Model Predictive Control for Engine Torque Coordination
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Solution Overview
Problem
Traditional engine control systems fail to accurately control engine output torque and do not provide rapid responses to control signals, nor coordinate torque control among various devices effectively.
Innovation Solution
A model predictive control (MPC) module identifies and selects target values for engine actuators based on cost analysis and predetermined ranges, while a fault diagnostic module diagnoses issues in the MPC module and adjusts control strategies accordingly, ensuring accurate torque control and rapid response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional engine control systems are used, then the system structure is simple, but the torque control precision is insufficient and response speed is slow
Solution Approach 1:
The control system is segmented into multiple independent control modules (throttle control module, spark control module, fuel control module, EGR control module, variable valve timing control module) that can operate autonomously yet coordinate through the MPC framework. This segmentation allows each module to specialize in specific control functions, improving overall torque control precision while maintaining manageable complexity through modular design.
Solution Approach 2:
The system implements dynamic control by using model predictive control (MPC) that continuously adjusts control strategies based on real-time engine operating conditions. The MPC module dynamically determines optimal target values for multiple actuators by predicting future system behavior and optimizing control actions, enabling rapid response to changing torque demands while maintaining precise control.
2Speed
If traditional engine control systems are used, then the device complexity is low, but the response speed to control signals is slow
Solution Approach 1:
The system performs preliminary action by using the MPC module to predict future engine states and pre-calculate optimal control actions before they are needed. The control modules are configured with target values determined in advance by the MPC, allowing the system to respond rapidly to torque requests without waiting for sequential processing of each control action.
Solution Approach 2:
The system merges multiple control functions (throttle, spark, fuel, EGR, variable valve timing) into a unified MPC framework that coordinates all actuators simultaneously. This merging allows the system to process multiple control decisions in parallel rather than sequentially, significantly improving response speed while the modular module structure keeps the overall complexity manageable.
3Reliability
If traditional engine control systems are used, then the control system is easy to implement, but the coordination among various torque control devices is poor
Solution Approach 1:
The MPC module serves as a universal control framework that simultaneously manages multiple torque control devices (throttle, spark, fuel, EGR, variable valve timing). Each control module is designed with multi-functionality to handle its primary control function while also participating in the coordinated torque control strategy. The fault diagnostic module provides universal monitoring across all actuators, detecting faults in any component and triggering appropriate responses.
Solution Approach 2:
The system implements comprehensive feedback mechanisms where the MPC module continuously monitors actual engine torque output and compares it with target torque values. The fault diagnostic module provides feedback on the health status of all actuators. This feedback enables the MPC to dynamically adjust control strategies to maintain optimal coordination among all torque control devices, ensuring reliable torque control while the modular structure keeps implementation complexity manageable.
Data Source
AI summary
A model predictive control (MPC) module: identifies sets of possible target values based on an engine torque request; determines predicted operating parameters for the sets of possible target values, respectively; determines cost values for the sets of possible target values, respectively; selects one of the sets of possible target values based on the cost values; and sets target values based on the possible target values of the selected one of the sets. An actuator module controls an engine actuator based on one of the target values. A fault diagnostic module selectively diagnoses a fault in the MPC module.