Agricultural Machine Control System for Fuel-Efficient Harvesting
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Solution Overview
Problem
Existing self-driving agricultural work machines face challenges in optimizing fuel consumption during harvesting processes, particularly when dealing with varying harvesting throughputs and field conditions, leading to inefficient engine operation and increased fuel usage.
Innovation Solution
A procedure that utilizes a preliminary recording system with sensors to gather geographical apron information, which is then combined with biomass forecast data to determine a forecasting bag. This information is used to adjust the drive engine settings, ensuring efficient processing of crops while maintaining a constant driving speed, thereby reducing fuel consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If the drive engine operates at constant high power to maintain ready availability, then the power availability is improved, but the fuel consumption increases
Solution Approach 1:
The engine power setting is dynamically adjusted based on real-time field conditions (crop density, plant population) detected by sensors and forecast data. The control system continuously adapts the engine power level to match actual harvesting requirements, transitioning from static high-power operation to dynamic power management that maintains readiness while reducing unnecessary energy consumption.
Solution Approach 2:
The system implements feedback control by continuously monitoring field conditions through detection systems and comparing actual harvesting throughput with forecasted values. Based on this feedback loop, the engine power setting is automatically adjusted to optimize the balance between power availability and fuel consumption, ensuring the engine operates at appropriate power levels rather than constant high power.
2Use of energy by moving object
If the engine power is reduced to save fuel, then the fuel consumption is improved, but the crop throughput processing capability deteriorates
Solution Approach 1:
The system performs preliminary detection of field conditions (crop density, plant population) before harvesting operations begin or change. Forecast data is obtained in advance to predict upcoming harvesting requirements. This preliminary information allows the control system to proactively adjust engine power settings to match anticipated throughput needs, preventing both over-powering and under-powering scenarios.
Solution Approach 2:
The engine power setting transitions from static to dynamic adjustment based on real-time detection of crop conditions and forecasted throughput requirements. The system continuously adapts power levels to match actual harvesting demands, ensuring sufficient power availability when needed while minimizing fuel consumption during lower throughput conditions.
3Ease of operation
If the driving speed is reduced to match transport vehicle synchronization, then the synchronization is improved, but the crop throughput processing capability deteriorates
Solution Approach 1:
The system dynamically adjusts engine power settings independently of driving speed to maintain optimal crop throughput processing capability. By decoupling the relationship between speed reduction and power reduction, the system can maintain lower speeds for synchronization while compensating with appropriate power levels to preserve harvesting efficiency.
4Measurement precision
If forecast accuracy is improved by using more detection systems, then the forecasting precision is improved, but the device complexity increases
Solution Approach 1:
The system combines multiple information sources (sensor detections of crop density and plant population with external forecast data) into a unified forecasting model. By merging these complementary data sources, the system achieves improved forecast accuracy without requiring each individual detection system to be overly complex, distributing the measurement burden across multiple simpler components.
Data Source
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AI summary
The present invention relates to a method for controlling at least one self-propelled agricultural machine (1) according to the preamble of claim 1. To provide a method that eliminates the disadvantages described in the prior art and enables fuel-efficient operation of the machine (1), the following method steps are proposed according to the invention: - Acquiring first foreground information of the geographic foreground (6) using the sensor (7) of the foreground detection system (5). - Transmitting the first foreground information to the control system (8). - Transmitting second foreground information to the control system (8), wherein the second foreground information comprises at least one piece of information from a biomass forecast map (27). - Determining a forecast accuracy (35, 36) of at least the first and/or second foreground information.- Determination of a motor setting of the drive motor (30) for driving the working machine (1) with a predetermined travel speed depending on at least the first foreground information and its prediction quality (35) and/or the second foreground information and its prediction quality (36) by means of the computing unit (9) of the control system (8).