Engine Control Map Updating for Real-Time Actuator Setpoint Optimization
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Solution Overview
Problem
Existing internal combustion engine control systems face challenges in efficiently managing emissions and performance due to limited computing power, part-to-part variations, and the inability of time-invariant control maps to adapt to real-time changes, leading to suboptimal operation and increased calibration complexity.
Innovation Solution
An internal combustion engine controller with a memory and processor that utilizes a map updating module to optimize actuator setpoints through a stratified sample search and line search algorithm, updating control maps in real-time to accommodate various operating conditions and reduce the number of required control maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-calibrated time-invariant control maps are used for engine control, then the control system is simple to implement, but the system cannot adapt to real-time changes and part-to-part variations
Solution Approach 1:
The control maps are transformed from static, time-invariant lookup tables into dynamic, adaptive structures that are continuously updated during engine operation. The processor automatically adjusts control parameters in real-time based on actual engine performance and operating conditions, enabling the system to adapt to part-to-part variations and changing environments without requiring manual recalibration.
Solution Approach 2:
The control system performs self-calibration by automatically updating its own control maps during operation. The processor uses real-time sensor data and performance feedback to autonomously adjust control parameters, eliminating the need for external intervention or complex manual calibration procedures while maintaining adaptability to real-time changes.
2Adaptability or versatility
If multiple different control maps are provided for different operating regimes, then the system can handle various operating conditions, but calibration complexity and cost increase
Solution Approach 1:
Instead of requiring separate control maps for different operating regimes, the system uses a single universal control map structure that adapts to all operating conditions through real-time updates. The processor dynamically adjusts the control parameters within the same map framework, eliminating the need for multiple pre-calibrated maps and significantly reducing calibration complexity while maintaining versatility across all operating regimes.
Solution Approach 2:
The control system transitions from static, regime-specific control maps to a single dynamic control map that automatically adapts its parameters based on real-time operating conditions. This dynamic approach allows the system to handle diverse operating regimes without requiring separate calibration for each regime, thereby reducing overall calibration complexity.
3Productivity
If model-based control with real-time predictions is implemented, then engine performance is optimized, but significant computational resources are required
Solution Approach 1:
The system implements a simplified model-based control approach that performs partial real-time optimization rather than complete predictive modeling. The processor focuses on adjusting key control parameters based on essential performance feedback, achieving sufficient optimization without requiring the full computational power of comprehensive model-based control systems.
Solution Approach 2:
The control system optimizes performance by dynamically adjusting control parameters in real-time based on simplified performance models and sensor feedback. This parameter-based approach achieves effective optimization with reduced computational requirements compared to full model-based predictive control, as it focuses on tuning parameters rather than solving complex predictive equations.
4Measurement precision
If the number of input variables in control maps is increased, then the control precision is improved, but memory requirements and map complexity increase exponentially
Solution Approach 1:
The system transitions from large, static control maps with many input variables to a more efficient structure where the processor dynamically calculates control parameters based on fewer key input variables. This dynamic approach maintains high control precision by using real-time adjustments rather than relying on exhaustive pre-calculated maps, thereby significantly reducing memory requirements.
Data Source
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AI summary
An internal combustion engine controller for controlling an internal combustion engine is provided. The internal combustion engine controller comprises a memory and a processor. The memory is configured to store a plurality of control maps, each control map defining a hypersurface of actuator setpoints for controlling an actuator of the internal combustion engine based on a plurality of input variables to the internal combustion engine controller. The processor comprises an engine setpoint module and a map updating module. The map updating module is configured to optimise one or more of the hypersurfaces of the control maps at the location defined by the plurality of input variables. The map updating module comprises an optimiser module configured to search for an optimised group of actuator setpoints wherein the map updating module updates the one or more hypersurfaces at the location defined by the plurality of input variables based on the optimised group of actuator setpoints. A method of controlling an internal combustion engine is also provided.