NOx Emission Model Correction via Adaptation Filter
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Solution Overview
Problem
Current NOx sensors in combustion engines have large response times and limited accuracy during transient operations, making efficient and reliable EGR control challenging due to their slow dynamics.
Innovation Solution
A method that corrects a fast but potentially inaccurate NOx emission model using a difference between measured and estimated values through an adaptation filter, such as an integrator or adaptive curve, to adjust EGR rates or NOx estimations, ensuring accurate NOx emissions estimation and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a NOx sensor is used for EGR control, then NOx emissions can be reduced, but the sensor's large response time and slow dynamics make efficient control difficult
Solution Approach 1:
The patent implements a feedback mechanism where the NOx sensor measurement is continuously fed back to the EGR controller. The controller uses this feedback to adjust the EGR rate dynamically, creating a closed-loop control system that adapts to changes in NOx emissions in real-time, thereby compensating for the sensor's inherent slow response through continuous adjustment.
Solution Approach 2:
The patent introduces dynamic adaptation by allowing the EGR control system to adjust its parameters based on real-time sensor data. The controller dynamically modifies the EGR rate according to the measured NOx levels, enabling the system to respond flexibly to changing emission conditions despite the sensor's physical limitations in response speed.
2Reliability
If a NOx sensor is used for EGR control, then NOx emissions can be reduced, but the sensor's limited accuracy during transient operations reduces control reliability
Solution Approach 1:
The feedback loop continuously monitors NOx levels and adjusts EGR control parameters accordingly. During transient operations, the system uses this feedback to compensate for measurement inaccuracies by dynamically adapting the control strategy, thereby maintaining reliable control despite reduced sensor accuracy during transient conditions.
Solution Approach 2:
The patent employs preliminary actions by anticipating transient conditions and pre-adjusting control parameters. The controller uses historical data and predictive algorithms to prepare for upcoming transient operations, adjusting EGR rates in advance to maintain control reliability even when sensor accuracy deteriorates during transient phases.
3Device complexity
If the sensor signal is used directly in EGR feedback loop, then control is simple, but the slow sensor dynamics introduce severe problems for maintaining robust NOx conversion
Solution Approach 1:
The patent enhances the feedback mechanism by introducing additional processing stages that analyze sensor data more thoroughly before adjusting EGR control. The controller uses sophisticated feedback algorithms that consider historical sensor readings, rate-of-change information, and contextual factors to make more robust control decisions, thereby maintaining NOx conversion reliability while managing system complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer between the sensor and the EGR controller. This intermediary component acts as a buffer and processor that filters, interpolates, and enhances sensor signals before they reach the control algorithm, thereby reducing the direct impact of slow sensor dynamics on control robustness while maintaining manageable system complexity.
Data Source
AI summary
The present invention provides for a system for controlling NOx emissions based on the calculation of an error given by the difference between a first measured value obtained from a NOx sensor (7) and a second one estimated from a NOx estimation. Said sensor (7) can be used in an adaptation loop, where an open-loop or closed-loop EGR control system is adapted such that the expected NOx emissions (from the EGR controller) match the ones measured with the NOx sensor under steady-state conditions.