EV Charging Location Classification Using Geolocation Clusters
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Solution Overview
Problem
Lack of reliable information on electric vehicle charging patterns leads to incorrect and delayed infrastructure-related decisions, as existing systems lack accurate and up-to-date data on customer charging behaviors.
Innovation Solution
An electric vehicle charging recommendation system that classifies charging station locations based on usage frequency and geolocation data, using machine learning algorithms to provide actionable insights for infrastructure development and user incentives, thereby enhancing the accessibility and utilization of EV charging infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional charging infrastructure planning methods are used, then infrastructure development can proceed with basic information, but the accuracy and timeliness of charging pattern data are insufficient leading to incorrect and delayed decisions
Solution Approach 1:
The patent introduces a server as an intermediary component that collects, processes, and analyzes charging data from multiple EVs and charging stations. This server acts as a mediator between the charging infrastructure and planning entities, aggregating reliable charging pattern information that would be impossible for individual components to obtain alone, thereby resolving the information availability problem
Solution Approach 2:
The system implements feedback mechanisms where charging data is continuously collected from EVs and charging stations, processed by the server to identify patterns, and then used to generate actionable insights for infrastructure planning. This closed-loop feedback system ensures that planning decisions are based on accurate, up-to-date charging patterns rather than outdated information
2Reliability
If comprehensive charging data from multiple vehicles is collected and analyzed, then accurate charging patterns can be identified for infrastructure planning, but the system complexity increases significantly
Solution Approach 1:
The patent divides the complex data collection and analysis system into distinct segmented components: EVs with onboard controllers, charging stations with data collectors, a central server for processing, and a user interface layer. Each component has a specific function, and the segmentation allows the system to handle large-scale data collection without overwhelming complexity at any single point
Solution Approach 2:
The server component is designed with multi-functionality, serving as a data collector, processor, analyzer, and communication hub simultaneously. This universal component consolidates multiple functions into a single system element, reducing overall system complexity while maintaining the ability to process comprehensive charging data from multiple vehicles
Data Source
AI summary
A method for providing an electric vehicle charging recommendation is disclosed. The method may include obtaining information associated with a plurality of charging events for a vehicle. The information may include a plurality of vehicle geolocations associated with the plurality of charging events. The method may further include generating a plurality of charging geolocation clusters from the plurality of vehicle geolocations. The method may additionally include determining a charging event occurrence frequency for each charging geolocation cluster. Further, the method may include classifying the charging geolocation clusters into a primary charging station location and routine charging station locations. Furthermore, the method may include defining a virtual polygon connecting the primary charging station location and the routine charging station locations, and transmitting the electric vehicle charging recommendation to a server based on the defined virtual polygon.