Motor Energy Prediction for Battery-Aware Task and Route Planning
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Solution Overview
Problem
Current methods for managing energy consumption in electric motors, particularly in automated warehouses and vehicles, do not effectively consider task-specific parameters, leading to inefficiencies and increased energy costs.
Innovation Solution
A method that utilizes Real-Time Traffic Management (RTTM) servers and on-board computers to predict energy consumption by integrating sensors, task assignment algorithms, geographic maps, power consumption databases, and current battery states to optimize task allocation and route planning for autonomous robots and vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional task assignment algorithms are used in automated warehouses, then operational costs and productivity are improved, but energy consumption is not optimized and battery lifetime is reduced
Solution Approach 1:
The patent changes the parameters considered in task assignment algorithms by integrating energy consumption predictions based on motor characteristics, task parameters, and environmental conditions. This allows the system to optimize not only for productivity but also for energy efficiency, resolving the contradiction between operational efficiency and energy consumption.
Solution Approach 2:
The system implements feedback mechanisms where energy consumption data from sensors is continuously fed back to the task assignment algorithm. This real-time feedback enables dynamic optimization of task allocation to minimize energy usage while maintaining productivity, addressing the contradiction between operational efficiency and energy consumption.
2Adaptability or versatility
If robots frequently shift between stationary and moving states to increase flexibility, then maneuverability is improved, but battery consumption becomes critical and energy efficiency deteriorates
Solution Approach 1:
The system performs preliminary calculations of energy consumption for different task scenarios before executing movements. By predicting energy requirements based on motor characteristics and task parameters, the system can plan movements more efficiently and reduce unnecessary battery consumption while maintaining flexibility and maneuverability.
3Speed
If energy consumption is not considered in task allocation, then task completion speed is maintained, but global efficiency of the robot fleet deteriorates
Solution Approach 1:
The task assignment algorithm is enhanced to serve multiple functions simultaneously: it maintains task completion speed while also optimizing for energy efficiency and overall fleet productivity. The integrated system considers motor characteristics, task parameters, and environmental conditions to achieve multi-objective optimization, resolving the contradiction between speed and global efficiency.
Data Source
AI summary
Presented is a method for predicting the energy consumption of electric motors and using these predictions as one of the factors in planning tasks that are carried out with the assistance of the motors. The method can be used for assigning tasks to robots in an automatic system for picking and placing boxes on shelves in a warehouse. Another application of the method is selecting the most energetically efficient route from several alternative routes for passenger and commercial vehicles comprising battery powered propulsion systems that include electric motors and on-board computers.