Harvester Power Prediction for Dynamic Subsystem Allocation
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Solution Overview
Problem
Agricultural harvesters face power deficits and inefficiencies due to varying power requirements during operations, leading to reduced performance, increased wear, and energy waste, as existing systems struggle to accurately predict and manage power distribution among main and auxiliary subsystems.
Innovation Solution
A control system that utilizes in-situ data combined with prior or predicted data to generate predictive models and maps, such as topographic, soil moisture, and crop moisture maps, to optimize power distribution by adjusting engine throttle and power allocation based on real-time and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a set amount of power is automatically provided to the material transfer system, then the material transfer operation can be performed, but power waste and decreased efficiency occur when the provided power exceeds the required power
Solution Approach 1:
The system dynamically adjusts the power provided to the material transfer system based on real-time power requirements. Instead of providing a fixed set amount of power, the control system continuously monitors power consumption of primary subsystems and auxiliary subsystems, and adjusts the power allocation to match actual needs, thereby eliminating power waste while ensuring adequate power supply for material transfer operations
Solution Approach 2:
The system implements a feedback mechanism where the control system continuously monitors the power consumption of primary subsystems and auxiliary subsystems, compares actual power usage against available power from the powerplant, and adjusts power allocation accordingly. This feedback loop prevents both power waste and power deficits by maintaining real-time balance between power supply and demand
2Ease of operation
If power is taken from other systems to meet auxiliary subsystem power requirements, then the auxiliary subsystem can operate, but the performance of primary subsystems is reduced
Solution Approach 1:
The control system proactively predicts power requirements for both primary and auxiliary subsystems before power deficits occur. By anticipating future power needs based on operational patterns and system demands, the system can prepare power allocation strategies in advance, preventing situations where auxiliary subsystems must draw power from primary subsystems and cause performance degradation
Solution Approach 2:
The system dynamically balances power allocation between primary and auxiliary subsystems based on real-time conditions. The control system continuously adjusts power distribution to ensure auxiliary subsystems receive adequate power without compromising primary subsystem performance, optimizing the overall system operation rather than treating power allocation as a fixed hierarchy
3Power
If the powerplant is operated at maximum capacity to meet peak power requirements, then sufficient power is available for all subsystems, but energy efficiency decreases and wear on the powerplant increases
Solution Approach 1:
The system dynamically adjusts the powerplant output to match actual system requirements rather than operating at maximum capacity continuously. The control system monitors real-time power consumption of all subsystems and adjusts the powerplant output accordingly, ensuring sufficient power is available when needed while avoiding unnecessary operation at maximum capacity, thereby improving energy efficiency and reducing wear
Solution Approach 2:
The system changes the operating parameters of the powerplant based on actual power requirements. Instead of maintaining a fixed maximum output setting, the control system adjusts powerplant parameters such as engine load and fuel injection rates to match the actual power demand of primary and auxiliary subsystems, optimizing both performance and efficiency
Data Source
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AI summary
An agricultural harvesting system includes a control system. The control system identifies a predictive value of a power characteristic based on a relationship between the power characteristic and a characteristic. The control system generates a control signal to control a controllable subsystem of a mobile agricultural harvesting machine based on the predictive value of the power characteristic.