Distance-Based EV Energy Transfer for Charging Queue Control
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Solution Overview
Problem
Current electric vehicle charging stations lack intelligence and cannot efficiently manage energy transfer based on the distance of the transport to the charging station, leading to inefficient energy distribution and potential delays.
Innovation Solution
A system where a charging station determines the estimated arrival time and remaining energy of a transport, notifies it to provide a portion of its energy, and adjusts the energy transfer based on delays, utilizing advanced communication and data processing to optimize energy distribution among multiple transports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a charging station provides power on demand without intelligence, then the charging station is simple to operate, but energy distribution is inefficient and queue times increase
Solution Approach 1:
The charging station performs preliminary actions by determining the estimated arrival time and remaining energy of approaching transports before they actually arrive. This allows the system to proactively notify transports about energy transfer requirements in advance, optimizing energy distribution efficiency without requiring complex real-time decision-making at the moment of arrival.
Solution Approach 2:
The system implements feedback by notifying transports about their estimated energy requirements and adjusting energy transfer instructions based on actual arrival times and delays. This closed-loop feedback mechanism enables intelligent energy distribution while maintaining manageable system complexity through automated adjustments.
2Productivity
If the charging station notifies transports to provide energy based on distance and arrival time, then energy transfer efficiency is optimized, but the system becomes more complex
Solution Approach 1:
The charging station performs multiple functions using a single integrated system: it tracks transport locations, estimates arrival times, calculates remaining energy requirements, and communicates with transports. This multi-functionality approach optimizes energy transfer efficiency while avoiding the need for separate specialized systems for each function.
Solution Approach 2:
Transports are empowered to self-manage their energy provision by receiving notifications about their energy requirements and autonomously deciding whether to provide energy based on their current state. This self-service approach reduces the complexity of centralized control while maintaining high energy transfer efficiency.
3Loss of time
If the charging station adjusts energy transfer based on transport delays, then queue times are reduced, but measurement and detection complexity increases
Solution Approach 1:
The system determines estimated arrival times and potential delays before transports actually arrive at the charging station. By performing this analysis in advance, the system can proactively adjust energy transfer instructions to minimize queue times without requiring complex real-time measurement and detection capabilities at the moment of arrival.
Data Source
AI summary
An example operation includes one or more of determining an estimated arrival time of a first transport to a charging station, determining an estimated remaining stored transport energy at the estimated arrival time of the first transport, notifying the first transport to provide a portion of the determined remaining stored transport energy and when a next transport is delayed to the charging station, notifying the first transport to provide an additional portion of the determined remaining stored transport energy based on the delay.