EV Charging Station Recommendation via Supply Demand Analysis
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Solution Overview
Problem
Existing route guidance systems for electric vehicles do not accurately predict charging waiting times and charging times, leading to inefficient route planning and potential long waiting times at charging stations.
Innovation Solution
A driving information display apparatus and method that sets zones based on geographic information, clusters charging stations, and analyzes demand and supply information to accurately predict charging waiting times and charging times, thereby providing optimal route guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If charging station recommendation is based only on vehicle conditions and driving route, then route guidance can be provided, but charging demand concentrates in specific areas causing long waiting times
Solution Approach 1:
The system collects real-time charging status information from multiple charging stations and feeds this information back to dynamically adjust routing recommendations. This feedback mechanism allows the system to identify charging stations with shorter waiting times and redirect vehicles away from congested stations, thereby reducing overall charging waiting time while maintaining effective route guidance.
Solution Approach 2:
The routing system transitions from static pre-planned routes to dynamic real-time routing that continuously adapts based on current charging station status. The system dynamically recalculates optimal charging stops by considering real-time factors such as charging availability, waiting times, and vehicle battery status, enabling flexible route adjustments that minimize waiting time while providing ongoing guidance.
2Measurement precision
If multiple charging stations are monitored for supply and demand analysis, then charging waiting time can be predicted, but system complexity increases
Solution Approach 1:
The system segments the monitoring scope into multiple independent charging station units, each with its own status parameters (availability, current demand, charging speed). By dividing the complex monitoring task into discrete station-level segments, the system can collect and analyze data from multiple stations without creating an unmanageably complex monolithic system, enabling precise waiting time prediction through aggregated segment data.
Solution Approach 2:
The system introduces an intermediary routing server that acts as a mediator between charging stations and vehicles. This intermediary collects status information from multiple charging stations, processes the data to predict waiting times, and generates optimized routing recommendations. The intermediary layer simplifies the overall system architecture by centralizing the complex analysis functions while maintaining manageable communication interfaces with individual stations.
Data Source
AI summary
In a driving information display apparatus and method for providing charging station recommendation information by considering supply and demand, the driving information display apparatus can include a processor configured to receive driving guidance information and vehicle location information and perform control to output a guidance screen corresponding to the driving guidance information, and a storage unit configured to store road information and algorithms driven by the processor. The driving guidance information can include route information including charging station route guidance generated based on destination information, target remaining charge level information, and the supply level information of one or more charging stations for each zone located on a route to a destination among a plurality of zones.