PM Sensor Response Time Prediction via Curve Fitting
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Solution Overview
Problem
Particulate matter (PM) sensors in engine exhaust systems are prone to contamination, leading to erroneous data due to impingement of larger particulates and water droplets, which affects their sensitivity and accuracy in measuring soot concentration and response time, thereby compromising emissions compliance and filter efficiency monitoring.
Innovation Solution
Collecting exhaust soot sensor data during steady-state conditions, fitting a quadratic curve to the data, and predicting the sensor response time based on this fit, allowing for independent regeneration of the PM sensor even before reaching the threshold soot load, thereby improving diagnostic completion ratios and accuracy of soot concentration measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If PM sensor data is collected continuously including noisy data, then more data is available for analysis, but measurement precision deteriorates due to contamination from larger particulates and water droplets
Solution Approach 1:
The patent segments sensor data into two categories: noisy data (contaminated by larger particulates and water droplets) and clean data (free from contamination). By separating and selectively processing only clean data segments, the system maintains measurement precision while still utilizing available data for response time prediction through curve fitting.
Solution Approach 2:
The patent applies local quality by treating different portions of the sensor data differently based on their quality. Clean data portions are used for curve fitting and response time prediction, while noisy data portions are discarded. This selective application of data quality standards ensures accurate measurements without wasting potentially useful clean data.
2Measurement precision
If sensor regeneration is delayed until actual response time is reached, then measurement accuracy is maintained, but diagnostic completion ratio decreases due to insufficient time in short drive cycles
Solution Approach 1:
The patent performs preliminary action by predicting the sensor response time using curve fitting on clean data collected during steady-state conditions. This prediction allows the system to proactively schedule sensor regeneration before the actual response time is reached, ensuring that diagnostics are completed within the available drive cycle time while maintaining measurement accuracy through selective use of clean data.
3Measurement precision
If noisy sensor data is discarded to maintain accuracy, then measurement precision improves, but loss of information increases due to discarded data
Solution Approach 1:
The patent applies parameter changes by transforming the approach to data utilization: instead of using all raw data directly, it changes the parameter of data selection criteria by introducing a noise threshold. Data points exceeding this threshold are identified as noisy and excluded, while clean data points are retained for curve fitting. This parameter-based filtering minimizes information loss by preserving all usable clean data.
4Productivity
If average response time is used for short drives, then diagnostic completion is possible, but measurement precision deteriorates due to insufficient actual measurement data
Solution Approach 1:
The patent performs preliminary action by collecting and analyzing clean sensor data during steady-state conditions to predict the response time using curve fitting. This prediction is made before the drive cycle ends, allowing the system to determine an accurate response time even in short drives without relying on averages from insufficient data, thereby maintaining both diagnostic completion and measurement precision.
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 method enhances the accuracy of PM sensor measurements by utilizing noise-free data for response time prediction, enabling more reliable regeneration and data collection within a drive cycle, thus improving emissions compliance and filter efficiency monitoring.
Implementation Method 1
sense concentration and/or flux of PM entrained in the exhaust gas based on a correlation between a measured change in electrical current and/or conductance at a sensor element and the amount of PM deposited on the element
Implementation Method 2
fitting a time-based curve to the collected data; predicting a sensor response time based on the curve fit
Data Source
AI summary
Methods and systems are provided for predicting response time of a particulate matter (PM) sensor and resetting the PM sensor upon completion of response time prediction, independent of actual or predicted soot load on PM sensor. Soot accumulation data collected during steady state vehicle operation may be fitted with a time-based polynomial function and sensor output and regeneration schedule may be estimated from the curve fit even if the overall signal is noisy.