Autonomous Mining Fleet Energy Storage Optimization
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Solution Overview
Problem
Existing mining machine fleets face challenges in optimizing energy storage utilization, leading to inefficiencies in productivity, cost, and safety within defined mining environments.
Innovation Solution
A system comprising a fleet of at least partially autonomous mining machines, interchangeable energy storages, machine sensors, spatial localization, and a fog- and/or cloud-computation system that computes and deploys optimized energy storage utilization based on data from mining machines, energy storages, and spatial localization, aiming to enhance productivity, reduce costs, and improve safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mining machines operate simultaneously in a working area with shared work assignments, then productivity is improved through coordinated operations, but energy storage utilization becomes inefficient due to lack of optimization
Solution Approach 1:
The fog- and/or cloud-computation system performs preliminary calculations to determine optimized energy storage utilization before deploying workflow information to mining machines. This includes computing optimal energy storage allocation, charging schedules, and task assignments in advance, allowing machines to operate efficiently without real-time optimization delays.
Solution Approach 2:
The system continuously receives data from mining machines regarding their location, energy consumption, and task completion status. This feedback loop enables the computation system to recalculate and update energy storage utilization optimization, adjusting workflow assignments dynamically to maintain both productivity and energy efficiency.
2Reliability
If mining machines require dedicated servicing areas for refueling or charging, then safety is improved by separating energy replenishment from working areas, but productivity decreases due to additional travel time to and from servicing areas
Solution Approach 1:
The system schedules energy storage replacement and charging activities in advance during periods of lower productivity demand. By predicting when machines will need energy replenishment and planning these operations beforehand, the system minimizes disruption to overall productivity while maintaining safety through dedicated servicing areas.
Solution Approach 2:
The workflow information deployed to mining machines is dynamically adjusted based on real-time data about machine locations, energy levels, and servicing area availability. This dynamic optimization allows the system to minimize travel time to servicing areas while maintaining safety separation, thereby reducing productivity loss.
3Adaptability or versatility
If interchangeable energy storages are used in mining machines, then adaptability is improved for different energy requirements, but device complexity increases due to management of multiple energy storages
Solution Approach 1:
The system extracts the complexity of managing interchangeable energy storages from the mining machines themselves and centralizes it in the fog- and/or cloud-computation system. The computation system handles tracking, matching, and assignment of energy storages to machines, while machines simply receive workflow information indicating which energy storage to use and when.
Solution Approach 2:
The fog- and/or cloud-computation system acts as an intermediary between interchangeable energy storages and mining machines. It computes optimal matching between energy storage characteristics and machine requirements, manages replacement schedules, and coordinates with servicing areas, thereby simplifying the interface between storages and machines while maintaining high adaptability.
Data Source
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AI summary
A system (1) comprising a fleet of at least partially autonomous mining machines (2) and a method of computing an optimized energy storage utilization in such a system (1). Data is acquired relating to: a shared work assignment (8); each respective mining machine (2); each respective energy storage (6); a location of each mining machine (2) and each energy storage (6) in the mining environment (3). The acquired data is provided to a fog- and/or cloud- computation system (13) which is used to compute an optimized energy storage (6) utilization with respect to at least one of the following optimization targets productivity, cost or safety. A workflow information for the performance of the shared work assignment (8) based on the optimized energy storage (6) utilization is deployed to the fleet of mining machines (2).