CN115571125A

CN115571125A - Model prediction control algorithm for PCC function of commercial vehicle

Info

Publication number: CN115571125A
Application number: CN202211246737.2A
Authority: CN
Inventors: 吕婷婷; 陈曼; 余建华; 刘双平; 杨卓; 谭宪琦; 关孟樵; 蒋江楚
Original assignee: Dongfeng Commercial Vehicle Co Ltd
Current assignee: Dongfeng Commercial Vehicle Co Ltd
Priority date: 2022-10-12
Filing date: 2022-10-12
Publication date: 2023-01-06

Abstract

The invention relates to a model predictive control algorithm for PCC (policy and charging control) functions of a commercial vehicle, which comprises the following steps: collecting vehicle state information and external environment information; calculating to obtain an optimal control torque, an optimal braking torque and an optimal gear in a time domain according to the vehicle state information and the external environment information; and outputting the optimal control torque, the optimal braking torque and the optimal gear in the time domain obtained by calculation in the S200 to a corresponding system of the vehicle for use. According to the method, the dynamic characteristics and delay of the actuator are well considered, and the real-time optimal solution (control torque and optimal gear) in a prediction time domain (mileage) is calculated through external road condition information obtained in advance; the purposes of good vehicle speed tracking performance and lowest fuel consumption in the prediction time domain are achieved.

Description

Model predictive control algorithm for PCC function of commercial vehicle

Technical Field

The invention relates to the technical field of commercial diesel vehicles, in particular to a model predictive control algorithm for PCC (policy and charging control) functions of a commercial vehicle.

Background

In recent years, with the continuous development of electronic and electrical systems, the economy, driving comfort and intellectualization of the whole vehicle become comprehensive consideration factors of more and more people, wherein the cruising driving function of the commercial vehicle is more prominent. The conventional cruise driving mainly comprises constant-speed cruise and self-adaptive cruise, wherein the constant-speed cruise only considers the current actual vehicle speed and the current target vehicle speed and adjusts the current actual vehicle speed and the target vehicle speed by a PID (proportion integration differentiation) method to ensure the following performance of the vehicle speed. Predictive cruise control is a high-level cruise control system that uses high-precision map data, i.e., GPS to determine vehicle position and predict road topography ahead, including grade (uphill, downhill), curvature, and controls cruise gear and torque in advance before entering uphill or downhill sections, in order to save fuel and follow cruise speed.

In the prior art, PID or dynamic programming is mostly adopted; typically, the chinese invention with the application number CN202210671096.9 and the name "predictive cruise control method, device, equipment and storage medium" discloses the following technical solutions:

acquiring road information in front of a vehicle, and reconstructing a road according to gradient according to the road information to obtain at least one road section;

and calculating a control target of a target road section in at least one road section through a cruise control algorithm based on a preset vehicle speed range and a preset gear range, and performing predictive cruise control on the vehicle according to the control target, wherein the control target comprises a target vehicle speed and a target gear.

Based on a preset vehicle speed range and a preset gear range, calculating a control target of a target road section in at least one road section through a cruise control algorithm, and performing predictive cruise control on the vehicle according to the control target, wherein the steps comprise:

obtaining an optional control state according to the vehicle speed range and the gear range, wherein the optional control state comprises an optional vehicle speed and an optional gear, the optional vehicle speed belongs to the vehicle speed range, and the optional gear belongs to the gear range;

inputting the selectable control state into a cruise control algorithm, and calculating at least one algorithm value;

and selecting the vehicle speed and the gear corresponding to the algorithm value with the minimum value to obtain the target vehicle speed and the target gear of the target road section, and performing predictive cruise control on the vehicle according to the target vehicle speed and the target gear.

The method comprises the following steps of calculating a control target of a target road section in at least one road section through a cruise control algorithm based on a preset vehicle speed range and a preset gear range, and performing predictive cruise control on a vehicle according to the control target, wherein the method comprises the following steps:

obtaining a cruise constant speed and an adjusting value, wherein the adjusting value is determined by driving autonomous setting or historical data testing;

and obtaining a vehicle speed range based on the cruise constant speed and the regulating value.

The algorithm items in the cruise control algorithm comprise the predicted energy consumption of the current road section, the vehicle speed change of the current road section relative to the previous road section, the gear change of the current road section relative to the previous road section, and the speed difference between the current vehicle speed and the cruise constant speed, wherein the predicted energy consumption of the target road section is determined according to the gradient of the target road section. The algorithm item of the gear change of the current road section relative to the previous road section comprises a gear change control coefficient which controls the gear change times in the anticipatory cruise process,

when the gear of the current road section is not changed relative to the gear of the previous road section, the control coefficient is 0;

when the gear change of the current road section relative to the gear change of the previous road section is more than one gear, the control coefficient is 1;

when the gear change of the current road section relative to the gear change of the previous road section is more than one gear, the control coefficient is 2. The method comprises the steps of obtaining road information in front of a vehicle, reconstructing a road according to gradient according to the road information, and obtaining at least one road section, wherein the steps comprise:

acquiring road information in front of a vehicle, wherein the road information comprises a gradient value of a road;

and dividing the gradient value into at least one gradient interval on the basis of a preset gradient amplitude, wherein one gradient interval corresponds to one road section, at least one road section is generated, and the gradient value is continuously divided. The cruise control algorithm is a penalty function:

the penalty function is:

n-1 is an end point road section in at least one road section, k is a current road section, i is a starting speed and a starting gear of the current road section, and J is an end point speed and an end point gear of the current road section.

The device comprises:

the acquisition module is used for acquiring road information in front of the vehicle and reconstructing a road according to the road information and the gradient to obtain at least one road section;

and the cruise module is used for calculating a control target of a target road section in at least one road section through a cruise control algorithm based on a preset vehicle speed range and a preset gear range, and performing predictive cruise control on the vehicle according to the control target, wherein the control target comprises a target vehicle speed and a target gear.

The core of the above invention application lies in: dividing the road into a plurality of road sections, calculating the change of a certain road section relative to the previous road section, and obtaining the optimal control target of the current road section in a reverse solving mode.

The defects of the prior art are as follows:

the existing PID control algorithm is single input/output, variable control of intermediate state variables cannot be considered, the PID control method has certain hysteresis, the dynamic programming method can realize optimal control, but the calculation amount is huge, the operation time is long, a large amount of chip resources are occupied, and the control instantaneity is relatively poor.

Disclosure of Invention

Aiming at the problems, the invention provides a model prediction control algorithm of a PCC function of a commercial vehicle, which aims to well consider the dynamic characteristic and delay of an actuator and calculate the real-time optimal solution (control torque and optimal gear) in a prediction time domain (mileage) through external road condition information acquired in advance; the purposes of good vehicle speed tracking performance and lowest fuel consumption in the prediction time domain are achieved.

In order to solve the problems, the technical scheme provided by the invention is as follows:

a model predictive control algorithm for PCC function of a commercial vehicle comprises the following steps:

s100, collecting vehicle state information and external environment information;

s200, calculating to obtain an optimal control torque, an optimal braking torque and an optimal gear in a time domain according to the vehicle state information and the external environment information;

and S300, outputting the optimal control torque, the optimal braking torque and the optimal gear in the time domain obtained by calculation in S200 to a corresponding system of a vehicle for use.

Preferably, the vehicle state information is transmitted to the MPC controller through the whole vehicle CAN, and comprises a cruise activation state, a cruise set speed, a vehicle actual speed, a current gear, an engine torque and a vehicle brake torque;

the external environment information is provided by an ADAS high-precision map module built in the TBOX, is transmitted to the MPC controller through the whole vehicle CAN and comprises the current position of the vehicle, the position of a future road and the information of the future road; the future road information includes a future road grade, a future road curvature.

Preferably, S200 specifically comprises the following steps:

s210, setting a target function; the target function comprises constraint conditions and terminal punishment indexes;

then setting vehicle parameters, controller parameters and the constraint conditions; wherein:

the vehicle parameters comprise a gearbox speed ratio, engine fuel consumption MAP, a tire radius and a vehicle load;

the controller parameters comprise a prediction time domain, iteration times, a first weight coefficient and a terminal punishability index weight coefficient;

the constraint conditions comprise speed constraint, engine torque constraint and gear constraint;

then, setting a variable initial value in an iteration process;

then, according to the current position, interpolating in an array for storing the future road information to obtain the road gradient in a prediction time domain, and then endowing the value to the future road gradient;

s220, optimizing the objective function;

s230, constructing a Hamiltonian;

s240, solving the objective function to obtain an explicit solution sequence of the optimal control variable; then, iteratively solving the optimal covariance variable of each step in the prediction time domain; the control variable corresponding to the optimal covariate is the optimal control variable;

s250, comparing the obtained speed with the speed limit of the current road, and according to the comparison result, performing the following operations:

if the obtained speed does not exceed the speed limit of the current road, outputting the optimal torque and the optimal gear at the next moment and the next moment;

s260, after the iteration is finished, judging whether a terminal constraint condition is met, and according to a judgment result, performing the following operations:

if the terminal constraint condition is met, outputting the predicted torque and gear;

and if the terminal constraint condition is not met, maintaining the torque and the gear at the current moment.

Preferably, the objective function in S200 is expressed by the following formula:

wherein: j is the objective function; v (k) is the velocity constraint; t is a unit of _e (k) Is the engine torque constraint; g (k) is the gear constraint; k is a radical of formula ₂ Punishment index weight coefficient is the terminal; v (N) is the terminal vehicle speed in the prediction time domain; v. of _ref For the driver to setAnd (5) determining the cruising speed.

Preferably, the objective function is optimized in S220 and expressed as follows:

wherein: q _t (k) Is an index of fuel economy; k is a radical of ₁ (v(N)-v _ref ) ² Tracking performance index for speed; k is a radical of ₁ Is the first weight coefficient.

Preferably, the hamiltonian in S230 is expressed as follows:

wherein: delta is a vehicle rotating mass conversion coefficient; m is the total mass of the vehicle; f _t Is vehicle driving force; f _f Is rolling resistance; f _w Is the air resistance; f _i Is the ramp resistance.

Preferably, the constraint condition in the process of solving the objective function in S240 is expressed by the following formula:

F _j (k)＝F _t (k)-F _f (k)-F _w (k)-F _i (k)

wherein: f _j (k) Is the constraint.

Preferably, the solving of the optimal covariance variables in S240 specifically includes the following steps:

s241, the Hamiltonian is arranged into a quadratic polynomial form and expressed according to the following formula:

H(T _e ,v,λ,k)＝Au ² +Bu+C

s242. With T _e V is an independent variable, and the optimal co-modal variable is solved; the optimal co-modal variables are expressed as follows:

λ(Te,v)

preferably, the ending condition of the iteration in S260 is expressed as follows:

|F(λ ₂ ⁽ⁿ⁾ (0)|≤ε

wherein: n is the number of iterations; ε is a small positive number.

Preferably, after the iteration in S260 is finished, the optimal engine torque is obtained and output.

Compared with the prior art, the invention has the following advantages:

1. the MPC control algorithm adopted by the invention can not only realize multi-input/output, but also consider the constraint of the intermediate state variable, thereby well considering the dynamic characteristic and delay of the actuator, and calculating the real-time optimal solution (control torque and optimal gear) in the prediction time domain (mileage) through the external road condition information known in advance;

2. the algorithm of the invention has the characteristics of fast operation and good real-time performance, thereby achieving the purposes of good vehicle speed tracking performance and lowest fuel consumption in the prediction time domain.

Drawings

FIG. 1 is a schematic diagram of a system architecture for implementing an embodiment of the present invention;

FIG. 2 is a schematic diagram of an algorithm flow according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of the optimization and derivation process of an MPC optimization control algorithm according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a process for solving optimal covariates according to an embodiment of the present invention;

FIG. 5a is a schematic diagram of the performance of the speed tracking at 60km/h in the real vehicle experiment according to the embodiment of the present invention;

FIG. 5b is a schematic diagram of the performance of the vehicle at a speed of 70km/h in the vehicle experiment according to the embodiment of the present invention.

Detailed Description

The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.

It should be noted that the present invention is based on a vehicle system as shown in fig. 1.

As shown in fig. 2, a model predictive control algorithm for PCC functionality of a commercial vehicle includes the following steps:

s100, vehicle state information and external environment information are collected.

In this embodiment, the vehicle state information is transmitted to the MPC controller through the entire vehicle CAN, and includes a cruise activation state, a cruise set speed, an actual speed of the vehicle, a current gear, an engine torque, and a vehicle braking torque.

In this embodiment, the vehicle state information is sent to the MPC controller via the CAN bus, and the MPC algorithm is activated when the cruise state is activated.

In the specific embodiment, the external environment information is provided by an ADAS high-precision map module built in the TBOX and is transmitted to the MPC controller through the whole vehicle CAN, and the external environment information comprises the current position of the vehicle, the future road position and the future road information; the future road information includes a future road gradient and a future road curvature.

In the embodiment, the ADAS high-precision map mainly applies POSN and PROFSHORT messages, namely the current position, the future road position and the future road information of the vehicle; and applying the reconstructed current position, the reconstructed future position and the corresponding gradient and curvature to an MPC control algorithm to be used as a longitudinal dynamics constraint condition for solving an objective function.

It should be noted that the MPC control algorithm supports multiple-input/multiple-output calculation, supports constraints of input/output variables and state variables, sets, optimizes and solves a target function to obtain optimal control torque, brake torque and gear in a prediction time domain, moves with the vehicle, and updates the optimal control target in the prediction time domain in a real-time iterative manner, so that in the cruise control process, the control effects of good vehicle speed tracking performance and lowest fuel consumption rate in a certain mileage are achieved.

It should be noted that, regarding the storage of road information, based on the ADAS v2 standard protocol, the map module sends the current position, the future position, and the corresponding gradient and curvature through the post and proshort messages, is limited by the data length (Offset max 8191 m) of the message and the transformation of the road ID, reconstructs the current position and the future position of the vehicle, and stores the reconstructed data (the current position, the future position, the corresponding gradient and curvature) in an array of a fixed length, the data update complies with the first-in first-out principle, that is, new data is stored in the last bit of the array, and simultaneously rejects the first data in the array, calculates the gradient corresponding to the distance position in the time domain according to the current position of the vehicle, models through longitudinal dynamics, and uses the modeling result as the constraint condition of the optimal solution.

It is noted that, as shown in fig. 3, the MPC controller predicts the optimum control torque, the optimum braking torque, and the optimum gear in the time domain based on the above-described vehicle state information and the external environment information. The implementation method comprises the steps of constructing a target function based on multi-factor constraint, applying a commercial vehicle dynamics model, an engine MAP model, a fuel consumption rate model and an MPC optimization control algorithm, ensuring good speed tracking performance and minimum fuel consumption within a certain mileage, and outputting the calculated optimal engine torque, braking torque and optimal gear signal to a corresponding system of the vehicle to achieve the purpose of controlling the vehicle speed and minimum fuel consumption.

In this embodiment, the future range is within 3km in the future.

And S200, calculating to obtain the optimal control torque, the optimal braking torque and the optimal gear in the time domain according to the vehicle state information and the external environment information.

It should be noted that the present application runs the MPC control algorithm through the VECU controller.

In this embodiment, S200 specifically includes the following steps:

s210, setting a target function; the objective function comprises constraint conditions and terminal punishment indexes.

Then setting vehicle parameters, controller parameters and constraint conditions; wherein:

the vehicle parameters include transmission speed ratio, engine fuel consumption MAP, tire radius, vehicle load.

The controller parameters comprise a prediction time domain, iteration times, a first weight coefficient and a terminal punishment index weight coefficient.

The constraints include speed constraints, engine torque constraints, and gear constraints.

Then, initial values of variables in the iterative process are set.

Then, according to the current position, in an array for storing future road information, interpolation is carried out to obtain the road gradient in the prediction time domain, and then the value is given to the future road gradient.

In this embodiment, the objective function in S200 is expressed by formula (1):

wherein: j is an objective function; v (k) is a velocity constraint; t is _e (k) Is an engine torque constraint; g (k) is gear constraint; k is a radical of ₂ Punishment index weight coefficient is a terminal; v (N) is the terminal vehicle speed in the prediction time domain; v. of _ref The cruising speed set for the driver.

S220, optimizing the objective function.

In this embodiment, the optimization objective function in S220 is expressed by equation (2):

wherein: q _t (k) Is an index of fuel economy; k is a radical of ₁ (v(N)-v _ref ) ² Tracking performance index for speed; k is a radical of ₁ Is a first weight coefficient.

S230, constructing a Hamiltonian.

In this embodiment, the hamiltonian in S230 is expressed by equation (3):

It should be noted that, the construction process of the hamiltonian is as follows:

s231, considering an optimal control problem and an optimal necessity condition, constructing a Hamiltonian equation according to the extreme value principle of the Hamiltonian, and expressing according to the formula (4):

wherein: lambda [ alpha ] ₁ (k + 1) is a covariate of state s (k); lambda [ alpha ] ₂ (k + 1) is a covariate of state v (k).

S232, according to the Ponderland minimum value principle, the optimal state variable x ^* (0) Optimum number control input u ^* (0) And corresponding covariate lambda ^* (k+1)＝[λ ₁ (k+1)λ ₂ (k+1)] ^T The Hamiltonian must be minimized and each term of the Hamiltonian does not contain s (k), so that λ is obtained ₁ (k) =0, so the iterative equation for the covariate is expressed in equation (5):

for equation (5), the boundary condition of the covariate is expressed by equation (6):

s233, according to S231-S232, the final Hamiltonian as formula (3) can be obtained.

S240, as shown in figure 4, solving the objective function to obtain the optimal control variable T _e Dominant solution sequence of v; then, iteratively solving the optimal covariate lambda of each step in the prediction time domain; and the control variable corresponding to the optimal covariate lambda is the optimal control variable.

In this embodiment, the constraint condition in the process of solving the objective function in S240 is expressed by equation (7):

F _j (k)＝F _t (k)-F _f (k)-F _w (k)-F _i (k) (7)

wherein: f _j (k) Are constraints.

It should be noted that the current and future road information sent by the ADAS high-precision map includes the current position, the slope and the curvature within the future 3km range, the MPC controller obtains the position and the slope of the road within the predicted time domain through interpolation, obtains a longitudinal dynamics equation through constructing a longitudinal dynamics model, and takes the equation (7) as the constraint condition of the solving process.

In this embodiment, the step of solving the optimal covariance variables in S240 specifically includes the following steps:

s241, for convenient solution, knowing through a fuel consumption fitting model and an objective function, and controlling a variable T _e The highest power of v is the square, so the Hamiltonian can be arranged into a quadratic polynomial form, expressed by the formula (8):

H(T _e ,v,λ,k)＝Au ² +Bu+C (8)

s242. At this time, the operation is changed to T _e V is an independent variable, and the optimal co-modal variable is solved; the optimal co-modal variables are expressed by equation (9):

λ(Te,v) (9)

it should be noted that the initial covariance variable λ is derived according to the control criterion which converts the optimal control problem solution into the optimal necessity condition and control process ₂ (0) To lambda ₂ And (N), therefore, the initial optimal control problem is converted into an optimization problem of the initial covariance variable value, and the optimal initial covariance variable value is determined, so that the terminal covariance variable value obtained under the drive of the optimal control criterion meets the terminal necessity condition. For the optimal control problem, the terminal covariance variable necessity condition is obtained from the formula (6) to obtain a terminal covariance variable function, which is expressed by the formula (10):

F(λ ₂ (0))＝λ ₂ (N)-2k ₂ (v(N)-v _d ) (10)

obtaining the optimal initial covariance variable value by rooting the terminal covariance variable function, namely solving F (lambda) ₂ (0))＝And 0, carrying out binary iterative solution in the solution process.

and if the obtained speed does not exceed the speed limit of the current road, outputting the optimal torque and the optimal gear at the next moment and the next moment.

if the terminal constraints are satisfied, the predicted torque and gear are output.

In this embodiment, the termination condition of the iteration in S260 is expressed by equation (11):

|F(λ ₂ ⁽ⁿ⁾ (0)|≤ε (11)

wherein: n is the number of iterations; ε is a small positive number.

In this embodiment, after the iteration in S260 is finished, the optimal engine torque is obtained and output, and cruise prediction control with good vehicle speed tracking performance and optimal fuel consumption within a certain mileage is implemented.

And S300, outputting the optimal control torque, the optimal braking torque and the optimal gear in the time domain obtained in the S200 to a corresponding system of the vehicle for use.

In this embodiment, the MPC controller outputs the solution result to the bus, and the solution result acts on the engine system, the transmission system, and the EBS system, respectively, and the entire vehicle sends a new state signal to the bus, thereby implementing closed-loop control.

To further demonstrate the significant technological advancement of the present invention over the prior art, the present embodiment also performed a real-world experiment:

as shown in FIGS. 5a and 5b, the effect was significant at 60kmh (speed fluctuation. + -. 3 km/h) and 70kmh (speed fluctuation. + -. 3 km/h).

Wherein: driver _ Requested _ Engine _ Mode (blue): cruise active state Cruise _ Control _ Set _ Speed (red): driver-set cruise Vehicle Speed, tachograph _ Vehicle _ Speed (green), vehicle Speed, MPC _ Te (purple): control torque output by the MPC algorithm.

As shown in Table 1, after the technology of the invention is adopted, the fuel saving rate is greatly improved by the display of real vehicle operation:

TABLE 1 oil-saving ratio comparison table

Algorithm	Testing mileage (Km)	Fuel consumption (L)	Average oil consumption (L/100 km)	Test conditions	Oil saving rate
						Existing PCC	435.7	135.1	31.007	Reference to	-
MPC	311.6	94.1	30.198	Test 1	2.6％
						MPC	428	130.7	30.537	Test 2	1.5％

It should be noted that the mileage and fuel consumption tested in table 1 are the data recorded in the meter.

In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. To those skilled in the art; various modifications to these embodiments will be readily apparent, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A model predictive control algorithm for PCC function of a commercial vehicle is characterized in that: comprises the following steps:

2. Model predictive control algorithm for a PCC functionality of a commercial vehicle according to claim 1, characterised in that: the vehicle state information is transmitted to the MPC controller through the whole vehicle CAN and comprises a cruise activation state, a cruise set speed, a vehicle actual speed, a current gear, an engine torque and a vehicle braking torque;

3. Model predictive control algorithm for a commercial vehicle PCC function according to claim 2, characterised in that: s200 specifically includes the following steps:

the controller parameters comprise a prediction time domain, iteration times, a first weight coefficient and a terminal punishment index weight coefficient;

then, setting an initial value of a variable in an iteration process;

s220, optimizing the objective function;

s230, constructing a Hamiltonian;

s240, solving the objective function to obtain an explicit solution sequence of the optimal control variable; then, iteratively solving the optimal covariate of each step in the prediction time domain; the control variable corresponding to the optimal covariate is the optimal control variable;

4. Model predictive control algorithm for a commercial vehicle PCC function according to claim 3, characterised in that: the objective function in S200 is expressed as follows:

wherein: j is the objective function; v (k) is the velocity constraint; t is _e (k) Is the engine torque constraint; g (k) is the gear constraint; k is a radical of ₂ Punishment index weight coefficient for the terminal; v (N) is the terminal vehicle speed in the prediction time domain; v. of _ref The cruising speed set for the driver.

5. Model predictive control algorithm for a commercial vehicle PCC function according to claim 4, characterised in that: in S220, the objective function is optimized and expressed as follows:

wherein: q _t (k) Is an index of fuel economy; k is a radical of formula ₁ (v(N)-v _ref ) ² Tracking performance index for speed; k is a radical of ₁ Is the first weight coefficient.

6. Model predictive control algorithm for a commercial vehicle PCC function according to claim 5, characterised in that: the hamiltonian in S230 is expressed as follows:

wherein:delta is a vehicle rotating mass conversion coefficient; m is the total mass of the vehicle; f _t Is vehicle driving force; f _f Is rolling resistance; f _w Is the air resistance; f _i Is the ramp resistance.

7. Model predictive control algorithm for a commercial vehicle PCC function according to claim 6, characterised in that: in S240, the constraint condition in the process of solving the objective function is expressed by the following formula:

F _j (k)＝F _t (k)-F _f (k)-F _w (k)-F _i (k)

wherein: f _j (k) Is the constraint.

8. Model predictive control algorithm for a PCC functionality of a commercial vehicle according to claim 7, characterised in that: the step of solving the optimal covariance variable in S240 specifically includes the following steps:

s241, arranging the Hamiltonian into a quadratic polynomial form, and expressing the Hamiltonian according to the following formula:

H(T _e ,v,λ,k)＝Au ² +Bu+C

λ(Te,v) 。

9. model predictive control algorithm for a PCC functionality of a commercial vehicle according to claim 8, characterised in that: the termination condition of the iteration in S260 is expressed as follows:

|F(λ ₂ ⁽ⁿ⁾ (0)|≤ε

wherein: n is the number of iterations; ε is a small positive number.

10. Model predictive control algorithm for a PCC function on a commercial vehicle according to claim 9, characterised in that: and after the iteration in the step S260 is finished, obtaining and outputting the optimal engine torque.