EV Charging Scheduler with Dynamic Queue Management
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Solution Overview
Problem
The inefficiency in scheduling electric vehicle charging due to limited charging infrastructure, where vehicles may occupy stations for extended periods while others wait, leading to difficulties in managing charging processes.
Innovation Solution
A system that includes controllers and processors in vehicles and charging stations to select and schedule charging based on the vehicle's state-of-charge, desired state-of-charge, and estimated departure time, allowing for queue management and notifications to users, ensuring efficient use of charging stations without displacing other vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If charging stations are limited in number, then infrastructure cost is reduced, but charging efficiency and utilization rate deteriorate
Solution Approach 1:
The system performs preliminary actions by having vehicles register their charging requirements in advance, calculate optimal charging schedules before arrival, and pre-book charging slots. This allows charging stations to prepare and allocate resources efficiently, maximizing utilization of limited infrastructure without requiring additional stations.
2Duration of action of moving object
If vehicles occupy charging stations for extended periods to complete charging, then charging completeness is improved, but availability for other vehicles deteriorates
Solution Approach 1:
The system dynamically adjusts charging schedules based on real-time conditions. Vehicles can modify their charging plans during the process, and the system re-allocates charging slots dynamically. This allows the charging station to serve multiple vehicles in a flexible sequence, improving overall throughput while ensuring each vehicle receives adequate charging time.
Solution Approach 2:
The system implements feedback mechanisms where charging status, queue position, and station availability are continuously monitored and communicated to vehicles. This enables informed decision-making by users about their charging plans, allowing them to adjust expectations or relocate if needed, thereby improving station turnover and availability.
3Measurement precision
If charging schedules are calculated based on multiple parameters, then scheduling accuracy is improved, but computational complexity deteriorates
Solution Approach 1:
The scheduling problem is segmented into manageable components: vehicle requirement analysis, station capacity assessment, queue management, and real-time adjustment. Each module handles specific calculations independently, reducing overall computational complexity while maintaining high scheduling accuracy through coordinated operation of these segmented functions.
Data Source
AI summary
A vehicle includes one or more controllers, programmed to responsive to receiving an indication of a destination, select a charger within a predefined geofence from the destination; responsive to detecting the vehicle becoming less than a predefined distance from the charger, calculate a proposed charging schedule based on a current state-of-charge (SOC) and a desired SOC; and join a queue for the charger with the proposed charging schedule.