Fluid machine and cooling device
US-2024328731-A1 · Oct 3, 2024 · US
US9765621B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9765621-B2 |
| Application number | US-201414519760-A |
| Country | US |
| Kind code | B2 |
| Filing date | Oct 21, 2014 |
| Priority date | Oct 21, 2014 |
| Publication date | Sep 19, 2017 |
| Grant date | Sep 19, 2017 |
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A method for controlling an internal combustion engine using a controller that controls an air flow path by adjusting at least one of a variable geometry turbine (VGT) and an exhaust gas recirculation (EGR) flow rate during engine operation. The method determines inputs, such as engine speed and fuel rate from the sensor data, and employs a switch based gain-scheduled explicit model predictive controller (MPC) responsive to the inputs to determine the air flow path.
Opening claim text (preview).
What is claimed is: 1. A method for controlling an internal combustion engine, comprising: determining inputs to a controller of the internal combustion engine; determining an airflow path using a switch based gain-scheduled explicit model predictive controller (MPC) and based on the inputs; and adjusting, with the controller, at least one of a variable geometry turbine (VGT) and an exhaust gas recirculation (EGR) flow rate to set the air flow path determined, wherein to determine the airflow path, the switched based gain-scheduled explicit MPC employs a gain scheduled matrix for different engine operating conditions and corresponding air flow dynamics, generates a diagonal matrix by extracting the diagonal elements of a gain scheduled matrix, generates an off-diagonal gain matrix by extracting the off-diagonal elements of a gain scheduled matrix, and stores a diagonal and an off-diagonal gain matrix in memory to be used when demanded. 2. The method of claim 1 wherein the explicit MPC, for a nominal engine operating condition, generates optimal control actions that fall within a real control constraint region. 3. The method of claim 1 wherein the explicit MPC, for a nominal engine operating condition, generates optimal control actions that may fall outside a real control constraint region. 4. The method of claim 3 further comprising: defining an input constraint matrix for the explicit MPC based on switching between the diagonal and the off-diagonal gain matrix. 5. The method of claim 4 wherein: switching is applied at an interval of half a sample time used in the explicit MPC; and for an even sample time the diagonal gain matrix is used and for an odd sample time the off-diagonal gain matrix is used. 6. The method of claim 1 wherein the inputs are engine speed and fuel rate. 7. The method of claim 2 wherein the nominal engine operating condition can be selected from combination of conditions such as idle speed, no load, or other designed conditions. 8. The method of claim 3 wherein the nominal engine operating condition can be selected from combination of conditions such as idle speed, no load, idle speed, no load, or other designed conditions. 9. A controller for an internal combustion engine, comprising: circuitry programmed to determine inputs to the controller, and determine an airflow path using a switch based gain-scheduled explicit model predictive controller (MPC) and based on the inputs, and adjust at least one of a variable geometry turbine (VGT) and an exhaust gas recirculation (EGR) flow rate to set the air flow path determined, wherein to determine the airflow path, the circuitry is further programmed to employ a gain scheduled matrix for different engine operating conditions and corresponding air flow dynamics, generate a diagonal gain matrix by extracting the diagonal elements of the gain scheduled matrix, generate an off-diagonal gain matrix by extracting the off-diagonal elements of the gain scheduled matrix, and store a diagonal and an off-diagonal gain matrix in memory to be used when demanded. 10. The controller of claim 9 wherein the explicit MPC, for a nominal engine operating condition, generates optimal control actions that fall within a real control constraint region. 11. The controller of claim 9 wherein the explicit MPC, for a nominal engine operating condition, generates optimal control actions that may fall outside a real control constraint region. 12. The controller of claim 11 , wherein the circuitry is further programmed to: define an input constraint matrix for the explicit MPC based on switching between the diagonal and the off-diagonal gain matrix that leads to optimal control actions falling back into the real control constraint region. 13. The controller of claim 12 wherein: switching is applied at an interval of half a sample time used in an explicit MPC; and for an even sample time the diagonal gain matrix is used and for an odd sample time the off-diagonal gain matrix is used. 14. The controller of claim 9 wherein the inputs are engine speed and fuel rate. 15. The controller of claim 10 wherein the nominal engine operating condition can be selected from combination of conditions such as idle speed, no load, or other designed conditions. 16. The controller of claim 11 wherein the nominal engine operating condition can be selected from combination of conditions such as idle speed, no load, or other designed conditions. 17. A system, comprising: one or more sensors to obtain an internal combustion engine speed and fuel flow rate data; and a controller including a processor configured to: determine a nominal plant behavior model from an off-nominal plant behavior model using switch gain scheduling, determine an optimal air flow control action for the off-nominal plant by applying the switch gain scheduling in combination with an explicit model predictive controller (MPC) that is designed for nominal plant operating condition, and adjust at least one of a variable geometry turbine (VGT) and an exhaust gas recirculation (EGR) flow rate of the internal combustion engine to set the air flow path determined, wherein to determine the airflow path, the controller employs a gain scheduled matrix for different engine operating conditions and corresponding air flow dynamics, generates a diagonal gain matrix by extracting the diagonal elements of the gain scheduled matrix, generates an off-diagonal gain matrix by extracting the off-diagonal elements of the gain scheduled matrix, and stores a diagonal and an off-diagonal gain matrix in memory to be used when demanded.
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