EV Charging Station Allocation Using Predicted Queue Availability
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Solution Overview
Problem
Existing charging station recommendation systems fail to accurately predict the status of charging stations due to lagging information exchange between user and charging end, leading to lengthy queuing times and poor user experience.
Innovation Solution
A method for smart allocation of charging stations that selects vehicles with regular charging behavior, predicts station status based on historical data, and plans navigation routes to minimize queuing time by allocating stations based on predicted availability and user habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing charging station recommendation schemes are used, then the system is simple to operate, but the information exchange between user end and charging end lags behind, causing inaccurate prediction of charging station status and lengthy queuing time
Solution Approach 1:
The system performs preliminary actions by predicting the status of charging stations before the user arrives. It analyzes historical charging data, vehicle charging behavior patterns, and real-time station status to forecast whether charging piles will be available when the user reaches the destination, allowing users to make informed decisions and avoid queues
Solution Approach 2:
The system establishes a feedback mechanism that continuously collects real-time charging station status data, vehicle charging completion information, and user navigation data. This feedback loop enables dynamic adjustment of predictions and recommendations, improving the accuracy of status prediction and reducing queuing time
2Reliability
If charging station allocation is based only on current status, then the allocation process is simple, but it cannot accurately predict future status when vehicle reaches the charging station
Solution Approach 1:
The system performs preliminary analysis of vehicle charging behavior patterns and historical charging data before making allocation decisions. It predicts future charging station status by analyzing past behavior, thereby improving the reliability of allocation without requiring complex real-time optimization during the charging event
Solution Approach 2:
The system transitions from static current-status-based allocation to dynamic prediction-based allocation. It continuously updates predictions based on real-time data flows including vehicle location, charging completion status, and station occupancy changes, making the allocation system adaptive and reliable
3Productivity
If more charging stations are provided in urban areas, then the supply meets increasing demand, but the cost of building and maintaining infrastructure increases
Solution Approach 1:
The system enables self-service by allowing users to independently query predicted charging station status and make informed decisions about where to charge. This reduces the need for extensive manual intervention and infrastructure expansion, as the system optimizes the utilization of existing charging stations through intelligent allocation
Solution Approach 2:
The system changes the operational parameters of existing charging stations by predicting their future status and guiding users to optimal charging locations and times. This maximizes the utilization rate of current infrastructure, effectively increasing service coverage without proportionally increasing the number of physical stations
Data Source
AI summary
A method for smart allocation of a charging station includes acquiring vehicle charging information, selecting, on a basis of the acquired vehicle charging information, a vehicle of which charging behavior is regular, and obtaining navigation information of a target vehicle and allocating a charging station to the target vehicle on a basis of the navigation information of the target vehicle and charging information of the vehicle of which charging behavior is regular.