Engine Control Optimization via Discrete-Continuous Variable Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing model-based control and regulation processes for combustion engines struggle with the integration of discrete maneuver variables, leading to complex structures that cannot be effectively displayed on an engine control unit.
Innovation Solution
A three-step process is implemented, where the optimizer first calculates a pre-optimized quality of quality by interpreting discrete maneuvers as continuous variables, then quantizes these variables into discrete settings using switching thresholds and hysteresis, and finally determines a post-optimized quality of quality based on the new discrete variables, which are fixed and not subject to further optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If branch and bound methods are used to optimize discrete control variables, then the optimization completeness is improved, but the computational complexity increases significantly making it infeasible for engine control units
Solution Approach 1:
The patent segments the control variables into two distinct groups: discrete control variables (binary decisions like valve activation, injection events) and continuous control variables (parameters like injection quantity, rail pressure). This segmentation allows different optimization strategies to be applied to each group - discrete variables are optimized through systematic enumeration while continuous variables use gradient-based optimization, thereby reducing overall computational complexity while maintaining optimization completeness.
Solution Approach 2:
The patent applies preliminary action by first determining the optimal discrete control variables before optimizing the continuous control variables. The discrete variables are fixed first based on their impact on combustion mode selection, and then the continuous variables are optimized given these fixed discrete settings. This sequential approach prevents the combinatorial explosion that would occur if all variables were optimized simultaneously.
2Measurement precision
If all possible combinations of discrete control variables are investigated, then the optimal solution accuracy is improved, but the computation time becomes prohibitive for real-time engine control
Solution Approach 1:
The patent segments the optimization process into two phases: first optimizing discrete control variables by evaluating only relevant combinations based on combustion modes, then optimizing continuous control variables. This segmentation avoids the need to evaluate all possible combinations of all control variables simultaneously, thereby reducing computation time while maintaining solution accuracy through targeted evaluation of discrete variable combinations.
Solution Approach 2:
The patent applies partial action by not evaluating all theoretically possible combinations of discrete control variables, but only those combinations that are physically meaningful and relevant to the current operating conditions and combustion modes. This partial evaluation approach maintains sufficient optimization accuracy while dramatically reducing the computational burden compared to exhaustive enumeration.
3Manufacturing precision
If discrete control variables are integrated into model-based control, then the control precision is improved, but the control structure complexity increases beyond what can be implemented on engine control units
Solution Approach 1:
The patent segments the control structure into distinct modules: a discrete control variable optimization module that determines binary decisions based on combustion modes, and a continuous control variable optimization module that optimizes injection parameters. This modular segmentation allows the complex control task to be divided into manageable subtasks that can be implemented on engine control units with limited computational resources.
Solution Approach 2:
The patent applies preliminary action by pre-defining combustion modes and their associated discrete control variable settings. During real-time control, the system first identifies the appropriate combustion mode and applies the corresponding discrete control settings, then optimizes continuous variables. This preliminary structuring of discrete controls based on combustion modes simplifies the real-time control structure while maintaining precision.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
What is proposed is a method for the model-based open-loop and closed-loop control of an internal combustion engine, in which a pre-optimized quality measure (J(VO)) is calculated on the basis of the operating situation (BS) by an optimizer in a first step, wherein, in the calculation of the pre-optimized quality measure (J(VO)), discrete manipulated variables having discrete settings are interpreted as continuous manipulated variables (SG(k)) having a continuous settings range, in which these continuous manipulated variables (SG(k)) are quantized and set as new discrete manipulated variables (SG(new)) having discrete settings in a second step, in which a post-optimized quality measure (J(NA)) is calculated on the basis of the new discrete manipulated variables (SG(new)) and the operating situation (BS) of the internal combustion engine (1) by the optimizer in a third step, and the post-optimized quality measure (J(NA)) is set as critical for the operating point of the internal combustion engine (1) by the optimizer (21).