IoT Charging Pile Recommendation for Queue and Failure Avoidance
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Solution Overview
Problem
New energy vehicles face long queues and potential charging pile failures at public charging stations, leading to inconvenience and inefficiency in charging services.
Innovation Solution
An IoT system for charging pile recommendation in a smart city, utilizing a charging pile management platform to determine candidate charging piles based on queuing and time information, predict failure rates, and provide personalized recommendations to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users rely on public charging piles on the streets, then charging service coverage is improved, but waiting time increases and reliability deteriorates due to long queues and charging pile failures
Solution Approach 1:
The system performs preliminary actions by predicting charging pile status and queue lengths before users arrive. It calculates expected waiting times and proactively recommends alternative charging piles that will be available sooner, allowing users to plan their routes in advance and avoid long queues at popular charging locations
Solution Approach 2:
The system implements real-time feedback by continuously monitoring charging pile status, queue lengths, and operational reliability. This feedback is used to dynamically update waiting time predictions and recommend the best charging piles based on current conditions, creating a closed-loop system that adapts to changing circumstances
2Adaptability or versatility
If users rely on public charging piles on the streets, then charging service coverage is improved, but reliability deteriorates due to charging pile failures
Solution Approach 1:
The system performs preliminary reliability assessment by predicting the probability of charging pile failures before users attempt to use them. It identifies charging piles with high failure risks based on historical data and current conditions, then recommends alternative piles with higher reliability scores, allowing users to avoid problematic charging locations in advance
Solution Approach 2:
The system implements continuous feedback by monitoring charging pile operational status and updating reliability predictions in real-time. When failures occur, the system learns from these events and adjusts future recommendations to avoid similarly unreliable charging piles, creating a self-improving recommendation system
3Ease of operation
If the system provides detailed charging pile recommendations including failure rate prediction, then service quality is improved, but system complexity increases
Solution Approach 1:
The system introduces a knowledge graph as an intermediary layer between raw charging pile data and user recommendations. The knowledge graph structures complex relationships between charging piles, their historical performance, and reliability metrics, making the data more manageable and enabling sophisticated predictions without overwhelming the recommendation engine with raw data complexity
Solution Approach 2:
The system creates simplified copies of complex charging pile information through the knowledge graph, which stores essential features and relationships in a structured format. This copying approach allows the system to work with condensed representations of charging pile data rather than processing all raw operational data, reducing computational complexity while preserving essential information for predictions
Data Source
AI summary
The present disclosure provides a method of charging pile recommendation for a new energy vehicle in a smart city. This method is executed by a charging pile management platform. This method includes: obtaining a charging request of a user by the user platform; based on the charging request, determining candidate charging piles; based on queuing information of the candidate charging piles and time information for going to the candidate charging piles, sorting the candidate charging piles to determine an object charging pile; obtaining a knowledge map; based on the knowledge map, determining the failure rate of the object charging pile; based on a failure rate of the object charging pile, determining recommendation information of the object charging pile; and based on the service platform, feeding back the recommendation information to the user through the user platform.