Particle Filter Regeneration Control via Dynamic Estimation Correction
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Solution Overview
Problem
Current methods for managing particulate filters in internal combustion engines face challenges in accurately estimating particle production, leading to either premature filter deterioration or underutilization, due to wide margins taken to avoid excess emissions, which result in inefficient regeneration and increased fuel dilution in oil.
Innovation Solution
A method that corrects particle production estimates using a correction factor based on the difference between actual and theoretical regeneration proportions, allowing for a smaller margin in estimation, with limits on the correction factor and additional corrective actions such as actuator control or warning signals, to ensure accurate regeneration triggering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large margin is taken in the estimation of particle production to cover 90% or 99% of emissions, then excess emissions are avoided and filter robustness is protected, but regeneration occurs more frequently leading to underutilization of filter capacity and increased fuel dilution in oil
Solution Approach 1:
The system uses feedback from pressure differential measurements across the filter to continuously update and refine the particle production estimate. The closed-loop load estimator compares actual pressure measurements with model predictions, adjusting the estimation margin dynamically based on real filter load conditions rather than using a fixed large margin for all cases.
Solution Approach 2:
The estimation margin is made dynamic rather than static. The system adapts the margin size based on operating conditions, filter age, and measured pressure differentials. This allows the system to use smaller margins when conditions are well-understood and larger margins when uncertainty is higher, optimizing both filter protection and regeneration timing.
2Object-affected harmful factors
If a large margin is taken in the estimation of particle production, then the risk of undetected excess emissions is reduced, but the accuracy of particle production estimation decreases
Solution Approach 1:
The closed-loop estimation system continuously refines the particle production estimate by comparing model predictions with actual pressure differential measurements. This feedback mechanism reduces the need for large safety margins while maintaining accuracy, as the system learns from actual operating data and adjusts its estimates accordingly.
Solution Approach 2:
The system performs preliminary characterization of the filter and operating conditions during normal operation, building up a knowledge base that enables more accurate future predictions. This preliminary action allows the system to reduce estimation margins over time as it becomes more confident in its predictions, while still protecting against excess emissions.
3Object-generated harmful factors
If regeneration is triggered by crossing a first threshold determined by open-loop load estimator, then regeneration is initiated earlier, but the filter robustness is underutilized and fuel dilution increases
Solution Approach 1:
The regeneration threshold is made dynamic based on filter conditions and operating parameters. Rather than using a fixed early threshold, the system adjusts the threshold based on measured pressure differentials, filter age, and operating conditions, allowing later regeneration timing when safe, thereby reducing fuel dilution while still preventing excess emissions.
Solution Approach 2:
The filter management system uses the filter's own performance data (pressure differential measurements) to determine optimal regeneration timing. This self-service approach allows the system to maximize filter utilization without external intervention, delaying regeneration only as long as the filter can safely handle the load, thus reducing unnecessary fuel consumption.
Data Source
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AI summary
The invention relates to a method for managing a filter (4) whose regeneration is triggered (6), according to the value of an ease parameter (7) characterizing the ease of performing the regeneration, either by crossing a first threshold with a value (10) determined by an open-loop load estimator (11) from a particle production estimate (14), or by crossing a second threshold with a value (12) determined by a closed-loop load estimator (13). According to the invention, the particle production estimate (14) is corrected (32) according to a discriminating error (29) between the proportion (23) of regenerations triggered by the closed-loop load estimator (13) and a theoretical proportion (27) of regenerations that should have been triggered by this estimator (13) given the average value (25) of the ease parameter (7).