EV Charging Content Personalization Using Duration Prediction
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Solution Overview
Problem
Existing vehicle systems lack the ability to efficiently deliver personalized content to vehicle occupants during charging sessions, considering factors like expected charging duration and occupant preferences.
Innovation Solution
A system that determines the expected charging duration of an electric vehicle at a charging point and delivers content to the vehicle based on this duration and the occupant's content preferences, using AI and machine learning to personalize the content and optimize its delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If content is delivered to the EV during charging, then user experience is improved, but system complexity increases
Solution Approach 1:
The patent introduces a content delivery system that acts as an intermediary between the charging infrastructure and the EV occupant. This system receives charging duration information from the charging point, queries content preferences from a server, and delivers appropriate content through the EV's display system. The intermediary nature of this system resolves the contradiction by automating the complex coordination between multiple components (charging point, server, EV system) while providing a simple, seamless user experience.
Solution Approach 2:
The system performs preliminary actions by determining the expected charging duration before content delivery begins, and by pre-querying content preferences from the occupant's profile data. This allows the system to prepare and deliver appropriate content in advance, improving user experience while managing system complexity through automated pre-processing of information.
2Loss of information
If personalized content is delivered based on occupant preferences, then content relevance is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts only the necessary information (content preferences) from the occupant's comprehensive profile data stored on the server. Rather than processing or storing all profile data locally in the EV, the system queries and extracts only the relevant content preference information needed for content delivery. This extraction approach improves content relevance while minimizing data processing requirements and storage needs in the vehicle system.
3Loss of time
If content delivery is optimized based on charging duration, then time utilization is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary determination of the expected charging duration by receiving this information from the charging point before content delivery begins. This advance knowledge allows the system to select and deliver content that is appropriately timed to the charging session, maximizing time utilization without requiring continuous or highly precise measurement during the charging process.
Data Source
AI summary
An example operation includes one or more of determining an expected charging duration of an electric vehicle (EV) at a charging point, determining content preferences of an occupant associated with the EV, wherein the content preferences are based on profile data of the occupant associated with the occupant, and delivering content to the EV, based on the expected charging duration and the content preferences.