Engine Control Loop Monitoring During Transient State Changes
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Solution Overview
Problem
Existing methods for monitoring engine systems, such as air mass, EGR rate, and boost pressure regulating systems, are unable to detect errors that occur during transient state changes, limiting their effectiveness in identifying performance issues and potential emission impairments.
Innovation Solution
A method and device that ascertain a characteristic value from preset and system variables during state changes, allowing for the detection of errors by mapping the difference between setpoint and actual values, using techniques like gradient analysis, integration, convolution, and adaptation variables to assess system performance and detect impairments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional monitoring methods are used for steady operating states, then monitoring capability in steady states is provided, but errors during transient state changes cannot be detected
Solution Approach 1:
The monitoring method transitions from static steady-state monitoring to dynamic transient-state monitoring by calculating characteristic values during state changes. The system evaluates the gradient of the preset value and determines monitoring relevance based on dynamic conditions, enabling error detection during transient operations where components are actually changing state.
Solution Approach 2:
The invention changes the monitoring parameters from simple steady-state variable comparisons to complex characteristic value calculations that integrate multiple parameters (preset value, system variable, gradient, time) during transient states. This parameter transformation enables the detection of errors that only manifest during dynamic transitions.
2Reliability
If monitoring is performed during transient state changes using characteristic values, then error detection during transients is enabled, but monitoring complexity increases
Solution Approach 1:
The monitoring process is segmented into distinct operational phases: steady-state monitoring using conventional methods, and transient-state monitoring using characteristic value calculations. The control unit determines monitoring relevance based on the gradient of the preset value, activating complex calculations only when transient conditions are detected, thus managing complexity through conditional segmentation.
Solution Approach 2:
The characteristic value serves as an intermediary parameter that bridges the gap between preset values and system variables during transient states. Instead of directly comparing raw variables, the system calculates a derived characteristic value that captures the dynamic relationship between setpoint and actual values, simplifying the monitoring logic while improving detection capability.
3Measurement precision
If the characteristic value is calculated using gradient analysis and integration, then detection precision for transient errors is improved, but computational requirements increase
Solution Approach 1:
The system applies gradient analysis and integration calculations selectively during transient state changes rather than continuously. By determining monitoring relevance based on preset value gradient thresholds, the system performs computationally intensive operations only when necessary, achieving high detection precision during transients while minimizing overall computational power consumption.
Data Source
AI summary
A method for monitoring a control loop or a regulating loop in a system, in particular in an engine system in a motor vehicle, is described. A characteristic value is ascertained from a preset value and a system variable of the regulating loop or the control loop during one or more state changes, and an error is detected as a function of the ascertained characteristic value.