Cubic Spline IMEP Computation Using Sparse Encoder Data
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Solution Overview
Problem
Existing methods for calculating indicated mean effective pressure (IMEP) in internal combustion engines require high-resolution crankshaft position and cylinder pressure data, leading to increased costs due to the need for advanced encoders, memory, and computing power.
Innovation Solution
The use of a cubic spline integration method that allows for the computation of IMEP using sparse input data, reducing the required resolution of crankshaft position and cylinder pressure data while maintaining high accuracy through the application of indirect integration and cubic spline techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution crankshaft position encoder and frequent cylinder pressure measurement are used, then IMEP calculation accuracy is improved, but system cost and computational complexity increase
Solution Approach 1:
The patent changes the mathematical parameters of the integration method from traditional trapezoidal integration to cubic spline integration. This parameter change allows the system to achieve the same or better IMEP calculation accuracy using lower-resolution crankshaft position data and less frequent cylinder pressure measurements, thereby reducing encoder resolution requirements and computational complexity
Solution Approach 2:
The patent replaces the mechanical requirement for high-resolution physical measurement systems with a mathematical computation approach. By using cubic spline integration, the system substitutes the need for high-resolution hardware encoding with a sophisticated mathematical algorithm that can accurately reconstruct the pressure-volume relationship from sparser data points
2Measurement precision
If high-resolution crankshaft position data is used, then IMEP calculation accuracy is improved, but data storage requirements and computing power increase
Solution Approach 1:
The patent changes the sampling parameter requirements by implementing cubic spline integration, which can accurately interpolate between data points. This allows the system to use coarser sampling intervals (e.g., 10-degree crank angle increments instead of 1-degree increments), dramatically reducing the quantity of crankshaft position and cylinder pressure data that needs to be stored and processed while maintaining IMEP calculation accuracy
3Measurement precision
If frequent cylinder pressure measurement is used, then IMEP calculation accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent changes the integration method parameter from simple trapezoidal rules to cubic spline integration. This mathematical parameter change enables the system to achieve accurate IMEP calculations with fewer measurement points by properly interpolating the pressure-volume relationship, thereby reducing computational resources and processing time while maintaining or improving accuracy
Solution Approach 2:
The patent applies preliminary mathematical transformation by fitting cubic spline curves to the discrete pressure and volume data points before performing the integration. This preliminary action of creating a smooth continuous function from discrete data allows for more efficient and accurate integration with fewer data points, improving computational efficiency
Data Source
AI summary
A method for computing indicated mean effective pressure (IMEP) in an internal combustion engine using sparse input data. The method uses a cubic spline integration approach, and requires significantly lower resolution crankshaft position and cylinder pressure input data than existing IMEP computation methods, while providing calculated IMEP output results which are very accurate in comparison to values computed by existing methods. By using sparse input data, the cubic spline integration method offers cost reduction opportunities for a manufacturer of vehicles, engines, and/or electronic control units, through the use of lower cost sensors and the consumption of less computing resources for data processing and storage.