Powertrain Control System Using Dynamic Node Look-Up Table
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Solution Overview
Problem
The complexity and cost associated with identifying actuator combinations for each speed-load point in engine optimization, driven by stringent fuel economy and emission standards, necessitate a more efficient approach than extensive dynamometer data collection and processing.
Innovation Solution
A hybrid approach that learns actuator settings at boundary conditions of the engine speed-load map and uses a dynamic node look-up table to interpolate or extrapolate settings for non-boundary conditions, reducing the need for extensive data collection and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If adaptive control schemes are used across the entire engine speed-load table at steady-state conditions, then actuator settings can be optimized for each speed-load point, but extensive data collection and processing time are required to visit each speed-load point
Solution Approach 1:
The engine speed-load map is segmented into boundary conditions and non-boundary conditions. Adaptive control is applied only to boundary conditions (edges of the speed-load map), while non-boundary conditions are handled through interpolation from boundary data, significantly reducing the number of points requiring direct adaptive control visits.
Solution Approach 2:
An engine model serves as an intermediary to interpolate actuator settings from boundary conditions to non-boundary conditions. This model-based approach allows accurate prediction of settings for unvisited speed-load points without requiring direct data collection at each point.
2Object-generated harmful factors
If adaptive control is applied to all speed-load points, then fuel economy and emissions are optimized, but the complexity and cost of data collection and processing increase significantly
Solution Approach 1:
The speed-load map is divided into boundary and non-boundary regions. Only boundary conditions require complex adaptive control processing, while non-boundary conditions use simpler interpolation methods, reducing overall system complexity and data processing requirements.
Solution Approach 2:
Actuator settings from boundary conditions are copied and interpolated to non-boundary conditions using the engine model. This approach avoids the need to collect and process data at every single speed-load point while maintaining optimization benefits.
3Measurement precision
If extensive dynamometer data collection is performed for each speed-load point, then accurate actuator settings are obtained, but the process becomes prolonged and costly
Solution Approach 1:
Data collection is segmented to focus only on boundary conditions rather than every speed-load point. The reduced set of boundary measurements, when combined with model-based interpolation, achieves comparable accuracy to full-map measurement while dramatically improving productivity.
Solution Approach 2:
The engine model acts as an intermediary that translates limited boundary condition measurements into accurate predictions for all non-boundary conditions, maintaining measurement precision while reducing the actual data collection workload.
Data Source
AI summary
Systems and methods are described for powertrain controls optimization. One method comprises adaptively learning engine settings for a sparse sample of a speed-load map, which includes engine operation at boundary conditions of a speed-load map, and generating a dynamic node look-up table based on the learned engine settings for the sparse sample. The dynamic node look-up table may provide engine settings for engine operation at speed-load points not explicitly learned during the adaptive learning.