EV Charging Point Detection for Dynamic Power Sharing
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Solution Overview
Problem
The EV charging infrastructure lags behind the growth in EV adoption, with issues such as insufficient charging points, broken chargers, high costs, confusion about payment, multiple connector types, and a lack of dynamic power sharing capabilities, leading to inefficiencies in charging electric vehicles.
Innovation Solution
A system and method to determine and predict the probability of dynamic power sharing capabilities at EV charging stations using a combination of GNSS data, vehicle sensor data, and real-time data from charge point operators, updating a database with dynamic power sharing indicators, and providing automated vehicle control signals and user interface displays to optimize charging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic power sharing capabilities are implemented at EV charging stations, then charging efficiency and power distribution are improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
The system performs preliminary detection of dynamic power sharing capabilities at EV charging stations before vehicles arrive. By pre-identifying stations with DPS capabilities and storing this information in a database, the system prepares charging optimization data in advance, allowing vehicles to receive optimized charging assignments without real-time complexity when they arrive at the station.
Solution Approach 2:
A centralized server acts as an intermediary between EV charging stations and vehicles. The server detects DPS capabilities, manages power sharing algorithms, and coordinates charging assignments. This intermediary handles the complex computations and communications, shielding individual charging stations and vehicles from the complexity while enabling efficient dynamic power sharing.
2Productivity
If real-time detection and prediction of dynamic power sharing is implemented, then charging optimization is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system uses automated detection methods where charging stations self-report their DPS capabilities and real-time power availability. Vehicles automatically query the database for optimized charging assignments. This self-service approach reduces the need for complex manual monitoring and intervention, simplifying the system while maintaining real-time optimization capabilities.
Solution Approach 2:
The system creates and maintains a database copy of DPS capability information and power availability data from multiple charging stations. This centralized data copy allows the server to perform optimization calculations efficiently without requiring direct real-time connections to all charging infrastructure, reducing system complexity while enabling comprehensive charging optimization.
3Measurement precision
If multiple data sources including GNSS and sensor data are integrated for EV location tracking, then charging location accuracy is improved, but information processing complexity increases
Solution Approach 1:
The system merges multiple data sources including GNSS data, vehicle sensor data, and charging station location data into a unified location determination process. By integrating these diverse data streams through a centralized server, the system achieves high location accuracy for vehicles and charging stations while managing processing complexity through unified data handling protocols and standardized interfaces.
Data Source
AI summary
A system, method, apparatus, etc., for electric vehicle supply equipment management. More specifically, at least an apparatus which may detect and/or predict the location of one of more dynamic power sharing electric vehicle supply equipment(s) such as a charge point, charge plug, etc. This information may be deduced from charge point data operators or generated by independent analysis.