METHODS AND SYSTEMS FOR POWERTRAIN OPTIMIZATION AND IMPROVED FUEL ECONOMY

• US 20080262712A1
• Filed: 04/18/2007
• Published: 10/23/2008
• Est. Priority Date: 04/18/2007
• Status: Active Grant

• Current Assignee: FCA US LLC (Stellantis NV)
• Original Assignee: Chrysler Group LLC (Stellantis NV)
• Inventors: Duty, Mark J., Papke, Melody
• Granted Patent: US 8,050,856 B2
• Time in Patent Office: Days
• US Class Current: 701/123
• CPC Class Codes: B60W 10/02 including control of driveline clutches
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/10 including control of change-speed gearings
• CPC Class Codes: B60W 30/1882 characterised by the working point of the engine, e.g. by using engine output chart
• CPC Class Codes: F02D 11/105 characterised by the function converting demand to actuation, e.g. a map indicating relations between an accelerator pedal position and throttle valve opening or target engine torque
• CPC Class Codes: F02D 2041/1433 using a model or simulation of the system
• CPC Class Codes: F02D 2200/0625 Fuel consumption, e.g. measured in fuel liters per 100 kms or miles per gallon
• CPC Class Codes: F02D 41/1406 with use of a optimisation method, e.g. iteration
• CPC Class Codes: Y02T 10/40 Engine management systems
• CPC Class Codes: Y02T 10/84 Data processing systems or methods, management, administration

Abstract

The technology described herein provides methods and systems for powertrain optimization and improved fuel economy including multiple displacement engine modeling and control optimization, automotive powertrain matching for fuel economy, cycle-based automotive shift and lock-up scheduling for fuel economy, and engine performance requirements based on vehicle attributes and drive cycle characteristics. Also provided is a reverse tractive road load demand simulation algorithm used to propagate a reverse tractive road load demand and a corresponding component torque and speed, derived from a vehicle speed trace, in a reverse direction through a powertrain system. Also provided is a dynamic optimization algorithm. The dynamic programming algorithm is applied to a matrix of fuel flow rates to find the optimal control path that maximizes the powertrain efficiency over a cycle.

Claims

1. A method for powertrain optimization and improved fuel economy in a vehicle, the method comprising:

utilizing a reverse tractive road load demand simulation algorithm to propagate a reverse tractive road load demand and a corresponding component torque and speed, the corresponding component torque and speed derived from a vehicle speed trace in a reverse direction through a powertrain system;

calculating required torque and speed from the vehicle speed trace;

propagating the required torque and speed backwardly though the powertrain system to a vehicle engine; and

controlling the vehicle engine and improving the fuel economy with the determined required engine torque and speed.

2. The method for powertrain optimization and improved fuel economy of claim 1, wherein the powertrain system further comprises each of wheels, axle, driveshaft, transfer case, transmission, torque converter, and engine vehicle subsystems, the method further comprising:

utilizing the reverse tractive road load demand simulation algorithm with a direction of power transfer flowing from the wheel, to the axle, to the driveshaft, to the transfer case, to the transmission, to the torque converter, and to the engine vehicle subsystem.

3. The method for powertrain optimization and improved fuel economy of claim 1, the method further comprising:

utilizing the reverse tractive road load demand simulation algorithm to simulate the required engine torque as a function of engine speed based on a plurality of vehicle attributes;

propagating the required torque and speed from the vehicle wheels through the powertrain for all possible component states;

utilizing the required engine torque to traverse different drive cycles as part throttle engine torque design requirements.

4. The method for powertrain optimization and improved fuel economy of claim 1, the method further comprising:

utilizing a dynamic optimization algorithm to calculate required fuel flow for each of a plurality of powertrain component control decisions;

identifying an optimal state for each of the plurality of powertrain components; and

controlling each of the plurality of powertrain components in the identified optimal state for each in order to improve fuel efficiency.

5. The method for powertrain optimization and improved fuel economy of claim 4, the method further comprising:

calculating a required fuel flow for each of a plurality of control decisions for each of a plurality of powertrain states at k=N−

1;

identifying a minimum required fuel flow and an optimal control decision for each of the plurality of powertrain states at k=N−

1;

calculating recursively a required fuel flow for each of a plurality of control decisions for each of a plurality of powertrain states for 0≦

k<

N−

1;

identifying a minimum required fuel flow and an optimal control decision for each of a plurality of powertrain states for 0≦

k<

N−

1;

determining a global optimum accumulated required fuel flow and initial powertrain state at k=0; and

creating air optimal state vector by sequencing the optimal control decision at each time step for 0≦

k≦

N−

1;

wherein k is a time step and N is a cycle duration.

6. The method for powertrain optimization and improved fuel economy of claim 4, the method further comprising:

utilizing the reverse tractive road load demand simulation algorithm to optimize cycle-based automotive shift and lock-up scheduling for improved fuel economy;

determining required fuel flow for all possible states within hardware constraints;

determining a cycle-based automotive shift and lock-up schedule for improved fuel economy;

applying the dynamic optimization algorithm to find an optimal control path that minimizes accumulated fuel flow; and

controlling a vehicle powertrain subsystem with the optimal control path to minimize the accumulated fuel flow.

7. The method for powertrain optimization and improved fuel economy of claim 4, the method further comprising:

utilizing the reverse tractive road load demand simulation algorithm to optimize a multiple displacement engine over a plurality of different drive cycles;

determining if there is enough torque available in a multiple displacement mode;

utilizing the dynamic optimization algorithm to find an optimal control path yielding a minimal accumulated fuel flow and an optimal control policy; and

controlling a vehicle powertrain subsystem with the optimal control path to yield minimal accumulated fuel flow and optimal control policy.

8. The method for powertrain optimization and improved fuel economy of claim 4, the method further comprising:

utilizing the reverse tractive road load demand simulation algorithm to determine a required fuel flow for all possible states within hardware constraints;

determining the required fuel flow for all possible states within hardware constraints;

utilizing the dynamic optimization algorithm to find an optimal control for a one or more powertrain component; and

iteratively calculating an optimal combination of powertrain components that performs well based on a desired cycle of interest.

9. The method for powertrain optimization and improved fuel economy of claim 1, wherein the reverse tractive road load demand simulation algorithm further comprises:

utilizing the following relationship;

10. The method for powertrain optimization and improved fuel economy of claim 5, wherein the dynamic optimization algorithm further comprises:

utilizing the following relationship for determining a total cost to be minimized;

11. A control system for powertrain optimization and improved fuel economy in a vehicle, the control system comprising:

a powertrain system;

a reverse tractive road load demand simulation algorithm, in operative communication with the powertrain system, operative to propagate a reverse tractive road load demand and a corresponding component torque and speed, the corresponding component torque and speed derived from a vehicle speed trace, in a reverse direction through the powertrain system;

wherein the control system comprises logic configured to,

calculate a required torque and speed from the vehicle speed trace,

propagate the required torque and speed backwardly though the powertrain system to a vehicle engine, and

control the vehicle engine and improving the fuel economy with the determined required engine torque and speed.

12. The control system for powertrain optimization and improved fuel economy of claim 11, wherein the powertrain system further comprises each of wheels, axle, driveshaft, transfer case, transmission, torque converter, and engine vehicle subsystems, the control system further comprising:

logic configured to utilize the reverse tractive road load demand simulation algorithm with a direction of power transfer flowing from the wheel, to the axle, to the driveshaft, to the transfer case, to the transmission, to the torque converter, and to the engine vehicle subsystem.

13. The control system for powertrain optimization and improved fuel economy of claim 11, the control system further comprising:

logic configured to;

utilize the reverse tractive road load demand simulation algorithm to simulate the required engine torque as a function of engine speed based on a plurality of vehicle attributes;

propagate the required torque and speed from the vehicle wheels through the powertrain for all possible component states; and

utilize the required engine torque to traverse different drive cycles as part throttle engine torque design requirements.

14. The control system for powertrain optimization and improved fuel economy of claim 11, the control system further comprising:

logic configured to;

utilize a dynamic optimization algorithm to calculate required fuel flow for each of a plurality of powertrain component control decisions,

identify an optimal state for each of the plurality of powertrain components, and

control each of the plurality of powertrain components in the identified optimal state for each in order to improve fuel efficiency.

15. The control system for powertrain optimization and improved fuel economy of claim 14, the control system further comprising:

logic configured to;

calculate a required fuel flow for each of a plurality of control decisions for each of a plurality of powertrain states at k=N−

1,

identify a minimum required fuel flow and an optimal control decision for each of the plurality of powertrain states at k=N−

1,

calculate recursively a required fuel flow for each of a plurality of control decisions for each of a plurality of powertrain states for 0≦

k<

N−

1,

identify a minimum required fuel flow and an optimal control decision for each of a plurality of powertrain states for 0≦

k<

N−

1,

determine a global optimum accumulated required fuel flow and initial powertrain state at k=0, and

create an optimal state vector by sequencing the optimal control decision at each time step for 0≦

k≦

N−

1,

wherein k is a time step and N is a cycle duration.

16. The control system for powertrain optimization and improved fuel economy of claim 14, the control system further comprising:

logic configured to;

utilize the reverse tractive road load demand simulation algorithm to optimize cycle-based automotive shift and lock-up scheduling for improved fuel economy,

determine required fuel flow for all possible states within hardware constraints,

determine a cycle-based automotive shift and lock-up schedule for improved fuel economy,

apply the dynamic optimization algorithm to find an optimal control path that minimizes accumulated fuel flow, and

control a vehicle powertrain subsystem with the optimal control path to minimize the accumulated fuel flow.

17. The control system for powertrain optimization and improved fuel economy of claim 14, the control system further comprising:

logic configured to;

utilize the reverse tractive road load demand simulation algorithm to optimize a multiple displacement engine over a plurality of different drive cycles,

determine if there is enough torque available in a multiple displacement mode,

utilize the dynamic optimization algorithm to find an optimal control path yielding a minimal accumulated fuel flow and an optimal control policy, and

control a vehicle powertrain subsystem with the optimal control path to yield minimal accumulated fuel flow and optimal control policy.

18. The control system for powertrain optimization and improved fuel economy of claim 14, the control system further comprising:

logic configured to;

utilize the reverse tractive road load demand simulation algorithm to determine a required fuel flow for all possible states within hardware constraints,

determine the required fuel flow for all possible states within hardware constraints,

utilize the dynamic optimization algorithm to find an optimal control for a one or more powertrain component, and

iteratively calculate an optimal combination of powertrain components that performs well based on a desired cycle of interest.

19. The control system for powertrain optimization and improved fuel economy of claim 11, wherein the reverse tractive road load demand simulation algorithm further comprises:

logic configured to utilizing the following relationship;

20. The control system for powertrain optimization and improved fuel economy of claim 15, wherein the dynamic optimization algorithm further comprises:

logic configured to;

utilize the following relationship for determining a total cost to be minimized;

21. A computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle, the programming configured to:

utilize a reverse tractive road load demand simulation algorithm,

propagate a reverse tractive road load demand and a corresponding component torque and speed, the corresponding component torque and speed derived from a vehicle speed trace, in a reverse direction through a powertrain system,

calculate a required torque and speed from the vehicle speed trace,

propagate the required torque and speed backwardly though the powertrain system to a vehicle engine, and

control the vehicle engine and improving the fuel economy with the determined required engine torque and speed.

22. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 21, wherein the powertrain system further comprises each of wheels, axle, driveshaft, transfer ease, transmission, torque converter, and engine vehicle subsystems, the programming configured to:

utilize the reverse tractive road load demand simulation algorithm with a direction of power transfer flowing from the wheel, to the axle, to the driveshaft, to the transfer case, to the transmission, to the torque converter, and to the engine vehicle subsystem.

23. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 21, the programming configured to:

utilize the reverse tractive road load demand simulation algorithm to simulate the required engine torque as a function of engine speed based on a plurality of vehicle attributes;

propagate the required torque and speed from the vehicle wheels through the powertrain for all possible component states; and

utilize the required engine torque to traverse different drive cycles as part throttle engine torque design requirements.

24. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 21, the programming configured to:

utilize a dynamic optimization algorithm to calculate required fuel flow for each of a plurality of powertrain component control decisions,

identify an optimal state for each of the plurality of powertrain components, and

control each of the plurality of powertrain components in the identified optimal state for each in order to improve fuel efficiency.

25. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 24, the programming configured to:

calculate a required fuel flow for each of a plurality of control decisions for each of a plurality of powertrain states at k=N−

1,

identify a minimum required fuel flow and an optimal control decision for each of the plurality of powertrain states at k=N−

1,

calculate recursively a required fuel flow for each of a plurality of control decisions for each of a plurality of powertrain states for 0≦

k<

N−

1,

identify a minimum required fuel flow and an optimal control decision for each of a plurality of powertrain states for 0≦

k<

N−

1,

determine a global optimum accumulated required fuel flow and initial powertrain state at k=0, and

create an optimal state vector by sequencing the optimal control decision at each time step for 0≦

k≦

N−

1,

wherein k is a time step and N is a cycle duration.

26. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 24, the programming configured to:

utilize the reverse tractive road load demand simulation algorithm to optimize cycle-based automotive shift and lock-up scheduling for improved fuel economy,

determine required fuel flow for all possible states within hardware constraints,

determine a cycle-based automotive shift and lock-up schedule for improved fuel economy,

apply the dynamic optimization algorithm to find an optimal control path that minimizes accumulated fuel flow, and

control a vehicle powertrain subsystem with the optimal control path to minimize the accumulated fuel flow.

27. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 21, the programming configured to:

utilize the reverse tractive road load demand simulation algorithm to optimize a multiple displacement engine over a plurality of different drive cycles,

determine if there is enough torque available in a multiple displacement mode,

utilize the dynamic optimization algorithm to find an optimal control path yielding a minimal accumulated fuel flow and an optimal control policy, and

control a vehicle powertrain subsystem with the optimal control path to yield minimal accumulated fuel flow and optimal control policy.

28. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 24, the programming configured to:

utilize the reverse tractive road load demand simulation algorithm to determine a required fuel flow for all possible states within hardware constraints,

determine the required fuel flow for all possible states within hardware constraints,

utilize the dynamic optimization algorithm to find an optimal control for a one or more powertrain component, and

iteratively calculate an optimal combination of powertrain components that performs well based on a desired cycle of interest.

29. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 21, the programming configured to:

utilize the following relationship;

30. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 21, the programming configured to:

utilize the following relationship for determining a total cost to be minimized;