Engine Torque Estimation via Sigmoidal Spark Timing Normalization
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Solution Overview
Problem
Internal combustion engines face challenges in accurately estimating torque output and efficiently controlling engine parameters to optimize fuel consumption and performance, particularly in deactivating and activating cylinders based on torque requests.
Innovation Solution
An engine control system that normalizes spark timing and other parameters using sigmoidal or exponential functions, and employs a mathematical model to estimate torque output, allowing for precise adjustment of engine actuators to achieve desired torque levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional torque estimation methods are used, then the system is simpler to implement, but the measurement precision of torque output is insufficient
Solution Approach 1:
The patent replaces traditional mechanical torque sensing methods with a mathematical modeling approach that uses sensor data (pressure, temperature, flow rates) and sigmoidal/exponential functions to estimate torque output. This substitution achieves high measurement precision without requiring complex mechanical torque sensors, thereby improving accuracy while managing system complexity through software-based solutions.
Solution Approach 2:
The patent transforms raw engine parameters (pressure, temperature, flow rates) into normalized parameters using sigmoidal and exponential functions. This parameter transformation enhances the correlation between model inputs and torque output, improving estimation accuracy. The normalized parameters enable the mathematical model to more accurately predict torque across different operating conditions without increasing hardware complexity.
2Reliability
If engine parameters are not normalized, then the mathematical model is simpler, but the correlation between model inputs and estimated torque is poor
Solution Approach 1:
The patent applies sigmoidal and exponential normalization functions to transform raw engine parameters into normalized parameters before inputting them to the mathematical model. This parameter change process significantly improves the correlation between model inputs and estimated torque, enhancing model reliability. The normalization ensures that parameters from different sensors and units are properly scaled and weighted, allowing the mathematical model to accurately capture torque relationships across diverse operating conditions.
3Use of energy by moving object
If precise torque estimation is achieved through normalization and mathematical modeling, then fuel consumption optimization is improved, but the ease of operation decreases
Solution Approach 1:
The patent implements a self-service control system where the mathematical model automatically estimates torque and the control module autonomously adjusts engine parameters (fuel injection timing, air intake, valve timing) to optimize fuel consumption. The system uses feedback from sensors and normalized parameters to continuously refine torque estimation and make real-time control decisions without requiring manual intervention, thereby improving energy efficiency while maintaining operational simplicity through automation.
Data Source
AI summary
An engine control system includes: a normalization module configured to normalize, to within a predetermined range of values, a spark timing of an engine and at least one other parameter of the engine, thereby producing a normalized spark timing and at least one normalized other parameter, respectively; a processing module configured to generate a sigmoidal spark timing by applying, to the normalized spark timing, one of (a) a sigmoidal function and a sinusoidal function; and an estimation module configured to estimate a torque output of the engine based on the normalized spark timing and the at least one normalized other parameter using a mathematical model.