EV Charging Routing Using Shared Preference and Availability Data
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Solution Overview
Problem
Existing systems lack efficient methods for electric vehicles to locate and navigate to alternate charging stations based on user preferences and vehicle-to-vehicle communication, leading to suboptimal charging experiences.
Innovation Solution
A method and system that utilize vehicle-to-vehicle communication and a centralized server to determine charging locations based on shared preferences, enabling routing to charging stations that align with user priorities, using blockchain technology for secure data management and authorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a vehicle uses traditional routing methods to locate charging stations, then the routing process is simple, but the charging location does not align with user preferences and vehicle-to-vehicle communication data
Solution Approach 1:
A server acts as an intermediary between vehicles and charging stations, receiving charging requests from vehicles, determining optimal charging locations based on user preferences and vehicle-to-vehicle communication data, and routing vehicles to appropriate charging stations. This mediator consolidates the complexity of preference matching and routing optimization in a centralized system rather than requiring complex distributed logic in each vehicle.
2Reliability
If a vehicle routes to the nearest charging station, then the routing time is short, but the charging station may not meet user preferences or availability requirements
Solution Approach 1:
The system performs preliminary determination of optimal charging locations by analyzing user preferences, charging station availability, and vehicle-to-vehicle communication data before routing the vehicle. The server pre-calculates the best charging station match, so when the vehicle receives routing instructions, it already has a optimized path to a reliable charging location rather than needing to search or trial multiple stations.
Solution Approach 2:
The system utilizes vehicle-to-vehicle communication to create feedback loops where vehicles share charging station usage data, availability information, and preference patterns. This feedback enables the server to continuously optimize routing decisions, balancing the need for reliable charging station matches with efficient routing time by learning from collective vehicle experiences.
3Measurement precision
If the system collects and processes detailed user preference data, then the charging location accuracy improves, but data security and management complexity increase
Solution Approach 1:
The server serves as a secure intermediary that collects, processes, and manages detailed user preference data centrally. Rather than requiring vehicles to independently handle sensitive preference information or share it directly with each other, the server mediates all data interactions, implementing security protocols and access controls to protect user information while enabling accurate charging location determination.
Data Source
AI summary
An example operation includes one or more of receiving, from a vehicle, a request for a charge, determining, based on one or more preferences related to the vehicle, a charging location used by another vehicle with the same preferences, and routing the vehicle to the charging location based on the determining.