EV Charging Request Feedback for Accurate Load Management
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Solution Overview
Problem
Users often inaccurately represent their energy needs and charging times when requesting charging sessions, leading to inefficient charge capacity utilization and poor load management at electric vehicle charging stations.
Innovation Solution
A system and method that receive and compare user-provided charge requests with actual usage data, determining accuracy based on predetermined thresholds, and taking corrective action to encourage users to adjust their requests for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users are provided with a templated interface that automatically populates charging time and energy needs, then the ease of operation is improved, but the accuracy of user-provided charging information deteriorates
Solution Approach 1:
The system monitors actual charging behavior (time plugged in, energy consumed) and compares it with user requests. When discrepancies are detected, the system provides feedback to the user through notifications or adjusted charging parameters, encouraging more accurate future requests while maintaining the simplicity of the templated interface.
Solution Approach 2:
The system dynamically adjusts charging parameters based on the user's historical accuracy. For users who consistently provide inaccurate information, the system may modify default values, adjust charging rates, or change timing parameters to optimize charge delivery while still using the templated interface.
2Productivity
If load management prioritizes users who request more energy and earlier charging, then the productivity of charge delivery is improved for those users, but the efficiency of charge capacity utilization deteriorates
Solution Approach 1:
The system uses feedback from actual charging sessions to adjust load management priorities. Users who consistently request more energy than they actually consume receive adjusted prioritization in subsequent sessions, reducing energy wastage while maintaining fair charge delivery for all users.
Solution Approach 2:
The system intentionally delivers partial charging in some cases where users request full charging, particularly when load management determines that full delivery would result in excessive energy consumption or inefficient capacity utilization. This approach optimizes overall system efficiency while still meeting user needs.
3Ease of operation
If the charging station accommodates all user requests as provided, then the ease of operation is maintained, but the efficiency of charge capacity utilization deteriorates
Solution Approach 1:
The system maintains the simple templated interface for users while implementing intelligent feedback loops in the background. The system analyzes actual charging patterns and adjusts capacity allocation, charging rates, and scheduling to optimize utilization without requiring users to manually adjust their requests or understand complex parameters.
Solution Approach 2:
The system automatically optimizes charge capacity utilization by monitoring actual charging behavior and self-adjusting charging parameters, scheduling, and resource allocation. This eliminates the need for user intervention while improving overall system efficiency beyond what simple request accommodation could achieve.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for truth and charging. One embodiment of a method includes receiving a charge request from a user for a charging station that includes an expected charging time and an expected energy usage for charging an EV, receiving data related to a charge at the charging station that is associated with the charge request, and comparing the expected charging time with the actual charging time and the expected energy usage with the actual energy usage. Some embodiments include determining whether the charge request was accurate, determining whether the user is accurate, and in response to determining that the user is not accurate, taking corrective action to encourage the user to adjust a future charge request to become more accurate.