Predictive Regeneration Control Using Driver Data
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Solution Overview
Problem
Existing exhaust gas aftertreatment systems, particularly diesel particle filters, face challenges in controlling regeneration cycles to avoid damaging temperature peaks and adverse engine operating conditions, which can lead to system instability or damage, especially when considering driving statuses that are harmful for regeneration.
Innovation Solution
A procedure that utilizes driver-specific information data, including driving habits and routes, to control regeneration cycles, predicting optimal regeneration phases and avoiding harmful conditions by comparing current and saved data using neuronal networks and a control device, allowing for pre-emptive adjustments in regeneration strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regeneration cycles are controlled using basic route information data, then temperature peaks can be avoided, but harmful driving statuses cannot be fully predicted and avoided
Solution Approach 1:
The system performs preliminary analysis of driver-specific information data (driving habits, typical routes, driving cycles) before regeneration cycles are initiated. By storing and analyzing this data in advance, the control device can predict upcoming driving statuses and plan regeneration cycles proactively, avoiding harmful conditions before they occur rather than reacting to them after detection.
Solution Approach 2:
The system continuously monitors actual driving behavior and compares it with stored driver-specific patterns. When deviations or critical patterns are detected, the system adjusts regeneration timing and parameters in real-time. This feedback loop enables the system to adapt to actual driving conditions while maintaining the benefits of predictive control based on historical data.
2Reliability
If regeneration is delayed to avoid harmful conditions, then system damage is reduced, but regeneration time and lost productivity increase
Solution Approach 1:
By analyzing stored driver-specific information data in advance, the system identifies optimal regeneration windows before the actual driving cycle begins. This allows regeneration to be scheduled during naturally favorable conditions (e.g., upcoming highway sections) rather than delaying until conditions are confirmed favorable, reducing overall regeneration time while maintaining system safety.
Solution Approach 2:
The system dynamically adjusts regeneration parameters (timing, duration, intensity) based on real-time comparison of actual driving behavior with stored patterns. This dynamic optimization allows the system to execute regeneration more efficiently by adapting to actual conditions, reducing the time penalty associated with scheduled regeneration cycles.
3Productivity
If driver-specific information data is used for predictive control, then regeneration optimization is improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The system extracts only the most relevant features from driver-specific information data (key driving habits, typical route characteristics, critical driving cycles) rather than processing complete raw datasets. This feature extraction approach maintains predictive accuracy while significantly reducing computational complexity and data processing requirements in the control device.
Solution Approach 2:
The system creates simplified representations or models of driver-specific driving patterns based on historical data. These models serve as lightweight proxies that can be quickly compared against actual driving behavior without requiring complex real-time analysis of complete historical datasets, reducing computational burden while maintaining predictive capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach minimizes negative impacts on the exhaust gas aftertreatment system and engine by optimizing regeneration, avoiding damaging conditions and improving system stability through data-driven predictive control of regeneration cycles.
Implementation Method 1
After a specific operating time a regeneration of the particle filter is necessary while the stored particles are oxidized. During this process heat is released.
Implementation Method 2
Reciprocating piston internal combustion engines, which are controlled and regulated electronically by an engine control signal, basically develop pollutants in the form of nitrous gases and particles during the conversion of the chemically connected fuel energy into heat.
Data Source
AI summary
Procedure for regenerating an exhaust gas after treatment system, especially a particle filter, of a combustion engine that is arranged in a motor vehicle, with regeneration cycles that are controlled by a control unit, whereby the control unit is provided with information data regarding the route and whereby the regeneration cycles are controlled with regard to the information data, is thereby characterized, in that the information data that regards the route contains driver specific information data.