Catalyst Regeneration Control Using Kalman Temperature Estimation
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Solution Overview
Problem
Existing closed-loop control systems for diesel particulate filter regeneration in internal combustion engines suffer from delayed and inaccurate sensor readings due to high thermal mass, leading to unstable temperature control and potential damage from uncontrolled exothermic reactions.
Innovation Solution
A Kalman filtering technique is employed to predict internal catalyst temperatures by combining sensor measurements with a simulation model, using an inner-outer loop architecture to adjust fuel injection based on accurate internal temperature estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a temperature sensor is placed at the outlet of the fuel burning catalyst for closed-loop control, then the regeneration process can be monitored, but the thermal mass of the catalyst brick causes delayed and inaccurate sensor readings that reduce controller stability
Solution Approach 1:
The patent applies preliminary action by using a simulation model to predict the internal catalyst temperature and outlet temperature in advance before the actual sensor measurements are available. The simulation model calculates temperature based on fuel injection amount, exhaust flow rate, and other parameters, providing forward-looking temperature estimates that enable proactive control adjustments before the physical temperature sensor readings become available or inaccurate.
Solution Approach 2:
The patent introduces an intermediary approach by combining simulation model outputs with actual sensor measurements through a weighted fusion algorithm. The simulation model acts as an intermediary that bridges the gap between fuel injection control and actual temperature feedback, providing corrected temperature estimates that account for catalyst thermal mass effects and measurement delays while maintaining controller stability.
2Strength
If the catalyst thermal mass is large to ensure structural integrity, then the catalyst can withstand high temperatures, but the temperature sensor at the outlet experiences significant delay in detecting temperature changes
Solution Approach 1:
The simulation model performs preliminary temperature calculation based on fuel injection parameters and exhaust conditions, predicting the temperature state before the thermal mass causes significant delay in sensor response. This allows the control system to anticipate temperature changes and adjust fuel injection accordingly, compensating for the time lag introduced by the catalyst's thermal mass.
Solution Approach 2:
The patent replaces reliance on the physical temperature sensor alone with a computational approach using a simulation model that calculates temperature based on thermodynamic principles and measured parameters. This substitutes the mechanical/physical measurement system with a computational model that can provide real-time temperature estimates without being affected by the catalyst's thermal mass delay.
3Productivity
If fuel injection is increased to accelerate regeneration, then the regeneration speed improves, but the temperature may become uncontrolled and damage the DPF filter
Solution Approach 1:
The patent implements feedback control by continuously monitoring actual temperature sensor readings and comparing them with simulation model predictions. The control algorithm adjusts fuel injection amount based on the difference between predicted and actual temperatures, ensuring that regeneration proceeds at an accelerated rate while maintaining temperature within safe limits to prevent DPF damage.
Solution Approach 2:
The control system dynamically adjusts fuel injection parameters based on real-time temperature measurements and simulation predictions. The fuel injection rate is continuously optimized to maintain the fastest possible regeneration speed while staying within safe temperature boundaries, adapting to changing operating conditions and preventing harmful temperature excursions.
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 stabilizes the regeneration process, preventing damage to the diesel particulate filter by accurately controlling temperature fluctuations and ensuring safe operation.
Implementation Method 1
receiving, via a Kalman filter, an initial estimation from a previous instance of time, wherein the initial estimation includes one or more first estimated inside temperature(s), a first estimated outlet temperature, or a combination thereof of an after-treatment (A/T) catalyst
Implementation Method 2
fuel may be injected late in an expansion stroke of the engine so that the fuel does not combust in the engine, but rather is exhausted to the A/T where it may burn and produce heat for regenerating
Implementation Method 3
hydrocarbon or soot accumulated in the DPF may combust during regeneration, and such rapid incineration increases the temperature faster than may be sensed by the control system
Implementation Method 4
Regeneration may be accomplished by oxidizing, e.g., burning off the accumulation trapped in the device
Implementation Method 5
an emissions treatment device may be disposed in the exhaust system of the engine, e.g., filters and After-Treatment (A/T) catalysts that generally operate by physically trapping the emission products or by chemically reacting with the emission products
Data Source
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AI summary
Systems and methods for controlling a regeneration process of catalyst(s) are disclosed. The method includes receiving, via Kalman filter (207), initial estimation from a previous instance of time. The initial estimation includes one or more first estimated inside temperature(s) and/or first estimated outlet temperature of A/T catalyst. An output from a simulation model (201) may be generated to calculate a mean and covariance. Sensor measurement covariance may be compared against the mean and covariance of the output to update Kalman filter gain and process covariance. A weighted average may be calculated between sensor measurements and mean of the output to generate a second estimation for the next instance of time, wherein weight is based on Kalman filter gain. The second estimation includes one or more second estimated inside temperature(s) and/or second estimated outlet temperature of A/T catalyst to control the mass flow rate in diesel engine via a closed loop control system.