DE102017113156A1 - Drive System Control with Model Predictive Control (MPC) - Google Patents

Abstract

A propulsion system, control system and method for optimizing fuel efficiency, the model predictive control systems for generating first and second predicted actual axle torques and first and second predicted actual fuel consumption rates based on first and second sets of possible target values, respectively. The sets of possible setpoints include commanded engine output torques and commanded gear ratios. First and second costs for the first and second sets of possible target values are respectively based on a first predetermined weight value, a second predetermined weight value, the first and second predicted actual axle torque, the first and second predicted actual fuel consumption rates, a requested axle torque, respectively requested engine output torque, a requested gear ratio and a requested fuel consumption rate determined. One of the first and second sets of possible setpoints is selected and set based on the lower cost.

Description

TECHNICAL AREA

The disclosure relates to a control system and method for a drive system of a motor vehicle having an engine and a transmission, and more particularly to a control system and method using multivariable control.

INTRODUCTION

Propulsion system control in a vehicle generally includes reading driver and vehicle inputs, such as accelerator pedal position, vehicle sensor data, and torque requests, and communicates these inputs to an engine control module (ECM) and a transmission control module (TCM). The ECM may calculate a desired axle torque from the driver and vehicle inputs. The desired axle torque can then be transmitted to the engine and the TCM. The engine will be controlled based on the desired axle torque to generate an actual axle torque. Meanwhile, the ECM calculates a desired speed or gear ratio from the desired axle torque and vehicle speed. The desired gear ratio is then transmitted to the transmission. The transmission is controlled based on the desired gear ratio to produce an actual gear ratio. The actual axle torque and the actual transmission ratio define the operating conditions of the motor vehicle.

While this system of propulsion system control is useful for its purpose, there is room in the art for improvements in the dynamic control of axle torque to balance power and fuel economy, particularly in CVT propulsion systems.

Engine control systems have been developed to control engine output torque and achieve desired torque. However, conventional engine control systems do not regulate the engine output torque with the required accuracy.

SUMMARY

The present disclosure provides a method and system for controlling a parameter, such as vehicle acceleration, in a vehicle drive system while optimizing fuel economy through model predictive control. In some embodiments, model predictive control is used to coordinate the engine and transmission and improve fuel efficiency and drivability.

In a form that may be disclosed together with or separately from other forms described herein, a method of controlling a drive system of a motor vehicle is provided. The method includes generating a first predicted actual axle torque and a first predicted actual fuel consumption rate based on a first set of possible setpoints, wherein the first set of possible setpoints includes a first commanded engine output torque and a first commanded gear ratio. The method also includes generating a second predicted actual axle torque and a second predicted actual fueling rate based on a second set of possible setpoints, wherein the second set of possible setpoints includes a second commanded engine output torque and a second commanded gear ratio. In practice, the method may also include determining a plurality of additional predicted actual axle torque values and predicted values of actual fuel consumption rate values based on additional sets of possible engine output torque and gearbox target values, but here two of each predicted value are described as minimum.

The method further includes determining first and second costs. The first cost is calculated for the first set of possible setpoints based on at least a first predetermined weighting value, a second predetermined weighting value, the first predicted actual axle torque, the first predicted actual fuel consumption rate, a requested axle torque, a requested engine output torque, a requested gear ratio, and a requested one Fuel consumption rate determined. The second cost for the second set possible setpoints are determined based on the at least one predetermined weighting value, the second predetermined weighting value, the second predicted actual axle torque, the second predicted actual fuel consumption rate, the requested axle torque, the requested engine output torque, the requested gear ratio, and the requested fuel consumption rate. The method may also include determining a plurality of additional costs for each set of possible engine output torque and transmission setpoint values in cases where more than two sets of possible setpoints are used, which is likely to occur in most optimization controls. First and second costs are described here as minimum.

The method includes selecting a first and second set of possible setpoints based on the lower first and second costs, and setting the desired setpoints based on the possible setpoints of the selected first and second sets. As mentioned above, well over two costs can be determined, and if so, the lowest cost is selected within any constraints. The method further includes controlling a vehicle parameter based on at least one desired setpoint.

In another form, which may be combined with or disclosed separately from the other forms herein, there is provided a vehicle drive control system for a vehicle having a transmission and a motor. The vehicle drive control system includes a prediction module for generating a first predicted actual axle torque and a first predicted actual fuel consumption based on a first set of possible setpoints, wherein the first set of possible setpoints includes a first commanded engine output torque and a first commanded gear ratio. The prediction module is further configured to generate a second predicted actual axle torque and a second predicted actual fueling rate based on a second set of possible setpoints, wherein the second set of possible setpoints includes a second commanded engine output torque including a second commanded gear ratio. In practice, the prediction module may also be configured to determine a plurality of additional predicted actual axle torque values and predicted actual fuel consumption rate values based on additional sets of possible engine output torque and gearbox target values, as is likely for an optimization control system.

The control system also includes a cost module configured to determine (at least) the first and second costs. The first costs for the first set of possible setpoints are based on at least a first predetermined weighting value, a second predetermined weighting value, the first predicted actual axle torque, the first predicted actual fuel consumption rate, a requested axle torque, a requested engine output torque, a requested gear ratio, and a requested one Fuel consumption rate determined. The second cost of the second set of possible setpoints is determined based on at least the first predetermined weighting value, the second predetermined weighting value, the second predicted actual axle torque, the second predicted actual fuel consumption rate, the requested axle torque, the requested engine output torque, the requested gear ratio, and the requested gear ratio Fuel consumption rate determined. The cost module may also be configured to determine a variety of additional costs for each additional set of possible engine output torque and transmission setpoint values in cases where more than two sets of possible setpoint values are used, which is likely to be the case with most optimization controls is. First and second costs are described here only as a minimum cost.

The control system further includes a selection module configured to select a first and second set (or additional sets) of possible setpoints based on the lower of the first and second costs (or additional determined cost) and sets of desired setpoints based on the possible setpoints of the setpoint selected first and second sentence.

In yet another form, which may be combined with or disclosed separately from other forms herein, a drive system for a motor vehicle is provided. The drive system includes a motor operable to power the vehicle. The engine has an engine output shaft for transmitting engine output torque. The drive system also includes a continuously variable transmission with a variator assembly including a first pulley and a second pulley. The first and second pulleys are rotatably connected by a rotatable device. At least one of the first and second pulleys includes a movable pulley translatable along an axis is for selectively changing a gear ratio between the engine output shaft and a transmission output shaft is. In addition, a drive axle is provided which is driven via the transmission output shaft. The drive axle is configured to output axle torque to a wheel set.

Furthermore, the drive system includes a control system with a prediction module, a cost module, and a selection module. The prediction module is configured to generate a first predicted actual axle torque and a first predicted actual fuel consumption based on a first set of possible setpoints. The first set of possible setpoints includes a first commanded engine output torque and a first commanded gear ratio. The prediction module is further configured to generate a second predicted actual axle torque and a second predicted actual fueling rate based on a second set of possible setpoints. The second set of possible setpoints includes a second commanded engine output torque and a second commanded gear ratio. As described above, additional sets of predicted values may be determined based on additional possible setpoints. The cost module is configured to determine the first and second costs (or additional costs based on additional possible setpoints). The first costs for the first set of possible setpoints are determined based on at least a first predetermined weighting value, a second predetermined weighting value, the first predicted actual axle torque, the first predicted actual fuel consumption rate, a requested axle torque, a requested engine output torque, a requested gear ratio, and a requested one Fuel consumption rate determined. The second costs for the second set of possible setpoints are determined based on at least the first predetermined weighting value, the second predetermined weighting value, the second predicted actual axle torque, the second predicted actual fuel consumption rate, the requested axle torque, the requested engine output torque, the requested gear ratio, and the requested gear ratio Fuel consumption rate determined. The selection module is configured to select a first and second set (or additional sets) of possible setpoints based on the lower of the first and second costs (or additional costs) and desired setpoints based on the possible setpoints of the selected first and second set (or additional setpoints) Sentences).

Additional additional properties may be disclosed and provided herein in any form, including but not limited to the following properties disclosed in paragraphs [0016] - [0023].

worin Te_a = vorausgesagtes tatsächliches Motorausgangsmoment; FR_a = vorausgesagte tatsächliche Kraftstoffverbrauchsrate; Rat_a = vorausgesagtes tatsächliches Übersetzungsverhältnis; Ta_a = vorausgesagtes tatsächliches Achsdrehmoment; Te_r = angefordertes Motorausgangsdrehmoment ; FR_r = angeforderte Kraftstoffverbrauchsrate; Rat_r = angefordertes Übersetzungsverhältnis; Ta_r = angefordertes Achsdrehmoment; Te_c = angewiesenes Motorausgangsdrehmoment; Rat_c = angewiesenes Übersetzungsverhältnis; Q_y = ein erster vorgegebener Gewichtungswert; Q_u = ein zweiter vorgegebener Gewichtungswert; Q_Δu = ein dritter vorgegebener Gewichtungswert; i = Index-Wert; k = Vorhersageschritt; und T = transponierter Vektor.For example, the first and second costs (or a number of a variety of costs in the optimization control plan) may be determined using the following cost equation:

where Te_a = predicted actual engine output torque; FR_a = predicted actual fuel consumption rate; Rat_a = predicted actual gear ratio; Ta_a = predicted actual axle torque; Te_r = requested engine output torque; FR_r = requested fuel consumption rate; Rat_r = requested ratio; Ta_r = requested axle torque; Te_c = instructed engine output torque; Rat_c = instructed gear ratio; Q _y = a first predetermined weighting value; Q _u = a second predetermined weighting value; Q _Δu = a third predetermined weighting _value ; i = index value; k = predictive step; and T = transposed vector.

In some forms, the cost equation may be applied iteratively to achieve the lowest cost for a plurality of sets of possible setpoints, wherein the plurality of sets of possible setpoints include the first and second sets of possible setpoints and the set of possible setpoints having the least cost wherein the set of possible setpoints having the least cost is defined as one of the first and second sets of possible setpoints.

In addition, the method and systems may be configured to determine an accelerator pedal position PP, an engine speed RPM, a vehicle speed V, and an air-fuel ratio AF. The requested axle torque Ta_r may then be determined based on the accelerator pedal position PP and the vehicle speed V; the requested fuel consumption rate FR_r based on the requested axle torque Ta_r, the vehicle speed V, the rotational speed RPM, and the air-fuel ratio AF; the requested gear ratio Rat_r can be calculated from the requested axle torque Ta_r and the vehicle speed V; and the requested engine output torque Te_r may be determined based on the requested axle torque Ta_r, the requested gear ratio Rat_r, and the final drive ratio FD.

worin x_k+1 = Zustandsvariable an einem Voraussageschritt k + 1; x_k = Zustandsvariable an einem Voraussageschritt k; A = eine erste Zustandsmatrix; B = eine zweite Zustandsmatrix; Te_c_k = Motorausgangsdrehmoment angewiesen an Voraussageschritt k; Rat_c_k = Übersetzungsverhältnis angewiesen an Voraussageschritt k; K_KF = eine Kalman-Filter-Verstärkung; Te_a_k = vorausgesagtes tatsächliches Motorausgangsmoment an Voraussageschritt k; FR_a_k = vorausgesagte tatsächliche Kraftstoffverbrauchsrate an Voraussageschritt k; Rat_a_k = vorausgesagtes tatsächliches Übersetzungsverhältnis an Voraussageschritt k; Ta_a_k = vorausgesagtes tatsächliches Achsdrehmoment an Voraussageschritt k; Te_m_k = gemessenes Motorausgangsdrehmoment an Voraussageschritt k; FR_m_k = gemessene Kraftstoffverbrauchsrate an Voraussageschritt k; Rat_m_k = gemessenes Übersetzungsverhältnis an Voraussageschritt k; Ta_m_k = gemessenes Achsdrehmoment an Voraussageschritt k; Ta_a_k+1 = vorausgesagtes tatsächliches Achsdrehmoment an Voraussageschritt k + 1; FR_a_k+1 = vorausgesagte tatsächliche Kraftstoffverbrauchsrate an Voraussageschritt k + 1; und C = eine dritte Zustandsmatrix ist.The first and second predicted actual axle torques (or any number of predicted actual axle torques) and the first and second predicted actual fuel consumption rates (or any number of predicted actual fuel consumption rates) may be determined using the following set of equations:

where x _{k + 1} = state variable at a prediction step k + 1; x _k = state variable at a prediction step k; A = a first state matrix; B = a second state matrix; Te_c _k = engine output torque instructed at prediction step k; Rat_c _k = gear ratio instructed on prediction step k; K _KF = a Kalman filter gain; Te_a _k = predicted actual engine output torque at prediction step k; FR_a _k = predicted actual fuel consumption rate at prediction step k; Rat_a _k = predicted actual gear ratio to prediction step k; Ta_a _k = predicted actual axle torque at prediction step k; Te_m _k = measured engine output torque at prediction step k; FR_m _k = measured fuel consumption rate at prediction step k; Rat_m _k = measured gear ratio at prediction step k; Ta_m _k = measured axle torque at prediction step k; Ta_a _{k + 1} = predicted actual Axle torque at prediction step k + 1; FR_a _{k + 1} = predicted actual fuel consumption rate at prediction step _k + 1; and C = a third state matrix.

In some forms, the first and second costs (or any number of costs) may be determined by the following more detailed cost equation: Cost = λ _a · (Ta_a _k - Ta_r) ² + λ _a · (Ta_a _{k + 1} - Ta_r) ² + λ _a · (Ta_a _{k + 1} - Ta_r) ² + λ _f · (FR_a _k - FR_r) ² + λ _f · (FR_a _{k + 1} -FR_r) ² + λ _f · (FR_a _{k + 2} -FR_r) ² + λ _e · (Te_c _k -Te_r) ² + λ _e · (Te_c _{k + 1} -Te_r) ² + λ _r · (Rat_c _k - Rat_r) ² + λ _r · (Rat_c _{k + 1} - Rat_r) ² + λ _Δr · (ΔRat_c _k ) ² + λ _Δr · (ΔRat_c _{k + 1} ) ² + λ _Δr · (ΔTe_c _k ) ² + λ _Δe · (ΔTe_c _{k + 1} ) ² where λ _a = a first predetermined weighting value; Ta _- a _k = predicted actual axle torque at prediction step k; Ta_r = requested axle torque; Ta_a _{k + 1} = predicted actual axle torque at prediction step _k + 1; Ta_a _{k + 2} = predicted actual axle torque at prediction step _k + 2; λ _f = a second predetermined weighting value; FR _- a _k = predicted actual fuel consumption rate at prediction step k; FR _- r = requested fuel consumption rate; FR_a _{k + 1} = predicted actual fuel consumption rate at prediction step _k + 1; FR_a _{k + 2} = predicted actual fuel consumption rate at prediction step _k + 2; λ _e = a third predetermined weighting value; Te_c _k = instructed engine output torque at prediction step k; Te_r = requested engine output torque; Te_c _{k + 1} = instructed engine output torque at prediction step _k + 1; λ _r = a fourth predetermined weighting value; Rat_c _k = controlled ratio of prediction step k; Rat_r = requested ratio; Rat_c _{k + 1} = controlled gear ratio at prediction step _k + 1; λ _Δr = a fifth predetermined weighting _value ; ΔRat_c _k = instructed gear ratio change to prediction step k; ΔRat _- c _{k + 1} = instructed gear ratio change to prediction step k + 1; λ _Δe = a sixth predetermined weighting _value ; ΔTe_c _k = change of requested torque to prediction step k; and ΔTe_c _{k + 1} = change of requested torque at prediction step _{k + 1} .

Ta_a _{k + 1} and FR_a _{k + 1} may each be equal to the first predicted actual axle torque and the first predicted actual fuel consumption rate when generated based on the first set of possible setpoints for Te_c _k and Rat_c _k . On the other Ta_a may each be equal to the second predicted actual axle torque and the second predicted actual fuel consumption rate _{k + 1} and FR_a _{k + 1,} when based on the second set of possible target values for Te _- c _k and Rat_c _k were generated.

In some forms, the control system may further include an actuation module configured to control a vehicle parameter, such as acceleration, based on at least one of the desired setpoints (a setpoint from the selected set of possible setpoints).

Furthermore, the control values may be limited by restrictions such as: Te _min <Te_c _k <Te _max ; Te _min <Te_c _{k + 1} <Te _max ; Rat _min <Rat_c _k <Rat _max ; Rat _min <Rat_c _{k + 1} <Rat _max ; ΔTe_c _k <ΔTe_c _max ; ΔTe_c _{k + 1} <ΔTe_c _max ; ΔRat_c _k <ΔRat_c _max ; and ΔRat_c _{k + 1} <ΔRat_c _max , where Te _min = a minimum possible engine output torque; Te _max = a maximum possible engine output torque; Rat _min = a minimum possible gear ratio; Rat _max = a maximum possible gear ratio; ΔTe_c _max = a maximum possible change in engine output torque; and ΔRat_c _max = a maximum possible gear ratio change.

Other features, aspects and advantages of the present disclosure will become apparent in the following description and the accompanying drawings, wherein like reference numbers refer to like components, elements or features.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.

1 FIG. 10 is a schematic diagram of a motor vehicle having an exemplary drive system in accordance with the principles of the present disclosure; FIG.

2 FIG. 12 is a schematic illustration of a drive control system for use with the drive system. FIG 1 in accordance with the principles of the present disclosure;

3 FIG. 12 is a schematic diagram of a control system for use with the drive control system. FIG 2 in accordance with the principles of the present disclosure;

4 is a schematic diagram illustrating further details of the control system 3 in accordance with the principles of the present disclosure;

5 Figure 12 is a schematic diagram illustrating further details of multivariable control of the control system 3 - 4 in accordance with the principles of the present disclosure; and

6 FIG. 12 is a flowchart illustrating a method of controlling a vehicle drive system in accordance with the principles of the present disclosure.

DESCRIPTION

Regarding 1 an exemplary motor vehicle is shown and generally numbered 9 characterized. The car 9 is presented as a car, but it should be clear that the motor vehicle 9 Any type of vehicle can be such as a truck, van, sport utility vehicle (SUV), etc. The motor vehicle 9 includes an exemplary powertrain 10 , It should be noted in advance that while a rear-wheel drive train 10 is shown, the motor vehicle 9 a front-drive drivetrain, without departing from the scope of the present disclosure.

The drive system 10 generally includes an engine 12 connected to a transmission 14 and a final drive unit 16 , The motor 12 may be a conventional internal combustion engine or an electric motor, hybrid engine, or any other type of prime mover without departing from the scope of the present disclosure. The motor 12 provides engine output torque to the transmission 14 via a crankshaft or engine output shaft 18 , The engine output torque may be through a flexplate and / or starter 20 with the gearbox 14 be transmitted. The starting device 20 may be a hydrodynamic device such as a fluid coupling or torque converter, a wet-running dual clutch or an electric motor. Torque is then released from the starting device 20 to the at least one transmission input shaft 22 transmitted.

The gear 14 can be a step transmission with planetary gears, a countershaft, a continuously variable transmission or limitless continuously variable transmission. Torque from the transmission input shaft 22 is via a gear ratio control unit 24 on a transmission output shaft 26 transfer. In general, the gear ratio control unit 24 a plurality of forward or reverse gears or gear ratios or an infinite number of forward or reverse gears or gear ratios between the transmission input shaft 22 and the transmission output shaft 26 ready.

wherein the gearbox 14 is a continuously variable transmission, the gear ratio control unit can 24 a variator assembly 24a with first and second pulleys 24b . 24c include rotatably coupled by an endless rotatable member 24d around the variable diameter pulleys 24b . 24c are arranged. At least one of the first and second pulleys 24b . 24c includes a movable disc 24e along an axis for selectively changing a gear ratio between the engine output shaft 18 and the transmission output shaft 26 ,

The transmission output shaft 26 communicates output torques to the driveline unit 16 , The final drive unit 16 generally involves a differential 28 , the axle torque through drive axles 30 to the drive wheels 32 transfers.

now to 2 wherein a drive control system for use with the exemplary drive system 10 generally with the reference number 34 is designated. The vehicle drive control system 34 includes a monitoring control module 36 in electronic communication with an engine control unit 38 and a transmission control unit 40 , The modules 36 . 38 and 40 can communicate over a vehicle network or cable network (CAN) bus. The vehicle drive control system 34 can communicate with or be in communication with various other control modules such as an on-board control unit or infotainment control module include. Alternatively, the supervisory control module 36 in the engine control unit 38 or transmission control unit 40 be integrated.

The monitoring control module 36 is a non-generalized electronic control device with a pre-programmed digital computer or processor 42 , Memory or non-transitory computer-readable medium 44 for storing data such as control logic, instructions, image data, lookup tables, etc., and a variety of input / output peripherals or ports 46 , The processor 42 is designed to execute the control logic or instructions.

The engine control unit 38 is a non-generalized electronic recording device with a pre-programmed digital computer or processor 48 , Memory or non-transitory computer-readable medium 50 for storing data such as control logic, instructions, image data, lookup tables, etc., and a variety of input / output peripherals or ports 52 , The processor 48 is designed to execute the control logic or instructions. The engine control unit 38 communicates with and controls the engine 12 ,

The transmission control module 40 is a non-generalized electronic control device with a pre-programmed digital computer or processor 54 , Memory or non-transitory computer-readable medium 56 for storing data such as control logic, instructions, image data, lookup tables, etc., and a variety of input / output peripherals or ports 58 , The processor 54 is designed to execute the control logic or instructions. The transmission control module 40 communicates with and controls the transmission 14 ,

The vehicle drive control system 34 is connected via a variety of sensors to the drive system 10 including an airflow sensor S2 of the engine 12 , an engine speed sensor S4, a transmission input shaft speed sensor S6, a transmission output shaft speed sensor S8, a vehicle speed sensor S10 and a pedal position sensor S12. The airflow sensor S2 and the engine speed sensor S4 communicate with the engine control unit 38 , The transmission input shaft speed sensor S6 and the transmission output shaft speed sensor S8 communicate with the transmission control device 40 , The vehicle speed sensor S10 and the pedal sender S12 communicate with the engine control unit 38 and the transmission control unit 40 ,

With reference to 3 and further reference to 1 and 2 Fig. 10 is a control diagram for the vehicle drive control system 34 shown. The control diagram represents a control system or method 100 for controlling a parameter such as vehicle acceleration during optimization of fuel efficiency using multivariable control. The tax system 100 includes a multivariable control 102 and a facility 103 that of the multivariable control 102 is controlled. The multivariable control 102 can iteratively an engine output torque Te 104 and a translation Ratio 106 for optimizing a fuel consumption rate FR and for obtaining an axle torque Ta. The axle torque Ta is the amount of torque at the vehicle axle 30 , Thus, inputs include multivariable control 102 a requested axle torque Ta_r based on driver and vehicle inputs, and a measured actual axle torque Ta_m and a measured fuel consumption rate FR_m.

The tax system 100 includes a motor torque control 108 , a gear ratio control 110 (which may be a variator control for CVT transmissions) and a vehicle dynamics module 112 , In some examples, the multivariable control is 102 stored by and is monitored by the monitoring module 36 executed, the engine torque control 108 is stored by and is from the engine control unit 38 executed and the ratio control 110 is stored by and is from the transmission control module 40 executed. The driving dynamics module 112 can be stored and executed by the engine control unit 38 , the transmission control module 40 or any other control module or combination of control modules.

The multivariable control 102 receives system limits 105 from the engine control 108 including a maximum engine output torque Te _max , a minimum engine output torque Te _min , a maximum engine output torque ΔTe _max, and a minimum change in engine output torque ΔTe _min . The multivariable control 102 also receives system limits 107 from the gear ratio control 110 with a maximum gear ratio Rat _max , a minimum Rat _min , a maximum gear ratio ΔR _max and a minimum gear ratio change ΔR _min .

With reference to 4 is another illustration of the tax system 100 shown the inputs and outputs to the multivariable controller 102 and the plant 103 , controlled by the multivariable control 102 , shows. For example, inputs to the multivariable controller 102 include a requested axle torque Ta_r and vehicle speed V. Feedback inputs of measured axle torque Ta_m and measured fuel consumption rate FR_m may also input to the multivariable controller 102 be. The outputs of the multivariable controller 102 may include a controlled engine output torque Te_c and a controlled gear ratio Rat_c. These controlled outputs or "u" variables (Te_c and Rat_c) of the multivariable controller 102 are inputs to the system 103 that the engine 12 and the gearbox 14 includes.

The instructed engine output torque Te_c is used to control the engine 12 used to provide an actual engine output torque Te_a that is actually applied to the transmission 14 transmitted engine output torque is. The regulated ratio Rat_c is used to control the transmission 14 for providing an actual gear ratio or pulley ratio Rat_a between the transmission input shaft 22 and the transmission output shaft 26 , Thus gives the plant 103 outputs the "y" variables whose values can be tracked, which may include actual engine torque Te_a, actual fuel consumption rate FR_a, actual transmission ratio (or pulley ratio) Rat_a, and actual axle torque Ta_a.

With reference to 5 are additional details of multivariable control 102 shown. The multivariable control 102 includes a stationary state observation module 200 which represents a reference generator. The steady state observer module determines reference values (desired or requested values) for the "u" variables (controlled quantities) and the "y" variables (the optimized outputs that can be tracked). For example, this is the steady state optimization module 200 configured to determine a requested engine output torque Te_r, a requested gear ratio Rat_r, a requested fuel consumption rate FR_r, and a requested axle torque Ta_r. The u- _refs include the requested engine output torque Te_r and the requested gear ratio Rat_r, while the y- _refs may include all four requested engine output torques Te_r, the requested gear ratio Rat_r, the requested fuel consumption rate FR_r, and the requested axle torque Ta_r. The u- _refs and y- _refs are values that are desirable during a steady state. The MPC module described below 202 optimizes the course of particular fuel consumption rate during the transition from one stationary state to another.

The requested axle torque Ta_r can be determined from the accelerator pedal position PP and the vehicle speed V, for example via Ta_r = f (PP, V). (1)

The requested axle torque Ta_r may be determined from a look-up table or 2D map of a vehicle speed V measured by vehicle speed sensor S10 and an accelerator pedal position PP measured by the pedal sender S12, in some examples.

The requested fuel consumption rate FR_r is based on the requested axle torque Ta_r, the vehicle speed V, the engine speed RPM, and the air-fuel ratio AF. For example: FR_r = f (Ta_r, V, RPM, AF). (2)

The engine speed RPM may be determined by the engine speed sensor S4. The air-fuel ratio AF is the ratio of the mass of air to the mass of fuel that can be indicated by, for example, a fuel control module.

The requested gear ratio Rat_r is based on the requested axle torque Ta_r and the vehicle speed V. For example: Rat_r = f (Ta_r, V). (3)

The requested engine output torque Te_r is based on the requested axle torque Ta_r, the requested gear ratio Rat_r and the final drive ratio FD (constant for a given vehicle). For example:

The "loss" factor may include mechanical losses such as friction and pulley clamping losses, for example.

Once the requested values are determined, the state-optimizer module gives them (the u_refs and the y_refs to the MPC module 202 , The MPC module 202 uses model predictive control and may also be referred to as a square programming solver such as a Dantzig QP solver.

A predictive module 204 is configured to predict at least the actual axle torque and an actual fuel consumption rate for use in the MPC module 202 , The prediction module 204 may also be referred to as a state observer using a Kalman filter. The predicted actual values 206 be from the prediction module 204 to the MPC module 202 output.

The prediction module 204 for example, is configured to generate at least a first predicted actual axle torque and a first predicted actual fuel consumption based on a first set of possible setpoints, wherein the first set of possible setpoints includes a first commanded engine output torque Te_c and a first commanded gear ratio Rat_c, and the prediction module 204 further configured to generate at least one second predicted actual axle torque and a second predicted actual fueling rate based on a second set of possible setpoints, wherein the second set of possible setpoints includes a second commanded engine output torque Te_c and a second commanded gear ratio Rat_c. In practice, a significantly larger number of predicted values may be generated based on additional sets of possible setpoints. The predicted actual values 206 will be sent to the MPC module 202 output.

The MPC module 202 contains a cost module 208 configured to determine first costs for the first set of possible set values Te_c, Rat_c based on at least first and second predetermined weight values, the first predicted actual axle torque, the first predicted actual fuel consumption rate, the requested axle torque Ta_r, the requested engine output torque Te_r, the requested gear ratio Rat_r and the requested fuel consumption rate FR_r. The same is the cost module 208 configured to determine a second cost of the second set of possible setpoints Te_c, Rat_c based on at least one of the first and second predetermined weighting values, the second predicted actual axle torque, the second predicted actual fuel consumption rate, the requested axle torque Ta_r, the requested engine output torque Te_r, the requested one Ratio ratio Rat_r and requested fuel consumption rate FR_r. Also, many additional costs may be determined based on additional sets of predicted values and setpoints for optimization at the lowest cost.

The MPC module 202 can also be a selection module 210 configured to select one of the first and second sets of possible setpoints Te_c, Rat_c (or additional sets of possible setpoints) based on the lower of the first and second costs (or additional costs) and desired setpoints Te_c, Rat_c based on the possible setpoints Te_c, Rat_c of the selected one of the first and second sets (or additional sets).

The selected setpoints Te_c and Rat_c are from the MPC module 202 to the plant 103 issued (see 3 and 4 ). The multivariable control 102 or the plant 103 may include an actuation module configured to control a vehicle parameter based on at least one of the desired (selected) setpoints Te_c, Rat_c. The acceleration of the vehicle 9 For example, it can be controlled to optimize the fuel consumption rate. In some forms, the actuation module may be within the vehicle dynamics module 112 represented in 3 to be included. Any vehicle system that varies a motor parameter may be referred to as an actuator module. For example, in some forms, the actuation module may vary engine ignition timing or throttle for controlling vehicle acceleration and / or axle torque.

worin Te_a = vorausgesagtes tatsächliches Motorausgangsdrehmoment; FR_a = vorausgesagte tatsächliche Kraftstoffverbrauchsrate; Rat_a = vorausgesagtes tatsächliches Übersetzungsverhältnis; Ta_a = vorausgesagtes tatsächliches Achsdrehmoment; Te_r = angefordertes Motorausgangsdrehmoment; FR_r = angeforderte Kraftstoffverbrauchsrate; Rat_r = angefordertes Übersetzungsverhältnis; Ta_r = angefordertes Achsdrehmoment; Te_c = angewiesenes Motorausgangsdrehmoment; Rat_c = angewiesenes Übersetzungsverhältnis; Q_y = ein erster vorgegebener Gewichtungswert; Q_u = ein zweiter vorgegebener Gewichtungswert; Q_Δu = ein dritter vorgegebener Gewichtungswert; i = Index-Wert; k = Voraussageschritt; und T = transponierter Vektor ist. In diesem Fall gibt es sind zwei Werte für die „u”-Variablen, u₁ und u₂, sodass i = 1, 2 ist und es gibt vier Werte für die „y”-Variablen, y₁, y₂, y₃, y₄, sodass i = 1, 2, 3, 4 ist. Der Voraussageschritt k ist die Vorhersage an einem aktuellen Schritt, der Voraussageschritt k + 1 ist eine Vorhersage einen Schritt voraus und der Voraussageschritt k + 2 ist eine Vorhersage zwei Schritte voraus. Wie vorstehend erläutert, können die y_ref und u_ref Werte bestimmt werden durch das stationäre Zustandsoptimierungsmodul 200.The cost module 202 may be configured to determine the first and second costs or any number of costs with the following cost equation (5):

where Te_a = predicted actual engine output torque; FR_a = predicted actual fuel consumption rate; Rat_a = predicted actual gear ratio; Ta_a = predicted actual axle torque; Te_r = requested engine output torque; FR_r = requested fuel consumption rate; Rat_r = requested ratio; Ta_r = requested axle torque; Te_c = instructed engine output torque; Rat_c = instructed gear ratio; Q _y = a first predetermined weighting value; Q _u = a second predetermined weighting value; Q _Δu = a third predetermined weighting _value ; i = index value; k = prediction step; and T = transposed vector. In this case, there are two values for the "u" variable, u ₁ and u _2, such that i = 1, 2, and there are four values for "y" variables, y _1, y _2, y ₃ , y ₄ , so that i = 1, 2, 3, 4. The prediction step k is the prediction at a current step, the prediction step k + 1 is a prediction one step ahead and the prediction step k + 2 is a prediction two steps ahead. As explained above, the y _ref and u _ref values can be determined by the steady state optimization module 200 ,

The first and second sets of costs or a greater number of sets of costs may be determined, in particular, by the following equation (6), which is an MPC equation having a prediction horizon of three and a control horizon of two: Cost = λ _a · (Ta_a _k - Ta_r) ² + λ _a · (Ta_a _{k + 1} - Ta_r) ² + λ _a · (Ta_a _{k + 2} - Ta_r) ² + λ _f · (FR_a _k - FR_r) ² + λ _f · (FR_a _{k + 1} -FR_r) ² + λ _f · (FR_a _{k + 2} -FR_r) ² + λ _e · (Te_c _k -Te_r) ² + λ _e · (Te_c _{k + 1} -Te_r) ² + λ _r · (Rat_c _k - Rat_r) ² + λ _r · (Rat_c _{k + 1} - Rat_r) ² + λ _Δr · (ΔRat_c _k ) ² + λ _Δr · (ΔRat_c _{k + 1} ) ² + λ _Δe · (ΔTe_c _k ) ² + λ _Δe · (ΔTe_c _{k + 1} ) ² (6) where λ _a = a first predetermined weighting value; Ta _- a _k = predicted actual axle torque at a prediction step k; Ta_r = requested axle torque; Ta_a _{k + 1} = predicted actual axle torque at a prediction step _k + 1; Ta _- a _{k + 2} = predicted actual axle torque at a prediction step k + 2; λ _f = a second predetermined weighting value; FR _- a _k = predicted actual fuel consumption rate at prediction step k; FR _- r = requested fuel consumption rate; FR_a _{k + 1} = predicted actual fuel consumption rate at prediction step _k + 1; FR_a _{k + 2} = predicted actual fuel consumption rate at prediction step _k + 2; λ _e = a third predetermined weighting value; Te_c _k = instructed engine output torque at prediction step k; Te_r = requested engine output torque; Te_c _{k + 1} = instructed engine output torque at prediction step _k + 1; λ _r = a fourth predetermined weighting value; Rat_c _k = controlled ratio of prediction step k; Rat_r = requested ratio; Rat_c _{k + 1} = controlled gear ratio at prediction step _k + 1; λ _Δr = a fifth predetermined weighting _value ; ΔRat_c _k = instructed gear ratio change to prediction step k; ΔRat _- c _{k + 1} = instructed gear ratio change to prediction step k + 1; λ _Δe = a sixth predetermined weighting _value ; ΔTe_c _k = change the commanded engine output torque at prediction step k; and ΔTe_c _{k + 1} = change of instructed engine output torque at prediction step _{k + 1} .

The cost equation (eg, equation (5) or (6)) may be applied iteratively at the lowest cost for a plurality of sets of possible setpoints Te_c, Rat_c, wherein the plurality of sets of possible setpoints Te_c, Rat _- c include the first and second sets of possible setpoints, as well as a number of other possible sets of setpoints for Te_c, Rat_c. Then the selection module 210 the set of possible target values Te_c, Rat _- c to select the plurality of target values at the lowest cost, wherein the set of possible target values Te_c, Rat _- c can be defined with the lowest cost as the first and second set of possible target values Te_c, Rat_c , Likewise, the cost module 208 generate c _- a surface appearance of the expense of possible sets of setpoints Te_c, advice. The cost module 208 and / or the selection module 210 can then determine the possible lowest cost set based on the curve slope of the cost curve.

worin A = eine erste Zustandsmatrix; B = eine zweite Zustandsmatrix; C = eine dritte Zustandsmatrix; Te_a_k = vorausgesagtes tatsächliches Motorausgangsmoment an Voraussageschritt k; FR_a_k = vorausgesagte tatsächliche Kraftstoffverbrauchsrate an Voraussageschritt k; Rat_a_k = vorausgesagtes tatsächliches Übersetzungsverhältnis an Voraussageschritt k; Ta_a_k = vorausgesagtes tatsächliches Achsdrehmoment an Voraussageschritt k; x_k = Zustandsvariable an Voraussageschritt k; Te_a_k+1 = vorausgesagtes tatsächliches Motorausgangsmoment an Voraussageschritt k + 1; FR_a_k+1 = vorausgesagten tatsächliche Kraftstoffverbrauchsrate mit der Voraussageschritt k + 1; Rat_a_k+1 = vorausgesagten Ist-Übersetzungsverhältnis an die Voraussageschritt k + 1; Ta_a_k+1 = vorausgesagtes tatsächliches Achsdrehmoment an Voraussageschritt k + 1; x_k+1 = Zustandsvariable an Voraussageschritt k + 1; Te_c_k = angewiesenes Motorausgangsdrehmoment an Voraussageschritt k; Rat_c_k = geregeltes Übersetzungsverhältnis an Voraussageschritt k; K_KF = eine Kalman-Filter-Verstärkung; Te_m_k = gemessenes Motorausgangsdrehmoment an Voraussageschritt k; FR_m_k = gemessene Kraftstoffverbrauchsrate an Voraussageschritt k; Rat_m_k = gemessenes Übersetzungsverhältnis an Voraussageschritt k; und Ta_m_k = gemessenes Achsdrehmoment an Voraussageschritt k. Der Voraussageschritt k ist ein Voraussageschritt bei der aktuellen Zeit (z. B. jetzt).The prediction module 204 can be a number of predicted actual variables 206 to the MPC module 202 for use in the cost equation (eg, equation (5) or (6)) by the cost module 208 provide. The prediction module 204 can use equations like the following to determine the predicted actual variables 206 use:

where A = a first state matrix; B = a second state matrix; C = a third state matrix; Te_a _k = predicted actual engine output torque at prediction step k; FR_a _k = predicted actual fuel consumption rate at prediction step k; Rat_a _k = predicted actual gear ratio to prediction step k; Ta_a _k = predicted actual axle torque at prediction step k; x _k = state variable at prediction step k; Te_a _{k + 1} = predicted actual engine output torque at prediction step _k + 1; FR_a _{k + 1} = predicted actual fuel consumption rate with the prediction step _k + 1; Rat_a _{k + 1} = predicted actual gear ratio to the prediction step _k + 1; Ta_a _{k + 1} = predicted actual axle torque at prediction step _k + 1; x _{k + 1} = state variable at prediction step k + 1; Te_c _k = instructed engine output torque at prediction step k; Rat_c _k = controlled ratio of prediction step k; K _KF = a Kalman filter gain; Te_m _k = measured engine output torque at prediction step k; FR_m _k = measured Fuel consumption rate at prediction step k; Rat_m _k = measured gear ratio at prediction step k; and Ta_m _k = measured axle torque at prediction step k. The prediction step k is a prediction step at the current time (for example, now).

The measured engine output torque Te_m may be detected by the engine torque sensor S4. The measured transmission ratio or pulley ratio Rat_m can be determined from the speed of the transmission input shaft 22 detected by the transmission input shaft speed sensor S6 and the speed of the transmission output shaft 26 detected by the transmission output shaft speed sensor S8.

The sets of equations for the prediction module can be rewritten with the matrix values used as follows:

Ta_a _{k + 1} and FR_a _{k + 1} may be defined as equal to each of the first predicted actual axle torque and the first predicted actual fuel consumption rate when _k based on the first set of setpoints for Te_c _k and Rat_c _k , and Ta_a _{k + 1} and FR_a _{k + 1} may be defined as or equal to each of the second predicted actual axle torque and the second predicted actual fuel consumption rate when generated based on the second set of possible setpoints for Te_c _k and Rat_c _k .

The cost equation (eg, equation (5) or (6)) may be subject to the following limitations 105 . 107 : Te _min <Te_c _k <Te _max ; Te _min <Te_c _{k + 1} <Te _max ; Rat _min <Rat_c _k <Rat _max ; Rat _min <Rat_c _{k + 1} <Rat _max ; ΔRat_c _k <ΔRat_c _max ; ΔRat_c _{k + 1} <ΔRat_c _max ; ΔTe_c _k <ΔTe_c _max ; and ΔTe_c _{k + 1} <ΔTe_c _max , where Te _min = a minimum possible engine output torque, Te _max = a maximum possible engine output torque, Rat _min = a minimum possible gear ratio, Rat _max = a maximum possible gear ratio, ΔRat_c _max = a maximum possible gear ratio change and ΔTe_c _max = a maximum possible change in engine output torque ,

The constants, matrices and gains listed above including A, B, C, K _KF , Q _y , Q _u , Q _Δu , λ _a , λ _f , λ _e , λ _r , λ _Δe , λ _Δr are parameters of the system were determined by tests, physical models or other means. In some variations, a system identification procedure becomes offline for example, in a calibration to identify the constants, matrices and gains, and also to define u ₀ and y ₀ . Once u ₀ and y _{0 are} known, then x ₀ can be computed by the prediction modulus equations (eg equations (7) - (12) or a subset thereof). Thereafter, each of the prediction module 204 and MPC module 202 Equations (eg equations (5) - (12) or a subset thereof) for obtaining initial values off-line. Then the control system 102 run online to constantly optimize the controlled parameters Te_c and Rat_c as the vehicle 9 passes through stationary and non-stationary states. The constants allow the cost to be determined based on the relationship between each and relative importance of each of the instructed values Te_c, Rat_c and tracked values (eg, FR_a, Ta_a, Rat_a, Te_a). The relationships are weighted to control the effect each relationship has on the cost.

In some forms, the MPC module can 202 generate the setpoints Te_c, Rat_c by determining possible sequences, sets or a surface containing the setpoints Te_c, Rat_c that can be used for N future control loops. The prediction module 204 may determine predicted responses to the possible sets of commanded setpoints Te_c, Rat_c using the prediction module equations (eg, equations (7) - (12) or a subset thereof). The prediction module 204 For example, it may determine a set of predicted actual axle torque Ta_a and a set of predicted actual fuel consumption rates FR_a for N control loops.

More specifically, a set of N values may be determined for each setpoint Te_c, Rat_c, and a set of M values for each predicted actual value Ta_a, FR_a may be determined from the N setpoints Te_c, Rat_c. The cost module 208 can then the cost value for each of the possible sets of setpoints Te_c, Rat_c determine, based on the predicted actual parameters Ta_a, FR_a (which Ta_a _k, Ta_a _{k + 1,} Ta_a _{k + 2,} FR_a _k, FR_a _{k + 1} and FR_a _{k + 2} , depending on the particular cost equation (5), (6) used). The selection module 210 may then select one of the possible sets of the setpoints Te_c, Rat_c based on the cost of the possible sets, respectively. The selection module 210 For example, it may select the possible set of setpoints Te_c, Rat_c with the least cost, while at the same time limiting the system 105 . 107 met (eg Te _min <Te_c _k <Te _max ; Te _min <Te_c _{k + 1} <Te _max ; Rat _min <Rat_c _k <Rat _max ; Rat _min <Rat_c _{k + 1} <Rat _max ; ΔTe_c _k <ΔTe_c _max ; ΔTe_c _{k + 1} <ΔTe_c _max ; ΔRat_c _k <ΔRat_c _max ; ΔRat_c _{k + 1} <ΔRat_c _max ).

In some forms, the fulfillment of the restrictions 105 . 107 be considered in the cost determination. The cost module 208 For example, the cost values may continue based on the restrictions 105 . 107 determine and the selection module 210 may select the possible set of target values Te_c, Rat_c that best achieves the axle torque request Ta while minimizing the fuel consumption rate FR determined to meet the constraints 105 . 107 ,

In steady state operation, the setpoints Te_c, Rat_c may be at or near the reference or the requested values Te_r, Rat_r, respectively. In transient operation, the MPC module 202 however, adjust the target values Te_c, Rat_c at a distance from the reference values Te_c, Rat_c for best possible achievement of the torque request Ta_r while simultaneously minimizing the fuel consumption rate FR and meeting the constraints 105 . 107 ,

In operation, the MPC module 202 determine the cost values for the possible sets of regulated and predicted values (u, y). The MPC module 202 can then select one of the possible sets with the least cost. The MPC module 202 Next, it may determine if the selected possible set has the restrictions 105 . 107 Fulfills. If so, the possible sentence can be used. If not, the MPC module determines 202 based on the selected possible sentence a possible sentence containing the restrictions 105 . 107 fulfilled and has the lowest cost. The MPC module 202 can the possible sentence that the restrictions 105 . 107 fulfilled and has the lowest cost, use.

With reference to 6 FIG. 12 is a flowchart showing an exemplary method of controlling the drive system. FIG 10 of the motor vehicle 9 represented and generally with 300 designated. The procedure 300 includes a step 302 generating a number of predicted actual axle torques (at least first and second predicted actual axle torques) and a number of predicted actual fuel consumption rates (at least first and second predicted actual fuel consumption rates) based on a number of sets (at least two) of possible target values. The first set of possible setpoints includes, for example, a first commanded engine output torque and a first commanded gear ratio, the second set of possible target values includes a second commanded engine output torque and a second commanded one instructed gear ratio and so on as desired. The step 302 For example, by the prediction module 204 represented in 5 be performed.

The procedure 300 then includes a step 304 determining costs for each set of possible setpoints. Each of the costs is determined based on at least first and second predetermined weighting values, the predicted actual axle torque for the particular set, the predicted actual fuel consumption rate for the particular set, the requested axle torque, the requested engine output torque, the requested gear ratio, and the requested fuel consumption rate. The step 304 can through the cost module 208 be executed.

The procedure 300 can also take a step 306 selecting a set of possible setpoints based on the lowest of the determined costs. Once the set of possible setpoints is identified based on the least cost, the method includes 300 one step 308 setting desired setpoints based on the possible setpoints of the selected set. In some variations, the possible set of setpoints is a set of constraints 105 . 107 as described above. Finally, the procedure can 300 one step 310 controlling a vehicle parameter such as acceleration based on at least one of the desired setpoints.

The procedure 300 can the steps 302 . 304 . 306 . 308 . 310 in any manner described above, such as by applying one of equations (1) - (12). Furthermore, the method 300 determining the requested axle torque, the requested engine output torque, the requested fuel consumption rate, and the requested gear ratio by, as explained above, determining the accelerator pedal position PP, the engine RPM, the vehicle speed V, the final drive ratio FD, and the air-fuel ratio AF and Apply of the above equations (1) - (4), if desired.

The terms control unit, control module, module, controller, controller, processor and the like refer to one or more combinations of application specific integrated circuits (ASIC), electronic circuit (s), central processing unit (s), e.g. B. Microprocessor (s) and their associated non-transitory memory components in the form of memory and data storage devices (read-only memory, programmable read-only memory, random access memory, hard disk memory, etc.). The non-transitory memory component is capable of providing machine-readable instructions in the form of one or more software or firmware programs or routines, combinatorial logic circuitry, drive / drive circuitry and devices, signal conditioning and buffering circuits, and others Store components that may be accessed by the one or more processors to provide a described functionality.

Drive / output circuitry and devices include analog / digital converter related devices that monitor sensor inputs at a predetermined polling frequency or in response to a triggering event. Software, firmware, programs, commands, control routines, code, algorithms, and similar terms refer to all instruction sets executable by a controller, such as a computer. B. Calibrations and look-up tables. Each controller executes control routines to provide the desired functions, including monitoring the inputs of sensor devices and other networked controllers, as well as executing control and diagnostic routines to control actuation of actuators. The routines can be used at regular intervals, such as. B. during running operation every 100 microseconds to run. Alternatively, routines may be executed in response to a triggering event.

The communication between the controllers and communications between controllers, actuators, and / or sensors may be accomplished via a direct cable connection, a networked communication bus connection, a wireless connection, or any other suitable communication connection. Communication content includes exchanging data signals in any suitable manner, including e.g. As electrical signals via a conductive medium, electromagnetic signals over the air, optical signals via optical fibers and the like.

Data signals may include, among other things, signals representing inputs from sensors, signals representing actuator commands, and communication signals between controllers. The term "model" refers to a processor-based or processor-executable code and associated calibration that simulates the physical existence of a device or physical process. As used herein, the term "dynamic" describes steps or processes that are performed in real time and characterized by monitoring or otherwise determining parameter states and periodically or periodically updating parameter states when executing a routine or between iterations in executing the routine.

The tax system 100 may be configured to perform each step of the method 300 , Thus, the entire description can be related to 1 to 6 through the tax system 100 on the in 6 shown method 300 be applied. Furthermore, the control system 100 include or be a controller that includes a number of control logics configured to perform the steps of methods 300 ,

The control (s) of the control system 100 may include a computer readable medium (also referred to as a processor readable medium) including any non-transitory (eg, concrete) media involved in providing data (eg, instructions) that could be read by a computer ( eg by the processor of a computer). Such a medium may be in any format, including but not limited to nonvolatile media and volatile media. Non-volatile media may be, for example, optical or magnetic disks and other persistent storage. For example, volatile media may include dynamic random access memory (DRAM), which may be a main memory. Such instructions may be transmitted by one or more transmission media, including coaxial cable, copper wire, and fiber optics, including the wires that include a system bus coupled to the processor. Some forms of a computer-readable medium include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, another magnetic medium, a CD-ROM, DVD, another optical medium, punched cards, punched tape, another hole-patterned physical medium, a RAM , a PROM, an EPROM, a FLASH EEPROM, another memory chip, or a storage cartridge or other medium from which a computer can read.

Lookup tables, databases, data repositories, or other data stores described herein may include various types of mechanisms for storing, accessing, and retrieving various types of data, including a hierarchical database, a set of files in a file system, an application database in a proprietary format, relational database management system (RDBMS), etc. Each of these data stores may be included in a computing device that employs a computer operating system, such as one of those listed above, and that may be accessed over a network in one or more of a variety of ways. A file system may be accessible through a computer operating system and may contain files stored in various formats. An RDBMS can use the Structured Query Language (SQL) in addition to a language to create, store, manipulate, and execute stored procedures, such as the PL / SQL language listed above.

The detailed description, as well as the drawings or figures, support and describe many aspects of the present disclosure. While certain aspects have been described in detail, various alternative aspects exist in the appended claims.

Claims (10)

A method of controlling a drive system of a motor vehicle, the method comprising: generating a first predicted actual axle torque and a first predicted actual fuel consumption rate based on a first set of possible setpoints, wherein the first set of possible setpoints includes a first commanded engine output torque and a first commanded one Translation ratio includes; Generating a second predicted actual axle torque and a second predicted actual fuel consumption rate based on a second set of possible setpoints, the second set of possible setpoints having a second commanded engine output torque including a second commanded gear ratio; Determining first costs for the first set of possible setpoints based on a first predetermined weighting value, a second predetermined weighting value, the first predicted actual axle torque, the first predicted actual fuel consumption rate, a requested axle torque, a requested engine output torque, a requested gear ratio, and a requested fuel consumption rate ; Determining second costs for the second set of possible setpoints based on the first predetermined weighting value, the second predetermined weighting value, the second predicted actual axle torque, the second predicted actual fuel consumption rate, the requested axle torque, the requested engine output torque, the requested gear ratio, and the requested fuel consumption rate ; Selecting one of the first and second sets of possible setpoints based on the lower of the first and second costs; Setting desired setpoints based on the possible setpoints of the selected one of the first and second sets; and controlling a vehicle parameter based on at least one of the desired setpoints. The method of claim 1, further comprising determining the first and second costs with the following cost equation:

wherein: Te_a = predicted actual engine output torque; FR_a = predicted actual fuel consumption rate; Rat_a = predicted actual gear ratio; Ta_a = predicted actual axle torque; Te_r = requested engine output torque; FR_r = requested fuel consumption rate; Rat_r = requested ratio; Ta_r = requested axle torque; Te_c = instructed engine output torque; Rat_c = instructed gear ratio; Q _y = the first predetermined weighting value; Q _u = the second predetermined weighting value; Q _Δu = a third predetermined weighting _value ; i = index value; k = prediction step; and T = transposed vector. The method of claim 2, further comprising applying the cost equation iteratively to achieve the lowest cost for a plurality of sets of possible setpoints, the plurality of sets of possible setpoints including the first and second sets of possible setpoints, the method further selecting the set of possible setpoints Setpoints of the plurality of setpoints with the lowest cost wherein the set of possible setpoints having the least cost is defined as one of the first and second sets of possible setpoints. The method of claim 2 or claim 3, further comprising: determining an accelerator pedal position (PP); Determining an engine speed (RPM); determining a vehicle speed (V); Determining an air-fuel ratio (AF); Determining the requested axle torque (Ta_r) based on the accelerator pedal position (PP) and the vehicle speed (V); Determining the requested fuel consumption rate (FR_r) based on the requested axle torque (Ta_r), the vehicle speed (V), the engine speed (RPM), and the air-fuel ratio (AF); Determining the requested gear ratio (Rat_r) based on the requested axle torque (Ta_r) and the vehicle speed (V); and determining the requested engine output torque (Te_r) based on the requested axle torque (Ta_r), the requested gear ratio (Rat_r) and a final drive ratio (FD), the method further comprising determining the first and second predicted actual axle torques and the first and second predicted actual Fuel consumption rates with the following equation system includes:

where x _{k + 1} = state variable at a prediction step k + 1; x _k = state variable at a prediction step k; A = a first state matrix; B = a second state matrix; Te_c _k = instructed engine output torque at prediction step k; Rat_c _k = instructed gear ratio on prediction step k; K _KF = a Kalman filter gain; Te_a _k = predicted actual engine output torque at prediction step k; FR_a _k = predicted actual fuel consumption rate at prediction step k; Rat_a _k = predicted actual gear ratio to prediction step k; Ta_a _k = predicted actual axle torque at prediction step k; Te_m _k = measured engine output torque at prediction step k; FR_m _k = measured fuel consumption rate at prediction step k; Rat_m _k = measured gear ratio at prediction step k; Ta_m _k = measured axle torque at predicting step k; Ta_a _{k + 1} = predicted actual axle torque at prediction step _k + 1; FR_a _{k + 1} = predicted actual fuel consumption rate at prediction step _k + 1; and C = a third state matrix, where Ta_a _{k + 1} and FR_a _{k + 1 are} equal to the first predicted actual axle torque and the first predicted actual fuel consumption rate when generated based on the first set of setpoints for Te_c _k and Rat_c _k and Ta_a _{k + 1} and FR_a _{k + 1 are} respectively equal to the second predicted actual axle torque and the second predicted actual fuel consumption rate when generated based on the second set of possible setpoints for Te_c _k and Rat_c _k . The method of claim 1 or claim 4, further comprising determining the first and second costs with the following cost equation: Cost = λ _a · (Ta_a _k - Ta_r) ² + λ _a · (Ta_a _{k + 1} - Ta_r) ² + λ _a · (Ta_a _{k + 2} - Ta_r) ² + λ _f · (FR_a _k - FR_r) ² + λ _f · (FR_a _{k + 1} -FR_r) ² + λ _f · (FR_a _{k + 2} -FR_r) ² + λ _e · (Te_c _k -Te_r) ² + λ _e · (Te_c _{k + 1} -Te_r) ² + λ _r · (Rat_c _k - Rat_r) ² + λ _r · (Rat_c _{k + 1} - Rat_r) ² + λ _Δr · (ΔRat_c _k ) ² + λ _Δr · (ΔRat_c _{k + 1} ) ² + λ _Δe · (ΔTe_c _k ) ² + λ _Δe · (ΔTe_c _{k + 1} ) ² where λ _a = the first predetermined weighting value; Ta_a _k = predicted actual axle torque at prediction step k; Ta_r = requested axle torque; Ta_a _{k + 1} = predicted actual axle torque at prediction step _k + 1; Ta_a _{k + 2} = predicted actual axle torque at prediction step _k + 2; λ _f = the second predetermined weighting value; FR_a _k = predicted actual fuel consumption rate at prediction step k; FR_r = requested fuel consumption rate; FR_a _{k + 1} = predicted actual fuel consumption rate at prediction step _k + 1; FR_a _{k + 2} = predicted actual fuel consumption rate at prediction step _k + 2; λ _e = a third predetermined weighting value; Te_c _k = instructed engine output torque at prediction step k; Te_r = requested engine output torque; Te_c _{k + 1} = instructed engine output torque at prediction step _k + 1; λ _r = a fourth predetermined weighting value; Rat_c _k = instructed gear ratio on prediction step k; Rat_r = requested ratio; Rat_c _{k + 1} = instructed gear ratio on prediction step _k + 1; λ _Δr = a fifth predetermined weighting _value ; ΔRat_c _k = instructed gear ratio change to prediction step k; ΔRat_c _{k + 1} = instructed gear ratio change to prediction step _k + 1; λ _Δe = a sixth predetermined weighting _value ; ΔTe_c _k = change of the commanded axle torque to prediction step k; and ΔTe_c _{k + 1} = change of the commanded axle torque to prediction step _{k + 1} , where Ta_a _{k + 1} and FR_a _{k + 1 are} respectively equal to the first predicted actual axle torque and the first predicted actual fuel consumption rate when based on the first set of target values for Te_c _k , and Rat_c _k and Ta_a _{k + 1} and FR_a _{k + 1 are} equal to the second predicted actual axle torque and the second predicted actual fuel consumption rate when generated based on the second set of possible setpoints for Te_c _k and Rat_c _k . A motor vehicle drive control system for a motor vehicle having a transmission and an engine, the motor vehicle drive control system comprising: a prediction module for generating a first predicted actual axle torque and a first predicted actual fuel consumption rate based on a first set of possible setpoints, wherein the first set of possible setpoints includes a first commanded engine output torque and a first commanded gear ratio, wherein the prediction module is further configured to generate a second predicted actual axle torque and a second predicted actual fueling rate based on a second set of possible setpoint values, the second set of possible setpoint values including a second commanded engine output torque and a second commanded gear ratio; a control module configured to: Determining first costs for the first set of possible setpoints based on a first predetermined weighting value, a second predetermined weighting value, the first predicted actual axle torque, the first predicted actual fuel consumption rate, a requested axle torque, a requested engine output torque, a requested gear ratio, and a requested fuel consumption rate ; and Determining second costs for the second set of possible setpoints based on the first predetermined weighting value, the second predetermined weighting value, the second predicted actual axle torque, the second predicted actual fuel consumption rate, the requested axle torque, the requested engine output torque, the requested gear ratio, and the requested fuel consumption rate ; and a selection module configured to select one of the first and second sets of possible setpoints based on the lower of the first and second costs and desired setpoints based on the possible setpoints of the selected one of the first and second sets. The motor vehicle drive control system of claim 6, further comprising: an actuation module configured to control a vehicle parameter based on at least one of the desired setpoint values; and a stationary state optimizer configured to: determine an accelerator pedal position (PP); Determining an engine speed (RPM); Determining a vehicle speed (V); Determining an air-fuel ratio (AF); Determining the requested axle torque (Ta_r) based on the accelerator pedal position (PP) and the vehicle speed (V); Determining the requested fuel consumption rate (FR_r) based on the requested axle torque (Ta_r), the vehicle speed (V), the engine speed (RPM), and the air-fuel ratio (AF); Determining the requested gear ratio (Rat_r) based on the requested axle torque (Ta_r) and the vehicle speed (V); and determining the requested engine output torque (Te_r) based on the requested axle torque (Ta_r), requested engine output torque (Rat_r), and final drive ratio (FD), wherein the prediction module is further configured to determine the first and second predicted actual axle torque and the first and second second predicted actual fuel consumption rates using the following equation system:

where x _{k + 1} = state variable at prediction step k + 1; x _k = state variable at prediction step k; A = a first state matrix; B = a second state matrix; Te_c _k = instructed engine output torque at prediction step k; Rat_c _k = instructed gear ratio on prediction step k; K _KF = a Kalman filter gain; Te_a _k = predicted actual engine output torque at prediction step k; FR_a _k = predicted actual fuel consumption rate at prediction step k; Rat_a _k = predicted actual gear ratio to prediction step k; Ta_a _k = predicted actual axle torque at prediction step k; Te_m _k = measured engine output torque at prediction step k; FR_m _k = measured fuel consumption rate at prediction step k; Rat_m _k = measured gear ratio at prediction step k; Ta_m _k = measured axle torque at prediction step k; Ta_a _{k + 1} = predicted actual axle torque at prediction step _k + 1; FR_a _{k + 1} = predicted actual fuel consumption rate at prediction step _k + 1; and C = a third state matrix, where Ta_a _{k + 1} and FR_a _{k + 1 are} each equal to the first predicted actual axle torque and the first predicted actual fuel consumption rate when generated based on the first set of setpoints for Te_c _k and Rat_c, and Ta_a _{k + 1} and FR_a _{k + 1} are equal to the second predicted actual axle torque and the second predicted actual fuel consumption rate, respectively, when generated based on the second set of possible setpoints for Te_c _k and Rat_c. A vehicle drive control system according to claim 6 or claim 7, wherein the cost module is configured to determine the first and second costs with the following cost equation:

where Te_a = predicted actual engine output torque; FR_a = predicted actual fuel consumption rate; Rat_a = predicted actual gear ratio; Ta_a = predicted actual axle torque; Te_r = requested engine output torque; FR_r = requested fuel consumption rate; Rat_r = requested ratio; Ta_r = requested axle torque; Te_c = instructed engine output torque; Rat_c = instructed gear ratio; Q _y = the first predetermined weighting value; Q _u = the second predetermined weighting value; Q _Δu = a third predetermined weighting _value ; i = index value; k = prediction step; and T = transposed vector. The vehicle drive control system of claim 6 or claim 7, wherein the cost module is further configured to determine the first and second costs with the following cost equation: Cost = λ _a · (Ta_a _k - Ta_r) ² + λ _a · (Ta_a _{k + 1} - Ta_r) ² + λ _a · (Ta_a _{k + 2} - Ta_r) ² + λ _f · (FR_a _k - FR_r) ² + λf · (FR_a _{k + 1} -FR_r) ² + λ _f · (FR_a _{k + 2} -FR_r) ² + λ _e · (Te_c _k -Te_r) ² + λ _e · (Te_c _{k + 1} -Te_r) ² + λ _r · (Rat_c _k - Rat_r) ² + λ _r · (Rat_c _{k + 1} - Rat_r) ² + _λΔr · (ΔRat_c _k ) ² + _λΔr · (ΔRat_c _{k + 1} ) ² + _λΔe · (ΔTe_c _k ) ² + λ _Δe · (ΔTe_c _{k + 1} ) ² wherein: λ _a = the first predetermined weighting value; Ta_a _k = predicted actual axle torque at prediction step k; Ta_r = requested axle torque; Ta_a _{k + 1} = predicted actual axle torque at prediction step _k + 1; Ta_a _{k + 2} = predicted actual axle torque at prediction step _k + 2; λ _f = the second predetermined weighting value; FR_a _k = predicted actual fuel consumption rate at prediction step k; FR_r = requested fuel consumption rate; FR_a _{k + 1} = predicted actual fuel consumption rate at prediction step _k + 1; FR_a _{k + 2} = predicted actual fuel consumption rate at prediction step _k + 2; λ _e = a third predetermined weighting value; Te_c _k = instructed engine output torque at prediction step k; Te_r = requested engine output torque; Te_c _{k + 1} = instructed engine output torque at prediction step _k + 1; λ _r = a fourth predetermined weighting value; Rat_c _k = instructed gear ratio on prediction step k; Rat_r = requested ratio; Rat_c _{k + 1} = angewiesenes ratio of predicting step k + 1; λ _Δr = a fifth predetermined weighting _value ; ΔRat_c _k = instructed gear ratio change to prediction step k; ΔRat_c _{k + 1} = instructed gear ratio change to prediction step _k + 1; λ _Δe = a sixth predetermined weighting _value ; ΔTe_c _k = commanded change of axle torque to prediction step k; and ΔTe_c _{k + 1} = commanded change of axle torque to prediction step _{k + 1} wherein Ta_a _{k + 1} and FR_a _{k + 1 are} respectively equal to the first predicted actual axle torque and the first predicted actual fuel consumption rate, if based on the first set of target values for Ta_c _k and Rat_c, and Ta_a _{k + 1} and FR_a _{k + 1 are} respectively equal to the second predicted actual axle torque and the second predicted actual fuel consumption rate when generated based on the second set of possible setpoints for Te_c _k and Rat_c , Drive system for a motor vehicle, comprising: a motor operable to drive the motor vehicle, the motor configuring an engine output shaft to transmit engine output torque; a continuously variable transmission having a variator assembly having a first pulley and a second pulley, the first and second pulleys being rotatably coupled by a rotatable member, at least one of the first and second pulleys having a movable pulley along an axis for selectively changing a gear ratio between the engine output shaft and a transmission output shaft; a drive axle configured to be driven via the transmission output shaft, the drive axle configured to output axle torque to a wheel set; and a control system comprising: a prediction module for generating a first predicted actual axle torque and a first predicted actual fuel consumption rate based on a first set of possible target values, the first set of possible target values including a first commanded engine output torque, a first commanded gear ratio, the prediction module further configured to generate a second predicted actual axle torque and a second predicted actual fuel consumption rate based on a second set of possible setpoints, the second set of possible setpoints having a second commanded engine output torque and a second commanded gear ratio; a cost module configured to: Determining first costs for the first set of possible setpoints based on a first predetermined weighting value, a second predetermined weighting value, the first predicted actual axle torque, the first predicted actual fuel consumption rate, a requested axle torque, a requested engine output torque, a requested gear ratio, and a requested fuel consumption rate ; and Determining second costs for the second set of possible setpoints based on the first predetermined weighting value, the second predetermined weighting value, the second predicted actual axle torque, the second predicted actual fuel consumption rate, the requested axle torque, the requested engine output torque, the requested gear ratio, and the requested fuel consumption rate ; and a selection module configured to select one of the first and second sets of possible setpoints based on the lower of the first and second costs and desired setpoints based on the possible setpoints of the selected one of the first and second sets.

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