EV Charging Point Recommendations Using Crowdsourced Vehicle Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing EV charging infrastructure faces challenges such as insufficient charging points, broken chargers, high costs, confusion about payment and charging time, and variability in connector types and charging speeds, making it difficult for EV users to find suitable charging stations based on their vehicle's characteristics and external factors.
Innovation Solution
A system that crowdsources data from EVs and mobile devices associated with EV users, utilizing real-time and historical charging data to generate a charging profile for each EV. This profile is used to predict charging needs and recommend suitable charging points, independent of charging point operators or data aggregators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If charging point operators or data aggregators are used to provide charging data, then data availability may be improved, but system complexity and dependency on external parties increases
Solution Approach 1:
The system enables EVs to autonomously collect their own charging data through onboard sensors and communication modules. The EVs self-report charging events, location, and battery status directly to the recommendation system, eliminating dependency on charging point operators or data aggregators to provide this information.
Solution Approach 2:
The patent introduces a neutral recommendation system that acts as an intermediary between EVs and charging infrastructure. This system collects data directly from EVs and provides recommendations without relying on charging point operators or commercial data aggregators, thereby reducing system complexity and external dependencies.
2Adaptability or versatility
If multiple charging points are available, then user choice is improved, but difficulty in identifying suitable charging station increases
Solution Approach 1:
The system continuously monitors EV battery status, location, and charging history, providing real-time feedback to generate personalized recommendations. The system learns from user behavior patterns and adjusts recommendations based on feedback about charging preferences, making it easier for users to identify suitable charging stations among multiple options.
Solution Approach 2:
The patent applies local quality by providing customized charging recommendations tailored to each EV's specific characteristics, battery status, and user preferences. Instead of generic information about all charging points, the system delivers location-specific, condition-specific recommendations that match the particular needs of each EV and user combination.
3Measurement precision
If real-time charging data is collected from multiple sources, then prediction accuracy is improved, but data collection complexity increases
Solution Approach 1:
EVs autonomously collect and report their own charging data through onboard sensors, communication modules, and battery management systems. This self-service approach simplifies data collection by eliminating the need for complex external monitoring infrastructure while maintaining high prediction accuracy through direct EV-reported data.
Data Source
AI summary
An apparatus configured to collect and predict charging data for an electric vehicle (EV) from a mobile device associated with a user or the EV or an infotainment unit of the EV.