Internal Combustion Engine Control Device Ignition Timing Correction
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Solution Overview
Problem
Conventional internal combustion engine control devices control ignition timing based solely on compression ratio, leading to errors that can cause improper combustion, knocking, or deterioration of thermal efficiency due to influences from parameters other than compression ratio.
Innovation Solution
An internal combustion engine control device utilizing a neural network model that receives multiple variables, including rotation speed and load, to output a control amount, with a first neural network model using a reference value and a second using a current value, correcting the ignition timing based on the difference or ratio between their outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If ignition timing is controlled based only on compression ratio, then control simplicity is maintained, but control precision deteriorates due to influence from other parameters
Solution Approach 1:
The control system is segmented into multiple independent neural network models, each responsible for specific correction aspects. The first neural network model corrects for reference value deviations, while the second corrects for current value deviations, allowing complex multi-parameter control to be broken down into manageable segments that can be processed independently and then combined.
Solution Approach 2:
Neural network models serve as intermediary components between the basic compression ratio control and the final ignition timing determination. These intermediaries process multiple input parameters (compression ratio, rotation speed, load, temperature) and transform them into corrected ignition timing values, effectively mediating between simple control inputs and precise control outputs.
2Measurement precision
If multiple parameters are considered for ignition timing control, then control precision improves, but device complexity increases
Solution Approach 1:
The neural network models are designed as universal correction mechanisms that handle multiple parameters simultaneously. A single neural network model can process compression ratio, rotation speed, load, and temperature inputs together to produce a unified correction value, eliminating the need for separate control mechanisms for each parameter and reducing overall system complexity.
Solution Approach 2:
The system transforms multiple physical parameters (compression ratio, rotation speed, load, temperature) into a standardized correction value through the neural network model. This parameter transformation approach allows diverse inputs to be processed uniformly, simplifying the control structure while maintaining the ability to account for all relevant factors in ignition timing determination.
Data Source
AI summary
Provided is an internal combustion engine control device capable of reducing a control error of the ignition timing as compared with the conventional technique. The internal combustion engine control device of the present disclosure includes a neural network model that receives three or more variables including at least a rotation speed, a load, and another specific variable of an internal combustion engine as inputs and outputs a control amount of the internal combustion engine. The neural network model includes a first neural network model having a reference value of the specific variable as an input and a second neural network model having a current value of the specific variable as an input. The internal combustion engine control device of the present disclosure corrects a reference value of the control amount calculated based on the rotation speed and the load using a difference or a ratio between the output of the first neural network model and the output of the second neural network model as a correction amount.