EV Charging Service Delivery Based on Predicted Dwell Time
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Solution Overview
Problem
Existing vehicle charging systems lack the ability to predict charging duration accurately and provide personalized services to occupants based on their preferences and time constraints during the charging process.
Innovation Solution
A system that predicts charging duration using data from the vehicle, individual preferences, and charging station information, and delivers services such as media content or additional services based on the predicted duration and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If charging duration is predicted and services are provided during charging time, then user satisfaction and charging experience are improved, but system complexity increases due to need for prediction algorithms and service coordination
Solution Approach 1:
The system performs preliminary actions by predicting the charging duration before the actual charging process begins. This prediction enables the system to pre-arrange and deliver services during the charging period, transforming an otherwise idle time into productive service delivery time, thereby improving user satisfaction without requiring complex real-time coordination during charging
2Adaptability or versatility
If personalized services are delivered based on user preferences and charging duration, then user satisfaction is improved, but data processing requirements and system complexity increase
Solution Approach 1:
User preferences and service options are collected and processed in advance, before the charging event occurs. This preliminary data gathering and processing allows the system to quickly deliver personalized services during charging without requiring complex real-time decision-making, thus improving adaptability while managing data processing complexity
Solution Approach 2:
The system enables users to pre-select their service preferences and requirements. This self-service approach allows users to define their own service parameters, reducing the burden on the system to analyze complex user needs in real-time, thereby achieving personalized service delivery with reduced data processing complexity
Data Source
AI summary
An example operation includes one or more of predicting a charging duration of a vehicle connected to a charging station, based on data related to the vehicle, an individual associated with the vehicle, and the charging station; determining a length of time the individual will be in the vehicle when the vehicle is connected to the charging station; in response to the length of time being less than the predicted charging duration, determining a service to provide to the individual based on the data; and delivering the service based on the length of time.