Peer-to-Peer EV Charging Matchmaking for Plug Compatibility
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Solution Overview
Problem
The incompatibility between electric vehicle (EV) charging infrastructure and EVs leads to difficulties in reliably predicting which charging infrastructure a driver will select, due to variability in the properties and capabilities of both.
Innovation Solution
A peer-to-peer EV charging system that utilizes a mobile application and a server system to match EVs with compatible charging infrastructure based on plug type, location, and user preferences, allowing for the reservation and payment for charging services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If charging infrastructure is distributed across geographic regions with varying properties and capabilities, then coverage area is improved, but compatibility with EVs deteriorates
Solution Approach 1:
The patent introduces a peer-to-peer matching system that acts as an intermediary between EV drivers and charging infrastructure owners. The system collects data about EV plug types, charging requirements, and infrastructure characteristics, then matches compatible pairs through a digital platform. This intermediary layer resolves the compatibility issue without requiring uniform infrastructure standards across different geographic regions.
Solution Approach 2:
The charging infrastructure is designed to serve multiple functions: it can charge different EV models with various plug types (J1772, NACS, CHAdeMO) through a single infrastructure unit. The system accommodates diverse charging needs (level 1, level 2, DC fast charging) within the same geographic location, making the infrastructure universally applicable to multiple EV types.
2Adaptability or versatility
If charging infrastructure properties vary by location, then local adaptability is improved, but prediction reliability deteriorates
Solution Approach 1:
The system implements feedback loops where charging sessions generate data about actual compatibility, charging speed, and user experience. This feedback is fed back into the matching algorithm to continuously improve prediction accuracy. The system learns from past matches to refine future recommendations, resolving the unreliability caused by varying local infrastructure properties.
Solution Approach 2:
The system performs preliminary matching and compatibility verification before the actual charging session. By pre-assessing plug type compatibility, charging rate requirements, and location suitability, the system eliminates incompatible matches in advance, ensuring high prediction reliability for the actual charging event.
3Adaptability or versatility
If EV plug types and charging capabilities vary, then vehicle-specific optimization is improved, but infrastructure compatibility deteriorates
Solution Approach 1:
The infrastructure is segmented into distinct functional components: plug type identification module, compatibility verification module, and charging delivery module. Each component handles a specific aspect of the charging process, allowing the system to support multiple plug types without increasing overall system complexity. The segmentation enables modular addition of new plug type support.
Data Source
AI summary
A vehicle includes a battery, one or more memories, and one or more processors. The battery is configured to provide power to propel the vehicle. The one or more processors are configured to execute instructions that are stored in the one or more memories. The instructions, when executed, cause the one or more processors to send data indicative of a plug type for charging the battery of the vehicle and a location of the vehicle, receive preferences for a user of the vehicle, receive identifying information for provider charging stations that are matched to the vehicle, display the identifying information for each of the provider charging stations that are matched to the vehicle in an order, and causing one of the provider charging stations to be reserved for future use by the vehicle.