Hybrid Harvester Power Prediction for Output and Storage Control
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Solution Overview
Problem
Conventional agricultural harvesters face inefficiencies due to inadequate power management, as they either reserve power that is not needed or operate at excessive levels, leading to wear and inefficiency, and struggle with managing power storage effectively.
Innovation Solution
A system that utilizes in-situ data and predictive models to manage power distribution and storage in hybrid agricultural harvesters, adjusting power output and charging/discharging electrical storage based on real-time and historical data to match power requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If power is reserved for auxiliary operations, then power availability for auxiliary operations is improved, but power usage efficiency deteriorates due to reserving power that is not needed
Solution Approach 1:
The system performs preliminary prediction of power requirements for auxiliary operations using a predictive model that processes historical data and current operational state. This allows the power management system to prepare and allocate power before auxiliary operations are actually needed, eliminating the need for conservative power reserves while ensuring power availability when required.
Solution Approach 2:
The system continuously monitors actual power consumption during auxiliary operations and compares it with predicted values. This feedback is used to refine the predictive model and adjust future power allocation decisions, improving both power availability and usage efficiency over time by learning from actual operational patterns.
2Reliability
If power is operated at excessive levels, then power availability is improved, but wear and inefficiency increase
Solution Approach 1:
The power management system dynamically adjusts power output levels based on real-time predictions of actual power requirements. Instead of operating at fixed excessive levels, the system continuously adapts power delivery to match actual needs, ensuring power availability when required while minimizing unnecessary operation at excessive levels that cause wear and inefficiency.
Solution Approach 2:
The system changes operational parameters (power output levels) based on predicted requirements for auxiliary operations. By adjusting power levels dynamically rather than maintaining constant excessive power, the system ensures adequate power availability while reducing harmful effects of unnecessary high-power operation such as increased wear and energy inefficiency.
3Reliability
If power storage is managed conservatively, then power storage reliability is improved, but power storage optimization deteriorates
Solution Approach 1:
The system predicts future power requirements for auxiliary operations in advance and proactively manages power storage accordingly. This allows the system to optimize charging and discharging schedules without compromising reliability, as the predictive model ensures power will be available when needed while maximizing the utilization of power storage capacity.
Solution Approach 2:
The system monitors actual power consumption during auxiliary operations and uses this feedback to refine predictions and optimize future power storage management. This continuous learning process improves both reliability and optimization by adjusting power storage strategies based on actual operational patterns rather than conservative estimates.
Data Source
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 hybrid agricultural harvesting machine based on the predictive value of the power characteristic.