Methods and systems for powertrain optimization and improved fuel economy
View Patent ↗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.
1. A computer implemented method for powertrain optimization and improved fuel economy in a vehicle, the method comprising:
(a) providing a modeled powertrain system and vehicle engine;
(b) 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 the modeled powertrain system comprising:
(i) calculating required torque and speed from the vehicle speed trace;
(ii) propagating the required torque and speed backwardly through the modeled powertrain system to the modeled vehicle engine; and
(c) determining fuel flow for each one of a plurality of states of the modeled powertrain system with the determined required engine torque and speed utilizing a dynamic optimization algorithm capable of executing a plurality of iterations to:
(i) calculate required fuel flow for each of a plurality of powertrain component control decisions for each of a plurality of powertrain states at k=N−1
(ii) identify a minimum required fuel flow and an optimal control decision for each of the plurality of powertrain states at k=N−1,
(iii) 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,
(iv) identify a minimum required fuel flow and an optimal control decision for each of a plurality of powertrain states for 0≦k<N−1,
(v) determine a global optimum accumulated required fuel flow and initial powertrain state at k=0, and
(vi) 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, and
(d) identifying an optimal state for each of the plurality of powertrain components, and
(e) controlling each of the plurality of powertrain components in the identified optimal state for each of the plurality of powertrain components in order to improve fuel efficiency.
2. The computer implemented method for powertrain optimization and improved fuel economy of claim 1 , wherein the modeled 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 computer implemented 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 computer implemented 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 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.
5. The computer implemented 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 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.
6. The computer implemented 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 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 plurality of powertrain components; and
iteratively calculating an optimal combination of powertrain components that performs well based on a desired cycle of interest.
7. The computer implemented 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:
F
=
A
+
Bv
+
Cv
2
+
m
ⅆ
v
ⅆ
t
+
m
g
sin
θ
wherein A, B, and C are the road load coefficients, v is the vehicle speed, m is the vehicle mass, g is gravity, and θ is the angle the road makes with the horizontal.
8. The computer implemented method for powertrain optimization and improved fuel economy of claim 1 , wherein the dynamic optimization algorithm further comprises:
utilizing the following relationship for determining a total cost to be minimized:
J
=
g
N
(
x
N
)
+
∑
k
=
0
N
-
1
L
k
(
x
k
,
u
k
)
wherein g N is the cost at time step N, L k is the instantaneous transition cost at time step k, and the system is modeled as a discrete time non-linear system of the form:
x k+1 =f k ( x k ,u k ), k =0,1 , . . . , N− 1
wherein x k defines the state of the system and u k defines the control variables to be selected at time k.
9. A control system for powertrain optimization and improved fuel economy in a vehicle, the control system comprising:
a modeled powertrain system;
a reverse tractive road load demand simulation algorithm, in operative communication with the modeled 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 modeled 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 through the modeled powertrain system to a vehicle engine,
control the vehicle engine and improving the fuel economy with the determined required engine torque and speed by (a) utilizing a dynamic optimization algorithm to (i) calculate required fuel flow for each of a plurality of powertrain component control decisions for each of a plurality of powertrain states at k=N−1, wherein k is a time step and N is a cycle duration, (ii) identify a minimum required fuel flow and an optimal control decision for each of the plurality of powertrain states at k=N−1, (iii) 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, (iv) identify a minimum required fuel flow and an optimal control decision for each of a plurality of powertrain states for 0≦k<N−1, (v) determine a global optimum accumulated required fuel flow and initial powertrain state at k=0, and (vi) create an optimal state vector by sequencing the optimal control decision at each time step for 0≦k≦N−1, and (b) identifying an optimal state for each of the plurality of powertrain components, and (c) controlling each of the plurality of powertrain components in the identified optimal state for each of the plurality of powertrain components in order to improve fuel efficiency.
10. The control system for powertrain optimization and improved fuel economy of claim 9 , wherein the modeled 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.
11. The control system for powertrain optimization and improved fuel economy of claim 9 , 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.
12. The control system for powertrain optimization and improved fuel economy of claim 9 , 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.
13. The control system for powertrain optimization and improved fuel economy of claim 9 , 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.
14. The control system for powertrain optimization and improved fuel economy of claim 9 , 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 plurality of powertrain components, and
iteratively calculate an optimal combination of powertrain components that performs well based on a desired cycle of interest.
15. The control system for powertrain optimization and improved fuel economy of claim 9 , wherein the reverse tractive road load demand simulation algorithm further comprises:
logic configured to utilizing the following relationship:
F
=
A
+
Bv
+
Cv
2
+
m
ⅆ
v
ⅆ
t
+
m
g
sin
θ
wherein A, B, and C are the road load coefficients, v is the vehicle speed, m is the vehicle mass, g is gravity, and θ is the angle the road makes with the horizontal.
16. The control system for powertrain optimization and improved fuel economy of claim 9 , wherein the dynamic optimization algorithm further comprises:
logic configured to:
utilize the following relationship for determining a total cost to be minimized:
J
=
g
N
(
x
N
)
+
∑
k
=
0
N
-
1
L
k
(
x
k
,
u
k
)
wherein g N is the cost at time step N, L k is the instantaneous transition cost at time step k, and the system is modeled as a discrete time non-linear system of the form:
x k+1 =f k ( x k ,u k ), k =0,1 , . . . , N− 1
wherein x k defines the state of the system and u k defines the control variables to be selected at time k.
17. 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 modeled powertrain system,
calculate a required torque and speed from the vehicle speed trace,
propagate the required torque and speed backwardly through the modeled powertrain system to a vehicle engine, and
control the vehicle engine and improving the fuel economy with the determined required engine torque and speed utilizing the following relationship for determining a total cost to be minimized:
J
=
g
N
(
x
N
)
+
∑
k
=
0
N
-
1
L
k
(
x
k
,
u
k
)
wherein g N is the cost at time step N, L k is the instantaneous transition cost at time step k, and the system is modeled as a discrete time non-linear system of the form:
x k+1 =f k ( x k ,u k ), k =0,1 , . . . , N− 1
wherein x k defines the state of the system and u k defines the control variables to be selected at time k.
18. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 17 , wherein the modeled 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.
19. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 17 , 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.
20. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 17 , 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.
21. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 20 , 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.
22. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 20 , 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.
23. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 20 , 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 plurality of powertrain components, and
iteratively calculate an optimal combination of powertrain components that performs well based on a desired cycle of interest.
24. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 17 , 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.
25. The computer readable medium encoded with programming for powertrain optimization and improved fuel economy in a vehicle of claim 17 , the programming configured to:
utilize the following relationship:
F
=
A
+
Bv
+
Cv
2
+
m
ⅆ
v
ⅆ
t
+
m
g
sin
θ
wherein A, B, and C are the road load coefficients, v is the vehicle speed, m is the vehicle mass, g is gravity, and θ is the angle the road makes with the horizontal.