Autonomous Scooter Fleet Self-Balancing and Charging
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Solution Overview
Problem
Managing a fleet of electric scooters in densely populated urban areas is challenging due to inefficient charging and distribution, as they often end up in suboptimal locations and face overcrowding at charging stations, leading to uneven utilization and reduced utility.
Innovation Solution
Implementing a system where autonomous electric scooters communicate with each other and a centralized server to self-manage their position, charge, and redistribute within a geographic area, using real-time data on demand and availability to optimize placement and battery life, allowing for swarming behavior to balance workload and usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If electric scooters are deployed in densely populated urban areas, then personal transportation convenience is improved, but fleet management difficulty increases due to charging requirements and distribution challenges
Solution Approach 1:
The patent implements autonomous scooters that self-manage their own charging and redistribution without human intervention. The scooters autonomously navigate to charging stations, monitor their own battery levels, and redistribute themselves based on demand patterns, transforming the fleet management system into a self-service operation that reduces management complexity while maintaining service availability
Solution Approach 2:
The system incorporates real-time feedback loops where scooters communicate their status (location, battery level, usage patterns) to a centralized management system, which then adjusts redistribution strategies dynamically. This feedback mechanism enables adaptive fleet management that responds to changing urban conditions, resolving the complexity of managing distributed vehicles across dense populations
2Productivity
If electric scooters are positioned in optimal locations based on traffic patterns, then utilization efficiency is improved, but they may be left at charging locations that do not have availability due to overcrowding
Solution Approach 1:
The patent implements dynamic charging station selection where scooters do not have fixed charging locations but instead adaptively choose charging stations based on real-time availability data. The system continuously monitors charging station capacity and redistributes scooters to underutilized stations, transforming the static charging assignment into a dynamic process that maintains both high utilization and reliable charging availability
Solution Approach 2:
The system changes the parameter of charging location assignment from fixed to variable, allowing scooters to operate at multiple charging stations throughout their service life. This parameter change enables the fleet to access a broader pool of charging resources, reducing the impact of overcrowding at any single location while maintaining optimal utilization patterns
3Productivity
If autonomous scooters communicate and self-manage position and charge, then distribution optimization is improved, but system complexity increases
Solution Approach 1:
The patent divides the fleet management system into autonomous individual scooter units, each capable of independent decision-making regarding charging and positioning. This segmentation distributes the computational and operational complexity across many simple units rather than requiring one complex centralized control system, enabling distribution optimization through coordinated autonomous behavior while managing overall system complexity
Solution Approach 2:
Each scooter in the fleet is designed as a universal platform with identical autonomous capabilities, communication interfaces, and charging interfaces. This universality allows any scooter to perform any function (transport, charging, redistribution) at any location, simplifying the system architecture by standardizing components while achieving optimized distribution through flexible deployment of identical multi-functional units
Data Source
AI summary
Autonomous vehicle fleet management systems are provided herein. An example method includes receiving, via a control module of a first electric vehicle, trip characteristics data associated with a second electric vehicle. The trip characteristics data includes information such as vehicle location, a trip destination, and a route plan associated with the second electric vehicle. The control module or a connected control server selects a charging station for recharging the first electric vehicle based at least in part on the trip characteristics data and at least one route optimization option associated with the first electric vehicle. The example method further includes determining a travel route to the charging station and navigating the first electric vehicle to the charging station along the travel route using an autonomous vehicle navigation system associated with the control module.