-Charger Pairing via Location and Charge Timing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack an efficient method to identify pairings between vehicles and charge stations, which is crucial for effective management and optimization of charging processes.
Innovation Solution
A method that accesses vehicle and charge station location data, as well as charge event start times, to determine couple likelihoods for candidate vehicle-to-charge station pairs by combining distance-based and timing-based likelihoods, and then identifies the most likely scenario based on overall likelihood calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If no pairing identification system is implemented, then system complexity remains low, but charging management efficiency deteriorates
Solution Approach 1:
The system segments the pairing identification process into distinct components: location data analysis, charge event timing correlation, and scenario evaluation. Each component processes specific data types independently before combining results, reducing overall system complexity while improving management efficiency.
Solution Approach 2:
The system introduces an intermediary processing layer that correlates vehicle location data with charge station data and charge event timings. This intermediary layer acts as a mediator between raw data sources and management decisions, enabling efficient charging management without requiring direct complex interactions between all system components.
2Measurement precision
If pairing identification uses only location data, then data processing requirements are low, but pairing accuracy deteriorates
Solution Approach 1:
The system merges multiple data sources including vehicle location data, charge station location data, and charge event timing data from both vehicle and charge station perspectives. By combining these diverse data types, the system achieves high pairing identification accuracy while distributing processing complexity across multiple data streams.
Solution Approach 2:
The system adds temporal dimension by incorporating charge event timing data alongside spatial location data. This multi-dimensional approach correlates where vehicles and charge stations are located with when charging events occur, significantly improving pairing accuracy by examining the problem from multiple dimensions simultaneously.
3Measurement precision
If the system evaluates all possible vehicle-to-charge station pairs, then pairing identification completeness is high, but computational time increases
Solution Approach 1:
The system evaluates multiple pairing scenarios but identifies and focuses on the most likely scenario based on correlated location and timing data. Rather than exhaustively processing all possible pairings with equal depth, the system performs partial evaluation of less likely scenarios while conducting detailed analysis of the most probable pairing, achieving high completeness with reduced computational time.
Solution Approach 2:
The system uses feedback from location data correlation and charge event timing analysis to iteratively refine pairing identification. By evaluating scenarios and using the results to guide further analysis, the system efficiently achieves complete pairing identification without uniformly processing all possible pairs, thereby reducing overall computational time while maintaining completeness.
Data Source
AI summary
The systems, methods, and devices for identifying pairings between vehicles and charge stations are described. In the absence of explicit communication or handshaking between a vehicle and a charge station, location data and charge timing data between charge stations and vehicles is compared. Probabilistic coupling between vehicles and charge stations is determined. Further, overall coupling likelihood for a plurality of vehicles and a plurality of charge stations is determined by comparing scenarios of different coupling combinations.