EV Charging Vehicle Recognition for Automatic Authorization
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Solution Overview
Problem
The shift to electric vehicles requires efficient and user-friendly charging infrastructure to manage charging sessions, as traditional refueling methods do not apply, and complex access to charging resources can deter adoption and create competitive disadvantages.
Innovation Solution
A system using image data and machine learning models to automatically recognize vehicles, authorize charging sessions, and manage charging infrastructure, including detecting blocked locations, without manual interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual authorization processes are used for EV charging, then system complexity is reduced, but charging authorization time increases and user experience deteriorates
Solution Approach 1:
The patent replaces manual authorization mechanisms with an automated image recognition system using cameras and machine learning models to identify vehicles and authorize charging sessions automatically, eliminating the need for manual intervention and reducing authorization time
Solution Approach 2:
The system enables self-service charging authorization where the EV charging station autonomously identifies vehicles through image data, matches them with user accounts, and initiates charging sessions without requiring manual user input or operator intervention
2Productivity
If automated image recognition systems are deployed, then charging authorization efficiency improves, but infrastructure cost and system complexity increase
Solution Approach 1:
The patent implements a multi-functional system where the image recognition infrastructure serves multiple purposes: vehicle identification, license plate recognition, charging authorization, and blocked location detection, thereby justifying the infrastructure investment through diverse operational benefits
Solution Approach 2:
The system introduces an intermediary processing layer that captures image data, processes it through machine learning models, and translates it into charging authorization decisions, bridging the gap between physical vehicle presence and digital charging management systems
3Measurement precision
If comprehensive vehicle recognition is implemented, then charging authorization accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent pre-trains machine learning models with extensive vehicle image data before deployment, and performs preliminary filtering of image data to identify relevant features, so that the actual vehicle recognition and authorization process can proceed quickly with high accuracy
Solution Approach 2:
The system segments the vehicle recognition process into distinct stages: image capture, pre-processing, feature extraction, vehicle identification, and authorization decision, allowing each stage to be optimized independently and processed in parallel where possible
Data Source
AI summary
Certain aspects of the present disclosure provide techniques electric vehicle charging management. In one example, a method includes: detecting a connection between an electric vehicle supply equipment and an electric vehicle; receiving image data depicting an electric vehicle charging scene; determining one or more candidate vehicles in the image data; for each respective candidate vehicle of the one or more candidate vehicles in the image data, determining one or more vehicle characteristics associated with the respective candidate vehicle; associating one candidate vehicle of the one or more candidate vehicles with the electric vehicle based on one or more vehicle characteristics associated with the one candidate vehicle; determining if a user account is associated with the one candidate vehicle associated with the electric vehicle; and determining a charging authorization decision for the electric vehicle.