CN114889448A

CN114889448A - Self-adaptive torque control method and system for wheel hub motor driven vehicle and storage medium

Info

Publication number: CN114889448A
Application number: CN202210499611.XA
Authority: CN
Inventors: 付翔; 唐茂家; 孙录哲; 万佳琦
Original assignee: Wuhan University of Technology WUT
Current assignee: Wuhan University of Technology WUT
Priority date: 2022-05-09
Filing date: 2022-05-09
Publication date: 2022-08-12

Abstract

The invention discloses a self-adaptive torque control method, a self-adaptive torque control system and a storage medium for a wheel hub motor driven vehicle, which can ensure the stability of the vehicle and improve the dynamic property of the vehicle. The method comprises the steps of constructing a road surface influence factor identification model according to characteristic parameters, wherein the characteristic parameters comprise rolling resistance difference, air resistance normalization proportion, ramp resistance normalization proportion, road surface adhesion difference variance and minimum road surface adhesion coefficient; predicting the road surface influence factor through a road surface influence factor identification model; constructing a multi-target optimizing self-adaptive torque control model, which comprises a top layer, a target decision layer and a torque distribution layer; inputting the actual vehicle speed, the expected vehicle speed and the road surface influence factor into the top layer, and predicting the total longitudinal demand torque; inputting the longitudinal required total moment into a target decision layer, and predicting vehicle motor torque, slope compensation moment and yaw moment; inputting the vehicle motor torque, the slope compensation torque and the yaw moment into a torque distribution layer, and predicting a target torque; the torque amount of the vehicle is adjusted according to the target torque.

Description

Self-adaptive torque control method and system for wheel hub motor driven vehicle and storage medium

Technical Field

The invention relates to the technical field of electric control of in-wheel motor driven vehicles, in particular to a self-adaptive torque control method, a self-adaptive torque control system and a storage medium for the in-wheel motor driven vehicles.

Background

The off-road vehicle usually undertakes special tasks such as rescue and relief work, military maneuver, resource exploration and the like, and usually runs on a road surface with variable running resistance, so the off-road vehicle has stronger off-road environment adaptability and higher maneuverability and safety under complex off-road conditions such as low adhesion, uneven fluctuation and the like. In the related technology, the four-wheel motor of the distributed driving off-road vehicle has the advantages of independently controllable torque, quick torque response and capability of meeting higher requirements on power reserve due to the excellent characteristics of low-speed and large-torque output. But the system is complicated in coupling and has the characteristics of nonlinearity and time variation, and besides, the overdrive characteristic of the system also brings challenges to vehicle dynamics control. In the related art, the research and application scenarios of longitudinal instability control are mainly on a hard road surface with a smooth road surface, and the influence of related road conditions such as road adhesion and road surface undulation on drive control is not considered enough, in particular to the problem of how to guarantee the stability of a vehicle and simultaneously exert the dynamic performance of the vehicle to the maximum extent.

Disclosure of Invention

In order to solve at least one of the above technical problems, the present invention provides an adaptive torque control method, system and storage medium for an in-wheel motor driven vehicle, which can improve the dynamic performance of the vehicle while ensuring the stability of the vehicle.

In one aspect, an embodiment of the present invention provides an adaptive torque control method for an in-wheel motor driven vehicle, including the following steps:

constructing a road surface influence factor identification model according to the acquired characteristic parameters, wherein the characteristic parameters comprise rolling resistance difference, air resistance normalization proportion, ramp resistance normalization proportion, road surface adhesion difference variance and minimum road surface adhesion coefficient;

predicting to obtain a road surface influence factor through the road surface influence factor identification model;

constructing a multi-target optimizing self-adaptive torque control model, wherein the multi-target optimizing self-adaptive torque control model comprises a top layer, a target decision layer and a torque distribution layer;

inputting the obtained actual vehicle speed, the expected vehicle speed and the road surface influence factor into the top layer, and predicting to obtain the longitudinal required total moment of the vehicle;

inputting the longitudinal required total moment into the target decision layer, and predicting to obtain vehicle motor torque, slope compensation moment and yaw moment;

inputting the vehicle motor torque, the slope compensation torque and the yaw moment into the torque distribution layer, and predicting to obtain a target torque;

adjusting an amount of torque of the wheel-hub driven vehicle according to the target torque.

The adaptive torque control method for the in-wheel motor driven vehicle, provided by the embodiment of the invention, has the following beneficial effects: in this embodiment, a road surface influence factor identification model is first constructed according to the acquired characteristic parameters including the rolling resistance difference, the air resistance normalization ratio, the ramp resistance normalization ratio, the road surface adhesion difference variance, and the minimum road surface adhesion coefficient, so that the road surface influence factor can be predicted through the road surface influence factor identification model. Then a multi-target optimizing self-adaptive torque control model comprising a top layer, a target decision layer and a torque distribution layer is built, the obtained actual speed and expected speed of the vehicle and the road surface influence factor are input into the multi-target optimizing self-adaptive torque control model, the longitudinal demand total moment of the vehicle is obtained through the prediction of the top layer in the multi-target optimizing self-adaptive torque control model, the motor torque, the slope compensation moment and the yaw moment of the vehicle are predicted through the target decision layer according to the longitudinal demand total moment output by the top layer, the target torque of the vehicle is obtained through the prediction of the torque distribution layer according to the motor torque, the slope compensation moment and the yaw moment of the vehicle predicted by the target decision layer, the torque of the in-wheel motor driven vehicle can be adjusted through the predicted target torque, the stability of the vehicle is guaranteed, the dynamic property of the vehicle is improved.

According to some embodiments of the invention, the predicting the road surface influence factor through the road surface influence factor identification model includes:

carrying out fuzzy processing according to the characteristic parameters to obtain working condition type information;

and defuzzifying the working condition type information to obtain the road surface influence factor.

According to some embodiments of the invention, the step of inputting the obtained actual vehicle speed and the expected vehicle speed and the road surface influence factor into the top layer to predict and obtain the longitudinal total torque demand of the vehicle comprises:

obtaining expected acceleration through model predictive control according to the actual vehicle speed, the expected vehicle speed and the road surface influence factor;

and predicting the total moment of the longitudinal demand through a longitudinal dynamic model according to the expected acceleration.

According to some embodiments of the invention, the decision-making layer of interest comprises a drive antiskid control intervention or exit mechanism;

the drive antiskid control intervention or exit mechanism comprises:

constructing a slip rate function according to the relation between the optimal slip rate and the slip rate of each wheel of the vehicle;

calculating to obtain longitudinal acceleration according to the slip rate function;

and constructing a triggering condition for driving the anti-skid control to intervene or exit according to the second deviation value of the longitudinal acceleration and the equivalent acceleration.

According to some embodiments of the invention, the objective decision layer further comprises a drive slip controller, a ramp longitudinal force compensator, and a yaw moment controller;

the step of inputting the longitudinal total demand torque into the target decision layer to predict and obtain the vehicle motor torque, the slope compensation torque and the yaw torque comprises the following steps:

constructing a vehicle power model according to the longitudinal required total moment, the driving torque, the driving resistance and the wheel radius; predicting to obtain the vehicle motor torque according to the vehicle power model;

predicting to obtain the slope information of the current position of the vehicle according to the slope value; predicting and obtaining a slope compensation torque according to the slope information;

acquiring a current yaw rate of the vehicle; calculating a first deviation value of the current yaw rate and an ideal yaw rate; and calculating to obtain the yaw moment through a PI feedback controller according to the first deviation value.

According to some embodiments of the invention, said inputting said vehicle motor torque, said hill compensation moment, and said yaw moment into said torque distribution layer, predicting a target torque, comprises:

constructing a relation function of the longitudinal driving force and the wheel driving torque of each wheel according to the longitudinal driving force and the wheel driving torque of each wheel of the vehicle;

optimizing by taking the total driving force as an optimization target according to the relation function to obtain a first target optimization function;

optimizing by taking the adhesion margin of each wheel of the vehicle as an optimization target according to the relation function to obtain a second target optimization function;

obtaining a hybrid optimization objective function according to the first objective optimization function and the second objective optimization function;

constructing a constraint condition according to the vehicle motor torque, the slope compensation moment and the yaw moment;

and calculating to obtain the target torque according to the hybrid optimization objective function and the constraint condition.

According to some embodiments of the invention, the constraints comprise yaw force constraints, driving antiskid control constraints, motor characteristic constraints, and road attachment condition constraints;

the construction constraint condition comprises the following steps:

and constructing a yaw force constraint condition, a driving antiskid control constraint condition, a motor characteristic constraint condition and a road attachment condition constraint condition.

In another aspect, an embodiment of the present invention further provides an adaptive torque control system for an in-wheel motor driven vehicle, including:

the first model construction module is used for constructing a road surface influence factor identification model according to the acquired characteristic parameters, wherein the characteristic parameters comprise rolling resistance difference, air resistance normalization proportion, ramp resistance normalization proportion, road surface adhesion difference variance and minimum road surface adhesion coefficient;

the road surface influence factor prediction module is used for predicting to obtain a road surface influence factor through the road surface influence factor identification model;

the second model building module is used for building a multi-target optimizing self-adaptive torque control model; the multi-target optimizing self-adaptive torque control model comprises a top layer, a target decision layer and a torque distribution layer;

the longitudinal demand total moment prediction module is used for inputting the acquired actual vehicle speed, the acquired expected vehicle speed and the road surface influence factor into the top layer and predicting to obtain the longitudinal demand total moment of the vehicle;

the target decision layer module is used for inputting the longitudinal required total moment into the target decision layer and predicting to obtain vehicle motor torque, slope compensation moment and yaw moment;

the torque distribution layer module is used for inputting the vehicle motor torque, the slope compensation torque and the yaw moment into the torque distribution layer and predicting to obtain a target torque;

and the torque control module is used for adjusting the torque amount of the wheel hub driving vehicle according to the target torque.

at least one processor;

at least one memory for storing at least one program;

when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the adaptive torque control method for the in-wheel motor driven vehicle according to the above embodiment.

In another aspect, embodiments of the present invention further provide a computer storage medium, in which a program executable by a processor is stored, where the program executable by the processor is used to implement the adaptive torque control method for the in-wheel motor driven vehicle according to the above embodiments when executed by the processor.

Drawings

FIG. 1 is a flow chart of an adaptive torque control method for an in-wheel motor driven vehicle according to an embodiment of the present invention;

FIG. 2 is a schematic block diagram of an adaptive torque control system for an in-wheel motor driven vehicle according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a seven-degree-of-freedom dynamic model of a wheel hub motor driven vehicle provided by an embodiment of the invention.

Detailed Description

The embodiments described in the embodiments of the present application should not be construed as limiting the present application, and all other embodiments that can be obtained by a person skilled in the art without making any inventive step shall fall within the scope of protection of the present application.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.

Before describing embodiments of the present application, the terms referred to in the present application will be explained.

Low adhesion ground: a flat road surface with a low road surface adhesion coefficient. Because the longitudinal and cornering stiffnesses of the tire are reduced, tire slip and cornering are likely to occur under longitudinal acceleration conditions. When the wheel with the low road surface has severe slip, the tire is taken as a research object, the rolling resistance calculated based on the wheel rotation dynamics is increased due to the increased wheel speed change rate, but the rolling resistance calculated based on the vertical force is not changed, and the difference of the rolling resistance is represented by the difference value of the two rolling resistances. The larger the difference of the rolling resistance is, the more obvious the road surface disturbance is, the nonlinear change of the rolling resistance is, the instability of the vehicle is easier, the vehicle control is more focused on the stability constraint, and the stability priority weight is increased. When the wheel has small degree of slip or no slip on the low-adhesion ground, the rolling resistance difference is small, but the longitudinal instability of the vehicle is caused by the fact that the wheel is easy to slip again due to small longitudinal and lateral deflection stiffness of the tire under the working condition, and the priority weights of two optimization targets are balanced to ensure the dynamic responsiveness at the same time. The size of the air resistance on the low-attachment road surface can indirectly represent the speed of the vehicle, when the air resistance is large, the speed of the vehicle is large at the moment, the external characteristic curve of the hub motor can know that the output torque is small, the condition that the vehicle state changes due to torque regulation is not easy to occur, meanwhile, the speed can preliminarily bind the high-dynamic intention of a driver, and the priority weight of the dynamic responsiveness can be properly improved.

Changing the adhesion to the ground: and the road surface adhesion coefficient of each wheel has large difference and changes, such as split and butt road surfaces. Under the working condition, the longitudinal stiffness and the cornering stiffness of each wheel are dynamically updated along with the road adhesion coefficient, so that the unstable state of large difference and oscillation of the tire utilization rate of each wheel is caused, and unpredictable changes such as unexpected yaw moment, increased slip rate and the like are generated. The low-adhesion road surface and the variable-adhesion road surface can be distinguished through the road surface adhesion difference. Similarly, under the condition of large rolling resistance difference, the whole vehicle stability weight priority is still required to be ensured to be large on the variable-attachment road surface, and under the condition of small rolling resistance difference, the weight priority of the dynamic responsiveness can be properly increased.

Distorted undulating topography: the high-adhesion road surface with irregular and undulating terrain. The wheels are easy to empty and lose adhesion under the working condition, when a certain wheel is empty, the wheel has no road surface adhesion constraint, more serious slip on a lower adhesion road surface occurs, the slip rate is higher, the estimated road surface adhesion coefficient is less than 0.1, the wheel can be judged to be empty according to the situation, and then the wheel is identified to be a distorted and fluctuated road surface. On a distorted and fluctuant road surface, the driving speed is generally low and is not easy to generate the instability phenomenon, the total driving resistance is increased by considering the fluctuation of the road surface, and the larger the gradient is, the larger the driving resistance is, and the larger the longitudinal dynamic loss of the whole vehicle is. In order to improve the quick maneuvering performance of the vehicle, the priority weight of the power responsiveness needs to be improved, and the power performance of the whole vehicle is enhanced.

Off-road vehicles are often used as important vehicles in special tasks such as rescue and disaster relief, military maneuvering, and resource exploration. In general, in these special tasks, the off-road vehicle travels back on the road with various road surfaces and traveling resistance, so that the off-road vehicle has higher requirements on maneuverability and safety under complex off-road conditions of low adhesion, uneven undulation and the like. In the related art, the four-wheel hub motor of the distributed driving off-road vehicle has the advantages of independently controllable torque, quick torque response and excellent low-speed and large-torque output. However, the distributed drive system has complicated coupling, non-linear and time-varying characteristics and also has an overdrive characteristic, which brings great difficulty in vehicle dynamics control. In addition, at present, the research and application scenes of the longitudinal runaway control are mainly on a hard road surface with a smooth road surface, and the influence of related road conditions such as road surface adhesion, road surface undulation and the like on the driving control is not considered enough.

Based on the above, the embodiment of the invention provides an adaptive torque control method for an in-wheel motor driven vehicle, which can improve the dynamic property of the vehicle while ensuring the stability of the vehicle. Referring to fig. 1, the method of the embodiment of the present invention includes, but is not limited to, step S110, step S120, step S130, step S140, step S150, step S160, and step S170.

Specifically, the method application process of the embodiment of the invention includes, but is not limited to, the following steps:

s110: and constructing a road surface influence factor identification model according to the acquired characteristic parameters, wherein the characteristic parameters comprise rolling resistance difference, air resistance normalization proportion, ramp resistance normalization proportion, road surface adhesion difference variance and minimum road surface adhesion coefficient.

S120: and predicting to obtain the road surface influence factor through the road surface influence factor identification model.

S130: and constructing a multi-target optimizing self-adaptive torque control model, wherein the multi-target optimizing self-adaptive torque control model comprises a top layer, a target decision layer and a torque distribution layer.

S140: and inputting the obtained actual vehicle speed, the expected vehicle speed and the road surface influence factor into the top layer, and predicting to obtain the longitudinal required total moment of the vehicle.

S150: and inputting the longitudinal required total moment into a target decision layer, and predicting to obtain the vehicle motor torque, the slope compensation moment and the yaw moment.

S160: and inputting the vehicle motor torque, the slope compensation moment and the yaw moment into a torque distribution layer, and predicting to obtain the target torque.

S170: and adjusting the torque amount of the wheel driven by the wheel hub according to the target torque.

In the working process of the embodiment, the embodiment first obtains the characteristic parameters, and then constructs the road surface influence factor identification model according to the obtained characteristic parameters. The acquired characteristic parameters comprise rolling resistance difference, air resistance normalization proportion, ramp resistance normalization proportion, road surface assistant difference variance and minimum road surface adhesion coefficient. Further, the road surface influence factor is obtained through prediction of the road surface influence factor identification model in the embodiment. Specifically, the present embodiment is based on a vehicle running balance equation when the vehicle climbs a slope, as shown in the following equation (1):

F _x ＝F _f +F _w +F _i +F _j (1)

wherein, the relevant parameters in the formula (1) are shown as the following formulas (2) to (6):

F _f ＝mgf _R cosθ (3)

F _i ＝mgsinθ (6)

wherein, in the above formulae (1) to (6), F _x As a driving force, F _f To rolling resistance, F _w As air resistance, F _i As ramp resistance, F _j For acceleration resistance, T _i (i ═ 1,2,3,4) is four-hub motor drive torque, i _g Is the reduction ratio of the speed reducer, r is the tire radius, m is the vehicle mass, θ is the gradient, f _R In order to be a rolling resistance coefficient,

are all constants, C _d Is a coefficient of resistance, A is the frontal area, v _x Is the vehicle and air relative velocity.

Meanwhile, referring to fig. 3, in the present embodiment, a seven-degree-of-freedom model of the entire vehicle is adopted, and the kinetic equations of the respective degrees of freedom are as follows:

the longitudinal kinetic equation is shown in the following formula (7):

the lateral kinetic equation is shown in the following formula (8):

the yaw dynamics equation is shown in the following equation (9):

wherein, in the above formulas (7) to (9), m represents the mass of the entire vehicle; i is _z Representing the rotational inertia of the whole vehicle around the z axis; v. of _x 、v _y Respectively, longitudinal and transverse vehicle speeds, a _x 、a _y Respectively representing longitudinal and transverse acceleration; δ represents a front wheel turning angle; gamma denotes the vehicle lateralThe swing angular speed; f _xi (i ═ 1,2,3,4) represents the respective drive wheel longitudinal force; f _yi (i ═ 1,2,3,4) represents each driving force lateral force; l _f 、l _r Respectively representing the distances from the front shaft and the rear shaft to the mass center of the whole vehicle; b is _l 、B _r Respectively representing the distance from the line of the left and right wheels to the center of mass.

In addition, the present example employs a Dugoff tire model, the tire longitudinal force of which is F _x As shown in the following formula (10), the lateral force F _y As shown in the following formula (11):

wherein f (L) is represented by the following formula (12), and L is represented by the following formula (13):

wherein in the above formula (10) to the above formula (13), λ represents a slip ratio, μ represents an estimated adhesion coefficient, and F _z Indicating the vertical force of the tire, alpha _i Indicates the tire slip angle, C _x 、C _y Represents the longitudinal stiffness and lateral stiffness of the tire, epsilon represents a speed influence factor, and L represents a boundary value.

Accordingly, in the present embodiment, the respective wheel side slip angle calculation formulas are respectively shown as the following expression (14) and the following expression (15):

wherein in the formula, alpha ₁ 、α ₂ 、α ₃ 、α ₄ The tire slip angles of the left front, right front, left rear and right rear wheels are shown, B is the track width, V _x 、V _y Respectively, longitudinal and lateral vehicle speeds.

Further, in this embodiment, the wheel is taken as a research object, the magnitude of the tire vertical load is related to the vehicle body attitude and the acceleration change, the influence of the suspension is ignored, and each vertical force formula is shown as the following formula (16):

wherein L represents the wheel base, h _g The height of the center of mass to the ground is indicated, theta indicates the pitch angle, and phi indicates the roll angle.

Then, the following formulae (17) and (18) are obtained from the wheel rotation dynamics:

T _fi ＝F _fi ·r＝F _zi ·fr (18)

wherein J in the formulae (17) and (18) _eq Representing moment of inertia of the wheel, ω _i Representing the rotational speed of each wheel, r representing the radius of the wheel, T _i Representing the driving torque, F _xi Indicating the longitudinal driving force, T, of each wheel _fi Representing the rolling resistance even moment, F _zi The vertical force of each wheel is shown, and f represents the rolling resistance coefficient.

Because the wheels have different states and the running resistances of the wheels are greatly different, when the tire is in a suspended state, a low-adhesion state and the like, the ground adhesion capability is reduced, and the rolling resistance can be suddenly reduced. Further, the present embodiment calculates the difference between the rolling resistance of the vehicle calculated by the vertical force equation and the actual rolling resistance calculated by the wheel rotation dynamics equationThe wheel state and the maximum difference value of the rolling resistance of the whole vehicle can be represented

As shown in the following formula (19):

wherein, in the formula f _R Representing the rolling resistance coefficient.

In addition, the magnitude of the air resistance can represent the change of the vehicle speed to a certain extent, and a driver generally passes through the vehicle at a medium and low speed in rough terrain such as large twist and a slope. The driver intention can be effectively bound through introducing the air resistance and judge, and the ramp resistance can characterize the road surface fluctuation degree to a certain extent simultaneously. In the embodiment, the ramp resistance normalization proportion and the air resistance normalization proportion are obtained as characteristic parameters, wherein the ramp resistance and the air resistance are both normalized proportions, and the ramp resistance normalization proportion

Normalized air resistance ratio

As shown in the following formula (20):

wherein, in the formula, F _{w_min} For the reference minimum value of the ramp resistance, F _{i_min} The minimum value of air resistance of the vehicle test is obtained; f _{w_max} For the reference maximum value of the ramp resistance, F _{i_max} The maximum value of the air resistance of the vehicle test is obtained.

Meanwhile, in the embodiment, the variance of the road surface estimation coefficient of each wheel is obtained through the road surface adhesion coefficient of each wheel, and the road surface adhesion difference of each wheel is represented. Among them, the present embodiment takes the minimum value of the road adhesion coefficient, i.e., the minimum road adhesion coefficient, as the characteristicAnd (5) characterizing the parameters. Variance sigma of difference of road surface adhesion of each wheel _μ ² As shown in the following formula (21):

further, the embodiment implements torque distribution by constructing a multi-target optimization adaptive torque control model including a top layer, a target decision layer, and a torque distribution layer. Specifically, the actual vehicle speed and the expected vehicle speed of the vehicle are obtained first, and the obtained actual vehicle speed and the obtained expected vehicle speed and the road surface influence factor predicted by the road surface influence factor identification model are input into the top layer of the multi-target optimization self-adaptive torque control model for prediction, so as to obtain the longitudinal demand total torque of the vehicle. Then, the longitudinal total demand torque is input into a target decision layer, and the vehicle motor torque, the slope compensation torque and the yaw moment are obtained through prediction of the target decision layer. Furthermore, the target torque is predicted by the vehicle motor torque, the slope compensation torque and the yaw moment input torque distribution layer, and the predicted target torque is applied to the torque control of the in-wheel motor driven vehicle, so that the vehicle stability is ensured, and the vehicle dynamic performance is improved.

In some embodiments of the present invention, the road surface influence factor is predicted by a road surface influence factor identification model, which includes but is not limited to the following steps:

and carrying out fuzzy processing according to the characteristic parameters to obtain the working condition type information.

In the embodiment, the obtained characteristic parameters are subjected to fuzzy processing to obtain corresponding working condition type information, and defuzzification processing is performed on the working condition type information to obtain the road surface influence factors. Specifically, in this embodiment, after obtaining the characteristic parameters including the rolling resistance difference, the air resistance normalization ratio, the ramp resistance normalization ratio, the road adhesion difference variance, and the minimum road adhesion coefficient, the characteristic parameters are subjected to the fuzzy processing by the membership function. In this embodiment, the rolling resistance difference and the road adhesion difference are divided into three fuzzy subsets, i.e., { S, M, B }, of small difference, medium difference and large difference. Correspondingly, the air resistance normalization proportion and the ramp resistance normalization proportion are blurred into three fuzzy subsets of a small proportion, a medium proportion and a large proportion, namely { S, M, B }; and dividing the minimum road adhesion coefficient estimated by the tire into three fuzzy subsets, namely { S, M, B } of empty wheels, medium-low adhesion and high adhesion. The working condition type information obtained in this embodiment includes a strong driving intention working condition, a relatively unstable working condition, a volatile stable working condition, and a relatively unstable working condition, which are respectively described as { B, M, S, N }. The fuzzy rule of this embodiment is shown in table 1 below, where "|" represents "or".

TABLE 1

Further, the embodiment defuzzifies the obtained working condition type information, so as to obtain the road surface influence factor κ. It should be noted that the road surface influence factor κ is only for complicated off-road conditions, and a smaller κ indicates a worse road surface condition, and the requirement for vehicle stability control is higher. Conversely, a larger κ indicates a better road surface condition, and requires higher vehicle dynamics and responsiveness. When the rolling resistance difference of the whole vehicle is large, the vehicle can be judged to be in a slip unstable state, the general requirement kappa is smaller at the moment, the vehicle is mainly controlled by stability, and the low-adhesion ground or the distorted undulating terrain can be distinguished by combining the road adhesion difference and the minimum road adhesion coefficient. If the road surface adhesion difference is large and the minimum road surface adhesion coefficient is an empty wheel, the situation that the four wheels have two states of the empty wheel and the wheel on the high-adhesion ground is shown, the current terrain is a distorted and fluctuated terrain, the maneuvering performance of the vehicle needs to be improved at the moment, the kappa value needs to be large, meanwhile, the kappa value is controlled within a proper range by combining the air resistance ratio normalization and the ramp resistance ratio normalization, and the requirements on power responsiveness and stability can be balanced.

In some embodiments of the present invention, the obtained actual vehicle speed, the expected vehicle speed and the road surface influence factor are input into the top layer, and the longitudinal total torque demand of the vehicle is predicted, including but not limited to the following steps:

and obtaining the expected acceleration through model prediction control according to the actual vehicle speed, the expected vehicle speed and the road surface influence factor.

And predicting the total longitudinal demand torque through a longitudinal dynamic model according to the expected acceleration.

In this embodiment, the expected acceleration of the vehicle is obtained through model predictive control according to the obtained actual vehicle speed, the expected vehicle speed and the road surface influence factor of the vehicle. And then predicting the total longitudinal demand torque through a longitudinal dynamic model according to the expected acceleration. Specifically, in the present embodiment, according to the deviation between the actual vehicle speed and the expected vehicle speed, the road surface influence factor is used as an adjustment parameter, the acceleration urgency degree is determined according to the road surface characteristics and the vehicle state, and the expected acceleration is obtained through model predictive control. And then predicting to obtain the longitudinal demand total moment through a longitudinal dynamic model. Illustratively, the present embodiment approximately represents the longitudinal motion characteristic of the whole vehicle through first-order inertia mitigation, as shown in the following formula (22):

wherein a represents the actual acceleration of the vehicle, a _ref Indicating a desired acceleration, τ, of the vehicle _s Representing a time constant.

Meanwhile, in the present embodiment, the vehicle speed v and the acceleration a are selected as the state variables, and the state equation can be expressed as shown in the following equation (23):

further, the above formula (23) can be written as shown in the following formula (24):

wherein the relevant parameters in the formula (24) are shown as the following formulas (25) to (28):

u＝a _ref (28)

in the above equations (24) to (28), x represents a system state variable, and u represents a control amount of the system.

Further, the present embodiment discretizes the continuous state equation of the above equation (24) by the euler method, as shown in the following equation (29):

wherein in the formula

B＝TB,C＝[1 0]. k denotes the current time of the system, k +1 denotes the next time of the system, I denotes the identity matrix, and T denotes the system sampling time.

Furthermore, in the embodiment, by introducing the road surface influence factor, the acceleration urgency degree is judged after fully considering the road surface characteristics and the vehicle state, and the response capability of the speed tracking controller and the vehicle stable state are balanced, so that the vehicle stability is enhanced while the tracking accuracy is ensured. Specifically, the objective function of the present embodiment is defined as shown in the following equation (30):

wherein k-1 represents the last time of the system, k represents the current sampling time, and H _P Denotes the prediction step size, H _C Denotes the step size, y _ref (k+i|k),i＝1,…,H _P Reference value u (k + i), i ═ 1, …, H indicating control output _c Indicating the control input at time k + i, y _p (k+i|k),i＝1,…,H _P A predicted value of the control output, Δ u (k + i), i ═ 1, …, H _c Representing the control input delta at time k + i and Q, R, S representing the output quantity, control delta, and control quantity weight matrix of the system. Kappa is a road surface influence factor, when the coefficient is large, the proportion of the third acceleration control quantity of the objective function is small, the response capability of the system is improved, and the system can quickly pass through the off-road surface; when the coefficient is smaller, the proportion of the acceleration control quantity is larger, and the stability of the system is improved. The second term of the objective function is the limit of the change rate of the acceleration, and the second term has the function of preventing the instability of the system under the off-road working condition due to overlarge impact after the system is controlled. Meanwhile, the embodiment ensures that the system has a feasible solution at any time by introducing the relaxation factor epsilon.

In actual conditions, the magnitude of the vehicle acceleration and the vehicle speed are limited, and the stability of a vehicle system needs to be considered, so that the solution of the problem is converted into a quadratic programming problem shown in the following formula (31):

wherein the above equation (31) satisfies the following equations (32) to (34), the following equation (32) is a constraint of acceleration, the following equation (33) is a constraint of acceleration change rate, and the following equation (34) is a constraint of vehicle speed:

u _min ≤u(k+i)≤u _max ,i＝0,1,…,H _c -1 (32)

Δu _min ≤Δu(k+i)≤Δu _max ,i＝0,1,…,H _c -1 (33)

y _min ≤y(k+i)≤y _max ,i＝0,1,…,H _c -1 (34)

in the embodiment, the quadratic programming problem of the above equation (31) is solved, so that the optimal control input increment of each control cycle is obtained

The first term of the optimal prediction is then taken as the input increment, resulting in the input as shown in equation (35) below:

u(k)＝u(k-1)+Δu _k ′ (35)

the embodiment can obtain the predicted vehicle speed v (k + i | k) and the expected acceleration a at the time k by the model prediction _req (k) Then, the vehicle running balance equation, namely the longitudinal dynamics equation, is integrated to obtain the following formula (36):

wherein, in the above formula (36)

Refers to the expected acceleration a of the vehicle _req From this, the total longitudinal desired drive torque, i.e. the longitudinal required total torque T, can be calculated _v As shown in the following formula (37):

in some embodiments of the invention, the decision-making layer of interest includes a drive antiskid control intervention or exit mechanism. Accordingly, the drive antiskid control intervention or exit mechanism includes, but is not limited to, the following steps:

and constructing a slip rate function according to the relation between the optimal slip rate and the slip rate of each wheel of the vehicle.

And calculating to obtain the longitudinal acceleration according to the slip rate function.

And constructing a triggering condition of the drive anti-skid control intervention or exit mechanism according to the second deviation value of the longitudinal acceleration and the equivalent acceleration.

In this embodiment, the embodiment first constructs a slip rate function according to the optimal slip rate and the slip rate relationship of each wheel of the vehicle, and then calculates the longitudinal acceleration of the vehicle according to the slip rate function. Further, a triggering condition of the driving anti-skid control intervention or exit mechanism is constructed according to the longitudinal acceleration and the second deviation value of the equivalent acceleration. Specifically, when the vehicle driving wheel slips, the present embodiment immediately activates asr (acceleration Slip regulation) by the set determination mechanism, and adjusts the motor torque to ensure the vehicle stability. While the vehicle wheels are not slipping, the present embodiment can control ASR without intervening control, so that the vehicle can respond to the total required driving torque. Illustratively, each wheel slip rate should follow the optimal slip rate, as shown in equation (38) below:

then the above formula is differentiated to obtain the longitudinal acceleration a _x As shown in the following formula (39):

further, the present embodiment limits the slip rate s of each wheel by introducing a relaxation factor ε _i As shown in the following formula (40):

s _i ≤s _opt +ε (40)

then, the present embodiment defines the deviation between the equivalent acceleration of the vehicle and the acceleration in the longitudinal direction of the vehicle as an intervention or exit mechanism based on an Adaptive maximum torque determination method (AMTD), that is, a trigger condition for driving the anti-skid control intervention or exit mechanism, as shown in the following equation (41):

wherein, in the formula, delta represents a threshold value,

the present embodiment is a technique for performing filtering processing using a moving window filter having a fixed width in order to suppress the influence of signal noise on the calculation of the wheel angular acceleration. The filtered wheel angular acceleration is shown in equation (42) below, where M is the width of the sliding window:

in this embodiment, the control variable of the ASR is four-wheel torque, and in order to realize rapid recognition and timely response of the control variable, the maximum limit torque T should be added to the trigger condition for driving the anti-skid control intervention or exit mechanism _AMTD . The maximum torque to operate the wheel in the stable region can be derived by the following equation (43):

in the formula, F _xi For longitudinal drive force, ψ represents the maximum torque calibration parameter.

In this embodiment, in order to ensure timely response and smooth transition of the driving antiskid control intervention or exit mechanism, in the initial state, the vehicle is in a stable state, the ASR is in an exit state, and the AMTD flag (driving antiskid flag) is 0. When the AMTD triggering condition is satisfied, the state machine enters an ASR intervention state, and all triggering conditions continuously exceed n (calibration parameters) sampling periods to prevent accidental false triggering, otherwise, the state machine enters an ASR exit state. In order to realize quick response of the intervention exit condition, when the two conditions of the AMTD are simultaneously met, the state can be switched in time.

In some embodiments of the invention, the objective decision layer includes a drive slip controller, a hill longitudinal force compensator, and a yaw moment controller. Correspondingly, the longitudinal required total torque is input into a target decision layer, and the vehicle motor torque, the slope compensation torque and the yaw moment are predicted, wherein the method comprises the following steps of:

constructing a vehicle power model according to the longitudinal required total moment, the driving torque, the driving resistance and the wheel radius; and predicting the vehicle motor torque according to the vehicle power model.

Predicting to obtain the slope information of the current position of the vehicle according to the slope value; and predicting the slope compensation torque through the slope information.

Acquiring the current yaw velocity of the vehicle; calculating a first deviation value of the current yaw angular velocity and the ideal yaw angular velocity; and calculating to obtain the yaw moment through a PI feedback controller according to the first deviation value.

In this particular embodiment, the objective decision layer includes a drive slip controller, a ramp longitudinal force compensator, and a yaw moment controller. The driving anti-skid controller constructs a wheel power model through longitudinal required total torque, driving torque, running resistance and wheel radius, and obtains vehicle motor torque according to the prediction of the vehicle power model. Specifically, the present embodiment simplifies the seven-degree-of-freedom model, and only adopts the quarter vehicle model, resulting in the following equation (44):

wherein J represents the moment of inertia of the electric wheel, T represents the driving torque, m' represents 1/4 vehicle masses, and F _r Indicating the running resistance.

In this embodiment, the wheel slip λ is selected as the state variable x, and the following formula (45) is obtained:

meanwhile, if the driving torque T is selected as the input amount u, that is, if u is T, the following formula (46) is obtained:

accordingly, the sliding mode function of the driving antiskid controller of the present embodiment is constructed as shown in the following formula (47):

e＝λ-λ _opt (47)

wherein λ is the wheel slip ratio, λ _opt For optimal slip rate.

Then, the sliding mode surface constructed in this embodiment is shown as the following formula (48), where c is the integral coefficient:

s＝e+c∫edt (48)

further, the following formula (49) is obtained by deriving the formula (48):

the approach rate of this embodiment is obtained by replacing sgn(s) with saturation function sat(s) to obtain the following formula (50):

therefore, the drive slip control input u is obtained as shown in the following equation (51):

when the drive antiskid control intervention or exit mechanism is in the intervention state, the drive torque and force of each motor are respectively shown in the following formulas (52) and (53):

T _ASRi ＝u _i (52)

wherein i is 0,1,2,3 are respectively shown in the tableShowing left front, right front, left rear, right rear wheels, i _g Indicates a reduction ratio, T _ASRi Indicating the decision torque to drive the antiskid controller, F _ASRi Representing forces, u, generated at ASR decision moments _i To drive the antiskid control rate.

In addition, the present embodiment predicts the slope information of the current position of the vehicle based on the slope value. And then predicting the slope compensation torque through the slope information. Specifically, according to the slope value θ, the present embodiment obtains the information of the current slope where the vehicle is located, and calculates the power lost after the vehicle enters the slope, that is, the corresponding slope compensation torque T _r . The compensation torque is then ultimately distributed to the individual wheels, thereby ensuring that the vehicle can achieve the desired control effect on the grade. For example, the magnitude of the slope compensation torque of the present embodiment is designed to be the same as the resultant force of the rolling resistance and the slope resistance, and to be opposite to the direction thereof, so that the influence of the running resistance by the slope can be quickly cancelled by the torque compensation. Meanwhile, in order to prevent excessive torque decision of the vehicle under the longitudinal instability working condition and increase the driving safety uncertainty, the embodiment sets that the slope compensation torque is invalid when the driving slip mark is inserted. The slope compensation torque of the present embodiment is shown by the following equation (54):

in addition, it should be noted that, in some embodiments of the present invention, in order to implement the feedforward dynamic compensation torque and effectively reduce the wheel slip instability probability with smaller axle load, the embodiment also performs the compensation of the feedforward torque by the axle load redistribution method. Wherein, the calculation formula of the axle load of the front and rear axles is shown as the following formula (55):

wherein, F _zf Denotes front axle load, F _zr Denotes the rear axle load, Δ W _ax Indicates the amount of axle load transfer, Δ W, caused by the acceleration of the entire vehicle _θ Representing the amount of axle load transfer caused by the slope, a being the distance from the front axle to the center of mass, b being the distance from the rear axle to the center of mass, W _f For horizontal road front axle static load, W _r The dead load of the rear axle on the horizontal road surface. Therefore, the rear axle control coefficient ψ is calculated as shown in the following equation (56):

then, in the present embodiment, the torque distribution of each wheel is performed by the rear axle control coefficient ψ as shown in the following expression (57):

in addition, in the present embodiment, the seven-degree-of-freedom vehicle model is simplified to the two-degree-of-freedom vehicle model, and the yaw moment controller is constructed based on the two-degree-of-freedom vehicle model. Specifically, the lateral and yaw kinematics equations are shown in equation (58) below:

wherein, v in the formula _x 、v _y Is the longitudinal and transverse speed, gamma is the transverse yaw speed of the whole vehicle, beta is the side deviation angle of the mass center, F _yf And F _yr Is the lateral force of the front axle and the rear axle, alpha _f 、α _r Is the slip angle of the front and rear wheels, and δ is the front axle angle.

Meanwhile, the wheel side force calculation formula is shown as the following formula (59), wherein k is _f 、k _r For the cornering stiffness of the front and rear axles, the formula for calculating the tire cornering angle is shown by the following equation (60):

the linear two-degree-of-freedom kinetic equation can be obtained in a comprehensive way and is shown as the following formula (61), wherein m is the mass of the whole vehicle, and I _Z For moment of inertia about the Z axis:

further, the ideal yaw rate of the vehicle is as follows (62):

wherein, K is m (a/K) _r -b/k _f )/L ² The steering characteristics are characterized for the stability factor. The lateral acceleration of the vehicle under the tire limit condition needs to meet the condition of the following equation (63):

in this embodiment, because the centroid slip angle is small during longitudinal driving, the two latter terms on the right side of the formula (63) can be removed, the first term is dominant, and at the same time, 15% of influence coefficients are introduced, so as to ensure the safety of the whole vehicle, and the above formula can be rewritten as shown in (64):

in summary, in the present embodiment, the formula of the desired yaw rate under the complex off-road condition is satisfied as shown in the following formula (65):

further, the yaw moment controller of the present embodiment is configured to make the PI feedback control (proportional-integral feedback control) horizontalA pendulum moment decision algorithm is used for calculating the current yaw angular velocity gamma and an ideal value gamma _d Deviation e of _γ The desired vehicle additional yaw moment Δ M is calculated by the PI feedback controller as the control amount of the yaw controller _z As shown in the following formula (66):

wherein, K in the formula _P Is a proportionality coefficient, T _I Is the integration time constant.

In this embodiment, in order to avoid the measurement error of the steering wheel angle sensor and the operation of the non-yaw moment controller caused by a slight steering wheel angle during straight driving, the present embodiment performs sensitivity design on the feedback controller, sets a deviation threshold value Δ γ (a small normal number), and sets a deviation threshold value | e |, where _γ |>Δ γ, the controller starts, so e _γ Rewriting is as shown in the following formula (67):

in some embodiments of the present invention, vehicle motor torque, hill compensation torque, and yaw moment are input into the torque distribution layer and the target torque is predicted, including but not limited to the following steps:

and constructing a relation function of the longitudinal driving force of each wheel and the driving torque of each wheel according to the longitudinal driving force of each wheel and the driving torque of each wheel of the vehicle.

And optimizing by taking the total driving force as an optimization target according to the relation function to obtain a first target optimization function.

And optimizing by taking the adhesion margin of each wheel of the vehicle as an optimization target according to the relation function to obtain a second target optimization function.

And obtaining a hybrid optimization objective function according to the first objective optimization function and the second objective optimization function.

And constructing constraint conditions according to the vehicle motor torque, the slope compensation moment and the yaw moment.

In this embodiment, a relationship function between each wheel longitudinal driving force and each wheel driving torque is first constructed through each wheel longitudinal driving force and each wheel driving torque of the vehicle, and then the relationship function is optimized with the total driving force as an optimization target to obtain a first target optimization function. Further, the relation function of the longitudinal driving force of each wheel and the wheel driving torque is optimized by taking the adhesion margin of each wheel of the vehicle as an optimization target, and a second target optimization function is obtained. And then obtaining a mixed optimization objective function according to the first objective optimization function and the second objective optimization function. Meanwhile, the target torque is calculated by the constructed constraint condition and the hybrid optimization objective function. Specifically, the control target of the torque distribution layer in the present embodiment is as shown in the following equation (68):

at the same time, according to each wheel longitudinal driving force F _xi And the driving torque T _i The relationship function is constructed as follows (69):

further, the present embodiment optimizes the relationship function shown in the above equation (69) with the total driving force as the optimization target, and obtains the first target optimization function as shown in the following equation (70), and the total driving force F required from the top layer _vd And determining to ensure the longitudinal driving requirement.

minJ ₁ ＝(F _x -F _vd ) ² (70)

In this embodiment, the smaller the vehicle tire utilization rate is, the larger the tire stability margin is, and by taking the adhesion margin of each wheel of the vehicle as an optimization target, the longitudinal stability of the vehicle can be improved, and the problem of the excessively high tire utilization rate of a single wheel of the environment can be solved, and at the same time, the wheel slip risk can be effectively reduced, and the obtained second target optimization function is shown as the following formula (71):

since the lateral force of the vehicle is difficult to control and the longitudinal force is generally larger than the lateral force during longitudinal driving, the above formula (71) is simplified to the following formula (72) in order to optimize the control target:

further, the present embodiment constructs a weight coefficient with the road surface influence factor κ. When kappa is smaller, the stability margin of the whole vehicle is poorer, and the specific gravity with the tire utilization rate as an optimization target is constructed to be large so as to ensure the stability of the vehicle; when κ is large, in order to satisfy the driving force demand, the specific gravity is constructed with the total driving force demand as the control target. Therefore, the hybrid optimization objective function is obtained according to the first objective optimization function and the second objective optimization function as shown in the following formula (73):

minJ＝ε·S·J ₁ +(1-ε)·W·J ₂ (73)

wherein, epsilon is kappa, and kappa is a road surface influence factor. S and W are balance weight factors for balancing J ₁ ,J ₂ The set road surface influence factor has obvious effect.

Meanwhile, according to the embodiment, constraint conditions are constructed according to the vehicle motor torque, the slope compensation moment and the yaw moment so as to ensure the stable running of the vehicle. Further, in the embodiment, the hybrid optimization objective function is solved by combining the constraint condition, so as to obtain the target torque.

In some embodiments of the invention, the constraints include yaw force constraints, drive slip control constraints, motor characteristics constraints, and road attachment constraints. Accordingly, constraints are constructed, including but not limited to the following steps:

In the present specific embodiment, the constraint conditions constructed in the present embodiment include a yaw force constraint condition, a drive antiskid control constraint condition, a motor characteristic constraint condition, and a road attachment condition constraint condition. Specifically, under a complex and variable off-road condition, driving forces of wheels are different due to the difference of road surface adhesion of the wheels of the vehicle, and in addition, when control intervention such as antiskid driving is performed, the problem that driving torques of the wheels are inconsistent is also caused due to only adjustment of a single-wheel driving state, and unexpected yaw motion is easy to occur, so that the yaw moment needs to be subjected to relevant constraint, and a constructed yaw force constraint condition formula is shown as the following formula (74):

M _zd ＝-B _r (F _x1 +F _x3 )+B _l (F _x2 +F _x4 ) (74)

in this embodiment, when the driving anti-skid control intervenes, the vehicle has severe slip, if the longitudinal driving torque is greater than the driving anti-skid control torque, the wheels will keep in a flying state, resulting in a loss of driving torque, and the dynamic performance of the vehicle will not increase, and at the same time, the longitudinal and lateral stability of the whole vehicle will be deteriorated, so the embodiment limits the driving force after the driving anti-skid control intervenes, and the constructed driving anti-skid control constraint condition is as shown in the following formula (75):

in addition, since the output torque of the motor is influenced by the performance of the motor, the maximum output torque is limited, and the peak torque T of the motor is combined in the torque control process in the embodiment _p The constraint conditions of the motor characteristics are constructed as shown in the following formula (76):

meanwhile, the maximum longitudinal force of the wheel is constrained by the road adhesion condition, which is constructed in the present embodiment as shown in the following equation (77):

-μ _i F _zi ≤F _xi ≤μ _i F _zi (77)

according to the inequality constraint conditions, the lower limit LB of the constraint conditions _i And an upper constraint UB _i The calculation formulas are shown as the following formulas (78) and (79), respectively:

wherein, ATMD Flag in the formula represents a Flag bit for driving an antiskid control intervention or exit mechanism.

It should be noted that, in some embodiments of the present invention, in the process of calculating the target torque according to the hybrid optimization objective function and the constraint condition, the hybrid optimization objective function is first converted into a quadratic programming standard form, as shown in the following formula (80):

wherein, x ═ F _x1 F _x2 F _x3 F _x4 ] ^T ，Aeq＝[-B _l B _r -B _l B _r ]，beq＝[M _z ]，

LB＝[LB ₁ ,LB ₂ ,LB ₃ ,LB ₄ ] ^T ，UB＝[UB ₁ ,UB ₂ ,UB ₃ ,UB ₄ ] ^T ，

c＝[-2SεF _vd -2SεF _vd -2SεF _vd -2SεF _vd ] ^T ，

In the present embodiment, the quadratic programming problem shown in the above equation (80) is solved to obtain the target driving force F for each wheel _xi Then, the target torque T of each wheel can be obtained _i As shown in the following formula (81):

an embodiment of the present invention also provides an adaptive torque control system for an in-wheel motor driven vehicle, including:

the first model building module is used for building a road surface influence factor identification model according to the obtained characteristic parameters, wherein the characteristic parameters comprise rolling resistance difference, air resistance normalization proportion, ramp resistance normalization proportion, road surface adhesion difference variance and minimum road surface adhesion coefficient.

And the road surface influence factor prediction module is used for predicting to obtain the road surface influence factor through the road surface influence factor identification model.

And the second model building module is used for building the multi-target optimizing self-adaptive torque control model. The multi-target optimizing self-adaptive torque control model comprises a top layer, a target decision layer and a torque distribution layer.

And the longitudinal demand total moment prediction module is used for inputting the acquired actual vehicle speed, the acquired expected vehicle speed and the road surface influence factor into the top layer and predicting to obtain the longitudinal demand total moment of the vehicle.

And the target decision layer module is used for inputting the longitudinal required total moment into the target decision layer and predicting to obtain the vehicle motor torque, the slope compensation moment and the yaw moment.

And the torque distribution layer module is used for inputting the vehicle motor torque, the slope compensation torque and the yaw moment into a torque distribution layer and predicting to obtain the target torque.

And the torque control module is used for adjusting the torque amount of the vehicle driven by the wheel hub according to the target torque.

Referring to fig. 2, an embodiment of the present invention further provides an adaptive torque control system for an in-wheel motor driven vehicle, including:

at least one processor 210.

At least one memory 220 for storing at least one program.

When the at least one program is executed by the at least one processor 210, the at least one processor implements the adaptive torque control method for the in-wheel motor driven vehicle as described in the above embodiments.

An embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions for execution by one or more control processors, e.g., to perform the steps described in the above embodiments.

One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.

While the preferred embodiments of the present invention have been described, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and such equivalent modifications or substitutions are to be included within the scope of the present invention defined by the appended claims.

Claims

1. An adaptive torque control method for an in-wheel motor driven vehicle, characterized by comprising the steps of:

2. The adaptive torque control method for the in-wheel motor driven vehicle according to claim 1, wherein the predicting the road surface influence factor through the road surface influence factor identification model comprises:

3. The adaptive torque control method for the in-wheel motor driven vehicle according to claim 1, wherein the step of inputting the acquired actual vehicle speed, the acquired expected vehicle speed and the road surface influence factor into the top layer to predict the longitudinal total torque demand of the vehicle comprises the following steps:

4. The in-wheel motor driven vehicle adaptive torque control method according to claim 1, wherein the objective decision layer comprises a drive anti-skid control intervention or exit mechanism;

the drive antiskid control intervention or exit mechanism comprises:

calculating to obtain a longitudinal acceleration according to the slip rate function;

5. The in-wheel motor driven vehicle adaptive torque control method according to claim 4, wherein the objective decision layer further comprises a drive slip controller, a hill longitudinal force compensator, and a yaw moment controller;

6. The adaptive torque control method for an in-wheel motor driven vehicle according to claim 1, wherein the inputting the vehicle motor torque, the hill compensation torque, and the yaw moment into the torque distribution layer, predicting a target torque, comprises:

7. The in-wheel motor driven vehicle adaptive torque control method according to claim 6, wherein the constraints include yaw force constraints, driving anti-slip control constraints, motor characteristic constraints, and road attachment condition constraints;

the construction constraint condition comprises the following steps:

8. An adaptive torque control system for an in-wheel motor driven vehicle, comprising:

the road surface influence factor identification module is used for identifying the road surface influence factor according to the acquired characteristic parameters, wherein the characteristic parameters comprise rolling resistance difference, air resistance normalization proportion, ramp resistance normalization proportion, road surface adhesion difference variance and minimum road surface adhesion coefficient;

9. An adaptive torque control system for an in-wheel motor driven vehicle, comprising:

at least one processor;

at least one memory for storing at least one program;

when executed by the at least one processor, cause the at least one processor to implement the in-wheel motor driven vehicle adaptive torque control method of any one of claims 1 to 7.

10. A computer storage medium in which a processor-executable program is stored, wherein the processor-executable program, when executed by the processor, is for implementing the in-wheel motor driven vehicle adaptive torque control method according to any one of claims 1 to 7.