CN110539647A - four-wheel independent drive electric vehicle torque real-time optimization distribution control method facing straight line running working condition - Google Patents

Abstract

the invention relates to a torque real-time optimal distribution control method of a four-wheel independent drive electric vehicle facing a straight-line running working condition, which is used for solving the torque distribution problem of the four-wheel independent drive electric vehicle taking a hub motor as a power unit under the straight-line running working condition, preparing a corresponding online optimal distribution control algorithm, further compensating and correcting an online optimization result by using an offline acquired optimal distribution coefficient table, separating the design coupling of a torque distribution function and a whole vehicle controller and being beneficial to the implementation of control software modular design; the invention realizes the torque optimized distribution of the four-wheel independent drive electric automobile under the straight line running working condition, can effectively improve the energy efficiency of the power assembly on the premise of meeting the driving intention, and simultaneously ensures that the dynamic property and the braking stability of the automobile meet the design indexes.

Description

four-wheel independent drive electric vehicle torque real-time optimization distribution control method facing straight line running working condition

Technical Field

the invention discloses a torque real-time optimal distribution control method for a four-wheel independent drive electric vehicle facing a straight line running working condition, belongs to the field of design and manufacture of new energy vehicles, and particularly relates to a torque distribution control technology for the four-wheel independent drive electric vehicle.

background

The birth of the automobile is only hundreds of years till now, but the development process of the human society is deeply influenced by the consumed petroleum resources and the caused traffic pollution. With the continuous consumption of petroleum resources, energy safety has become a serious challenge that must be faced by survival and development in many countries including china; meanwhile, the urgent need for energy conservation and emission reduction and the increasingly stringent emission regulations in human society have led automobile manufacturers to consciously recognize the necessity of developing electric automobiles.

The four-wheel independent drive electric automobile taking the hub motor as a power unit becomes one of the future electric automobile development directions acknowledged in the industry by virtue of the simplified chassis structure, quick torque response and accurate control execution; the free distribution of torque among four wheels endows the automobile with more flexible performance space due to the structural advantage of the drive-by-wire control, the straight-line running is the most frequently encountered working condition in the use of the automobile, and the torque distribution strategy aiming at the working condition can obviously influence the economy, the dynamic property and the braking property of the automobile.

under the relatively limited energy density of the battery, the endurance mileage becomes an important obstacle for preventing the electric vehicle from occupying the mainstream market, so that the torque distribution strategy of the four-wheel independent drive electric vehicle taking energy conservation as the target is widely concerned. However, in practical engineering application, it is extremely difficult to obtain the road adhesion coefficient in real time, which may cause that the existing torque distribution control method is difficult to implement or that a certain axle is locked or slipped too early due to the absence of necessary constraint information, thereby affecting the dynamic performance and braking performance of the vehicle; meanwhile, the online numerical solution of the optimization problem is not beneficial to guaranteeing the real-time performance of the automobile control. Therefore, a real-time torque optimal distribution method for the four-wheel independent drive electric vehicle under the condition of lacking of road adhesion information needs to be established.

disclosure of Invention

The invention provides a torque real-time optimization distribution control method for a four-wheel independent drive electric vehicle facing a straight line running working condition, which can implement optimized torque distribution control on the premise of meeting driving intentions by calibrating control parameters with clear physical meanings and small quantity, effectively improve the energy efficiency of a power assembly of the four-wheel independent drive electric vehicle, and simultaneously ensure that the dynamic property and the braking stability of the vehicle meet design indexes.

The technical scheme adopted by the invention for solving the technical problems is as follows:

A torque real-time optimal distribution control method for a four-wheel independent drive electric vehicle facing a straight line running working condition comprises the following steps:

the first step is as follows: acquiring real-time required torque and real-time speed of the electric automobile through a vehicle control unit of the electric automobile;

the second step is that: judging a rotating speed interval where the rotating speed of the motor corresponding to the acquired real-time vehicle speed is located, wherein a minimum interval is taken, and forming a rotating speed vector by the endpoint of the minimum interval;

the third step: judging the real-time state of the electric automobile according to the acquired real-time required torque, wherein when the real-time required torque is zero, the electric automobile is judged to be in a zero output mode, when the real-time required torque is larger than zero, the electric automobile is judged to be in a driving mode, and when the real-time required torque is smaller than zero, the electric automobile is judged to be in a braking mode;

the fourth step: acquiring an optimized distribution coefficient vector corresponding to the rotating speed vector and a matched vehicle speed vector according to the judged real-time state of the electric vehicle;

The fifth step: calculating a real-time optimal distribution coefficient corresponding to the real-time vehicle speed by utilizing linear interpolation according to the optimal distribution coefficient vector obtained in the fourth step and the matched vehicle speed vector;

and a sixth step: modifying the real-time optimal distribution coefficient obtained in the fifth step by using an energy optimal torque distribution coefficient table manufactured off-line, and obtaining the optimal torque distribution coefficient under the real-time vehicle speed through a preferred decision function;

The seventh step: transmitting the optimal torque distribution coefficient obtained in the sixth step to a torque output calculation function to obtain a final torque output instruction, and then respectively sending the obtained final torque output instruction to a motor controller of a hub motor corresponding to the electric automobile;

as a further preferred aspect of the present invention,

defining the real-time required torque of the electric automobile as Td, the real-time speed as Vx, and the rotating speed vector composed of endpoints in a minimum interval as [ n1, n2], wherein the subscript is t to indicate that the electric automobile is in a driving mode, the subscript is g to indicate that the electric automobile is in a braking mode, and the subscript t/g indicates the driving mode or the braking mode;

when the automobile is in a driving mode, acquiring upper and lower distribution coefficient limits Kmin, t and Kmax, t according to a driving constraint calculation function, and then solving an optimized distribution coefficient vector [ Kt,1, Kt,2] corresponding to a rotating speed vector [ n1, n2] and matched vehicle speed vectors [ Vx1, Vx2] according to an adhesion-energy combined optimization function, wherein the unit of ni is rpm, and the subscript i is 1 or 2;

when the automobile is in a braking mode, obtaining upper and lower distribution coefficient limits Kmin, g and Kmax, g according to a braking constraint calculation function, and then obtaining an optimized distribution coefficient vector [ Kg,1, Kg,2] and a vehicle speed vector [ Vx1, Vx2] corresponding to a rotating speed vector [ n1, n2] according to an adhesion-energy combined optimization function, wherein ni represents the rotating speed of the motor, the unit of ni is rpm, and the subscript i is 1 or 2;

as a further preferable aspect of the present invention, when the automobile is in the driving mode, the upper and lower distribution coefficient limits Kmin, t and Kmax, t are obtained based on a driving constraint calculation function formed by the formula (2),

tf, max (ni) and Tr, max (ni) are maximum torques determined by the motor characteristics, are obtained by an external characteristic curve of the motor, and ni represents the rotating speed of the motor; for the hub motors with the motor controllers capable of feeding back the maximum torque signals, Tf, max (ni) and Tr, max (ni) can also be directly obtained from the feedback signals, and when the maximum torque signals fed back by the left and right hub motors on the same axis are inconsistent, a smaller value is taken for calculation;

When the automobile is in a braking mode, obtaining upper and lower distribution coefficient limits Kmin, g and Kmax, g according to a braking constraint calculation function, wherein the braking constraint calculation function is formed by formulas (3) and (4),

wherein Lr is the distance from the center of mass of the whole vehicle to the rear axle, hg is the height of the center of mass, L is the distance between the vehicle and the axle, M is the mass of the whole vehicle, G is the acceleration of gravity, G is the gravity of the whole vehicle, Rw is the rolling radius of the wheels, Tf, min (ni) and Tr, min (ni) are the algebraic minimum torque determined by the characteristics of the motor, wherein the specified regenerative braking is negative torque, which is obtained by the external characteristic curve of the motor, ni represents the rotating speed of the motor, and the rotating speeds are considered to be approximately equal under the condition of straight-line driving because the front wheel and the rear wheel adopt the same hub motor;

for the hub motors with the motor controllers capable of feeding back the minimum torque signals, Tf, min (ni) and Tr, min (ni) can also be directly obtained from the feedback signals, and when the maximum torque signals fed back by the left and right hub motors on the same axis are inconsistent, a larger value is taken for calculation;

as a further preferred embodiment of the present invention, the analytical form of the optimal solution of the optimization indicator function consumed in the adhesion-energy joint optimization function comprises the steps of,

Step 4.1: the optimization index function is shown in equation (5),

Wherein λ 1 and λ 2 respectively represent an optimization weight of road surface adhesion utilization and an optimization weight of motor energy consumption, Fzf and Fzr respectively represent loads of a front axle and a rear axle of a vehicle, Σ Phub represents a sum of power consumption of four hub motors, Tfl and Tfr respectively represent left and right wheel output torques of the front axle, Trl and Trr respectively represent left and right wheel output torques of the rear axle, Td represents a real-time required torque, Ti represents an output torque of each wheel, subscript i represents fl, fr, rl and rr, Rw represents a rolling radius of the wheel, and JEAA is an optimization index function;

Step 4.2: the power loss characteristic field obtained by the motor bench test is calculated by adopting a formula (6) to obtain a motor power consumption characteristic field,

wherein T represents the motor torque measured by the test and the unit is N m; n represents the motor rotating speed measured by the test, and the unit is revolutions per minute, namely rpm; ploss is the power loss measured for each group (T, n), obtained by subtracting the mechanical power output at the wheel end from the input direct current power, in kw; phub represents the electric power consumption of the hub motor, the unit is kw, positive torque represents driving, negative torque represents braking, positive power represents the power consumed by the motor from the battery, negative power represents the power fed back from the motor to the battery, and test rotating speed data in a characteristic field is made into a motor rotating speed working condition point data table according to a test result;

and 4.3, step: according to two modes of braking and driving, the relationship of the power consumption of the motor with the change of the torque under different rotating speeds is respectively fitted by adopting a cubic polynomial mode, the fitting formula is shown as (7), wherein a, b, c and d are parameters to be fitted and correspond to the rotating speed working condition point data table manufactured in the step (4.2)

P＝a(n)T+b(n)T+c(n)T+d(n) (7)

Step 4.4: since the left and right wheel torque outputs should be the same in the straight running condition, the front axle left and right wheel output torque Tfl-Tfr-Tf and the rear axle left and right wheel output torque Trl-Trr-Tr are specified, and the specific expression is shown in (8).

the proportion of the output torque of the front wheels accounting for 50% of the required torque is defined as K, the proportion is the distribution coefficient of the torque of the front axle and the rear axle, and Td is the real-time required torque;

step 4.5: substituting the formulas (6), (7) and (8) into the formula (5), obtaining an analytical expression of a quadratic polynomial of the optimization problem about K, wherein the analytical expression is shown as (9), and coefficients are shown as formulas (10) - (12);

J(K)＝τK-τK+ε (9)

td is a real-time required torque, lambda 1 and lambda 2 respectively represent an optimization weight for road adhesion utilization and an optimization weight for motor energy consumption, Fzf and Fzr respectively represent loads of a front axle and a rear axle of a vehicle, a, b, c and d are parameters to be fitted, Rw is a rolling radius of a wheel, and JEAA represents an optimization index function;

step 4.6: the first order partial derivative about K is calculated for jeaa (K) in equation (9) and is made equal to zero, the extreme point of the function can be obtained, and then the second order partial derivative about K is calculated for the function, so as to obtain the monotonicity information of the function, and the results of the extreme point and the second order partial derivative are shown in equations (13) and (14).

the second-order partial derivative about K is calculated for the optimization index function JEAA, and Kex represents a possible extreme point of the JEAA function;

step 4.7: according to the principle of minimum value, the global minimum value point of the optimization problem can be obtained by a classification discussion mode, and the specific method is as follows,

4.7.1, when τ 1<0, judge that Kex represents the distance between one possible extreme point of the JEAA function and the partition coefficient boundary,

If Kex is more than or equal to Kmax and t/g, making Kt/g, i equal to Kmin and t/g;

if Kex is less than or equal to Kmin and t/g, let Kt/g, i be Kmax and t/g;

if Kmin, t/g < Kex < Kmax, t/g, let Kt/g, i ═ argmax | κ -Kex | (κ ∈ { Kmin, t/g, Kmax, t/g });

4.7.2, when τ 1>0, judging whether Kex is in the feasible region, if Kex > Kmax, t/g, making Kt/g, i ═ Kmax, t/g; if Kex is less than Kmin, t/g, let Kt/g, i equal to Kmin, t/g; otherwise, let Kt/g, i ═ Kex.

4.7.3, when τ 1 is 0, judging the sign of τ 2, if τ 2 is greater than 0, let Kt/g, i be Kmax, t/g; otherwise, let Kt/g, i ═ Kmin, t/g;

wherein Kt/g, i represents that the optimization index function (5) in the 4.1 step obtains a value K of a global minimum value under a driving mode or a braking mode, a subscript i is 1 or 2, Kmin, t and Kmax are upper and lower limits of a distribution coefficient obtained according to a driving constraint calculation function when the automobile is in the driving mode, and Kmin, g and Kmax, g are upper and lower limits of the distribution coefficient obtained according to a braking constraint calculation function when the automobile is in the braking mode;

As a further preferred aspect of the present invention, the step of obtaining the optimized distribution coefficient vector [ Kt/g,1, Kt/g,2] corresponding to the rotation speed vector [ n1, n2] and the matched vehicle speed vector [ Vx1, Vx2] by an analytical method of the optimized solution of the optimization index function includes:

step 5.1: the rotating speed vector formed by the endpoints of the minimum interval is [ n1, n2 ];

step 5.2: judging whether the input data meets the solving requirement of the optimization problem,

If the motor is in the driving mode and the required torque exceeds the total peak torque of the motor, correcting the required torque into the sum of all the peak torques, and then solving an optimized distribution coefficient vector [ Kt,1, Kt,2] corresponding to the rotating speed n1 and n2 according to the optimized solving method in the step 4.7 and the fitting data in the step 4.3;

if the motor is in the braking mode, when the total required torque is smaller than the total regenerative braking torque which can be provided by the motor or a feasible region obtained by a braking constraint calculation function is an empty set, the total required torque is Kg, i is KI; otherwise, according to the optimization solving method in the step 4.7 and the fitting data in the step 4.3, solving a corresponding optimized distribution coefficient vector [ Kg,1, Kg,2 ];

step 5.3: converting the rotating speed vector [ n1, n2] into a matched vehicle speed vector [ Vx1, Vx2] according to a formula (15)

Wherein Rw is the rolling radius of the wheel, ni represents the rotating speed of the motor, subscript i is 1 or 2, n1, and n2 is obtained by the real-time vehicle speed; as a further preferable aspect of the present invention, in the fifth step, the calculation formula of the real-time optimal distribution coefficient corresponding to the real-time vehicle speed by using linear interpolation is as follows:

The Kt/g, i represents that under a driving mode or a braking mode, the optimization index function (5) in the step 4.1 obtains a K value of a global minimum value, a subscript i is 1 or 2, Vx is a real-time vehicle speed, Vxi is a matched vehicle speed vector, and the subscript i is 1 or 2;

as a further preferred aspect of the present invention, in the sixth step, the step of forming the energy-optimized torque distribution coefficient table created off-line includes the steps of:

step 7.1: the energy-optimal torque distribution coefficient calculation method is shown in equation (16),

pd (·) is a motor power calculation function and can be calculated by a linear interpolation method from a motor power consumption characteristic field in the step 4.2, a constrained calculation formula is the formulas (2) - (4), Vx is a real-time vehicle speed, Td is a real-time required torque of the electric vehicle, and K is a proportion of a front wheel output torque to 50% of the required torque;

and 7.2: respectively acquiring corresponding energy optimal distribution coefficients for the problems under the conditions of each vehicle speed Vx and required torque Td by utilizing a genetic algorithm optimizing function provided by a Matlab optimizing tool box, and manufacturing a two-dimensional numerical table about the vehicle speed and the required torque;

As a further preference of the present invention, the preferred decision function in the sixth step is formed by equation (17),

K＝arg min J(κ) (κ∈{K,K}) (17)

wherein, the function JEAA can be known in formula (5), but the motor power consumption part is directly obtained by interpolation calculation of a motor power consumption characteristic field; koff is obtained by a calculation method of an energy optimal torque distribution coefficient and is obtained by looking up a table according to the current required torque Td and the vehicle speed Vx; kt/g is obtained from the formula (1);

as a further preferable aspect of the present invention, in the seventh step, the torque output calculation function includes the steps of:

step 9.1, determining torque request commands of hub motors of front and rear axles according to a formula (18), wherein the output torques of the left and right wheels are the same due to the fact that the hub motors belong to the straight-line driving working condition

the Td is a real-time required torque of the electric vehicle, and since the torque outputs of the left and right wheels under the straight-line driving condition should be the same, the output torque of the left and right wheels of the front axle is specified to be Tfl ═ Tfr ═ Tf, and the output torque of the left and right wheels of the rear axle is specified to be Trl ═ Trr ═ Tr;

9.2, if the driving mode is adopted, the result is directly output;

if the motor is in the braking mode, further judging whether the torque output calculated by the formula (18) exceeds the regenerative braking torque boundary of the motor;

if the electric machine is unable to provide sufficient braking torque, the remaining torque is made up by friction braking according to the maximum regenerative torque output currently allowed;

if the motor can provide the torque calculated by the formula (18), the output calculation result is directly output.

through the technical scheme, compared with the prior art, the invention has the following beneficial effects:

1. according to the method, the real-time acquisition of the road surface adhesion information is not needed in the control process, the global optimal torque distribution result aiming at the proposed optimization index function can be acquired in the vehicle-mounted controller in real time, the energy efficiency of the power assembly of the four-wheel independent drive electric vehicle is effectively improved, and meanwhile, the dynamic performance and the braking stability of the vehicle are ensured to meet the design index;

2. according to the invention, by designing an optimization index function comprehensively considering the energy consumption of the power assembly and the road adhesion utilization rate, a feasible domain boundary calculation model of the distribution coefficient is established, which completely considers the external characteristics of the motor, the braking safety and the braking regulation limit, so that the dependence of a control algorithm on the real-time acquisition of the road adhesion coefficient can be effectively avoided;

3. the invention develops the online optimization allocation control algorithm by deducing the global optimal solution of the designed optimization problem, and solves the real-time puzzlement of online solution of the optimization problem.

drawings

the invention is further illustrated with reference to the following figures and examples.

FIG. 1 is a schematic diagram of the application of the preferred embodiment of the present invention;

FIG. 2 is a graph of the field versus external characteristic of the motor loss power characteristic of the preferred embodiment of the present invention;

FIG. 3 is a table of motor speed operating point data and corresponding motor coefficient fit table in accordance with a preferred embodiment of the present invention;

Fig. 4 is a flowchart of a control method of the preferred embodiment of the present invention.

Detailed Description

the present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.

the invention relates to a torque real-time optimal distribution control method for a four-wheel independent drive electric vehicle facing a straight line running working condition, which comprises the following steps of:

the first step is as follows: acquiring real-time required torque and real-time speed of the electric automobile through a vehicle control unit of the electric automobile;

the second step is that: judging a rotating speed interval where the rotating speed of the motor corresponding to the acquired real-time vehicle speed is located, wherein a minimum interval is taken, and forming a rotating speed vector by the endpoint of the minimum interval;

the third step: judging the real-time state of the electric automobile according to the acquired real-time required torque, wherein when the real-time required torque is zero, the electric automobile is judged to be in a zero output mode, when the real-time required torque is larger than zero, the electric automobile is judged to be in a driving mode, and when the real-time required torque is smaller than zero, the electric automobile is judged to be in a braking mode;

The fourth step: acquiring an optimized distribution coefficient vector corresponding to the rotating speed vector and a matched vehicle speed vector according to the judged real-time state of the electric vehicle;

the fifth step: calculating a real-time optimal distribution coefficient corresponding to the real-time vehicle speed by utilizing linear interpolation according to the optimal distribution coefficient vector obtained in the fourth step and the matched vehicle speed vector;

and a sixth step: modifying the real-time optimal distribution coefficient obtained in the fifth step by using an energy optimal torque distribution coefficient table manufactured off-line, and obtaining the optimal torque distribution coefficient under the real-time vehicle speed through a preferred decision function;

the seventh step: transmitting the optimal torque distribution coefficient obtained in the sixth step to a torque output calculation function to obtain a final torque output instruction, and then respectively sending the obtained final torque output instruction to a motor controller of a hub motor corresponding to the electric automobile;

the vehicle controller is commonly used in a centralized driving electric vehicle at present, and plays a role in analyzing the operation of a person on an accelerator pedal and a brake pedal into a real-time required torque and processing a signal of the vehicle, such as calculating a real-time vehicle speed. Because a general electric vehicle only has one motor, the obtained required torque can be directly sent to a motor controller, but for a four-wheel independent drive electric vehicle, because a plurality of motors exist, after a required torque signal analyzed by a vehicle controller is obtained, torque distribution needs to be further carried out by using the method provided by the invention, so that a torque request instruction for each in-wheel motor is generated;

based on the background, the application provides a control method for real-time optimized distribution of the torque of the four-wheel independent drive electric vehicle on the premise of a straight line running working condition;

example (b):

fig. 1 shows a specific form of the control method applied to a torque distribution control module, where the torque distribution control module is functionally connected to a vehicle controller, a motor controller of a hub motor, and a friction brake controller, and performs torque optimal distribution on a driver required torque Td analyzed by the vehicle controller according to current vehicle speed information Vx, and respectively sends the distributed front wheel motor torque Tf, rear wheel motor torque Tr, front wheel friction brake torque Tbf, and rear wheel friction brake torque Tbr to controllers of corresponding actuators, thereby implementing driving and braking control of an automobile.

it should be noted that, the so-called torque distribution module and the vehicle controller are functionally divided, and do not represent that related functions must be placed in two independent controllers, and the functions of the vehicle controller and the functions of the torque distribution control module can be integrated into one controller to operate under the permission of hardware resources.

Earlier stage work:

Defining the real-time required torque of the electric automobile as Td, the real-time speed as Vx,

performing bench test on the power loss characteristic of a hub motor used by the electric automobile by using a dynamometer to obtain a power loss characteristic field and an external characteristic curve of the motor, as shown in fig. 2; the positive torque represents driving, the negative torque represents regenerative braking, the positive power represents power consumed by the motor from the battery, and the negative power represents power fed back to the battery by the motor; the motor power loss is obtained by subtracting the mechanical power output by the wheel end from the input direct current power under different rotating speed and torque combinations; the external characteristic curves are the maximum torque and the minimum torque which can be output by the motor at different rotating speeds; defining subscript t to indicate that the electric vehicle is in a driving mode, subscript g to indicate that the electric vehicle is in a braking mode (i.e., regenerative braking mode), subscript t/g to indicate either the driving mode or the braking mode;

FIG. 4 shows the specific implementation steps of the preferred embodiment:

the first step is as follows: acquiring a current vehicle demand torque Td and a real-time vehicle speed Vx by a vehicle controller;

the second step is that: judging a rotating speed interval (taking a minimum interval) where the rotating speed of the motor corresponding to the real-time vehicle speed is located by utilizing a motor rotating speed working condition point data table obtained by a hub motor bench test, forming a rotating speed vector [ n1, n2] by using the minimum interval endpoint,

the motor rotating speed working condition point data table is obtained in the following mode:

the power loss characteristic field obtained by the wheel hub motor bench test is calculated by adopting a formula (6) to obtain a motor power consumption characteristic field,

Wherein T represents the motor torque measured by the test and the unit is N m; n represents the motor rotating speed measured by the test, and the unit is revolutions per minute, namely rpm; ploss is the power loss measured for each group (T, n), obtained by subtracting the mechanical power output at the wheel end from the input direct current power, in kw; phub represents the electric power consumption of the hub motor, the unit is kw, and according to the test result, the test rotating speed data in the characteristic field is made into a motor rotating speed working condition point data table;

The third step: judging the real-time state of the electric automobile according to the acquired real-time required torque, wherein when the real-time required torque is zero, the electric automobile is judged to be in a zero output mode, when the real-time required torque is larger than zero, the electric automobile is judged to be in a driving mode, and when the real-time required torque is smaller than zero, the electric automobile is judged to be in a braking mode;

the fourth step: obtaining an optimized distribution coefficient vector corresponding to the rotating speed vector and a matched vehicle speed vector according to the judged real-time state of the electric vehicle, and comprising the following steps of:

the rotating speed vector formed by the endpoints of the minimum interval is [ n1, n2 ];

judging whether the input data meets the solving requirement of the optimization problem,

if the motor is in the driving mode and the required torque exceeds the total peak torque of the motor, correcting the required torque into the sum of all the peak torques, and then solving an optimized distribution coefficient vector [ Kt,1, Kt,2] corresponding to the rotating speeds n1 and n2 according to an optimized solving method and fitting data;

if the motor is in the braking mode, when the total required torque is smaller than the total regenerative braking torque which can be provided by the motor or a feasible region obtained by a braking constraint calculation function is an empty set, the total required torque is Kg, i is KI; otherwise, according to the optimization solving method and the fitting data, solving a corresponding optimized distribution coefficient vector [ Kg,1, Kg,2 ];

converting the rotating speed vector [ n1, n2] into a matched vehicle speed vector [ Vx1, Vx2] according to a formula (15)

wherein Rw is the rolling radius of the wheel, ni represents the rotating speed of the motor, subscript i is 1 or 2, n1, and n2 is obtained by the real-time vehicle speed;

Specifically, the method comprises the following steps: defining the distribution coefficient of the front and rear axle torques as K,

when the vehicle is in a zero output mode, the output of all the motors is zero;

when the automobile is in a driving mode, acquiring upper and lower distribution coefficient limits Kmin, t and Kmax, t according to a driving constraint calculation function, and then solving an optimized distribution coefficient vector [ Kt,1, Kt,2] corresponding to a rotating speed vector [ n1, n2] and matched vehicle speed vectors [ Vx1, Vx2] according to an adhesion-energy combined optimization function, wherein the unit of ni is rpm, and the subscript i is 1 or 2;

When the automobile is in a braking mode, obtaining upper and lower distribution coefficient limits Kmin, g and Kmax, g according to a braking constraint calculation function, and then obtaining an optimized distribution coefficient vector [ Kg,1, Kg,2] and a vehicle speed vector [ Vx1, Vx2] corresponding to a rotating speed vector [ n1, n2] according to an adhesion-energy combined optimization function, wherein ni represents the rotating speed of the motor, the unit of ni is rpm, and the subscript i is 1 or 2;

the more detailed description is: when the automobile is in a driving mode, acquiring upper and lower distribution coefficient limits Kmin, t and Kmax, t according to a driving constraint calculation function, wherein the driving constraint calculation function is formed by a formula (2),

tf, max (ni) and Tr, max (ni) are maximum torques determined by the motor characteristics, are obtained by an external characteristic curve of the motor, and ni represents the rotating speed of the motor; for the hub motors with the motor controllers capable of feeding back the maximum torque signals, Tf, max (ni) and Tr, max (ni) can also be directly obtained from the feedback signals, and when the maximum torque signals fed back by the left and right hub motors on the same axis are inconsistent, a smaller value is taken for calculation;

When the automobile is in a braking mode, obtaining upper and lower distribution coefficient limits Kmin, g and Kmax, g according to a braking constraint calculation function, wherein the braking constraint calculation function is formed by formulas (3) and (4),

wherein Lr is the distance from the center of mass of the whole vehicle to the rear axle, hg is the height of the center of mass, L is the distance between the vehicle axles, M is the mass of the whole vehicle, G is the acceleration of gravity, G is the gravity of the whole vehicle, Rw is the rolling radius of the wheels, and KI in the formula (3) is derived from the curve of the vehicle brake I, so that the aim of preventing the rear wheels from locking before the front wheels in the braking process is fulfilled, and the braking stability is guaranteed; the KECE is obtained by deducing the braking strength requirement proposed by an ECE braking law, and aims to avoid the problem that the braking distance is prolonged due to the premature locking of a front shaft in the braking process;

tf, min (ni) and Tr, min (ni) are algebraic minimum torques determined by the motor characteristics, wherein the specified regenerative braking is negative torque and is obtained by a motor external characteristic curve, ni represents the motor rotating speed, and the rotating speeds are considered to be approximately equal under the straight-line running working condition because the front wheel and the rear wheel adopt the same wheel hub motor;

for the hub motors with the motor controllers capable of feeding back the minimum torque signals, Tf, min (ni) and Tr, min (ni) can also be directly obtained from the feedback signals, and when the maximum torque signals fed back by the left and right hub motors on the same axis are inconsistent, a larger value is taken for calculation;

in order to avoid the defect that the prior art only takes energy conservation as an optimization target and ignores the change of the road adhesion coefficient or takes the road adhesion coefficient as a known condition, an optimization index function which considers the road adhesion utilization optimization and the energy consumption of the electric drive assembly at the same time as shown in a formula (5) is established;

the analytical form of the optimal solution of the optimization index function consumed in the attachment-energy joint optimization function comprises the following steps,

The optimization index function is shown in equation (5),

wherein λ 1 and λ 2 respectively represent an optimization weight of road surface adhesion utilization and an optimization weight of motor energy consumption (which can be calibrated through a vehicle road test in practical application), Fzf and Fzr respectively represent loads of a front axle and a rear axle of a vehicle, Σ Phub represents a sum of consumed power of four in-wheel motors, Tfl and Tfr are respectively front axle left and right wheel output torques, Trl and Trr are respectively rear axle left and right wheel output torques, Td is a real-time required torque, Ti is an output torque of each wheel, subscript i represents fl, fr, rl, rr, Rw is a wheel rolling radius, and JEAA is an optimization index function;

since the left and right wheel torque outputs should be the same in the straight running condition, the front axle left and right wheel output torque Tfl-Tfr-Tf and the rear axle left and right wheel output torque Trl-Trr-Tr are specified, and the specific expression is shown in (8).

the proportion of the output torque of the front wheels accounting for 50% of the required torque is defined as K, the proportion is the distribution coefficient of the torque of the front axle and the rear axle, and Td is the real-time required torque;

According to two modes of braking and driving, the relationship of the power consumption of the motor with the change of the torque under different rotating speeds is respectively fitted by adopting a cubic polynomial mode, the fitting formula is shown as (7), wherein a, b, c and d are parameters to be fitted and correspond to the rotating speed working condition point data table manufactured in the second step

P＝a(n)T+b(n)T+c(n)T+d(n) (7)

The fitting coefficient is made into a motor fitting coefficient table corresponding to the electrode rotating speed working condition data table as shown in fig. 3, and the parameters are obtained through the fitting process described in the step. The parameters are different when being fitted at different rotating speeds, so that (n) is added in the formula (7) to indicate that the parameters are related to the rotating speed;

the global optimal analytic solution of the optimization problem is established, the real-time defect caused by the prior art that the optimization problem is solved by mostly depending on-line numerical iteration is avoided,

substituting the formulas (6), (7) and (8) into the formula (5), obtaining an analytical expression of a quadratic polynomial of the optimization problem about K, wherein the analytical expression is shown as (9), and coefficients are shown as formulas (10) - (12);

J(K)＝τK-τK+ε (9)

td is a real-time required torque, lambda 1 and lambda 2 respectively represent an optimization weight for road adhesion utilization and an optimization weight for motor energy consumption, Fzf and Fzr respectively represent loads of a front axle and a rear axle of a vehicle, a, b, c and d are parameters to be fitted, Rw is a rolling radius of a wheel, and JEAA represents an optimization index function;

the first order partial derivative about K is calculated for jeaa (K) in equation (9) and is made equal to zero, the extreme point of the function can be obtained, and then the second order partial derivative about K is calculated for the function, so as to obtain the monotonicity information of the function, and the results of the extreme point and the second order partial derivative are shown in equations (13) and (14).

the second-order partial derivative about K is calculated for the optimization index function JEAA, and Kex represents a possible extreme point of the JEAA function;

according to the principle of minimum value, the global minimum value point of the optimization problem can be obtained by a classification discussion mode, and the specific method is as follows,

When τ 1<0, it is judged that Kex represents the distance between one possible extreme point of the JEAA function and the partition coefficient boundary,

if Kex is more than or equal to Kmax and t/g, making Kt/g, i equal to Kmin and t/g;

if Kex is less than or equal to Kmin and t/g, let Kt/g, i be Kmax and t/g;

if Kmin, t/g < Kex < Kmax, t/g, let Kt/g, i ═ argmax | κ -Kex | (κ ∈ { Kmin, t/g, Kmax, t/g });

when tau 1 is greater than 0, judging whether Kex is in the feasible region, and if Kex is greater than Kmax, t/g, making Kt/g, i be Kmax, t/g; if Kex is less than Kmin, t/g, let Kt/g, i equal to Kmin, t/g; otherwise, let Kt/g, i ═ Kex.

when τ 1 is equal to 0, the sign of τ 2 is judged, and if τ 2 is greater than 0, Kt/g, i is equal to Kmax, t/g; otherwise, let Kt/g, i ═ Kmin, t/g;

the optimization index function (5) obtains a K value of a global minimum value under a driving mode or a braking mode, wherein Kt/g, i represents that a subscript i is 1 or 2, Kmin, t and Kmax are obtained according to a driving constraint calculation function when an automobile is in the driving mode, and Kmin, g and Kmax, g are obtained according to a braking constraint calculation function when the automobile is in the braking mode;

according to the analytical form of the optimal solution of the optimization index function, the optimal distribution coefficient vector [ Kt/g,1, Kt/g,2] corresponding to the rotating speed vector [ n1, n2] and the matched vehicle speed vector [ Vx1, Vx2] are solved; the fifth step: the calculation formula for calculating the real-time optimal distribution coefficient corresponding to the real-time vehicle speed by utilizing linear interpolation is as follows:

and Kt/g, i represents that the optimization index function (5) in the step 4.1 obtains a K value of a global minimum value under a driving mode or a braking mode, subscript i is 1 or 2, Vx is a real-time vehicle speed, Vxi is a matched vehicle speed vector, and subscript i is 1 or 2.

and a sixth step: modifying the real-time optimal distribution coefficient obtained in the fifth step by using an energy optimal torque distribution coefficient table manufactured off-line, and obtaining the optimal torque distribution coefficient under the real-time vehicle speed through a preferred decision function;

the step of forming the off-line manufactured energy optimal torque distribution coefficient table comprises the following steps:

the energy-optimal torque distribution coefficient calculation method is shown in equation (16),

pd (·) is a motor power calculation function and can be calculated by a linear interpolation method from a motor power consumption characteristic field in the step 4.2, a constrained calculation formula is the formulas (2) - (4), Vx is a real-time vehicle speed, Td is a real-time required torque of the electric vehicle, and K is a proportion of a front wheel output torque to 50% of the required torque;

respectively acquiring corresponding energy optimal distribution coefficients for the problems under the conditions of each vehicle speed Vx and required torque Td by utilizing a genetic algorithm optimizing function provided by a Matlab optimizing tool box, and manufacturing a two-dimensional numerical table about the vehicle speed and the required torque;

preferably the decision function is formed by equation (17),

K＝arg min J(κ)(κ∈{K,K}) (17)

wherein, the function JEAA can be known in formula (5), but the motor power consumption part is directly obtained by interpolation calculation of a motor power consumption characteristic field; koff is obtained by a calculation method of an energy optimal torque distribution coefficient and is obtained by looking up a table according to the current required torque Td and the vehicle speed Vx; kt/g is obtained from the formula (1);

The seventh step: transmitting the optimal torque distribution coefficient obtained in the sixth step to a torque output calculation function, obtaining a final torque output command, and then sending the obtained final torque output command to a motor controller of at least one in-wheel motor of the electric vehicle, wherein the torque output calculation function comprises the following steps:

determining torque request commands of hub motors of front and rear axles according to a formula (18), wherein the output torques of left and right wheels are the same due to the fact that the hub motors belong to the straight-line driving working condition

the Td is a real-time required torque of the electric vehicle, and since the torque outputs of the left and right wheels under the straight-line driving condition should be the same, the output torque of the left and right wheels of the front axle is specified to be Tfl ═ Tfr ═ Tf, and the output torque of the left and right wheels of the rear axle is specified to be Trl ═ Trr ═ Tr;

If the driver is in the driving mode, directly outputting the result;

if the motor is in the braking mode, further judging whether the torque output calculated by the formula (18) exceeds the regenerative braking torque boundary of the motor;

if the electric machine is unable to provide sufficient braking torque, the remaining torque is made up by friction braking according to the maximum regenerative torque output currently allowed;

if the motor can provide the torque calculated by the formula (18), the output calculation result is directly output.

it will be understood by those skilled in the art that, unless otherwise defined, all terms (including 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. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The meaning of "and/or" as used herein is intended to include both the individual components or both.

the term "connected" as used herein may mean either a direct connection between components or an indirect connection between components via other components.

in light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (9)

1. a four-wheel independent drive electric vehicle torque real-time optimization distribution control method facing a straight line running working condition is characterized in that: the method comprises the following steps:

the first step is as follows: acquiring real-time required torque and real-time speed of the electric automobile through a vehicle control unit of the electric automobile;

the second step is that: judging a rotating speed interval where the rotating speed of the motor corresponding to the acquired real-time vehicle speed is located, wherein a minimum interval is taken, and forming a rotating speed vector by the endpoint of the minimum interval;

the third step: judging the real-time state of the electric automobile according to the acquired real-time required torque, wherein when the real-time required torque is zero, the electric automobile is judged to be in a zero output mode, when the real-time required torque is larger than zero, the electric automobile is judged to be in a driving mode, and when the real-time required torque is smaller than zero, the electric automobile is judged to be in a braking mode;

the fourth step: acquiring an optimized distribution coefficient vector corresponding to the rotating speed vector and a matched vehicle speed vector according to the judged real-time state of the electric vehicle;

the fifth step: calculating a real-time optimal distribution coefficient corresponding to the real-time vehicle speed by utilizing linear interpolation according to the optimal distribution coefficient vector obtained in the fourth step and the matched vehicle speed vector;

And a sixth step: modifying the real-time optimal distribution coefficient obtained in the fifth step by using an energy optimal torque distribution coefficient table manufactured off-line, and obtaining the optimal torque distribution coefficient under the real-time vehicle speed through a preferred decision function;

The seventh step: and transmitting the optimal torque distribution coefficient obtained in the sixth step to a torque output calculation function to obtain a final torque output instruction, and then respectively sending the obtained final torque output instruction to a motor controller of the hub motor corresponding to the electric automobile.

2. the real-time torque optimal distribution control method for the four-wheel independent drive electric vehicle facing the straight-line running working condition according to claim 1, characterized by comprising the following steps of:

defining the real-time required torque of the electric automobile as Td, the real-time speed as Vx, and the rotating speed vector composed of endpoints in a minimum interval as [ n1, n2], wherein the subscript is t to indicate that the electric automobile is in a driving mode, the subscript is g to indicate that the electric automobile is in a braking mode, and the subscript t/g indicates the driving mode or the braking mode;

when the automobile is in a driving mode, acquiring upper and lower distribution coefficient limits Kmin, t and Kmax, t according to a driving constraint calculation function, and then solving an optimized distribution coefficient vector [ Kt,1, Kt,2] corresponding to a rotating speed vector [ n1, n2] and matched vehicle speed vectors [ Vx1, Vx2] according to an adhesion-energy combined optimization function, wherein the unit of ni is rpm, and the subscript i is 1 or 2;

When the automobile is in a braking mode, upper and lower distribution coefficient limits Kmin, g and Kmax, g are obtained according to a braking constraint calculation function, and then an optimal distribution coefficient vector [ Kg,1, Kg,2] and a vehicle speed vector [ Vx1, Vx2] corresponding to a rotating speed vector [ n1, n2] are obtained according to an adhesion-energy combined optimization function, wherein ni represents the rotating speed of the motor, the unit of the rotating speed is rpm, and the subscript i is 1 or 2.

3. the real-time torque optimal distribution control method for the four-wheel independent drive electric vehicle facing the straight-line running working condition as claimed in claim 2, wherein:

when the automobile is in a driving mode, acquiring upper and lower distribution coefficient limits Kmin, t and Kmax, t according to a driving constraint calculation function, wherein the driving constraint calculation function is formed by a formula (2),

Tf, max (ni) and Tr, max (ni) are maximum torques determined by the motor characteristics, are obtained by an external characteristic curve of the motor, and ni represents the rotating speed of the motor; for the hub motors with the motor controllers capable of feeding back the maximum torque signals, Tf, max (ni) and Tr, max (ni) can also be directly obtained from the feedback signals, and when the maximum torque signals fed back by the left and right hub motors on the same axis are inconsistent, a smaller value is taken for calculation;

When the automobile is in a braking mode, obtaining upper and lower distribution coefficient limits Kmin, g and Kmax, g according to a braking constraint calculation function, wherein the braking constraint calculation function is formed by formulas (3) and (4),

wherein Lr is the distance from the center of mass of the whole vehicle to the rear axle, hg is the height of the center of mass, L is the distance between the vehicle and the axle, M is the mass of the whole vehicle, G is the acceleration of gravity, G is the gravity of the whole vehicle, Rw is the rolling radius of the wheels, Tf, min (ni) and Tr, min (ni) are the algebraic minimum torque determined by the characteristics of the motor, wherein the specified regenerative braking is negative torque, which is obtained by the external characteristic curve of the motor, ni represents the rotating speed of the motor, and the rotating speeds are considered to be approximately equal under the condition of straight-line driving because the front wheel and the rear wheel adopt the same hub motor;

for the hub motors with the motor controllers capable of feeding back the minimum torque signals, Tf, min (ni) and Tr, min (ni) can also be directly obtained from the feedback signals, and when the maximum torque signals fed back by the left and right hub motors on the same axis are inconsistent, a larger value is taken for calculation.

4. the real-time torque optimal distribution control method for the four-wheel independent drive electric vehicle facing the straight-line running working condition according to claim 3, characterized by comprising the following steps of: the analytical form of the optimal solution of the optimization index function consumed in the attachment-energy joint optimization function comprises the following steps,

Step 4.1: the optimization index function is shown in equation (5),

wherein λ 1 and λ 2 respectively represent an optimization weight of road surface adhesion utilization and an optimization weight of motor energy consumption, Fzf and Fzr respectively represent loads of a front axle and a rear axle of a vehicle, Σ Phub represents a sum of power consumption of four hub motors, Tfl and Tfr respectively represent left and right wheel output torques of the front axle, Trl and Trr respectively represent left and right wheel output torques of the rear axle, Td represents a real-time required torque, Ti represents an output torque of each wheel, subscript i represents fl, fr, rl and rr, Rw represents a rolling radius of the wheel, and JEAA is an optimization index function;

Step 4.2: the power loss characteristic field obtained by the motor bench test is calculated by adopting a formula (6) to obtain a motor power consumption characteristic field,

Wherein T represents the motor torque measured by the test and the unit is N m; n represents the motor rotating speed measured by the test, and the unit is revolutions per minute, namely rpm; ploss is the power loss measured for each group (T, n), obtained by subtracting the mechanical power output at the wheel end from the input direct current power, in kw; phub represents the electric power consumption of the hub motor, the unit is kw, positive torque represents driving, negative torque represents braking, positive power represents the power consumed by the motor from the battery, negative power represents the power fed back from the motor to the battery, and test rotating speed data in a characteristic field is made into a motor rotating speed working condition point data table according to a test result;

and 4.3, step: according to two modes of braking and driving, the relationship of the power consumption of the motor with the change of torque under different rotating speeds is respectively fitted by adopting a cubic polynomial mode, the fitting formula is shown as (7), wherein a, b, c and d are parameters to be fitted and correspond to a rotating speed working condition point data table manufactured in the step 4.2

P＝a(n)T+b(n)T+c(n)T+d(n) (7)

step 4.4: since the left and right wheel torque outputs should be the same in the straight running condition, the front axle left and right wheel output torque Tfl-Tfr-Tf and the rear axle left and right wheel output torque Trl-Trr-Tr are specified, and the specific expression is shown in (8).

the proportion of the output torque of the front wheels accounting for 50% of the required torque is defined as K, the proportion is the distribution coefficient of the torque of the front axle and the rear axle, and Td is the real-time required torque;

step 4.5: substituting the formulas (6), (7) and (8) into the formula (5), obtaining an analytical expression of a quadratic polynomial of the optimization problem about K, wherein the analytical expression is shown as (9), and coefficients are shown as formulas (10) - (12);

J(K)＝τK-τK+ε (9)

Td is a real-time required torque, lambda 1 and lambda 2 respectively represent an optimization weight for road adhesion utilization and an optimization weight for motor energy consumption, Fzf and Fzr respectively represent loads of a front axle and a rear axle of a vehicle, a, b, c and d are parameters to be fitted, Rw is a rolling radius of a wheel, and JEAA represents an optimization index function;

Step 4.6: the first order partial derivative about K is calculated for jeaa (K) in equation (9) and is made equal to zero, the extreme point of the function can be obtained, and then the second order partial derivative about K is calculated for the function, so as to obtain the monotonicity information of the function, and the results of the extreme point and the second order partial derivative are shown in equations (13) and (14).

the second-order partial derivative about K is calculated for the optimization index function JEAA, and Kex represents a possible extreme point of the JEAA function;

step 4.7: according to the principle of minimum value, the global minimum value point of the optimization problem can be obtained by a classification discussion mode, and the specific method is as follows,

4.7.1, when τ 1<0, judge that Kex represents the distance between one possible extreme point of the JEAA function and the partition coefficient boundary,

if Kex is more than or equal to Kmax and t/g, making Kt/g, i equal to Kmin and t/g;

if Kex is less than or equal to Kmin and t/g, let Kt/g, i be Kmax and t/g;

if Kmin, t/g < Kex < Kmax, t/g, let Kt/g, i ═ argmax | κ -Kex | (κ ∈ { Kmin, t/g, Kmax, t/g });

4.7.2, when τ 1>0, judging whether Kex is in the feasible region, if Kex > Kmax, t/g, making Kt/g, i ═ Kmax, t/g; if Kex is less than Kmin, t/g, let Kt/g, i equal to Kmin, t/g; otherwise, let Kt/g, i ═ Kex.

4.7.3, when τ 1 is 0, judging the sign of τ 2, if τ 2 is greater than 0, let Kt/g, i be Kmax, t/g; otherwise, let Kt/g, i ═ Kmin, t/g;

And Kt/g, i represents a value K of a global minimum value obtained by the optimization index function (5) in the 4.1 step under a driving mode or a braking mode, a subscript i is 1 or 2, when the automobile is in the driving mode, Kmin, t and Kmax, t are upper and lower limits of a distribution coefficient obtained according to a driving constraint calculation function, and when the automobile is in the braking mode, Kmin, g and Kmax, g are upper and lower limits of a distribution coefficient obtained according to a braking constraint calculation function.

5. The real-time torque optimal distribution control method for the four-wheel independent drive electric vehicle facing the straight-line running working condition according to claim 4, characterized by comprising the following steps of: the method for solving the optimal distribution coefficient vector [ Kt/g,1, Kt/g,2] corresponding to the rotating speed vector [ n1, n2] and the matched vehicle speed vector [ Vx1, Vx2] through the analytic form of the optimal solution of the optimal index function comprises the following steps:

step 5.1: the rotating speed vector formed by the endpoints of the minimum interval is [ n1, n2 ];

step 5.2: judging whether the input data meets the solving requirement of the optimization problem,

if the motor is in the driving mode and the required torque exceeds the total peak torque of the motor, correcting the required torque into the sum of all the peak torques, and then solving an optimized distribution coefficient vector [ Kt,1, Kt,2] corresponding to the rotating speed n1 and n2 according to the optimized solving method in the step 4.7 and the fitting data in the step 4.3;

if in the braking mode, when the total required torque is smaller than the total regenerative braking torque which can be provided by the motor or the feasible region obtained by the braking constraint calculation function is an empty set, let Kg, i be KI, where the subscript i represents 1 or 2; otherwise, according to the optimization solving method in the step 4.7 and the fitting data in the step 4.3, solving a corresponding optimized distribution coefficient vector [ Kg,1, Kg,2 ];

step 5.3: converting the rotating speed vector [ n1, n2] into a matched vehicle speed vector [ Vx1, Vx2] according to a formula (15)

wherein Rw is the rolling radius of the wheel, ni represents the rotating speed of the motor, subscript i is 1 or 2, n1, and n2 is obtained by the real-time vehicle speed.

6. the real-time torque optimal distribution control method for the four-wheel independent drive electric vehicle facing the straight-line running working condition according to claim 5, characterized by comprising the following steps of: in the fifth step, the calculation formula of the real-time optimized distribution coefficient corresponding to the real-time vehicle speed by utilizing linear interpolation is as follows:

and Kt/g, i represents that the optimization index function (5) in the step 4.1 obtains a K value of a global minimum value under a driving mode or a braking mode, subscript i is 1 or 2, Vx is a real-time vehicle speed, Vxi is a matched vehicle speed vector, and subscript i is 1 or 2.

7. The real-time torque optimal distribution control method for the four-wheel independent drive electric vehicle facing the straight-line running working condition according to claim 6, characterized by comprising the following steps of: in the sixth step, the step of forming the off-line manufactured energy optimal torque distribution coefficient table includes the following steps:

step 7.1: the energy-optimal torque distribution coefficient calculation method is shown in equation (16),

pd (·) is a motor power calculation function and can be calculated by a linear interpolation method from a motor power consumption characteristic field in the step 4.2, a constrained calculation formula is the formulas (2) - (4), Vx is a real-time vehicle speed, Td is a real-time required torque of the electric vehicle, and K is a proportion of a front wheel output torque to 50% of the required torque;

And 7.2: and respectively acquiring corresponding energy optimal distribution coefficients for the problems under the conditions of the vehicle speed Vx and the required torque Td by utilizing a genetic algorithm optimizing function provided by a Matlab optimizing tool box, and making a two-dimensional data table about the vehicle speed and the required torque.

8. the real-time torque optimal distribution control method for the four-wheel independent drive electric vehicle facing the straight-line running working condition according to claim 7, characterized in that: the preferred decision function in the sixth step is formed by equation (17),

K＝arg minJ(κ) (κ∈{K,K}) (17)

Wherein, the function JEAA can be known in formula (5), but the motor power consumption part is directly obtained by interpolation calculation of a motor power consumption characteristic field; koff is obtained by a calculation method of an energy optimal torque distribution coefficient and is obtained by looking up a table according to the current required torque Td and the vehicle speed Vx; kt/g is obtained from equation (1).

9. the real-time torque optimal distribution control method for the four-wheel independent drive electric vehicle facing the straight-line running working condition according to claim 8, characterized by comprising the following steps of:

In the seventh step, the torque output calculation function includes the steps of:

step 9.1, determining torque request commands of hub motors of front and rear axles according to a formula (18), wherein the output torques of the left and right wheels are the same due to the fact that the hub motors belong to the straight-line driving working condition

the Td is a real-time required torque of the electric vehicle, and since the torque outputs of the left and right wheels under the straight-line driving condition should be the same, the output torque of the left and right wheels of the front axle is specified to be Tfl ═ Tfr ═ Tf, and the output torque of the left and right wheels of the rear axle is specified to be Trl ═ Trr ═ Tr;

9.2, if the driving mode is adopted, the result is directly output;

if the motor is in the braking mode, further judging whether the torque output calculated by the formula (18) exceeds the regenerative braking torque boundary of the motor;

if the electric machine is unable to provide sufficient braking torque, the remaining torque is made up by friction braking according to the maximum regenerative torque output currently allowed;

if the motor can provide the torque calculated by the formula (18), the output calculation result is directly output.