CN108162965B

CN108162965B - Method and system for controlling vehicle gear shifting under cruise control

Info

Publication number: CN108162965B
Application number: CN201711247061.8A
Authority: CN
Inventors: A·M·达马托; D·P·菲尔沃; J·O·米歇里尼; J·佩卡尔; O·桑廷; J·T·马伦
Original assignee: Ford Global Technologies LLC; Honeywell International Inc
Current assignee: Ford Global Technologies LLC; Honeywell International Inc
Priority date: 2016-12-05
Filing date: 2017-12-01
Publication date: 2022-12-06
Anticipated expiration: 2037-12-01
Also published as: DE102017128503A1; RU2748955C2; RU2017138783A3; RU2017138783A; CN108162965A

Abstract

The invention relates to a method and a system for controlling vehicle gear shifting under cruise control. Methods and systems are provided for improving performance of a vehicle operating in a cruise control mode, wherein a controller adjusts torque output from the vehicle to maintain vehicle speed within a desired range while preventing unnecessary downshifts. The method and system include adapting a vehicle dynamics model and a vehicle fuel consumption model that provide inputs to a nonlinear model predictive controller.

Description

Method and system for controlling vehicle gear shifting under cruise control

Cross Reference to Related Applications

This application is a continuation-in-part application entitled "METHOD and System FOR VEHICLE CRUISE CONTROL" (METHOD AND SYSTEM FOR VEHICLE CRUISE CONTROL) filed on 22/2/2016, U.S. patent application Ser. No.15/049,603. U.S. patent application Ser. No.15/049,603 requests priority from U.S. provisional patent application No.62/146,880 entitled "METHOD and System FOR VEHICLE CRUISE CONTROL" (METHOD AND SYSTEM FOR VEHICLE CRUISE CONTROL) "filed on 13.4.2015. U.S. patent application Ser. No.15/049,603 also claims priority from U.S. provisional patent application No.62/148,095 entitled "System and method FOR Fuel Economy OPTIMIZATION IN CRUISE CONTROL" (SYSTEM AND APPROACH FOR FUEL ECONOMY OPTIZATION IN CRUISE CONTROL) filed 2015, 4/15. The entire contents of the above referenced application are incorporated herein by reference in their entirety for all purposes.

Technical Field

The present description relates generally to methods and systems for controlling transmission shifting for a vehicle operating in a cruise control mode, in which a vehicle driver requests automatic control of vehicle speed.

Background

The vehicle may automatically control its speed to a desired speed via the controller with little or no input from the vehicle driver. One example way in which the controller adjusts the vehicle speed is to operate the vehicle in a cruise control mode. The cruise control mode may be described as a vehicle operating mode in which the vehicle speed is maintained within a desired vehicle speed range defined via upper and lower vehicle speed thresholds, without the driver requesting torque from the vehicle power source. The controller maintains the vehicle speed within a desired speed range via adjusting a torque output of a power source of the vehicle. Thus, the vehicle speed is maintained within a desired speed range via increasing and decreasing the torque output of the vehicle power source. One way for the controller to maintain vehicle speed is to proportionally adjust the torque output from the vehicle power source based on an error in vehicle speed. The controller may apply a proportional/integral/derivative (PID) algorithm or some similar variant to regulate the torque output of the vehicle power source and maintain the vehicle speed within a desired vehicle speed range. However, PID vehicle speed control algorithms are conservative in that they rely primarily on current or present vehicle speed errors to provide a corrected vehicle speed trajectory. Thus, and since the vehicle is often operating in a higher gear in cruise control mode, the controller may make large changes to the torque it requests from the vehicle power source. The hunting of the requested torque may cause the transmission to downshift, which may increase vehicle fuel consumption and disturb the driver.

Disclosure of Invention

The inventors herein have recognized the above-mentioned problems and have developed a vehicle system comprising: a vehicle including a motive torque source; and a controller in the vehicle, the controller comprising executable instructions stored in non-transitory memory, the instructions comprising an adaptive non-linear model predictive cruise control program including a powertrain torque threshold to reduce transmission shifts while maintaining vehicle speed within a first vehicle speed threshold and a second vehicle speed threshold.

By adapting the vehicle model and providing an output from the adapted vehicle model to the non-linear model predictive cruise control routine and constraining the driveline torque to a value less than the torque for a transmission downshift, it may be possible to provide the technical effect of reducing transmission shifts when operating the vehicle in a cruise control mode. Transmission shifting may be reduced at least in part based on the inferred road grade information. Additionally, adapting the vehicle model and the vehicle fuel consumption model in real time while the vehicle is in the cruise control mode allows the non-linear model predictive cruise control mode to adjust the torque control strategy from a constant torque output to a pulsed creep torque output, thereby allowing multiple torque solution strategies from the controller to be used for the same driving conditions, except for changes in the vehicle fuel consumption model or other changes in engine operating characteristics due to fuel properties. The fuel economy optimization strategy is thus automatically selected based on the actual characteristics of the vehicle fuel consumption model and system constraints.

The present description may provide several advantages. In particular, the method may reduce the tendency of the transmission to shift while operating the vehicle in a speed control mode. Further, the method may reduce the operating cost of the vehicle via reducing fuel consumption. Further, the method may improve vehicle drivability.

The above advantages and other advantages and features of the present invention will be readily apparent from the following detailed description when taken alone or with reference to the accompanying drawings.

It should be appreciated that the summary above is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. It is not intended to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.

Drawings

FIG. 1 illustrates an example vehicle that may be included in the systems and methods described herein;

FIG. 2 illustrates an example vehicle and its electronic field of view;

FIG. 3 illustrates an example vehicle power source;

FIG. 4 illustrates an example vehicle powertrain including a vehicle power source;

FIG. 5 shows a block diagram of an example vehicle cruise control system;

FIGS. 6A and 6B illustrate an example method for adaptive non-linear model predictive cruise control with fuel optimization and possibly with neutral selection;

FIG. 7 illustrates a detailed example method for optimization of non-linear model predictive cruise control (i.e., at Sequential Quadratic Programming (SQP) iterations);

FIG. 8 illustrates a detailed example method for non-linear model predictive cruise control with fuel optimization and with neutral selection;

FIGS. 9A and 9B illustrate example vehicle fuel consumption models; and

FIG. 10 illustrates an example vehicle cruise control sequence with neutral selection.

Detailed Description

The following description relates to systems and methods for improving operation of a vehicle operating in a cruise control mode. FIG. 1 shows a non-limiting example vehicle for operating in a cruise control mode, where the controller applies a non-linear model predictive cruise control algorithm with fuel optimization. FIG. 2 illustrates an example vehicle and an electronic horizon providing input to an adaptive nonlinear model predictive cruise control algorithm. Fig. 3 and 4 illustrate a non-limiting vehicle power source within a vehicle driveline. FIG. 5 is a block diagram of an example vehicle cruise control system. A method for operating a vehicle under cruise control (including one example variation of an adaptive non-linear model predictive cruise control algorithm) is provided in fig. 6A-8. An example vehicle power source fuel consumption model is shown in fig. 9A and 9B. FIG. 10 is an example vehicle cruise control mode operating sequence.

Referring now to fig. 1, a vehicle 100 includes a controller 12 for receiving sensor data and adjusting actuators. The controller 12 may cause the vehicle 100 to operate in a cruise control mode, wherein the vehicle speed is maintained within a desired vehicle speed range defined by an upper vehicle speed threshold and a lower vehicle speed threshold. In some examples, the controller 12 may cooperate with additional controllers to operate the vehicle 100. The vehicle 100 is shown with a Global Positioning System (GPS) receiver 130. The satellites 102 provide time-stamp information to the GPS receiver 130, which the GPS receiver 130 communicates to the vehicle position determination system 140. The vehicle position determination system 140 communicates current and future road grade data to the controller 12. The vehicle 100 may also be equipped with an optional camera 135 for viewing road conditions in the path of the vehicle 135. For example, the camera 135 may capture road conditions from the road-side symbol 166 or display. The vehicle position determination system 140 may alternatively gather information for determining the position of the vehicle from the stationary broadcast tower 104 via the receiver 132. In some examples, the vehicle 100 may also include a sensor 138 for determining the proximity of the vehicle in the travel path of the vehicle 100. The sensor 138 may be laser, acoustic or radar based.

In this example, the vehicle 100 is shown as a passenger vehicle. However, in some examples, the vehicle 100 may be a commercial vehicle, such as a freight semi-trailer and truck, a train, or a ship.

Referring now to FIG. 2, an example vehicle 100 and a distance 210 corresponding to an electronic field of view of the vehicle are shown. The vehicle 100 generates an electronic field of view (e.g., a data vector) comprised of road grade information for the road 214. The electronic field of view is made up of a plurality of blocks 220 or segments, and the blocks have a single signal of related or corresponding road slope or slope. The length of the block may be based on distance or time. The road grade information is provided for a predetermined distance 210 or a predetermined amount of time in the travel path of the vehicle. The road grade information may be provided to controller 12 shown in FIG. 1. For example, the road grade may be provided for a predetermined distance (e.g., 1500 meters) in the path of the vehicle 100. Alternatively, the road grade may be provided for a predetermined amount of time in the future of the path of travel of the vehicle. For example, road grade may be provided for the next 10 seconds or about 1833 meters of a vehicle traveling at 110 Km/hr. The road grade data may be stored in the memory of the vehicle position determination system 140 shown in FIG. 1, or it may be determined based on road altitude values stored in the memory. In one example, the road grade value may be retrieved from memory by indexing the memory based on the vehicle location and heading. Road grade values occurring over a predetermined distance or time may be stored in memory as an array or vector, and updates to the array may be provided as the vehicle moves on a first-in-first-out basis. For example, if a road grade value is provided for every 100 meters of road surface, then the array of road grade data corresponding to 1500 meters includes 15 blocks and their corresponding road grade values. The road grade value may change gradually between blocks.

Referring now to FIG. 3, an example vehicle power source is shown. In this example, the vehicle power source is a spark ignition engine. However, the vehicle power source may be a diesel engine, a turbine, or an electric machine.

FIG. 3 is a schematic diagram showing one cylinder of multi-cylinder engine 330 in engine system 300. Engine 330 may be controlled at least partially by a control system including controller 12 and by input from a vehicle operator 382 via an input device 380. In this example, the input device 380 includes an accelerator pedal and a pedal position sensor 384 for generating a proportional pedal position signal PP.

Combustion chamber 332 of engine 330 may include a cylinder formed by cylinder walls 334 in which a piston 336 is disposed. Piston 336 may be coupled to crankshaft 340 such that reciprocating motion of the piston is translated into rotational motion of the crankshaft. Crankshaft 340 may be coupled to at least one drive wheel of a vehicle via an intermediate transmission system. Additionally, a starter motor may be coupled to crankshaft 340 via a flywheel to enable a starting operation of engine 330.

Combustion chamber 332 may receive intake air from intake manifold 344 via intake passage 342 and may exhaust combustion gases via exhaust passage 348. Intake manifold 344 and exhaust passage 348 are selectively communicable with combustion chamber 332 via respective intake valve 352 and exhaust valve 354. In some examples, combustion chamber 332 may include two or more intake valves and/or two or more exhaust valves.

In this example, intake valve 352 and exhaust valve 354 may be controlled by cam actuation via respective

cam actuation systems

351 and 353.

Cam actuation systems

351 and 353 may each include one or more cams and may use one or more of a cam profile switching system (CPS), variable Cam Timing (VCT), variable Valve Timing (VVT), and/or Variable Valve Lift (VVL) system that may be operated by controller 12 to vary valve operation. The position of intake valve 352 and exhaust valve 354 may be determined by

position sensors

355 and 357, respectively. In alternative examples, intake valve 352 and/or exhaust valve 354 may be controlled by electric valve actuation. For example, cylinder 332 may alternatively include an intake valve controlled via electric valve actuation and an exhaust valve controlled via cam actuation including CPS and/or VCT systems.

A fuel injector 369 is shown coupled directly to the combustion chamber 332 for injecting fuel directly into the combustion chamber in proportion to the pulse width of the signal received from the controller 12. In this manner, the fuel injector 369 provides so-called direct injection of fuel into the combustion chamber 332. For example, the fuel injector may be mounted in the side of the combustion chamber or in the top of the combustion chamber. Fuel may be delivered to fuel injectors 369 by a fuel system (not shown) including a fuel tank, fuel pump, and fuel rail. In some examples, combustion chamber 332 may alternatively or additionally include a fuel injector disposed in intake manifold 344 in a configuration that provides so-called port injection of fuel into the intake port upstream of combustion chamber 332.

Spark is provided to combustion chamber 332 via spark plug 366. The ignition system may further include an ignition coil (not shown) for increasing the voltage supplied to the spark plug 366. In other examples, such as a diesel engine, spark plug 366 may be omitted.

Intake passage 342 may include a throttle 362 having a throttle plate 364. In this particular example, controller 12 may vary the position of throttle plate 364 by providing an electric motor or actuator included within throttle 362, a configuration commonly referred to as Electronic Throttle Control (ETC). In this manner, throttle 362 may be operated to vary the intake air provided to combustion chambers 332 of other engine cylinders. The position of throttle plate 364 may be provided to controller 12 via a throttle position signal. Intake passage 342 may include a mass air flow sensor 120 and a manifold air pressure sensor 322 for sensing the amount of air entering engine 330.

Exhaust gas sensor 327 is shown coupled to exhaust passage 348 upstream of emission control device 370 depending on the direction of exhaust flow. Sensor 327 may be any suitable sensor for providing an indication of exhaust gas air/fuel ratio such as a linear oxygen sensor or UEGO (universal or wide-range exhaust gas oxygen), a two-state oxygen sensor or EGO, a HEGO (heated EGO), a NOx, HC, or CO sensor. In one example, upstream exhaust gas sensor 327 is a UEGO configured to provide an output, such as a voltage signal proportional to the amount of oxygen present in the exhaust gas. Controller 12 converts the oxygen sensor output to an exhaust gas air-fuel ratio via an oxygen sensor transfer function.

Emission control device 370 is shown disposed along exhaust passage 348 downstream of exhaust gas sensor 327. Device 370 may be a Three Way Catalyst (TWC), NOx trap, various other emission control devices, or combinations thereof. In some examples, during operation of engine 330, emission control device 370 may be periodically reset by operating at least one cylinder of the engine within a particular air-fuel ratio.

The controller 12 is shown in fig. 3 as a microcomputer that includes a microprocessor unit (CPU) 302, an input/output port (I/O) 304, an electronic storage medium for executable programs and calibration values, shown in this particular example as a read only memory chip (ROM) 306 (e.g., non-transitory memory), a Random Access Memory (RAM) 308, a keep alive accessor (KAM) 310, and a data bus. Controller 12 may receive various signals from sensors coupled to engine 330, including, in addition to those signals previously discussed: a measurement of intake Mass Air Flow (MAF) from mass air flow sensor 320; engine Coolant Temperature (ECT) from temperature sensor 323 coupled to cooling sleeve 314; an engine position signal from a Hall effect sensor 318 (or other type) that senses the position of the crankshaft 340; throttle position from throttle position sensor 365; ambient humidity from humidity sensor 375, barometric pressure from barometric pressure sensor 376, and Manifold Absolute Pressure (MAP) signal from sensor 322. The engine speed signal may be generated by controller 12 based on a crankshaft position sensor 318. The manifold pressure signal also provides an indication of vacuum or pressure within the intake manifold 344. Note that various combinations of the above sensors may be used, such as with a MAF sensor and without a MAP sensor, or vice versa. During engine operation, engine torque may be estimated based on the output of the MAP sensor 322 and engine speed. Additionally, the sensor, along with the detected engine speed, may be the basis for estimating the charge (including air) drawn into the cylinder. In one example, a crankshaft position sensor 318, which also functions as an engine speed sensor, may produce a predetermined number of equally spaced pulses per revolution of the crankshaft.

Storage medium read-only memory 306 can be programmed with computer readable data representing non-transitory instructions executable by processor 302 for performing at least some portions of the methods described below as well as other variations that are anticipated but not specifically listed.

During operation, each cylinder within engine 330 typically undergoes a four stroke cycle: the cycle includes an intake stroke, a compression stroke, an expansion stroke, and an exhaust stroke. Generally, during the intake stroke, the exhaust valve 354 closes and the intake valve 352 opens. Air is introduced into combustion chamber 332 via intake manifold 344 and piston 336 moves to the bottom of the cylinder to increase the volume within combustion chamber 332. The position at which piston 336 is near the bottom of the cylinder and at the end of its stroke (e.g., when combustion chamber 332 is at its largest volume) is typically referred to by those of skill in the art as Bottom Dead Center (BDC).

During the compression stroke, the intake valve 352 and the exhaust valve 354 are closed. Piston 336 moves toward the cylinder head to compress the air within combustion chamber 332. The point at which piston 336 ends its stroke and is closest to the cylinder head (e.g., when combustion chamber 332 is at its smallest volume) is commonly referred to by those skilled in the art as Top Dead Center (TDC). In a process hereinafter referred to as injection, fuel is introduced into the combustion chamber. In a process hereinafter referred to as ignition, the injected fuel is ignited by known ignition means, such as spark plug 366, resulting in combustion.

During the expansion stroke, the expanding gases push the piston 336 back to BDC. Crankshaft 340 converts piston motion into rotational torque of the rotating shaft. Finally, during the exhaust stroke, exhaust valve 354 opens to release the combusted air-fuel mixture to exhaust manifold 348 and the piston returns to TDC. Note that the above is presented as an example only, and that intake and exhaust valve opening and/or closing timings may vary, such as to provide positive or negative valve overlap, late intake closing, or various other examples.

As described above, FIG. 3 shows only one cylinder of a multi-cylinder engine, and each cylinder may similarly include its own set of intake/exhaust valves, fuel injectors, spark plugs, and the like.

Referring now to FIG. 4, a schematic diagram of a vehicle powertrain system 400 is shown. The powertrain 400 may be powered by an engine 330 as shown in more detail in fig. 3. In one example, the engine 330 may be a gasoline engine. In alternative examples, other engine configurations may be employed, such as a diesel engine. The engine 330 may be started with an engine starting system (not shown). Additionally, the engine 330 may generate or adjust torque via a torque actuator 404 (such as a fuel injector, throttle, cam, etc.).

Engine output torque may be transmitted to a torque converter 406, which may be referred to as a component of the transmission, to drive a multi-speed automatic transmission 408 by engaging one or more clutches, including a forward clutch 410. Torque converter 406 includes an impeller 420 that transmits torque to a turbine 422 via a hydraulic fluid. One or more dog clutches 424 may be engaged to change the gear ratio between the engine 310 and the wheels 414. The output of the torque converter 406 may in turn be controlled by a torque converter lock-up clutch 412. Thus, when the torque converter lock-up clutch 412 is fully disengaged, the torque converter 406 transmits torque to the automatic transmission 408 via fluid transfer between the torque converter turbine 422 and the torque converter impeller 420, thereby achieving torque multiplication. In contrast, when the torque converter lock-up clutch 412 is fully engaged, engine output torque is transferred directly to the input shaft of the transmission 408 via the torque converter clutch 412. Alternatively, the torque converter lock-up clutch 412 may be partially engaged, thereby enabling the amount of torque transmitted to the transmission to be adjusted. Controller 12 may be configured to adjust the amount of torque transmitted through the torque converter by adjusting the torque converter lock-up clutch in response to various engine operating conditions or based on a driver engine operation request.

The torque output from the automatic transmission 408 may in turn be transferred to wheels 414 to propel the vehicle. Specifically, the automatic transmission 408 may adjust the input drive torque at an input shaft (not shown) in response to vehicle travel conditions prior to transmitting the output drive torque to the wheels. Vehicle speed may be determined via speed sensor 430.

Additionally, the wheels 414 may be locked by engaging wheel brakes 416. In one example, the wheel brakes 416 may be engaged in response to the driver pressing their foot on a brake pedal (not shown). In a similar manner, the wheels 414 may be unlocked by disengaging the wheel brakes 416 in response to the driver releasing his foot from the brake pedal.

Referring now to FIG. 5, a block diagram of an example vehicle cruise control system is shown. Cruise control system 500 includes vehicle sensors as shown at block 502. Vehicle sensors may include, but are not limited to, sensors for determining vehicle power source torque, speed, energy or fuel consumption, ambient conditions, distance measuring devices, GPS signals, road conditions, and driver inputs. The driver inputs may include a desired vehicle speed, a brake pedal position, an accelerator pedal position, a higher vehicle speed threshold, and a lower vehicle speed threshold. The vehicle sensor information may be input to an e-horizon 510, controller constraints 508, a vehicle dynamics model 512, a model predictive cruise control optimizer 530, a recursive least squares parameter adaptor 504, a vehicle fuel consumption model 514, an engine torque model 516, and a lead vehicle model 518.

The e-horizon box 510 may be included in the controller 12 of fig. 1, or it may be included in the vehicle position determination system 140 shown in fig. 1. The electronic horizon may consist of an array of memory locations or vectors of data, and the array may include a plurality of road slope values that describe the road slope of the road on which the vehicle is traveling. In one example, the electronic horizon extracts road slope values from a database describing road conditions (e.g., slope values stored in memory, slope values extracted from a three-dimensional map of the surface of the earth). The road slope value may include a road slope at a current location of the vehicle and a road slope value ahead of the vehicle in a travel path of the vehicle. The road grade may be converted to a road angle. The e-horizon box 510 updates the array or vector of road grade values at selected times and sends the updated array to the cruise control optimizer 530. The road grade value may be provided for a predetermined travel time in the future or for a predetermined distance in front of the vehicle.

At block 512, the cruise control system 500 includes a vehicle dynamics model. The vehicle dynamics model is physics-based and can be described as:

where m is the vehicle mass, v is the vehicle speed, F _Trac Is the tractive effort, which is defined as:

F _Aero is the aerodynamic drag, which is defined as:

F _Roll is the tire rolling resistance, which is defined as:

F _Grade is the gradient force, which is defined as:

wherein the wheel braking force is F _brake The driveline loss is ξ _DL (ii) a Effective wheel radius of R _WH (ii) a The transmission gear ratio is γ (G); the selected gear ratio is G; the final gear ratio of the vehicle is R _FDR (ii) a The power source braking torque is T; the ambient air density is ρ; the frontal area of the vehicle is a; aerodynamic drag coefficient of vehicle is C _d (ii) a The gravitational acceleration is g; coefficient of rolling resistance of tire is k ₁ And k ₂ (ii) a The road angle is

(ii) a t is time; and the vehicle mass is m.

The vehicle dynamics model is simplified as:

wherein beta is ₁ -β ₄ Are the adaptive coefficients. This simplification allows for β ₁ -β ₄ Recursive Least Squares (RLS) adaptation of terms or another suitable method. The adaptive parameters improve vehicle dynamic model performance, and the improved vehicle dynamic model performance improves non-linear model predictive controller performance. The adaptive parameters are adjustable to compensate for changes in vehicle mass, wind, tire conditions, and other vehicle operating conditions. The vehicle dynamics model can be modeled by adding an interference term d _v Is further enlarged. d _v The value of (c) may be estimated more frequently than the beta term, and in one example, it may be estimated via an extended kalman filter.

At block 514, the cruise control system 500 includes a vehicle fuel consumption model. The vehicle fuel consumption model estimates vehicle fuel consumption and it provides an input for optimizing vehicle fuel economy in optimizer block 530. Vehicle fuel consumption is expressed in the form of a polynomial:

wherein

Is a fuel flow to a power source of the vehicle; and c ₀ -c ₅ Are the adaptive coefficients. Vehicle fuel flow model allowance c ₀ -c ₅ Recursive Least Squares (RLS) adaptation of terms or another suitable method. The adaptive parameters improve vehicle fuel consumption model performance, and the improved vehicle fuel consumption model performance improves non-linear model predictive controller performance.

At block 504, the cruise control system 500 includes a recursive least squares parameter estimator for adjusting the β and c coefficients for the vehicle dynamics model and the vehicle fuel consumption model. It is desirable to adjust the β and c coefficients as vehicle operating conditions change so that a desired level of controller performance can be achieved. The recursive least squares estimator recursively adapts the parameter vector x to satisfy the system of equations (in matrix form):

y _k ＝H _k x+v _k 。

the new parameters are estimated as:

wherein H _k Is an m x n matrix, K _k Is the n x m estimator gain, and y _k -H _k x _k-1 Is a correction term. Wherein the noise v _k With zero mean and covariance R _k . Estimator gain K _k Sum covariance matrix P _k Is updated as follows:

P _k ＝(I-K _k H _k )P _k-1

the recursive least squares estimator is initialized by:

wherein, when x is unknown, P ₀ = ∞ I, and when x is known, P ₀ =0. Actual vehicle data used in the vehicle dynamics model and the vehicle fuel consumption model are collected, and the model coefficients are adjusted using recursive least squares.

At block 516, cruise control system 500 includes an engine torque model for the power source of the vehicle. The engine torque model describes the delay in engine torque production since engine torque is requested. The engine torque model may be expressed as:

where τ is expressed as engine speed N _e And the required torque T _d The time constant of the function of (a); and where T is engine or power source output torque. The torque demand is a function of an integral variable in the transmission or memory in the neutral flag. Specifically, the engine torque demand is:

T _d ＝T _in (1-N _fl )+T _idle N _fl

wherein T is _in Is the input torque; n is a radical of hydrogen _fl Is a neutral flag (e.g., 1 for neutral and 0 for a certain gear); and T _idle Is the engine idle torque. The engine speed is also a function of the transmission in neutral flag:

wherein N is _idle Is the engine idle speed, v ₁ Is the vehicle speed and the other variables are as previously described.

At block 518, the cruise control system 500 includes a model of the lead vehicle, or the vehicle being followed by the vehicle operating in the cruise control mode. The leading vehicle model is applied to a system that knows the vehicles in the path of the vehicle operating in the cruise control mode. The leading vehicle model has little leading vehicle information, but it is used to predict when vehicle acceleration is allowed and when vehicle deceleration may be expected. The lead vehicle can be modeled as:

wherein v is ₁ Is the actual speed of the lead vehicle, a ₁ Is the acceleration of the leading vehicle, and τ ₁ Is a time constant that represents the time constant of the expected acceleration. The distance between the lead vehicle and the vehicle operating in cruise control mode may be expressed as:

wherein D ₁ Is the distance between the lead vehicle and the vehicle operating in the cruise control mode, and v is the speed of the vehicle operating in the cruise control mode. The speed of the lead vehicle may be estimated from the radar or laser distance measuring devices of the following vehicles.

At block 506, the vehicle cruise control system 500 includes a cost function. The cost function describes the control objective or goal for the optimizer 530. For example, the cost function may attempt to minimize fuel consumption, maintain vehicle speed within a predetermined vehicle speed range defined by an upper vehicle speed limit and a lower vehicle speed limit, maintain a minimum distance between vehicles, and limit torque output of vehicle power to less than a threshold torque. The specific details of one example cost function are described at 708 of FIG. 7.

At block 508, the cruise control system 500 operating constraints are determined based on driver input and/or based on variables or functions stored in memory. In one example, the driver may input a desired vehicle speed, and the upper and lower vehicle speed thresholds may be determined based on the desired vehicle speed. For example, the driver may input a desired vehicle speed of 100KPH, and an upper threshold speed of 110KPH and a lower threshold of 90KPH may be determined by adding and subtracting offset values from the desired vehicle speed. In other examples, the vehicle system may adjust the upper threshold vehicle speed based on the posted road speed. For example, if the driver selects a desired vehicle speed of 90KPH and the road speed limit is 100KPH, the upper threshold vehicle speed may be adjusted to 100KPH. The maximum power source torque and the minimum vehicle following distance may be predetermined and stored in memory. Alternatively, the driver may input the constraint condition value. Additionally, desired vehicle speed and speed constraints may be temporarily adjusted via driver application of an accelerator pedal.

Cruise control system 500 also uses knowledge of a transmission downshift schedule to avoid downshifts when vehicle speed can remain within upper and lower vehicle speed thresholds (e.g., first and second vehicle speed thresholds). In one example, cruise control system 500 provides an adjustable powertrain torque limit that constrains a torque provided by a powertrain torque source (e.g., engine 330) to be less than a threshold amount of torque minus a torque reserve (reserve) offset. In other words, the driveline output torque or transmission input shaft torque is not allowed to exceed the adjustable driveline torque limit or threshold minus the torque reserve offset. The threshold amount of powertrain torque may be a function of engine speed, barometric pressure, humidity, and engine temperature. The threshold amount of powertrain torque may be constrained to be below a transmission input shaft torque for which the transmission is scheduled to downshift from the currently engaged transmission gear. In this way, a transmission downshift may be prevented or prevented when the vehicle speed is within the first and second vehicle speed thresholds. The control system 500 may query a transmission shift schedule stored in the controller memory to determine a transmission input shaft torque at which the transmission is scheduled to downshift from the currently engaged transmission gear. At block 530, the cruise control system 500 applies the inputs from blocks 506 through 518 to determine an optimal torque command or demand to output to the power source of the vehicle. Additionally, the optimizer 530 may selectively disengage the transmission forward gear, thereby placing the transmission in neutral (e.g., the unengaged transmission gear disengages the power source from the wheels) to cause the vehicle to coast and increase vehicle fuel economy. After the transmission was previously shifted to neutral, the optimizer 530 may selectively engage the forward transmission gears to maintain or increase vehicle speed. The optimizer uses sequential quadratic programming (see fig. 7 for additional details) to solve the optimization problem. Additional details regarding the operation of the optimizer are provided in the description of fig. 6A-8.

The optimizer also constrains the transmission input shaft torque to be less than the torque at which the transmission is scheduled to downshift from the currently engaged transmission gear so that the engine is less likely to enter a higher fuel consuming engine operating region. The transmission input shaft torque may be constrained via limiting engine torque. Engine torque may be limited by disallowing engine airflow beyond a threshold and/or constraining spark advance to a threshold amount less than spark advance. However, if the vehicle speed drops below the lower vehicle speed threshold, the optimizer allows the transmission to downshift by overriding the powertrain torque limit that constrains the torque produced by the powertrain torque source to be less than the torque at which the transmission is scheduled to downshift from the currently engaged transmission gear. In particular, the driveline torque is allowed to exceed a value at which the transmission is scheduled to downshift from the currently engaged transmission gear, so that the vehicle speed can be more closely controlled to a value between the upper and lower vehicle speed thresholds. In this way, powertrain torque may be constrained to reduce the likelihood of transmission downshifts, except when vehicle speed is not within a desired speed range.

For example, cruise control system 500 may index a transmission shift schedule stored in controller memory to determine a transmission downshift when the powertrain output torque or transmission input shaft torque is 240N-m at the current vehicle speed. Cruise control system 500 prevents the powertrain output torque from reaching or exceeding 240N-m so that a gear shift can be avoided or prevented. By avoiding gear shifts, the engine may operate more efficiently and consume less fuel. However, if the vehicle speed is not within the range between the first vehicle speed and the second vehicle speed, the adjustable driveline torque limit may be increased to a value greater than the transmission input shaft torque for which the transmission is scheduled to downshift from the currently engaged transmission gear. Thus, the transmission can be shifted, so that the vehicle speed can be maintained.

At block 520, the transmission of the vehicle may be shifted to neutral such that the vehicle starts or coasts, or alternatively, the transmission of the vehicle may be shifted to a forward gear to accelerate the vehicle. The transmission of the vehicle may be shifted to neutral by releasing hydraulic pressure on the dog clutch via a gear control solenoid. The transmission of the vehicle may be shifted into a forward gear (e.g., 5 th gear) by applying hydraulic fluid pressure to the transmission dog clutch via a gear control solenoid.

At block 522, the power source output torque of the vehicle may be adjusted. If the power source is an engine, engine torque may be increased via adjusting one or more of throttle position, spark timing, fuel injection timing, and cam timing or phase. If the power source is an electric machine, the machine torque may be adjusted by varying the current supplied to the electric machine.

Thus, the cruise control system of FIG. 5 provides a torque command to the vehicle power source and a gear or neutral command to the transmission to optimize vehicle fuel economy when the vehicle is operating in a cruise control mode. The controller solves the cruise control problem by applying a sequence quadratic programming.

The system of fig. 1-5 provides a vehicle system comprising: a vehicle including a motive torque source; and a controller in the vehicle, the controller comprising executable instructions stored in non-transitory memory, the instructions including an adaptive non-linear model predictive cruise control routine that includes a powertrain torque threshold to reduce transmission shifts when vehicle speed is maintained within first and second vehicle speed thresholds. Vehicle systems include those in which the driveline torque threshold is a function of engine speed, barometric pressure, and humidity. The vehicle system includes wherein the driveline torque threshold is a torque value that is less than a torque at which the transmission downshifts at a current vehicle speed. The vehicle system further includes constraining the driveline torque output to be less than the driveline torque threshold. Vehicle systems include wherein powertrain torque is constrained via constraining engine airflow to less than a threshold.

The system of fig. 1-5 also provides a vehicle system comprising: a vehicle including a motive torque source; and a controller in the vehicle, the controller comprising executable instructions stored in non-transitory memory, the instructions including an adaptive non-linear model predictive cruise control program with shift neutral state activation and a transmission shift reduction strategy that constrains the driveline torque to less than a threshold. Vehicle systems include wherein powertrain torque is constrained via constraining spark advance to be less than a threshold. The vehicle system includes wherein the transmission shift reduction strategy includes a driveline torque threshold.

In some examples, the vehicle system further includes additional instructions for constraining the driveline output torque to be less than a driveline torque threshold. Vehicle systems include those in which the driveline torque threshold is a function of engine speed, barometric pressure, and humidity.

Referring now to fig. 6A and 6B, an example method 600 for adaptive non-linear model predictive cruise control with fuel optimization is shown. At least some portions of method 600 may be included in a system as shown in fig. 1-5 as executable instructions stored in non-transitory memory. The instructions may provide a control program. Additionally, method 600 may include the methods of fig. 7 and 8. Further, the method of fig. 6A and 6B may provide the sequence of operations shown in fig. 10. The methods of fig. 6A-8 may be performed in real time in a vehicle traveling on a road.

At 602, method 600 initializes control parameters. The control parameters to be initialized in the model and optimization routine may include, but are not limited to, current vehicle speed, current power source output torque, current power source speed, current power source fuel consumption rate, current road angle on which the vehicle is traveling, and selected transmission gear. After the control parameters are initialized, method 600 proceeds to 604.

At 604, method 600 judges whether or not the cruise control mode is desired. The cruise control mode may be determined to be desired in response to the driver actuating a button, switch, or issuing a voice command indicating a desire to enter the cruise control mode. During the cruise control mode, the torque output of the power source is adjusted via controller 12 to maintain vehicle speed within a desired speed range defined by an upper speed threshold (e.g., 100 KPH) and a lower speed threshold (e.g., 90 KPH). Thus, the vehicle torque output is adjusted to maintain the desired vehicle speed. If the driver applies the brakes, operates a button, a switch, or issues a voice command, it may be determined that the cruise control mode is not desired. If method 600 determines that the cruise control mode is desired, the answer is yes and method 600 proceeds to 606. Otherwise, the answer is no and method 600 exits.

At 606, the method 600 receives new data from the system sensors and memory. Sensor data may include, but is not limited to, vehicle speed, road grade or slope, power source torque output, power source fuel or energy consumption, power source speed, and currently selected transmission gear. The data from the memory may include, but is not limited to, cruise control constraints, desired vehicle speed, minimum vehicle following distance to a lead vehicle, and controller tuning parameters. After new data is received, method 600 proceeds to 608.

At 608, the method 600 modifies or updates the β and c coefficients for the vehicle dynamics model and the vehicle fuel consumption model described at

blocks

512 and 514 of FIG. 5. The β and c coefficients are adjusted using recursive least squares with exponential forgetting, or another suitable method, based on the new data received at 606. The revised model is the basis for the system state observer, which is also updated or revised based on the revised model. After the model coefficients are modified, method 600 proceeds to 610.

At 610, method 600 applies nonlinear model predictive control to solve for the optimal torque trajectory without neutral engagement. The non-linear model predictive control is applied to a grade entry in the electronic field of view that extends from a current position of the vehicle to a forward-most position of the electronic field of view. The nonlinear model predictive control outputs an optimal torque value for an entry in the electronic field of view based on constraints in the cost function described at block 708 of fig. 7. After the non-linear model prediction control without neutral is applied, method 600 proceeds to 612.

At 612, method 600 determines an expected fuel economy value E0 for a predicted horizon (e.g., road grade data in an electronic horizon) for a situation when the vehicle is not operating with the transmission in neutral. In one example, method 600 estimates fuel economy for a block in an electronic field of view (e.g., an interval between slope values in the electronic field of view) by indexing a power source fuel or vehicle energy consumption model with the optimal torque value and power source speed for the block determined at 610. The vehicle energy consumption model stores empirically determined fuel or energy consumption rates and outputs the rates. The fuel or energy consumption for the block is stored to memory and method 600 proceeds to 614.

At 614, the method determines a maximum time for neutral engagement. The time is based on the time to achieve the lower vehicle speed threshold. The maximum time is determined by inputting the current operating conditions of the vehicle into the vehicle model described at block 512 of fig. 5, setting the engine brake torque to zero, and solving for the time it takes for the vehicle to coast or coast to a lower vehicle speed threshold. After the maximum time for neutral engagement is determined, method 600 proceeds to 616.

At 616, nonlinear model predictive control is applied to solve for the optimal torque trajectory with neutral engagement. In one example, the non-linear model predictive control is applied only to the first grade entry in the electronic field of view ahead of the current position of the vehicle to limit the computational load. However, in other examples, the nonlinear model predictive control may be extended to the length of the electronic horizon by increasing the computational load on the controller. The non-linear model predictive control outputs a transmission state control variable requesting the transmission to enter neutral or engage the forward transmission gear for entry in the electronic field of view based on constraints in the cost function described at block 506 of fig. 5. In addition, the nonlinear model predictive control outputs the engine idle torque when the neutral gear is determined to be the desired state. For the block in which neutral engagement is considered in the electronic field of view, the nonlinear model predictive controller varies the simulation conditions to simulate when the transmission is in neutral and when the power source is in idle or lower power output conditions. The non-linear model predictive control with neutral is described in more detail in the description of fig. 8. After the nonlinear model prediction control with neutral is applied, method 600 proceeds to 618.

At 618, method 600 determines an expected fuel economy value E1 for the predicted horizon (e.g., road grade data in an electronic horizon) for the case when the vehicle is operating with the transmission in neutral. In one example, method 600 estimates fuel economy for a block in an electronic field of view (e.g., an interval between slope values in the electronic field of view) by indexing a power source fuel or vehicle energy consumption model with the optimal torque value and power source speed for the block determined at 610. The vehicle energy consumption model stores empirically determined fuel or energy consumption rates and outputs the consumption rates. The fuel or energy consumption for the blocks in the electronic view vector is stored to memory and method 600 proceeds to 620.

At 620, method 600 judges whether or not it is desired to operate the vehicle with the transmission of the vehicle in neutral. In one example, in response to the expected fuel economy value E1 being greater than the expected fuel economy E0, the answer is yes and method 600 proceeds to 622. In other words, if operating the vehicle in neutral when the vehicle speed is within the upper and lower speed thresholds provides greater fuel economy, then the answer is yes and method 600 proceeds to 622. If the expected fuel economy value E1 is greater than the expected fuel economy value E0, or if the vehicle speed is expected to be less than the lower threshold vehicle speed when the vehicle's transmission is in neutral, then the answer is no and method 600 proceeds to 630.

At 622, method 600 selects a control trajectory for a transmission of the vehicle in neutral. This trajectory is the output from step 616 and it includes a vector or array that requests the transmission of the vehicle to operate in neutral in at least one block of the electronic horizon. The trajectory also includes a torque demand vector or array for operating the power source of the vehicle at idle or another low energy consumption state (e.g., stopping engine operation or motor rotation). After selecting the desired control trajectory, method 600 proceeds to 632.

At 630, method 600 selects a control trajectory for a transmission of the vehicle to be engaged in a forward gear. The trajectory is the output from step 610 and it includes a torque demand for maintaining the vehicle speed within the upper and lower vehicle speed thresholds. The torque demand is also provided to minimize vehicle fuel consumption. After selecting the desired control trajectory, method 600 proceeds to 632.

At 632, method 600 applies the control action to the actuator and waits for the next sampling phase. The control action taken is for causing the vehicle to operate in the electronic view block corresponding to the current vehicle position. The control action is based on the trajectory selected at 622 or 630. If the control action includes changing the transmission operating state of the vehicle from neutral to forward or vice versa, the state of one or more transmission clutches may be changed to shift the transmission into neutral or forward. The power source output of the vehicle may be adjusted via changing a state of a torque actuator (such as throttle position, cam timing, spark advance, fuel injection timing, or an amount of current applied to the electric machine) in response to a change in requested power source torque. After the control action is applied to the vehicle, method 600 returns to 604.

Referring now to FIG. 7, a detailed example of a numerical method for non-linear model predictive cruise control is shown. The method uses Sequential Quadratic Programming (SQP) to solve a nonlinear optimization problem. The jth iteration of the SQP solver can be represented as:

where f, S are the coefficients of the local quadratic approximation of the cost function J at x.

The new iteration is given by:

x ^j+1 ＝x ^j +α ^j ·Δx ^j ，

wherein alpha is _j Is a suitable step size. Alpha is alpha _j Is important to ensure fast convergence of the algorithm. In general, suitable values can be found by applying a line search algorithm. For systems with relatively mild non-linearity, the step size can be chosen as a constant, but it should also be chosen such that the cost function is reduced in all foreseeable cases.

At 704, method 700 receives new data from 606 of fig. 6A. Alternatively, the method 700 may receive data from memory and vehicle sensors, as described at 606 of fig. 6A. After new data is received, method 700 proceeds to 706.

At 706, method 700 performs simulation and linearization. The simulation and linearization performed on the model is described at blocks 512-518. Assume that the nonlinear system is described by:

y(t)＝g(x(t)，u(t))

where x is the system state, u is the system input, y is the system output, and f and g represent functions. Modeling a system in a predictive or electronic field of view may be accomplished by solving the above ordinary differential equations by a suitable solver, such as the basic forward Euler method. Euler method at time t _k ＝t ₀ +kT _s One step of (a) may be represented as:

x(t _k+1 )≈x(t _k )+T _s ·f(x(t _k )，u(t _k ))

the above ordinary differential equation is at t _k ＝t ₀ +kT _s At point (b) of

The linearization in (a) can be expressed as:

wherein

The linearized system is discretized to obtain a finite parameterization at the system input and in a one-step prediction:

Δx(t _k+1 )＝A _k Δx(t _k )+B _k Δu(t _k )

Δy(t _k )＝C _k Δx(t _k )＝D _k Δu(t _k )

the approximate discretization can be expressed as:

linearization the sensitivity matrix H for the trajectory used for prediction in each sampling phase is evaluated for each block of the system input that can be formed as a function of the electronic or prediction field of view. The linearized prediction of the system output can be expressed as:

wherein

After the simulation and linearization are performed, method 700 proceeds to 708.

At 708, method 708 builds a Quadratic Programming (QP) problem. The QP problem is built based on a cost function and constraints. In one example, the cost function may be expressed as:

where J is a cost function variable, N is a predicted view based on a vector or array of electronic views, q _N Is a penalty for tracking the expected vehicle speed at the end of the prediction horizon, q _mavg Is a penalty for average fuel consumption, q, over a predicted horizon _vavg Is a penalty for average vehicle speed tracking, and r _T Is a torque command activity.

The first term in the cost function represents the terminal penalty (vehicle speed at the end of the prediction horizon N). The second term is the average fuel consumption over the prediction horizon. The third term is the average vehicle speed within the prediction horizon. Finally, the fourth term is the torque activity penalty δ _T (t _k )＝T(t _k )-T(t _k-1 ) Or the engine or power source torque is changed between k steps. The cost function constraint may be expressed as:

v _min -ε ₁ (t _k )≤v(t _k )≤v _max +ε ₁ (t _k )，k＝1，2，...，N _vlim

T _min ≤T(t _k )≤T _max ，k＝1，2，...，N _c

D _min +t _pmin v(t _k )≤D _l (t _k )+ε ₂ (t _k )，k＝1，2，...，N _Dlim

wherein N is _vlim Is the number of points of vehicle speed limit, N _Dlim Is a leading vehicle distance limit in the predicted horizon, and where ε ₁ (t _k ) And ε ₂ (t _k ) Is the secondary softening variable. Auxiliary softening variable epsilon ₁ (t _k ) And ε ₂ (t _k ) Ensuring the feasibility of the resulting nonlinear optimization problem, the vehicle speed limit and the distance to the leading vehicle limit by introducing an auxiliary softening variable epsilon ₁ (t _k ) And epsilon ₂ (t _k ) Is treated as a soft constraint. Note that the minimum distance to the lead vehicle includes two portions. First part D _min Is a specified minimum distance, and a second portion t _pmin v(t _k ) Is shown to arrive between the host vehicle and the lead vehicle _min Time t of the specified minimum time of the gap _pmin And (4) parameterizing.

The optimization variable J is the torque trajectory (and softening variable) within the prediction horizon. Blocking techniques are used to reduce the number of optimization variables with the goal of reducing real-time computation and memory allocation. Therefore, the control action to adjust the power source torque is not calculated at each sampling phase within the prediction horizon. Alternatively, several sample times are blocked (grouped), and the control action within each block is assumed to be fixed (e.g., unchanged). This can be expressed as a linear transformation of the optimization variable (torque)

Wherein B is _bl Is a transformation (block) matrix and torque trajectory

Wherein n is _bl Is the total number of blocks, and b is the designation of each individual blockA vector of lengths of the number of samples in (a). Vector quantity

Become a substitute original trajectory

New optimization variables of (2). After the QP problem is established, method 700 proceeds to 710.

At 710, the method 700 solves the QP problem. The final QP approximation in the jth SQP iteration can be described as:

wherein

Is the trajectory, j is the iteration,

is a vector of softening variables of the vehicle speed limit, where v _min ≤ε _v (t _k )≤v _max ，

Is a vector of softening variables for the distance limitation to the leading vehicle, where D _min ≤ε _D (t _k )，J ^j Is the jth iteration of the cost function as described above,

is and is made of

Given a softened vehicle speed limit, and

is and is made of

The distance to the lead vehicle to be softened given limits the associated cost function. After the QP problem is solved, the method 700 proceeds to 712.

At 712, the method 700 updates or modifies the solution. According to the SQP solver described above, the trajectory can be modified or updated to:

the corrected torque trace is the starting point for the next (j + 1) th SQP iteration. After the solution is corrected, method 700 proceeds to 714.

At 714, the method 700 determines whether the solution has converged to the optimal solution. In some examples, the solution may be compared to a cost function. However, for problems that are progressively further away from the field of view, the solution may be determined to converge within a predetermined number of iterations (e.g., 1 or 2). If the method 700 determines that the solution has converged to the optimal solution, the answer is yes and the method 700 exits or returns to 610 of FIG. 6A.

Thus, when the vehicle is operating in forward gear, the method of FIG. 7 adjusts the torque command supplied to the power torque source in response to the distance between the vehicle operating in cruise control mode and a lead vehicle in front of the vehicle operating in cruise control mode. The method of FIG. 7 also determines an optimal vehicle speed profile based on the constraints.

Referring now to FIG. 8, an example method for non-linear model predictive cruise control with fuel optimization and with neutral selection is shown. Neutral selection refers to the ability of the controller to change the transmission operating state of the vehicle from forward to neutral or vice versa to improve vehicle fuel economy in cruise control mode. By commanding the transmission to neutral, it may be possible to increase or maintain vehicle speed on flat or negative road grades because engine braking and some driveline losses do not resist a portion of the gravitational forces acting on the vehicle when the transmission is shifted to neutral.

Speed variatorThe operating state is a binary variable having a value of 0 for the transmission not being in neutral and a value of 1 for the transmission being in neutral. If the electronic or predictive horizon includes N _d The number of possible combinations of transmission operating states for operating the transmission in a gear or neutral is

Thus, the algorithm of FIG. 8 may be performed

To achieve a minimum cost function. However, to reduce the computational load on the controller, it may be desirable to iterate only once for the first block in the electronic or predictive field of view.

At 804, method 800 receives new data from 606 of FIG. 6A. Alternatively, the method 800 may receive data from a data store and vehicle sensors, as described at 606 of fig. 6A. After new data is received, method 800 proceeds to 806.

At 806, method 800 selects one or more predetermined neutral trajectories over the electronic or predictive horizon, where a suitable combination of neutral engagements have a fixed duration and starting position in the electronic or predictive horizon. All predetermined neutral trajectories are evaluated along with corresponding calculated torque trajectories for vehicle fuel economy and constraint violations. Trajectories that have not been evaluated are selected at 806.

At 808, the SQP process, as described at 706-714 of FIG. 7, is executed to determine a torque trajectory corresponding to the predetermined neutral trajectory selected at 806. Additionally, a corresponding estimate of fuel economy Ei over the prediction horizon is calculated and stored to memory as described at 612 of fig. 6A. It should be noted that for evaluating neutral engagement, when a neutral engagement condition is evaluated, the power source torque is set to a low value (such as zero) or engine idle torque so that power source energy consumption or fuel consumption is accurate. After the SQP process is executed, method 800 proceeds to 810.

At 810, method 800 determines whether all combinations of neutral engagements have been evaluated. If so, the answer is yes and method 800 proceeds to 812. Otherwise, the answer is no and method 800 returns to 806 and the next neutral trajectory is evaluated.

At 812, method 800 selects a neutral trajectory and a corresponding torque trajectory that provides the best fuel economy from the combination of neutral trajectories. The method 800 exits or returns to 620 of fig. 6B.

Thus, the method of FIG. 8 evaluates operation of a vehicle with the transmission in neutral when the vehicle is in a cruise control mode and the vehicle is not actually necessarily in neutral. The evaluation is based at least in part on road conditions expected to be encountered by the vehicle at a future time, the road conditions at the future time being based on a map of road conditions stored in the memory.

The method of fig. 6A to 8 provides a vehicle cruise control method that includes: receiving vehicle information from one or more sensors to a controller; providing a torque command in response to an output of an adaptive non-linear model predictive cruise control routine executed by a controller, the torque command being constrained to a value less than a torque for a transmission downshift at a current vehicle speed; and adjusting a torque actuator of the motive torque source in response to the torque command. The method includes wherein the torque command is constrained via a driveline torque threshold. The method includes wherein the driveline torque threshold is a function of engine speed, barometric pressure, and humidity. The method includes wherein the driveline torque threshold is a torque value that is less than a torque value for a transmission downshift at the current vehicle speed.

Referring now to FIG. 9A, a graph of an example convex vehicle fuel consumption model is shown. In some examples, the vehicle fuel consumption model may also be referred to as a map. The vertical axis represents the fuel flow rate to the engine. The horizontal axis represents engine torque. The axis pointing into the paper is the engine speed. The vehicle fuel consumption model stores empirically determined values of fuel consumption or usage rates corresponding to selected engine speeds and torques. When joined together as shown and viewed from the horizontal axis, the fuel consumption values form a convex surface. The vehicle fuel consumption model shape may vary with fuel type (e.g., gasoline, alcohol), engine ambient conditions, and other conditions. Vehicle fuel consumption model shapes (e.g., convex, non-convex, affine) can affect controller output because the optimal torque solution is also optimized for minimum fuel consumption. The vehicle fuel model shape and expected controller output may be determined from values of coefficients of a polynomial that describes the vehicle fuel consumption model. For example, if the coefficients indicate that the vehicle fuel model is convex, the controller described in fig. 6A-8 provides a narrow band torque request that varies between an upper torque threshold and a lower torque threshold, the lower torque threshold being greater than zero torque. While the requested torque does change to maintain vehicle speed, the torque solution output by the controller of fig. 6A-8 may be referred to as a constant torque solution for the lug vehicle fuel consumption model. The narrow band torque request for the convex vehicle consumption model output by the controller includes a lower torque threshold that is a greater torque than the lower torque threshold for the non-convex vehicle fuel consumption model. In addition, the narrow band torque request for the lug vehicle fuel consumption model output by the controller includes an upper torque threshold that is a lower torque than the upper torque threshold for the non-lug vehicle fuel consumption model.

Referring now to FIG. 9B, a graph of an example non-convex vehicle fuel consumption model is shown. In some examples, the vehicle fuel consumption model may also be referred to as a map. The vertical axis represents the fuel flow rate to the engine. The horizontal axis represents engine torque. The axis pointing into the paper is the engine speed. The vehicle fuel consumption model stores empirically determined values of fuel consumption or usage rates corresponding to selected engine speeds and torques. When joined together as shown and viewed from the horizontal axis, the fuel consumption values form a non-convex surface. The torque solution output from the controller of fig. 6A-8 may be referred to as a pulse-and-glide torque solution. The torque request is pulse shaped, swinging from a requested zero torque request at the wheels to a higher torque value than if the same vehicle were traveling the same route when the fuel model was convex. Thus, for the same vehicle traveling the same route under the same conditions outside the fuel consumption model shape, the controller of fig. 6A-8 outputs a constant torque solution for the convex vehicle fuel consumption model and a pulsed creep torque solution for the non-convex fuel consumption model. The pulsed creep torque solution sometimes requires zero torque at the wheels, causing the vehicle to creep or idle. Neutral engagement may be particularly useful for non-convex fuel consumption models, as neutral engagement may extend coasting or zero torque durations. The impulse coasting torque solution also requests a greater torque than the constant torque solution. Thus, a controller torque solution (e.g., constant torque or pulsed coasting) may be determined via the vehicle fuel consumption model shape.

Referring now to FIG. 10, an example vehicle cruise control sequence for the system of FIGS. 1-5 and the method of FIGS. 6A-8 is shown. The vertical markers at times T1-T4 indicate times of interest during the sequence. All curves occur at the same time and under the same vehicle operating conditions. In the cruise control mode, wheel torque commands or power source torque requests are provided via a controller other than the driver. The controller has the objective of influencing the torque demand output by the controller in the cruise control mode, and this objective may be defined at least in part by constraints such as minimizing fuel consumption, maintaining vehicle speed within upper and lower threshold speeds that define a desired vehicle speed range, neutral use benefit, maximum neutral duration, and maximum wheel torque. The controller may vary the torque demand in the cruise control mode to maintain the vehicle speed within a desired vehicle speed range without driver input to the controller or without a driver requesting torque from the power source. Thus, the torque command in cruise control mode may be based on a desired vehicle speed requested by the driver. The controller may adjust the torque of the motive torque source to achieve a desired vehicle speed.

The first plot from the top of fig. 10 is a plot of calculated maximum neutral duration over time. The maximum neutral duration corresponds to the amount of time that the transmission may be in neutral while maintaining the vehicle speed within the desired vehicle speed range. The maximum neutral duration may be estimated via a model as described in the method of fig. 6A-8. The horizontal axis represents time, and time increases from the left side of the curve to the right side of the curve. The vertical axis represents the maximum neutral duration, and the maximum neutral duration increases in the direction of the vertical axis arrow. Horizontal line 1002 represents a threshold maximum neutral duration that would be exceeded by a transmission that is commanded to neutral with the transmission in a gear.

The second curve from the top of fig. 10 is a plot of calculated neutral usage benefit over time. The use benefit corresponds to vehicle fuel economy when the transmission of the vehicle is shifted to neutral. The neutral use benefit may be estimated by determining vehicle fuel economy for when the transmission is in neutral. The horizontal axis represents time, and time increases from the left side of the curve to the right side of the curve. The vertical axis represents neutral use benefit, and the benefit increases in the direction of the vertical axis arrow. Horizontal line 1004 represents a threshold neutral use benefit that would be exceeded by a transmission that is to be commanded to neutral if the transmission were in a gear.

The third plot from the top of fig. 10 is a plot of transmission state versus time. The transmission state indicates whether the transmission is in neutral or in a gear. The horizontal axis represents time, and time increases from the left side of the curve to the right side of the curve. The vertical axis represents the variator state. When the trajectory is at a higher level near the vertical axis arrow, the transmission is in neutral. When the trajectory is at a lower level near the horizontal axis, the transmission is in a certain gear.

The fourth curve from the top of fig. 10 is a plot of vehicle speed versus time. The horizontal axis represents time, and time increases from the left side of the curve to the right side of the curve. The vertical axis represents vehicle speed, and vehicle speed increases in the direction of the vertical axis arrow. Horizontal line 1006 represents a lower threshold vehicle speed. The purpose of the vehicle cruise control is to maintain the vehicle speed above the threshold 1006 when the vehicle is in cruise control mode.

At time T0, the vehicle is in cruise control mode. The maximum neutral duration is increasing from a medium level and the vehicle speed is decreasing from a higher level within the cruise control desired vehicle speed range. Such a condition may indicate that the vehicle is approaching the top of an uphill grade. Neutral use benefits are increasing from lower levels and the transmission is in forward gear.

Between time T0 and time T1, vehicle speed continues to decrease and the maximum neutral duration increases to a value above threshold 1002. The neutral use benefit remains below threshold 1004 and the transmission remains in the forward gear. The transmission state does not change even if the maximum neutral duration exceeds the threshold 1002 at a time since the benefit of neutral usage does not exceed the threshold 1004.

At time T1, the transmission changes state from forward gear to neutral. In response to the maximum neutral duration being greater than the threshold 1002 and the benefit of neutral use being greater than the threshold 1004, the transmission is shifted to neutral. In response to the transmission being in neutral, the vehicle speed begins to slowly decrease.

Between time T1 and time T2, the vehicle speed continues to decrease and the transmission remains in neutral. The maximum neutral duration falls below threshold 1002 and the benefit of neutral usage falls below threshold 1004. However, the transmission remains in neutral to prolong the fuel economy benefits of the vehicle in neutral or idle coasting.

At time T2, the vehicle speed drops to a threshold level 1006, and in response to the vehicle speed being at or below the threshold level 1006, the transmission is shifted into a forward gear. The maximum neutral duration is less than threshold level 1002 and the neutral usage benefit is less than threshold level 1004.

Between time T2 and time T3, the transmission remains engaged in the forward gear and the vehicle speed increases. The maximum neutral duration also exceeds threshold 1002. The neutral use benefit is less than the threshold 1004. The transmission does not enter neutral because the threshold 1004 is not exceeded.

At time T3, the transmission changes state from forward to neutral. In response to the maximum neutral duration being greater than the threshold 1002 and the benefit of neutral use being greater than the threshold 1004, the transmission is shifted to neutral. In response to the transmission being in neutral, the vehicle speed begins to slowly decrease.

Between time T3 and time T4, the vehicle speed continues to decrease and the transmission remains in neutral. The maximum neutral duration falls below threshold 1002 and the benefit of neutral usage falls below threshold 1004. The transmission is maintained in neutral to extend the fuel economy benefits of a vehicle coasting or idling.

At time T4, the vehicle speed drops to a threshold level 1006, and in response to the vehicle speed being at or below the threshold level 1006, the transmission is shifted into a forward gear. The maximum neutral duration is less than a threshold level 1002 and the neutral use benefit is less than a threshold level 1004.

In this way, the transmission may be selectively shifted to or from neutral to extend vehicle fuel economy. The vehicle controller selectively shifts to neutral depending on vehicle operating conditions and road grade or road slope value in predicted or electronic field of view. In response to negative road grade and other vehicle conditions, the controller may shift the transmission to neutral. Additionally, the controller may act to shift the transmission to neutral in response to a change in road grade from a positive grade to a negative grade or a zero grade as determined from a prediction or an electronic horizon.

Note that the example control and estimation routines included herein can be used with various engine and/or vehicle system configurations. Additionally, the methods described herein may be a combination of actions taken by a controller of the physical world and instructions within the controller. The control methods and programs disclosed herein may be stored as executable instructions in non-transitory memory and executed by a control system including a controller in conjunction with various sensors, actuators, and other engine hardware. The specific routines described herein may represent one or more of any number of processing strategies such as event-driven, interrupt-driven, multi-tasking, multi-threading, and the like. As such, various acts, operations, and/or functions described may be performed in the sequence illustrated, in parallel, or in some cases omitted. Likewise, the order of processing is not necessarily required to achieve the features and advantages of the example embodiments described herein, but is provided for ease of illustration and description. One or more of the illustrated acts, operations, and/or functions may be repeatedly performed depending on the particular strategy being used. Additionally, the described acts, operations, and/or functions may graphically represent code to be programmed into the non-transitory memory of the computer readable storage medium in the engine control system, wherein the described acts are enabled by execution of instructions in the system, including the various engine hardware components, in cooperation with the electronic controller.

This concludes the description. Numerous variations and modifications will occur to those skilled in the art upon reading the specification without departing from the spirit and scope of the invention. For example, I3, I4, I5, V6, V8, V10, and V12 engines operating in natural gas, gasoline, diesel, or alternative fuel configurations may benefit from the present description.

The claims hereof particularly point out certain combinations and subcombinations regarded as novel and nonobvious. These claims may refer to "an" element or "a first" element or the equivalent thereof. Such claims should be understood to include incorporation of one or more such elements, neither requiring nor excluding two or more such elements. Other combinations and subcombinations of the disclosed features, functions, elements, and/or properties may be claimed through amendment of the present claims or through presentation of new claims in this or a related application. Such claims, whether broader, narrower, equal, or different in scope to the original claims, also are regarded as included within the subject matter of the present disclosure.

Claims

1. A vehicle system, comprising:

a vehicle including a motive torque source;

a transmission coupled to the motive torque source; and

a controller in the vehicle, the controller comprising executable instructions stored in non-transitory memory, wherein the controller is configured to:

sensing a vehicle speed;

providing a torque command to the motive torque source in response to an output of an adaptive non-linear model predictive cruise control routine, the torque command determined based on a shape of a vehicle fuel consumption model; and

operating the driveline torque source to output a driveline torque that is constrained to be below a driveline torque threshold, wherein a transmission downshift is in response to the driveline torque being equal to or greater than the driveline torque threshold at a sensed vehicle speed.

2. The vehicle system of claim 1, wherein the controller is further configured to adapt the vehicle fuel consumption model.

3. The vehicle system of claim 1, wherein the controller is further configured to: operating the transmission in a forward gear in response to a convex fuel consumption model; and operating the transmission in a neutral state in response to the non-convex fuel consumption model.

4. The vehicle system of claim 1, wherein the controller is further configured to provide a pulse shaped torque to the motive torque source in response to a convex fuel consumption model.

5. The vehicle system of claim 1, wherein the source of motive torque comprises an engine, and constraining the driveline torque below the driveline torque threshold comprises constraining engine airflow to less than a threshold.

6. A vehicle system, comprising:

a vehicle including a motive torque source;

a transmission coupled to the motive torque source; the transmission includes a neutral state and a forward gear; and

sensing a vehicle speed;

providing a torque command to the motive torque source in response to an output of an adaptive non-linear model predictive cruise control routine;

operating the driveline torque source to output a driveline torque that is constrained to be below a driveline torque threshold, wherein a transmission downshift is responsive to the driveline torque being equal to or greater than the driveline torque threshold at a sensed vehicle speed;

operating the transmission in the forward gear in response to a convex fuel consumption model; and is

Operating the transmission in the neutral state in response to a non-convex fuel consumption model.

7. The vehicle system of claim 6, wherein the driveline torque is constrained by adjusting spark advance.

8. The vehicle system of claim 6, wherein the controller is further configured to maintain the vehicle speed between an upper vehicle speed threshold and a lower vehicle speed threshold.

9. The vehicle system of claim 8, wherein the controller is further configured to: in response to the vehicle speed being less than the lower vehicle speed threshold, operating the driveline torque source to output the driveline torque greater than the driveline torque threshold.

10. The vehicle system of claim 9, wherein the driveline torque threshold is determined based on a transmission shift schedule stored in the non-transitory memory of the controller.

11. A vehicle cruise control method, comprising:

receiving vehicle information from one or more sensors to a controller;

sensing a vehicle speed;

providing a torque command in response to an output of an adaptive non-linear model predictive cruise control routine executed by the controller, the torque command determined based on a shape of a vehicle fuel consumption model, the torque command constrained to be less than a driveline torque threshold, wherein a transmission downshift is in response to the torque command being equal to or greater than the driveline torque threshold at a sensed vehicle speed; and

adjusting a torque actuator of a powered torque source in response to the torque command.

12. The method of claim 11, further comprising downshifting the transmission in response to the vehicle speed being less than a lower vehicle speed threshold.

13. The method of claim 12, further comprising: operating the transmission in a forward gear in response to a convex fuel consumption model; and operating the transmission in a neutral state in response to the non-convex fuel consumption model.

14. The method of claim 12, further comprising: when the vehicle fuel consumption model is non-convex,

responsive to vehicle speed increasing to an upper vehicle speed threshold, adjusting gear selection to a neutral state, adjusting the torque actuator to deliver idle torque, and coasting the vehicle, an

In response to the vehicle speed decreasing to a lower vehicle speed threshold, adjusting the gear selection to a forward gear, adjusting the torque actuator to deliver a torque greater than the idle torque, and accelerating the vehicle.