Worksite Charger Layout Optimization for Electrified Machine Scheduling
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Solution Overview
Problem
The introduction of electrified machines at worksites leads to issues such as reduced productivity, increased energy demand, poor scheduling, and higher costs due to suboptimal placement of chargers, which are not adjusted based on the worksite layout.
Innovation Solution
A method and system for optimizing charger locations using a distributed controller to calculate worksite metrics, scores, and select charging locations based on historical data and real-time factors, transmitting signals to machines and chargers to improve efficiency and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If chargers are placed at fixed locations without optimization, then device complexity is reduced, but productivity decreases and energy demand increases
Solution Approach 1:
The charger placement system transitions from static to dynamic by continuously calculating optimal charger locations based on real-time worksite metrics, machine locations, and historical data. The distributed controller updates charger positions dynamically to adapt to changing worksite conditions, thereby improving productivity without requiring overly complex manual reconfiguration processes.
Solution Approach 2:
The system implements feedback mechanisms by calculating worksite metrics based on historical data and real-time information, then using these metrics to determine optimal charger placements. The system continuously monitors productivity, energy consumption, and machine locations, feeding this information back into the optimization algorithm to improve charger positioning over time.
2Loss of energy
If charger locations are optimized dynamically, then energy demand is reduced, but device complexity increases
Solution Approach 1:
The control system is segmented into a distributed controller architecture where multiple controllers operate independently at different worksite locations. Each controller manages local charger optimization based on regional metrics, reducing the computational burden and complexity of a centralized system while still achieving overall energy efficiency through localized optimization.
Solution Approach 2:
The system enables chargers and machines to self-optimize their positioning and charging schedules based on pre-calculated worksite metrics and historical patterns. The distributed controller provides the optimization framework, but the actual charging decisions and location adjustments are made autonomously by the machines and chargers themselves, reducing the operational complexity of the overall system.
3Productivity
If charger placement is optimized based on historical metrics, then cost is reduced, but measurement precision requirements increase
Solution Approach 1:
The system calculates worksite metrics with higher precision than strictly necessary for basic operation, using comprehensive historical data and multiple measurement parameters. This excessive measurement precision ensures that even under varying worksite conditions, the optimization decisions remain accurate and reliable, ultimately reducing costs through improved efficiency.
Data Source
AI summary
Systems and methods for worksite geographic optimization may include one or more processors configured to calculate a worksite metric for a first worksite layout, the worksite metric based on a worksite cost and a historic worksite metric during a previous time period. In some embodiments, the first worksite layout differs from a second worksite layout. The processor(s) calculate a score corresponding to a machine at the first worksite layout, and select a charging location for the machine at the first worksite layout, according to the score and the worksite metric. The processor(s) can transmit a first signal corresponding to the charging location to the first machine, and a second signal to a charger of the plurality of chargers at the charging location.