Multi-Agent EV Battery Swap Scheduling via IoV Edge Computing
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Solution Overview
Problem
Existing methods for scheduling electric vehicle battery swaps struggle to balance the workload among swap stations, leading to overcrowding at some stations and idleness at others, resulting in inefficient resource utilization and long waiting times for users.
Innovation Solution
A method utilizing multi-agent reinforcement learning and edge computing to dynamically optimize battery swap scheduling, where roadside units act as relay nodes, and vehicles and infrastructure collaborate to identify swap areas with the highest service capacity, ensuring balanced service rates and quality across all stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If battery swap stations pursue high service occupation rate with long waiting queues, then station utilization improves, but user waiting time increases and self-benefit maximization deteriorates
Solution Approach 1:
The patent implements dynamic scheduling that adjusts battery allocation and swap station assignments in real-time based on changing demand patterns, vehicle arrival rates, and station status. This dynamic approach allows the system to optimize both utilization and waiting time continuously rather than relying on static predetermined schedules.
Solution Approach 2:
The system incorporates feedback mechanisms where user waiting times, station occupancy rates, and swap completion data are continuously monitored and fed back to the scheduling algorithm. This feedback loop enables the system to learn from past performance and adjust future assignments to balance utilization efficiency with user convenience.
2Productivity
If multiple adjacent swap stations operate independently with self-benefit maximization, then individual station efficiency improves, but overall system coordination deteriorates causing overcrowding and idleness
Solution Approach 1:
The patent merges the scheduling decisions of multiple independent swap stations into a unified coordinated system. The centralized scheduling algorithm considers the status and capacity of all stations simultaneously, directing vehicles to appropriate stations based on overall system optimization rather than individual station interests, thereby balancing load distribution across the network.
Solution Approach 2:
The scheduling system serves multiple functions simultaneously: it optimizes individual station utilization, balances overall system load, minimizes user waiting time, and coordinates vehicle routing. This multi-functional approach allows a single scheduling framework to address both individual efficiency and system-wide coordination.
3Adaptability or versatility
If predetermined swap time is disorderly and swap location is random, then scheduling flexibility improves, but resource waste increases with many idle swap stations
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal swap times and locations based on predicted demand patterns, vehicle routes, and station capacity. Rather than leaving decisions entirely random or purely reactive, the scheduling algorithm proactively assigns swap appointments that balance flexibility with resource utilization efficiency.
Solution Approach 2:
The patent dynamically changes scheduling parameters such as swap time windows, station assignments, and battery allocation based on real-time system state. This parameter adjustment allows the system to maintain flexibility in responding to unexpected events while preventing resource waste through data-driven optimization of swap operations.
Data Source
AI summary
A method for scheduling EV battery swap based on IoV, in which the roadside units regard battery-swap station clusters with a high degree of potential cooperation as a whole, and gather them into a single battery swap area; taking a service rate of a cluster of swap stations as an assessment target, and mainly examining a service capability, a service quality, and location information of each of swap station nodes themselves, and a current state of those electric vehicles that require to swap battery; providing a solution of the best joint actions for the overall electric vehicles to maintain the overall service balance of every swap station and improve a long-term performance of Internet of Vehicles. According to the invention, a battery of the electric vehicles can be swapped as soon as possible, and every battery swap station can maintain business balance.