Method and system for determining thermal state

Patent metadata

Field	Value
Publication number	US-10995688-B2
Application number	US-201916431199-A
Country	US
Kind code	B2
Filing date	Jun 4, 2019
Priority date	Jun 4, 2019
Publication date	May 4, 2021
Grant date	May 4, 2021

Abstract

Systems and methods are provided for determining a temperature of a thermal system that includes fluid conduits. A sensor monitors a current state of the temperature. A controller receives a signal from the sensor that is representative of the current state; determines a flow in the fluid conduits; determines a noise covariance of the thermal system; processes a thermal model of the thermal system; predicts a next-step state of the parameter at a time after the current state; and corrects the next-step state based, at least in-part, on the noise covariance resulting in a corrected next-step state.

First claim

What is claimed is:

1. A system for determining temperature, comprising:
a thermal system including plural fluid conduits;
a pump disposed in the thermal system;
plural valves disposed in the thermal system;
a sensor disposed to monitor a current state of a parameter of the thermal system; and
a controller configured to:
receive, from the sensor, a signal representative of the current state;
receive from the pump, a speed signal;
receive, from actuators of the plural valves, position signals;
determine, based on the speed signal and the position signals, flows in the plural fluid conduits;
determine a noise covariance of the thermal system;
process a thermal model of the thermal system;
predict, based on the thermal model and the flows in the plural fluid conduits, a next-step state of the parameter at a time after the current state; and
correct the next-step state based, at least in-part, on the noise covariance resulting in a corrected next-step state.

2. The system of claim 1 , further comprising an internal combustion engine, wherein the controller is further configured to control operation of an internal combustion engine based on the corrected next-step state including to assign, based on the flows, the noise covariance value to the sensor, and to discount a temperature input provided by the sensor in relation to a reduction in the flows.

3. The system of claim 2 , wherein the sensor is configured to monitor a coolant temperature of a coolant in the fluid conduits, and wherein the controller is configured to determine, using the coolant temperature, a wall temperature of the internal combustion engine, where the wall temperature is a cylinder block wall temperature of the internal combustion engine, wherein the controller is configured to control combustion in the internal combustion engine based on the wall temperature.

4. The system of claim 1 , wherein the plural valves are configured to control the flow through branches of the fluid conduits, wherein the controller is configured to determine, based on positions of the plural valves, the flow in the branches, wherein the plural valves comprise a block valve controlling coolant flow through an engine, a heater valve controlling flow through a heater core, and a radiator valve controlling flow through a radiator.

5. The system of claim 1 , wherein the controller is configured to correct the predicted next-step state using a Kalman filter approach, including by designating a reliability of the sensor based on a position of one of the plural valves, and designating the reliability as unreliable when the one of the plural valves is closed.

6. The system of claim 5 , comprising an engine, wherein the controller is configured to predict the next-step state based on inputs representing an operational state of the engine at time k−1, and using a nonlinear model, and to correct the predicted next-step state using a measurement of the current state sampled at time k.

7. The system of claim 5 , wherein the controller is configured to designate the reliability as reliable when the one of the plural valves is full open.

8. The system of claim 1 , wherein the controller is configured to determine, based on the flows, whether the sensor is unreliable, and when the sensor is unreliable as indicated by the flows, to predict the next-step state without use of the current state.

9. The system of claim 1 , wherein the controller is configured to determine, when the flows in the fluid conduits are approximately zero, a temperature in the thermal system using a high noise covariance to discount sensed temperature values.

10. The system of claim 1 , wherein the controller is configured to:
receive temperature inputs of current state temperature readings from a plurality of temperature sensors;
calculate next-step state predictions for select parameters;
determine, based on rates of the flows, the noise covariance;
correct the next-step state predictions based on the current state temperature readings and the noise covariance determination.

11. A method of determining states of a parameter, the method comprising:
receiving, by the controller, a position signal for each of plural valves and a speed signal for a pump, wherein the pump and the plural valves are connected in a network of fluid conduits;
estimating, by a controller and based on a flow model of the network of fluid conduits and actuator feedback for the position signals of each of the plural valves, branch flows in the network of fluid conduits;
determining, by the controller using the estimated branch flows, a noise covariance level for a sensor in the network of fluid conduits;
processing, by the controller, a thermal model;
predicting, by the controller and based on the thermal model and the branch flows, a next-step state of the parameter; and
correcting, by the controller, the next-step state based, at least in-part, on the noise covariance; and
operating, by the controller, at least one actuator based on the corrected next-step state.

12. The method of claim 11 , comprising:
designating a reliability of the sensor based on a position of one of the plural valves;
designating the reliability as unreliable when the one of the plural valves is closed; and
determining the corrected next-step state by the controller and based on a Kalman filter observer, temperature measurements, the predicted next-step state, linearized model parameters, and the noise covariance.

13. The method of claim 11 , comprising:
assigning, based on the branch flows, the noise covariance value to the sensor;
discounting a temperature input provided by the sensor in relation to a reduction in the flows; and
controlling, by the controller, operation of an internal combustion engine based on the corrected next-step state.

14. The method of claim 11 , comprising:
monitoring, by the sensor, a coolant temperature of a coolant in the fluid conduits, and
determining, by the controller and using the coolant temperature, a wall temperature of a wall surrounding a cylinder of an internal combustion engine; and
controlling combustion the internal combustion engine based on the wall temperature.

15. The method of claim 11 , comprising:
controlling, by the plural valves, the branch flows in the network of fluid conduits; and
determining, by the controller and based on positions of the plural valves, the flow in the branches.

16. The method of claim 11 , wherein predicting the next-step state comprises using inputs representing an operational state of an engine at time k−1 and a nonlinear thermal system model, and
wherein correcting the next-step state comprises using a measurement of a current state sampled at time k.

17. The method of claim 11 , comprising:
determining, by the controller and based on a flow rate of the branch flows, whether the sensor is unreliable, and
predicting, when the sensor is determined unreliable as indicated by the flow rate, the next-step state without factoring in the current state.

18. The method of claim 17 , comprising:
determining, when a flow rate in the network of fluid conduits is approximately zero, a temperature using a high noise covariance to discount a temperature value received from a sensor.

19. The method of claim 11 , comprising:
correcting the predicted next-step state by the controller processing a Kalman filter observer using temperature measurements, the next-step state prediction, linearized model parameters, and the noise covariance.

20. A method

Assignees

• Gm Global Tech Operations Llc

Inventors

• Chen Jun
• Edwards David E
• Hu Yiran
• Sun Min
• Heinzen Adam J
• Smith Michael A

Classifications

• F02D2041/1417

  Kalman filter · CPC title

• G01K13/026

  of moving liquids · CPC title

• F02D43/04

  using only digital means · CPC title

• F01P7/167

  by adjusting the pre-set temperature according to engine parameters, e.g. engine load, engine speed · CPC title

• F01P2025/30

  Engine incoming fluid temperature · CPC title