Autonomous Vehicle Dispatch Routing for Battery-Aware Maintenance
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Solution Overview
Problem
Current autonomous vehicle fleet management systems face inefficiencies in dispatching vehicles to maintenance facilities due to variations in energy consumption based on environmental and vehicle-specific factors, leading to suboptimal battery charging and maintenance scheduling.
Innovation Solution
Implementing a system that predicts energy usage for routing autonomous vehicles to maintenance facilities based on distance, travel conditions, vehicle-specific factors, and demand, using data from sensors and machine learning algorithms to determine optimal routing and charging strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If autonomous vehicles are dispatched to maintenance facilities using traditional routing methods, then the dispatch process is simple, but energy consumption varies suboptimally and vehicle availability decreases
Solution Approach 1:
The system performs preliminary actions by predicting energy consumption before routing decisions are made. The fleet management server calculates predicted energy consumption for multiple potential routes to maintenance facilities, considering various factors such as distance, traffic conditions, and vehicle characteristics, and selects the optimal route in advance, thereby optimizing energy consumption while maintaining manageable system complexity
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual energy consumption and comparing it with predicted values. This feedback loop allows the fleet management server to refine its routing predictions and adjust future dispatch decisions, improving energy optimization over time while maintaining a structured approach to routing complexity
2Loss of time
If autonomous vehicles are routed without considering predicted energy consumption, then routing decisions are made quickly, but battery charging timing is suboptimal and vehicle downtime increases
Solution Approach 1:
The fleet management server performs preliminary energy consumption predictions and routing calculations before vehicles need to be dispatched to maintenance facilities. By pre-calculating the most energy-efficient routes and predicting when vehicles will need charging, the system optimizes battery charging timing and reduces vehicle downtime without requiring complex real-time decision-making during vehicle operation
Solution Approach 2:
The system enables autonomous vehicles to effectively self-manage their maintenance needs by providing them with predicted energy consumption data and optimal routing information. Vehicles can autonomously navigate to maintenance facilities at optimally timed intervals based on their predicted energy levels, reducing the need for complex centralized control while minimizing downtime
3Productivity
If traditional dispatch methods are used, then the system is easy to operate, but fleet efficiency and vehicle availability are reduced
Solution Approach 1:
The fleet management server performs multiple functions within a single centralized system: it manages routine maintenance scheduling, predicts energy consumption for various routes, determines optimal routing to maintenance facilities, and coordinates vehicle dispatch. This multi-functional approach improves fleet efficiency by optimizing multiple aspects of vehicle management simultaneously while maintaining a unified, manageable system architecture
Solution Approach 2:
The system optimizes fleet efficiency by dynamically changing key parameters such as routing decisions, dispatch timing, and maintenance scheduling based on predicted energy consumption values. By adjusting these parameters according to real-time and historical data, the system improves vehicle availability and fleet productivity while maintaining a structured approach to managing system complexity
Data Source
AI summary
Systems and techniques are provided for dispatching autonomous vehicles to maintenance facilities. An example process can include receiving, from a plurality of autonomous vehicles (AVs), AV location data and AV battery data; determining, based on the AV location data and AV dispatch information, a first predicted energy usage for routing each of the plurality of AVs to one or more waypoints; determining, based on AV maintenance facility location data, a second predicted energy usage for routing each of the plurality of AVs to one or more AV maintenance facilities; determining, based on the first predicted energy usage, the second predicted energy usage, and the AV battery data, a projected battery charge state for each of the plurality of AVs; and sending routing instructions to one or more of the plurality of AVs that are based on the projected battery charge state.