Predictive Vehicle Power Control With Tolerance-Band Intervention
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Solution Overview
Problem
Existing driver models for vehicle control focus solely on current vehicle speed requirements, leading to frequent acceleration and deceleration processes that increase fuel consumption and pollutant emissions, failing to simulate human driving behavior effectively.
Innovation Solution
A method that involves determining a target power profile curve with a tolerance band and an expectation characteristic curve to predict future vehicle behavior, enabling control commands only when the expectation characteristic curve intersects the tolerance band, thereby reducing unnecessary corrective actions and optimizing fuel efficiency and emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a driver model is oriented solely on current vehicle speed requirement, then the vehicle speed control is simple and responsive, but frequent acceleration and deceleration processes occur leading to higher fuel consumption and increased pollutant emissions
Solution Approach 1:
The driver model performs preliminary actions by predicting future vehicle speed using a prediction model before actual speed deviations occur. The model calculates expected speed values ahead of time and prepares control commands in advance, allowing the vehicle to maintain smoother speed profiles and avoid frequent acceleration/deceleration cycles that waste fuel.
Solution Approach 2:
The driver model dynamically adapts its control strategy by continuously updating predictions based on current vehicle state and road conditions. The model adjusts acceleration and deceleration commands dynamically based on predicted future speed requirements, enabling optimal fuel efficiency while maintaining responsive speed control.
2Device complexity
If a driver model is oriented solely on current vehicle speed requirement, then the control system is simple and fast, but it fails to simulate human driver behavior effectively
Solution Approach 1:
The prediction model performs preliminary calculations of future vehicle speed based on current conditions, mimicking how human drivers anticipate road conditions and plan their driving actions ahead of time. This allows the system to simulate human-like proactive driving behavior rather than just reactive responses to current speed deviations.
Solution Approach 2:
The driver model incorporates feedback mechanisms where prediction results are continuously compared with actual vehicle speed, and the model adjusts its predictions based on prediction errors. This feedback loop enables the system to learn and adapt its behavior patterns, improving its ability to simulate human driver responses over time.
3Speed
If frequent acceleration and deceleration processes are used to maintain vehicle speed requirement, then the vehicle responds quickly to speed changes, but pollutant emissions increase
Solution Approach 1:
The prediction model performs preliminary assessment of future speed requirements and plans acceleration/deceleration actions in advance to minimize unnecessary engine operations. By predicting when speed adjustments will be needed, the system avoids frequent start-stop cycles that generate high pollutant emissions while maintaining responsive vehicle speed control.
Solution Approach 2:
The driver model changes control parameters dynamically by adjusting acceleration and deceleration rates based on predicted future conditions. Instead of using fixed aggressive control parameters that cause frequent sharp speed changes, the model adapts parameters to achieve smoother transitions that reduce emissions while maintaining acceptable speed responsiveness.
Data Source
AI summary
The present invention relates to a device having at least one computing unit, which is configured to ascertain a specified target power profile curve (213) for a vehicle, to determine a tolerance band (211) for the target power profile curve (213), wherein the tolerance band (211) is limited by an upper limit line (215) and a lower limit line (217), to determine an expectation characteristic curve (219) for a power of the vehicle to be expected in the future by extrapolating a power development of the vehicle at a current setting of the vehicle for a specified temporal prediction window (229), and to enable a control command (315, 319) to be provided by the driver model in order to modify the setting of the vehicle in the event that the expectation characteristic curve (219) intersects at least one of the upper limit line (215) and the lower limit line (217) of the tolerance band (211) within the temporal prediction window (229).