Reserved Charging Column Activation Using Vehicle Approach Data
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Solution Overview
Problem
Existing methods for reserving electric vehicle charging stations do not fully optimize charging efficiency and convenience, as they may lead to waiting times or inefficient charging due to unsuitable station selection and lack of real-time data utilization.
Innovation Solution
A method where a cloud server reserves a designated charging column based on vehicle and station data, using a movable mechanical barrier and sensor-identified electric vehicles, optimizing charging column selection and activation through approach information, and providing visual signals for convenience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a cloud server reserves a charging column based on basic charging schedules, then charging station availability is improved, but charging efficiency and convenience are not fully optimized due to lack of real-time data utilization
Solution Approach 1:
The system implements feedback by continuously collecting real-time data from multiple sources including vehicle sensors (state of charge, battery temperature, charging power requirements), charging station status (available columns, current charging activity, operational status), and historical charging patterns. This feedback loop enables the machine learning model to dynamically optimize charging column assignment, transitioning from static scheduling to adaptive real-time decision-making that simultaneously improves reliability and productivity
Solution Approach 2:
The system transitions from static charging schedules to dynamic optimization by using machine learning models that continuously adapt to changing conditions. The model dynamically adjusts charging column assignments based on real-time vehicle requirements, station status, and predicted demand patterns, enabling the system to respond flexibly to varying charging needs and maximize both availability and efficiency
2Quantity of substance
If a charging station has multiple charging columns, then charging capacity is improved, but selecting the optimal charging column becomes complex without intelligent determination
Solution Approach 1:
The system implements self-service by enabling charging columns to effectively 'select themselves' for specific vehicles through automated machine learning-based assignment. The model automatically matches vehicle requirements (power needs, charging speed, battery characteristics) with appropriate column capabilities without manual intervention, allowing the system to autonomously manage the complexity of multi-column selection while maximizing charging capacity utilization
3Device complexity
If manual activation of charging columns is used, then system simplicity is maintained, but waiting times increase and driver satisfaction decreases
Solution Approach 1:
The system applies preliminary action by pre-assigning optimal charging columns to vehicles before they arrive at the charging station. The machine learning model processes vehicle requirements and station status in advance to determine the best column assignment, preparing the system ahead of time so that when the vehicle arrives, the optimal column is already identified and ready for immediate activation, thereby reducing waiting time without significantly increasing complexity
Solution Approach 2:
The system replaces manual mechanical activation processes with automated electronic control. Instead of drivers manually selecting and activating charging columns, the machine learning model automatically determines optimal assignments and triggers electronic activation signals to the selected columns, substituting manual operations with automated intelligent decision-making and electronic control systems
Data Source
AI summary
A method for reserving a charging column of a charging station is disclosed and may include determining a designated charging column of a selected charging station by a cloud server, reserving the designated charging column of the selected charging station by the cloud server, and ascertaining approach information concerning driving of the electric vehicle within the selected charging station to the designated charging column by the cloud server.