Model-Based Engine Control Optimizing Injection and Gas Path
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Solution Overview
Problem
Current methods for controlling internal combustion engines require extensive coordination and data modeling, leading to inefficiencies and information loss due to the complexity of characteristic curves and linearized models, which complicates precise setpoint calculations and emission compliance.
Innovation Solution
A method that uses a combustion model to calculate injection system setpoint values and a gas path model to calculate gas path setpoint values, with an optimizer minimizing a quality measure within a prediction horizon to set definitive operating points, eliminating the need for characteristic curves and allowing precise emission control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If characteristic curves and characteristic diagrams are used to control the internal combustion engine, then the engine behavior can be determined as a function of power request, but the expenditure on coordination increases significantly due to the large number of curves and diagrams that must be correlated
Solution Approach 1:
The patent replaces the mechanical approach of using extensive characteristic curves and diagrams with a mathematical model-based system. The control unit uses a mathematical model that directly calculates injection quantity and start of injection from engine operating parameters, eliminating the need to store and coordinate numerous characteristic curves while maintaining control accuracy.
Solution Approach 2:
The patent changes the control parameters from using pre-defined characteristic curves to using dynamic mathematical model calculations. The mathematical model takes engine speed, load, and temperature as inputs and computes optimal injection parameters in real-time, transforming the control approach from static lookup tables to dynamic parameter optimization.
2Device complexity
If mathematical models are used to reduce coordination expenditure, then the number of characteristic curves is reduced, but information loss occurs due to linearization of the gas path model
Solution Approach 1:
The patent implements a feedback mechanism where the system calculates a quality measure based on emission constraints and engine performance requirements, then uses an optimizer to adjust injection and gas path setpoint values. This closed-loop feedback ensures that linearized models do not lose critical information by continuously refining control parameters to meet quality targets.
Solution Approach 2:
The patent introduces dynamic optimization by calculating a quality measure within a prediction horizon and using an optimizer to minimize this measure by adjusting setpoint values. This dynamic approach allows the system to adapt to changing operating conditions in real-time, compensating for any information loss from model linearization through continuous optimization.
3Adaptability or versatility
If trained data is modeled using Bayesian networks or neural networks, then injection quantity and start of injection can be adapted, but extensive test bench running is required to learn the data first
Solution Approach 1:
The patent enables the control system to determine optimal injection parameters independently through mathematical modeling and real-time optimization, without requiring extensive pre-training on test benches. The system uses readily available sensor data and a mathematical model to self-adjust injection quantity and timing based on current engine conditions and emission constraints.
Solution Approach 2:
The patent performs preliminary calculations of the quality measure and optimal setpoint values within a prediction horizon before actual engine operation. This allows the system to pre-determine optimal injection parameters based on anticipated operating conditions, eliminating the need for time-consuming test bench learning phases while maintaining adaptability.
4Ease of manufacture
If the gas path model is linearized to simplify calculations, then computation is easier, but a loss of information is unavoidable
Solution Approach 1:
The patent changes the quality measure parameters to account for emission constraints and performance requirements in a way that compensates for linearization. By carefully selecting and weighting the parameters in the quality measure function, the system maintains precision despite using simplified linearized models for gas path calculations.
Solution Approach 2:
The patent uses feedback through the optimizer that minimizes the quality measure to compensate for information loss from linearization. The continuous adjustment of setpoint values based on quality measure feedback ensures that simplified calculations do not result in permanent information loss, as the system iteratively refines parameters to meet precise quality targets.
Data Source
AI summary
A method for the model-based open-loop and closed-loop control of an internal combustion engine, in which injection system set points for activating the injection system actuator are calculated as a function of a torque setpoint via a combustion model, and gas path set points for activating the gas path actuators are calculated via a gas path model. A measure of quality is calculated by an optimizer as a function of the injection system set points and the gas path set points. The measure of quality is minimized by the optimizer by changing the injection system set points and gas path set points within a prediction horizon. By using the minimized measure of quality, the injection system set points and gas path set points are set by the optimizer as definitive for adjusting the operating point of the internal combustion engine.