Charging Station Recommendation Using DTE and Availability Analysis
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Solution Overview
Problem
Existing charging station recommendation systems are prone to errors due to limitations in real-time information management and static data, leading to unreliable charging station recommendations, which can cause anxiety for mobility apparatus drivers when they arrive at destinations with insufficient charge.
Innovation Solution
A method and system that determines a recommended charging station by considering the charging amount, distance to empty (DTE), driving level, and surrounding data of charging stations, ensuring a stable charging option is available before the mobility apparatus runs out of charge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing charging station recommendation logic is used, then various attributes of chargers and routes are comprehensively considered, but the recommendation is exposed to errors due to limitations in real-time information management and static data
Solution Approach 1:
The system pre-calculates and stores historical charging data, traffic patterns, and charging station performance metrics before they are needed for recommendation. This preliminary preparation of data allows the system to quickly provide reliable recommendations without real-time calculation delays, addressing both the comprehensiveness and accuracy requirements
Solution Approach 2:
The system implements a feedback mechanism where actual charging outcomes, user satisfaction, and charging station status are continuously monitored and fed back into the recommendation algorithm. This closed-loop feedback improves recommendation accuracy over time by learning from past performance data while maintaining comprehensive attribute consideration
2Device complexity
If a charging station is recommended based on static data, then the recommendation process is simple, but the system cannot cope with real-time changes in charging station availability and traffic conditions
Solution Approach 1:
The recommendation system transitions from static to dynamic by continuously updating charging station status, traffic conditions, and user preferences in real-time. The system adapts its recommendations based on current conditions while maintaining a relatively simple underlying algorithm structure, balancing complexity and adaptability
Solution Approach 2:
The system creates a multi-functional platform that handles both simple static recommendations and complex real-time adaptations through a unified algorithm framework. This universal approach allows the same system to serve multiple purposes without requiring separate complex subsystems
3Ease of operation
If the navigation service provides charging station recommendations to relieve driving anxiety, then user confidence is improved, but the system may recommend charging stations that are actually unavailable due to information errors
Solution Approach 1:
The system prepares backup charging station options in advance and provides uncertainty estimates with recommendations. When the primary recommended station shows signs of potential unavailability, the system has pre-prepared alternative options ready, cushioning against the risk of recommending unavailable stations while maintaining user confidence
Solution Approach 2:
Real-time feedback from charging stations about actual availability, user reports of charging success/failure, and live traffic data continuously update the system's confidence in its recommendations. This feedback loop ensures that recommendations remain reliable while maintaining ease of use
Data Source
AI summary
Methods and systems for recommending a charging station are described. According to one embodiment, a method comprises obtaining a charging amount of a mobility apparatus, obtaining a distance to empty (DTE) using the charging amount, obtaining a driving level using the charging amount, obtaining a first charging station existing within the DTE and surrounding data of the first charging station, and determining the first charging station or a second charging station existing within the DTE as a recommended charging station using the driving level and an analysis result of the surrounding data of the first charging station.