Virtual Exhaust Temperature Estimation for Transient Engine Sensing
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Solution Overview
Problem
Existing temperature sensing methods in internal combustion engines, such as those using exhaust manifold temperature sensors, suffer from dynamic measurement delays, especially during transient events, making them inaccurate for real-time temperature estimation.
Innovation Solution
A method that combines raw-sensed data from sensors with modeled data using a physics-based model, filtered with a low-pass filter having a variable time constant based on operating conditions, to calculate virtual temperatures, which compensates for transient conditions and improves accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a simple temperature sensor is used, then the device complexity is reduced and cost is lowered, but the measurement precision deteriorates due to dynamic measurement delays during transient events
Solution Approach 1:
The patent introduces a controller as an intermediary that processes sensor data through a physics-based model and filtering algorithm. The controller acts as a mediator between the simple temperature sensor and the engine control system, transforming inaccurate raw sensor data into accurate virtual temperature data through computational processing rather than using a complex expensive sensor directly
Solution Approach 2:
The patent replaces the need for a complex physical temperature sensing system with a computational system. Instead of using a sophisticated temperature sensor that can handle transient conditions, the system uses a simple sensor combined with a physics-based model and low-pass filter implemented in software/firmware to achieve accurate temperature measurement
2Measurement precision
If a simple temperature sensor is used, then the manufacturing cost is reduced, but the measurement precision deteriorates during transient events
Solution Approach 1:
The controller serves as a cost-effective intermediary that processes data from inexpensive sensors. Rather than investing in expensive high-precision sensors, the system uses a low-cost sensor combined with computational processing in the controller to achieve the same measurement accuracy, significantly reducing manufacturing costs
Solution Approach 2:
The patent creates a virtual copy of the temperature measurement through computational modeling. The physics-based model creates a virtual temperature signal that replicates what a complex sensor would measure, allowing the use of simple physical sensors while achieving accurate measurements through the virtual model
3Measurement precision
If a physics-based model with filtering is used, then the measurement precision is improved, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-establishing the physics-based model relationships and filter parameters before transient events occur. The model is continuously ready to process sensor data, and the low-pass filter is pre-configured with appropriate time constants, allowing rapid processing during transient events without computational setup delays
Solution Approach 2:
The system dynamically adjusts the low-pass filter time constant based on operating conditions to optimize the balance between filtering accuracy and processing speed. During rapid transient events, the filter time constant is adjusted to provide faster response, while during steady-state operation, it provides stronger filtering for higher precision
Data Source
AI summary
A system or method for determining virtual data of a system, relative to a measurement point having a sensor located nearby, is determined by a controller. The system calculates modeled data at the measurement point, filters the modeled data to determine filtered data, and calculates a differential between the modeled data and the filtered data to determine a compensation term. The system also determines raw-sensed data from the sensor at the measurement point, and combines that raw-sensed data with the compensation data to calculate the virtual data at the measurement point. In some configurations, the modeled data is determined from a physics-based model. Furthermore, filtering the modeled data may include using a low-pass filter, and a time constant for the low-pass filter may be calculated based on operating conditions of the system.