Engine Control Unit Data Population Using Virtual Measurements
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for populating engine control units with data require numerous measurements across the entire operating range, leading to inefficiencies and increased costs, especially when dealing with a growing number of influencing variables, and fail to accurately represent engine behavior without interpolation noise.
Innovation Solution
A global data-based model is introduced, which reduces the number of measurements needed by 'learning' from adjacent operating points and providing model uncertainty calculations, allowing for virtual measurements that are noise-free and suitable for optimization, enabling efficient data population and adaptation across various vehicle variants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a grid measurement of all influencing parameters is performed to accurately represent engine behavior, then measurement precision is improved, but the number of measurements and time required increases significantly
Solution Approach 1:
The patent segments the measurement process into two distinct phases: (1) a reduced set of actual measurements taken at selected operating points, and (2) virtual measurements generated through interpolation algorithms. This segmentation allows the system to avoid time-consuming grid measurements while maintaining accuracy by combining limited real data with computationally-generated intermediate values.
Solution Approach 2:
The patent creates virtual copies of measurement data through interpolation algorithms that generate synthetic measurement values at operating points between those actually measured. These virtual measurements copy the behavior patterns observed in real measurements while filling gaps without requiring additional physical measurements, thereby reducing measurement time while preserving accuracy.
2Adaptability or versatility
If the number of influencing variables increases in engine control functions, then system functionality is improved, but the complexity of data population and measurement management increases
Solution Approach 1:
The patent implements a self-service mechanism where the interpolation algorithm automatically generates virtual measurements for all combinations of influencing variables without requiring manual measurement planning or data input for each variable combination. The system serves itself by computationally deriving the necessary data population from a reduced set of actual measurements, thereby managing complexity internally rather than requiring external coordination.
Solution Approach 2:
The patent creates a universal measurement approach that handles any number of influencing variables through a single interpolation framework. The same algorithmic process works regardless of whether there are 3 or 10 influencing variables, providing multi-functionality that adapts to different system complexities without requiring different measurement strategies or increasing procedural complexity.
3Measurement precision
If measurements are performed at every operating point to ensure data accuracy, then measurement precision is improved, but the quantity of measurements required increases
Solution Approach 1:
The patent performs preliminary actions by strategically selecting a minimal set of operating points for actual measurement that will serve as the foundation for generating all other required data. These preliminary measurements are chosen to maximize their utility for subsequent virtual measurement generation, thereby reducing the total number of measurements needed while maintaining overall data accuracy across the entire operating range.
Solution Approach 2:
The patent introduces an intermediary computational layer (interpolation algorithm) that mediates between the limited actual measurements and the complete set of required data points. This intermediary generates virtual measurements that bridge the gap between measured and unmeasured operating points, allowing accurate representation of all operating conditions without measuring each one directly.
Data Source
AI summary
A method is provided for populating a function for a control unit with data, in which method measurements are performed on a system at different measuring points on a test stand, and a global data-based model is set up based on the obtained measured values, and virtual measurements which simulate real measurements on the test stand are carried out on the global data-based model, and uncertainties for virtual measured values of the virtual measurements are determined from the global data-based model, the uncertainties of the virtual measured values being taken into account when populating the function for the control unit with data.