Torque distribution method and system based on distributed dual-motor drive vehicle

Abstract

The application provides a torque distribution method and system based on a distributed double-motor driven vehicle, and belongs to the technical field of pure electric vehicles. The method comprises the following steps: obtaining discrete values of a torque distribution coefficient at each step in a value range, establishing a longitudinal dynamics model of the vehicle based on a distributed double-motor driving mode, defining a target function according to the longitudinal dynamics model, combining a preset constraint condition and the target function to optimize a rolling prediction optimization problem with optimal energy consumption, solving the rolling prediction optimization problem based on the Pontryagin minimum principle and a dichotomy iteration method to obtain optimal control quantities, calculating energy consumption values under each discrete value based on the optimal control quantities, and selecting a target discrete value corresponding to a minimum energy consumption value as an optimal torque distribution coefficient. Through the application, the respective shortcomings of the existing distributed double-motor driving scheme and torque distribution method can be comprehensively solved, the torque distribution coefficient meeting the optimal energy consumption condition is found, and the optimal energy saving and consumption reduction effect is achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of pure electric vehicle power distribution, and particularly relates to a torque distribution method and system based on a distributed dual-motor drive vehicle. BACKGROUND

[0002] With the popularization of environmental protection knowledge, environmental protection and energy saving have gradually been paid attention to by people, and some new energy equipment has gradually replaced traditional energy equipment; among them, new energy electric vehicles are popular among people due to the advantages of energy saving and environmental protection. At present, the driving technology actually applied to mass-produced pure electric vehicles is generally "single motor + single gear reducer", which has high technical maturity, but due to the fixed transmission speed ratio that can be provided, a larger capacity motor and battery have to be used in order to meet the vehicle performance; therefore, the driving technology of the current pure electric vehicle presents the characteristics of "multi-motor" and "multi-gear". The dual-motor power system has been widely concerned by the academic and industrial circles due to its advantages of flexible adjustment of working state and improvement of brake energy recovery efficiency, and mature centralized dual-motor drive and distributed dual-motor drive technologies have appeared.

[0003] Researchers have carried out a lot of related work on the distributed dual-motor drive mode, and the main research prospects at present have two aspects: 1. The driving control mode of the dual-motor is still a control difficulty, although the research topic of driving speed planning by using road information has been widely carried out, but the vehicle speed energy saving control at the whole vehicle level still needs to be further optimized. 2. The torque distribution method of the dual-motor is still a control difficulty, although the existing torque distribution scheme based on rules can achieve good control effect by relying on accurate calibration MAP, but the energy consumption optimization problem at the whole vehicle level still needs to further consider the transmission system. Therefore, how to comprehensively solve the respective shortcomings of the current distributed dual-motor driving scheme and torque distribution method, find the torque distribution coefficient that meets the energy consumption optimization condition, and achieve the optimal energy saving and consumption reduction effect is particularly important. SUMMARY

[0004] In order to solve the above technical problems, the application provides a torque distribution method and system based on a distributed dual-motor drive vehicle.

[0005] On the one hand, the application provides a torque distribution method based on a distributed dual-motor vehicle, which comprises:

[0006] The value range of the torque distribution coefficient λ of the distributed dual-motor vehicle is set as λ∈[a, b], and 0

[0007] The value range is divided into k steps, and the increment m of each step of the torque distribution coefficient is calculated as m=(b-a) / k, so as to obtain the discrete value of each step of the torque distribution coefficient in the value range;

[0008] establish a longitudinal dynamics model of the vehicle based on a distributed dual-motor driving mode;

[0009] define a target function according to the longitudinal dynamics model;

[0010] optimize a rolling prediction optimization problem with the lowest energy consumption in combination with preset constraints and the target function;

[0011] solve the rolling prediction optimization problem based on the Pontryagin Minimum Principle and a dichotomy iteration method to obtain optimal control quantities related to energy consumption calculation;

[0012] calculate energy consumption values under each of the discrete values based on the optimal control quantities, and select a target discrete value corresponding to the minimum energy consumption value as the optimal torque distribution coefficient.

[0013] Preferably, the specific steps of establishing the longitudinal dynamics model of the vehicle based on the distributed dual-motor driving mode include:

[0014] Based on the distributed dual-motor driving mode of the vehicle, the output torques of the front and rear electrodes are calculated through a torque formula; wherein the torque formula is:

[0015]

[0016] In the formula, T f represents the output torque of the front axle / front wheel, T r represents the output torque of the rear axle / rear wheel, T represents the total output torque of the vehicle, η represents the total mechanical efficiency of the vehicle transmission system, i ff , i fr represent the front and rear main reducer transmission ratios, T mf is the output torque of the front motor, and T mr is the output torque of the rear motor.

[0017] Based on the energy consumption model of the two electrodes, a longitudinal dynamics model of the vehicle is established; wherein the longitudinal dynamics model is:

[0018] P = P f + P r

[0019]

[0020] In the formula, P f represents the output power of the front motor, P r represents the output power of the rear motor, n mf represents the speed of the front motor, and n mr represents the speed of the rear motor.

[0021] Preferably, the speed of the front motor nmf and the rear motor speed n mr Specifically,

[0022]

[0023] wherein r w represents the vehicle wheel radius, and v represents the vehicle speed.

[0024] Preferably, the specific step of defining the target function according to the longitudinal dynamics model comprises:

[0025] simplifying the longitudinal dynamics model into a single degree of freedom model so that the longitudinal motion of the vehicle is characterized by a speed and acceleration formula, wherein the speed and acceleration formula is specifically:

[0026]

[0027] wherein Δt represents the time interval from the kth step to the (k+1)th step, s(k+1) and s(k) represent the driving distance at the (k+1)th step and the kth step, v(k+1) and v(k) represent the driving speed at the (k+1)th step and the kth step, and a(k) represents the acceleration at the kth step;

[0028] obtaining a vehicle traction advance function by converting the vehicle road conditions according to the vehicle overall traction function and the speed and acceleration formula; wherein the vehicle traction advance function is specifically:

[0029]

[0030] wherein F t (k) represents the vehicle traction, F W (k) represents the air resistance, F i (k) represents the slope resistance, F f (k) represents the rolling resistance, and δ is a vehicle rotational mass conversion coefficient, and M is the total mass of the vehicle.

[0031] defining a target function of the vehicle within a prediction time domain according to the vehicle traction advance function and in combination with the control target of minimizing the total energy consumption of the front and rear motors within the prediction time domain N and the vehicle speed being close to the set vehicle speed; wherein the target function is specifically:

[0032] wherein,

[0033] L[x(k),u(k),k]=P·△t+κ1·(v(k)-v d ) 2

[0034]

[0035] P Δt = v (k) - v (k-1) Δt d wherein v d ) 2 represents a speed tracking index, (v(N)-v d ) 2 represents a terminal penalty index.

[0036] Preferably, the preset constraint condition is specifically:

[0037] v min ≤v(k)≤v max ;

[0038] n m,min ≤n m (k)≤n m,max ;

[0039] T m,min (n m (k))≤T m (n m (k))≤T m,max (n m (k));

[0040] wherein v min , v max represent the minimum value and the maximum value of the vehicle speed respectively, n m,min , n m,max represent the minimum value and the maximum value of the motor speed respectively, T m,min (n m (k)), T m,max (n m (k)) represent the minimum torque and the maximum torque of the electrode respectively when the speed of the electrode is n m (k).

[0041] Preferably, the rolling prediction optimization problem is specifically:

[0042]

[0043] P Δt = v (k) - v (k-1) Δt d wherein v d ) 2 represents a speed tracking index, (v(N)-v d ) 2represents a terminal penalty index, Δt represents a time interval from the kth step to the (k+1)th step, s(k+1), s(k) represent driving distances at the (k+1)th step and the kth step, v(k+1), v(k) represent driving speeds at the (k+1)th step and the kth step, F t (k) represents a vehicle traction force, F W (k) represents an air resistance, F i (k) represents a slope resistance, F f (k) represents a rolling resistance, δ is a vehicle rotational mass conversion coefficient, and M is a total mass of the vehicle.

[0044] Preferably, F w (k) = C D Aρ·v 2 (k) / 2, F i (k) = Mgsinα(k), F f (k) = Mgf·cosθ(k);

[0045] In the formula, C D represents an air resistance coefficient, A represents a windward area, ρ represents an air density, g represents a gravitational acceleration, f represents a wheel rolling resistance coefficient, and θ(k) represents a road slope at the kth step.

[0046] Preferably, the optimal control quantity at least includes a wheel rolling resistance coefficient, a windward area, a motor-to-wheel deceleration ratio of a front axle and a rear axle, and a power transmission efficiency.

[0047] In another aspect, the application provides a torque distribution system based on a distributed dual-motor vehicle, comprising: a setting module configured to set a value range of a torque distribution coefficient λ of the distributed dual-motor vehicle, λ∈[a, b], and 0

[0048] A calculation module configured to divide the value range into k steps and calculate an increment m of the torque distribution coefficient at each step, m = (b-a) / k, to obtain a discrete value of the torque distribution coefficient at each step within the value range.

[0049] A building module configured to build a longitudinal dynamics model of the vehicle based on a distributed dual-motor driving mode;

[0050] A defining module configured to define an objective function according to the longitudinal dynamics model;

[0051] An optimization module configured to optimize a receding horizon optimization problem with optimal energy consumption in combination with a preset constraint condition and the objective function;

[0052] A solving module configured to solve the receding horizon optimization problem based on a Pontryagin minimum principle and a bisection iteration method to obtain an optimal control quantity related to energy consumption calculation.

[0053] The selecting module is used for calculating energy consumption values under each of the discrete values based on the optimal control amount, and selecting a target discrete value corresponding to a minimum energy consumption value as an optimal torque distribution coefficient.

[0054] Compared with the prior art, the present application has the following beneficial effects:

[0055] The torque distribution method provided by the present application uses a distributed double-motor driving scheme, a double-motor energy consumption model and a longitudinal dynamics model considering road slope to form a control problem, further solves optimal control amounts related to energy consumption in a rolling optimization framework, and finally solves torque distribution coefficients respectively by using a cyclic solution method, so as to obtain torque distribution coefficients ensuring optimal energy consumption. By using the torque distribution method provided by the present application, for a distributed double-motor driving scheme of an electric vehicle, the optimal energy consumption can be ensured, torque distribution coefficients meeting the optimal energy consumption condition can be found, and the optimal energy saving and consumption reduction effect can be achieved; the respective shortcomings of the existing distributed double-motor driving scheme and torque distribution method can be comprehensively solved. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative labor based on these drawings.

[0057] Figure 1 The flow chart of the torque distribution method based on the distributed double-motor driven vehicle provided by the embodiment 1 of the present application is shown in the figure;

[0058] Figure 2 The typical distributed driving scheme of the double-motor electric vehicle provided by the embodiment 1 of the present application is shown in the figure;

[0059] Figure 3 The cyclic solution flow of the torque distribution coefficient provided by the embodiment 1 of the present application is shown in the figure;

[0060] Figure 4 The structure block diagram of the torque distribution system based on the distributed double-motor driven vehicle corresponding to the method of the embodiment 1 provided by the embodiment 2 of the present application is shown in the figure;

[0061] Figure 5 The hardware structure schematic diagram of the electronic device provided by the embodiment 3 of the present application is shown in the figure.

[0062] Explanation of reference signs:

[0063] 10 - setting module;

[0064] 20 - calculation module;

[0065] 30 - establishing module, 31 - calculation unit, 32 - establishing unit;

[0066] 40 - defining module, 41 - simplifying unit, 42 - converting unit, 43 - defining unit;

[0067] 50 - optimizing module;

[0068] 60 - solving module;

[0069] 70 - selecting module;

[0070] 80 - bus, 81 - processor, 82 - memory, 83 - communication interface. DETAILED DESCRIPTION

[0071] Example implementations will now be described with reference to the drawings. However, example implementations can be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the inventive concept of example implementations to those skilled in the art.

[0072] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the

[0073] The block diagrams in the drawings show only the functionality of the example implementations and do not imply any particular physical or architectural arrangement of the example implementations. For example, functions shown as discrete blocks in the example implementations can be implemented in monolithic form with separate hardware or firmware components. Alternatively, the functions can be implemented in shared or distributed hardware or firmware components. In another example, the functions can be implemented in a stand-alone or multiple-processor hardware architecture, or in a software architecture that is executed by a processor or processors of a general-purpose computer, a special-purpose computer, or a network-based controller.

[0074] The flow diagrams depicted in the drawings show example implementations only, and are not necessarily to be construed as reflecting an order of execution or the necessity of all illustrated operations or steps. For example, some operations or steps can be performed in a different order, or can be combined or partially combined with other operations or steps, and the order of execution can vary from example to example.

[0075] In particular, Figure 1 Fig. 1 shows a flow diagram of a method for distributing torque of a vehicle driven by two electric motors according to an embodiment of the present disclosure.

[0076] AsFigure 1 As shown, the torque distribution method based on the distributed dual-motor drive vehicle of the embodiment includes the following steps:

[0077] S101, set the value range of the torque distribution coefficient λ of the distributed dual-motor vehicle λ∈[a, b], and 0

[0078] Specifically, the schematic diagram of the typical distributed drive scheme of the dual-motor electric vehicle of the embodiment is as shown in the figure. Figure 2 By defining the torque distribution coefficient λ between the front and rear axles / wheels, i.e. the ratio of the front axle torque to the total output torque of the dual-motor electric vehicle, λ=T f / T,T f +T r =T. Where T is the total output torque of the vehicle, T f is the output torque of the front axle / front wheel, and T r is the output torque of the rear axle / rear wheel.

[0079] S102, divide the value range into k steps and calculate the increment m of each step of the torque distribution coefficient λ=(b-a) / k to obtain the discrete value of each step of the torque distribution coefficient in the value range.

[0080] Specifically, for a dual-motor driven electric vehicle, the torque distribution coefficient satisfies λ∈[a, b], where 0 Figure 3 As shown, the torque distribution coefficient is calculated based on the energy consumption optimal torque distribution coefficient cycle solving process. First, define the initialization parameters: the numerical values of the upper limit b and the lower limit a of the value range; when i=0, λ0=a, where i is the main reduction ratio; the numerical value of the torque distribution coefficient value range division k, then the increment of the torque distribution coefficient is m=(b-a) / k, thereby obtaining the discrete value of each step of the torque distribution coefficient λ in the value range [a, b].

[0081] S103, establish a longitudinal dynamics model of the vehicle based on the distributed dual-motor drive mode.

[0082] Further, the specific steps of step S103 include:

[0083] S1031, based on the distributed dual-motor drive mode of the vehicle, calculate the output torque of the front and rear electrodes through the torque formula.

[0084] Specifically, since the torque between the front and rear axles / wheels is transmitted from the motor through the front and rear main reducers, the output torque of the two motors can be obtained. The torque formula is:

[0085]

[0086] In the formula, T fT represents the output torque of the front axle / front wheel, T r T represents the output torque of the rear axle / rear wheel, T represents the total output torque of the vehicle, η represents the total mechanical efficiency of the vehicle transmission system, i ff , i fr T represents the transmission ratio of the front and rear main reducers, T mf is the output torque of the front motor, T mr is the output torque of the rear motor.

[0087] S1032, based on the energy consumption model of the two electrodes, a longitudinal dynamics model of the vehicle is established.

[0088] Specifically, the vehicle traction force F t (K) of the embodiment is expressed as follows:

[0089]

[0090] wherein r w is the wheel radius, since the motor power is related to its torque and speed, for the energy consumption model of the front and rear motors (defining the front motor output power as P f , and the rear motor output power as P r ), a quadratic polynomial form about the current motor torque T mf , T mr and speed n mf , n mr can be established, that is, the longitudinal dynamics model of the vehicle can be obtained, which is as follows:

[0091]

[0092] wherein P f represents the front motor output power, P r represents the rear motor output power, n mf represents the front motor speed, n mr represents the rear motor speed. Wherein the front motor speed n mf and the rear motor speed n mr are specifically:

[0093]

[0094] wherein r w represents the wheel radius of the vehicle, and v represents the vehicle speed.

[0095] S104, defining a target function according to the longitudinal dynamics model.

[0096] Further, the specific steps of step S104 include:

[0097] S1041, the longitudinal dynamics model is simplified into a single degree of freedom model, so that the longitudinal motion of the vehicle is characterized by a speed and acceleration formula.

[0098] Specifically, the longitudinal dynamics model is simplified into a single degree of freedom model, and the longitudinal motion of the vehicle can be described by the vehicle travel distance s(k), the vehicle travel speed v(k) and the vehicle longitudinal acceleration α(k). The specific description can be characterized by a speed and acceleration formula. Wherein, the speed and acceleration formula is specifically:

[0099]

[0100] In the formula, Δt represents the time interval from the kth step to the (k+1)th step, s(k+1) and s(k) represent the travel distance of the (k+1)th step and the kth step, v(k+1) and v(k) represent the travel speed of the (k+1)th step and the kth step, and α(k) represents the acceleration of the kth step.

[0101] S1042, according to the vehicle whole vehicle traction function and the speed and acceleration formula, the whole vehicle traction forward function is obtained by combining the vehicle road condition.

[0102] Specifically, according to the vehicle longitudinal dynamics equation, the vehicle needs to overcome various running resistances to realize traction forward during running, and thus the whole vehicle traction forward function can be obtained, which is specifically as follows:

[0103]

[0104] In the formula, F t (k) represents the vehicle traction force, F W (k) represents the air resistance, F i (k) represents the slope resistance, F f (k) represents the rolling resistance, and δ is the vehicle rotational mass conversion coefficient, and M is the total mass of the vehicle.

[0105] Wherein, F w (k) = C D Aρ·v 2 (k) / 2, F i (k) = Mgsinα(k), F f (k) = Mgf·cosθ(k); In the formula, C D represents the air resistance coefficient, A represents the windward area, ρ represents the air density, g represents the gravitational acceleration, f represents the wheel rolling resistance coefficient, and θ(k) represents the road slope of the kth step. It should be noted that the road slope value changes with the change of the vehicle position.

[0106] S1043, according to the whole vehicle traction advancing function, and in combination with the control target that the total energy consumption of the front motor and the rear motor is minimum and the vehicle speed is close to the set vehicle speed in the prediction time domain N, a target function of the vehicle in the prediction time domain is defined.

[0107] Specifically, the target of the optimization problem is to make the total energy consumption of the front motor and the rear motor minimum in the prediction time domain, and the vehicle speed is close to the set vehicle speed of the driver. In order to achieve the control target, the embodiment is realized by defining the target function of the vehicle in the prediction time domain. Wherein, the target function is specifically:

[0108]

[0109] In the formula, P·Δt represents the energy consumption efficiency index, v d represents the set reference vehicle speed, κ1, κ2 represents the weight coefficient with the dimension of kg, (v(k)-v d ) 2 represents the speed tracking index, (v(N)-v d ) 2 represents the terminal penalty index. It should be noted that the energy consumption efficiency index makes the energy consumption efficiency optimal in the prediction time domain N; the speed tracking index is used to track the reference speed to avoid excessive speed deviation; the terminal penalty index is used to ensure that the vehicle speed reaches the vicinity of the reference speed at the terminal time.

[0110] S105, in combination with the preset constraint condition and the target function, the energy consumption optimal rolling prediction optimization problem is optimized.

[0111] Specifically, for the above control problem, the limitation of the vehicle longitudinal speed and the range of the motor speed and torque need to be considered while achieving the target, so the constraint condition needs to be set. The constraint condition of the embodiment is specifically:

[0112] v min ≤v(k)≤v max ;

[0113] n m,min ≤n m (k)≤n m,max ;

[0114] T m,min (n m (k))≤T m (n m (k))≤T m,max (n m (k));

[0115] In the formula, v min , v max respectively represent the minimum value and the maximum value of the vehicle speed, n m,minn m,max T represents the minimum and maximum values ​​of the motor speed, respectively. m,min (n m (k)), T m,max (n m (k) represents the electrode rotation speed n. m (k) represents the minimum and maximum torque output by the electrode. It should be noted that n varies depending on the driving scenario (primarily considering vehicle speed and speed limit information). m,min n m,max The two values ​​will change accordingly; when the motor speed is n m (k) In this case, the maximum output torque of the motor is T. m,max (n m (k)), minimum output torque is T m,min (n m (k)). Combining the above objective function and constraints, we can optimize the energy-optimal rolling prediction optimization problem, which is specifically as follows:

[0116]

[0117] In the formula, P·Δt represents the energy efficiency index, and v d This represents the set reference vehicle speed, κ1 and κ2 represent weighting coefficients in kg, and (v(k)-v d ) 2 This represents the speed tracking performance index, (v(N)-v d ) 2 Let F represent the terminal penalty index, Δt represent the time interval from step k to step (k+1), s(k+1) and s(k) represent the distance traveled in steps (k+1) and k, respectively, and v(k+1) and v(k) represent the speeds in steps (k+1) and k, respectively. t (k) represents the vehicle's traction force, F W (k) represents air resistance, F i (k) represents the ramp resistance, F f (k) represents rolling resistance, δ is the vehicle rotational mass conversion factor, and M is the total vehicle mass.

[0118] Among them, F w (k)=C D Aρ·v 2 (k), F i (k)=Mgsinα(k), F f (k)=Mgf·cosθ(k); where, C D Let θ(k) represent the air resistance coefficient, A represent the frontal area, ρ represent the air density, g represent the gravitational acceleration, f represent the wheel rolling resistance coefficient, and θ(k) represent the road slope at the k-th step.

[0119] S106, based on the Pontryagin minimum principle and the dichotomy iteration method, the rolling prediction optimization problem is solved to obtain the optimal control quantity related to energy consumption calculation.

[0120] Specifically, when solving the optimal control problem with classical variational method, it is assumed that the control vector u(t) is not limited in any way, i.e. the allowable control set can be regarded as the entire p-dimensional control space, and at this time the control variation δu can be taken arbitrarily. At the same time, it is also strictly required that the Hamilton function H is continuously differentiable with respect to u. In this case, it is effective to solve the optimal control problem by applying the variational method. The result of the Pontryagin minimum principle is very close to that of the classical variational method, but it overcomes the limitations of the classical variational method and has a wider range of application. The dichotomy iteration method determines the root interval, divides the interval into two equal parts, and gradually reduces the root interval by judging the sign of f(x) until the root interval is small enough, and the approximate root that meets the accuracy requirement can be obtained. In this embodiment, the Pontryagin minimum principle and the dichotomy iteration method are combined to improve the optimization effect. In this embodiment, the optimal control quantity at least includes the wheel rolling resistance coefficient, the windward area, the speed reduction ratio between the front and rear axle motors and the wheels, and the power transmission efficiency.

[0121] S107, based on the optimal control quantity, the energy consumption value under each discrete value is calculated, and the target discrete value corresponding to the minimum energy consumption value is selected as the optimal torque distribution coefficient.

[0122] Specifically, referring to Figure 3 , the value of the torque distribution coefficient value domain is divided into k parts, the increment of the torque distribution coefficient is obtained, and the front and rear motor energy consumption values Q i , Q f,λi and the sum Q r,λi under the current λ λi are calculated based on the energy consumption optimal rolling optimization problem. If the program has been calculated to the upper limit of the value domain, i.e. λ i = b, then λ opt = arg min(Q λi ) is the optimal torque distribution coefficient, and if the program has not been calculated to the upper limit of the value domain, the increment needs to be supplemented and the calculation of the previous step continues until the loop ends.

[0123] In summary, the vehicle speed energy-saving optimization problem considered in this embodiment, taking into account the characteristics of distributed dual-motor drive, establishes a vehicle longitudinal dynamics model and a dual-motor power model. Considering system constraints, it solves a rolling prediction optimization problem based on optimal energy consumption, thereby obtaining the optimal control quantity related to energy consumption calculation. Using a cyclical solution method for the optimal control quantity and torque distribution coefficient, the possible value range of the torque distribution coefficient is divided into k equal parts in the current prediction time domain. Combining the optimal control quantity from the previous step, the energy consumption value under each torque distribution coefficient is solved, and finally, the torque distribution coefficient with the minimum energy consumption value (i.e., the optimal distribution coefficient) is selected. By adopting the above steps, the distributed dual-motor drive scheme for pure electric vehicles can achieve the goal of ensuring optimal energy consumption, finding the torque distribution coefficient that satisfies the optimal energy consumption condition to achieve the best energy-saving and consumption-reducing effect. It can comprehensively solve the respective shortcomings of existing distributed dual-motor drive schemes and torque distribution methods, as well as address the comprehensive challenge of dual-motor torque distribution methods relying too heavily on precise MAP calibration.

[0124] Example 2

[0125] This embodiment provides a structural block diagram of a system corresponding to the method described in Embodiment 1. Figure 4 This is a structural block diagram of the torque distribution system for a distributed dual-motor driven vehicle according to this embodiment, as shown below. Figure 4 As shown, the system includes:

[0126] The setting module 10 is used to set the range of the torque distribution coefficient λ of the distributed dual-motor vehicle, λ∈[a,b], and 0<a<b<1;

[0127] Calculation module 20 is used to divide the value range into k steps and calculate the increment m = (ba) / k of the torque distribution coefficient per step, so as to obtain the discrete value of the torque distribution coefficient per step in the value range.

[0128] Module 30 is established to build a longitudinal dynamics model of the vehicle based on the distributed dual-motor drive mode;

[0129] Definition module 40 is used to define an objective function based on the longitudinal dynamics model;

[0130] Optimization module 50 is used to optimize the energy-efficient rolling prediction optimization problem by combining preset constraints and the objective function;

[0131] The solution module 60 is used to solve the rolling prediction optimization problem based on the Pontryagin minimum principle and the bisection iteration method to obtain the optimal control quantity related to energy consumption calculation.

[0132] The selecting module 70 is configured to calculate the energy consumption value corresponding to each of the discrete values based on the optimal control value, and select a target discrete value corresponding to the minimum energy consumption value as the optimal torque distribution coefficient.

[0133] Further, the establishing module 30 comprises:

[0134] The operation unit 31 is configured to calculate the output torque of the front and rear electrodes by a torque formula based on the distributed dual-motor driving mode of the vehicle.

[0135] The establishing unit 32 is configured to establish a longitudinal dynamics model of the vehicle based on the energy consumption model of the two electrodes.

[0136] Further, the defining module 40 comprises:

[0137] The simplifying unit 41 is configured to simplify the longitudinal dynamics model into a single degree of freedom model, so that the longitudinal motion of the vehicle is represented by a speed and acceleration formula.

[0138] The conversion unit 42 is configured to convert the vehicle traction advance function according to the vehicle traction function and the speed and acceleration formula in combination with the road condition of the vehicle.

[0139] The defining unit 43 is configured to define a target function of the vehicle in the prediction time domain according to the vehicle traction advance function in combination with the control target of minimizing the total energy consumption of the front and rear motors and the vehicle speed close to the set vehicle speed in the prediction time domain N.

[0140] It should be noted that each of the above modules can be a functional module or a program module, which can be implemented by software or hardware. For the modules implemented by hardware, each of the above modules can be located in the same processor, or each of the above modules can be located in different processors in any combination.

[0141] Embodiment 3

[0142] In combination Figure 1 The torque distribution method based on the distributed dual-motor driving vehicle described above can be implemented by an electronic device. Figure 5 The hardware structure of the electronic device according to the embodiment is shown in the figure.

[0143] The electronic device can include a processor 81 and a memory 82 storing computer program instructions.

[0144] In particular, the processor 81 can include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or can be configured to implement one or more integrated circuits that embody the embodiments of the present application.

[0145] The memory 82 can include a mass storage for data or instructions. By way of example, and without limitation, the memory 82 can include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash drive, a compact disc (CD), a DVD, a magneto-optical disk, a tape drive, a USB drive, or any combination of two or more of these. The memory 82 can be removable and / or non-removable (or fixed) as appropriate. The memory 82 can be internal or external as appropriate. In certain embodiments, the memory 82 is a non-volatile memory. In certain embodiments, the memory 82 includes a Read-Only Memory (ROM) and a Random-Access Memory (RAM). The ROM can be a mask-programmed ROM, a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), an Electrically Alterable ROM (EAROM), or a FLASH memory, or any combination of two or more of these, as appropriate. The RAM can be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), which can be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended Data Output Dynamic Random-Access Memory (EDODRAM), a Synchronous Dynamic Random-Access Memory (SDRAM), or the like, as appropriate.

[0146] The memory 82 can be used to store or buffer various data files required for processing and / or communication, and possible computer program instructions executed by the processor 81.

[0147] The processor 81 reads and executes the computer program instructions stored in the memory 82 to implement the torque distribution method based on the distributed dual-motor driven vehicle in the above-mentioned embodiment 1.

[0148] In some embodiments, the electronic device can further include a communication interface 83 and a bus 80. In which, as shown in the figure, the processor 81, the memory 82, the communication interface 83 are connected through the bus 80 and complete the communication between each other. Figure 5

[0149] The communication interface 83 is used to realize the communication between each module, device, unit and / or equipment in the embodiments of the present application. The communication interface 83 can also realize the data communication between other components, such as: external equipment, image / data acquisition equipment, database, external storage and image / data processing workstation, etc.

[0150] ​Bus 80 includes hardware, software, or both, to couple components of the device to each other and to couple components of the device to other devices. What is considered a component of the device can vary depending on the embodiment of the present application. As examples, a bus 80 can include, but is not limited to, a data bus, an address bus, a control bus, an expansion bus, a local bus, etc. In one embodiment, for example, without limitation, the bus 80 can include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand (IB) interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or another suitable bus or a combination of two or more of these. Where appropriate, bus 80 can include one or more buses. Although this application describes and shows a particular bus, this application contemplates any suitable bus or interconnect.

[0151] The electronic device can obtain a torque distribution system based on a distributed dual-motor driven vehicle, and execute the torque distribution method based on the distributed dual-motor driven vehicle of embodiment 1.

[0152] In addition, in combination with the torque distribution method based on the distributed dual-motor driven vehicle in embodiment 1 described above, the present embodiment can provide a storage medium for implementation. The storage medium has computer program instructions stored thereon; the computer program instructions are executed by a processor to implement the torque distribution method based on the distributed dual-motor driven vehicle of embodiment 1.

[0153] Any technical features in the above-described embodiments can be combined in any manner, and for the sake of brevity, not all possible combinations are described, however, any combination of the technical features is considered to be within the scope of the present specification.

[0154] The above-described embodiments are merely preferred embodiments of the present application and are not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A torque distribution method for a distributed dual-motor vehicle, characterized in that, include: The range of the torque distribution coefficient λ for the distributed dual-motor vehicle is defined as λ∈[a,b], and 0<a<b<1; Divide the value range into k steps and calculate the increment of the torque distribution coefficient in each step m = (ba) / k to obtain the discrete value of the torque distribution coefficient in each step within the value range. A longitudinal dynamics model of the vehicle is established based on the distributed dual-motor drive mode; Based on the longitudinal dynamics model, an objective function is defined, wherein, based on the vehicle's traction forward function and combined with the control objectives of minimizing the total energy consumption of the front and rear motors and ensuring the vehicle speed approaches the set speed within the prediction time domain N, the objective function for the vehicle in the prediction time domain is defined; the specific objective function is as follows: in, In the formula, P·Δt represents the energy efficiency index, and v d This represents the set reference vehicle speed, κ1 and κ2 represent weighting coefficients in kg, and (v(k)-v d ) 2 This represents the speed tracking performance index, (v(N)-v d ) 2 Indicates a punitive indicator for the terminal; The optimal rolling prediction optimization problem with optimal energy consumption is optimized by combining preset constraints and the objective function. Based on the Pontryagin minimum principle and the bisection iterative method, the rolling prediction optimization problem is solved to obtain the optimal control quantity related to energy consumption calculation. Based on the optimal control quantity, the energy consumption value under each discrete value is calculated, and the target discrete value corresponding to the minimum energy consumption value is selected as the optimal torque distribution coefficient.

2. The torque distribution method for a distributed dual-motor vehicle according to claim 1, characterized in that, The specific steps for establishing the longitudinal dynamics model of the vehicle based on the distributed dual-motor drive mode include: Based on the vehicle's distributed dual-motor drive mode, the output torque of the front and rear electrodes is calculated using a torque formula; wherein, the torque formula is: In the formula, T f T represents the output torque of the front axle / front wheels. r The output torque of the rear axle / rear wheel is represented by T, the total output torque of the traction vehicle is represented by η, and the total mechanical efficiency of the vehicle's transmission system is represented by i. ff i fr T represents the transmission ratio of the front and rear main reducers. mf T is the output torque of the front motor. mr This refers to the output torque of the rear motor; Based on the energy consumption model of the two electrodes, a longitudinal dynamics model of the vehicle is established; wherein, the longitudinal dynamics model is: P=P f +P r In the formula, P f P represents the output power of the front motor. r Indicates the output power of the rear motor, n mf Indicates the front motor speed, n mr This indicates the speed of the rear motor.

3. The torque distribution method for a distributed dual-motor vehicle according to claim 2, characterized in that, The front motor speed n mf and the speed n of the rear motor mr Specifically: In the formula, r w The radius of the vehicle's wheels is represented by 'v', and the vehicle speed is represented by 'v'.

4. The torque distribution method for a distributed dual-motor vehicle according to claim 1, characterized in that, The specific steps for defining the objective function based on the longitudinal dynamics model include: The longitudinal dynamics model is simplified to a single-degree-of-freedom model so that the longitudinal motion of the vehicle can be characterized by velocity and acceleration formulas; wherein, the velocity and acceleration formulas are specifically: In the formula, Δt represents the time interval from step k to step (k+1), s(k+1) and s(k) represent the distance traveled in step (k+1) and step k, respectively, v(k+1) and v(k) represent the speeds of travel in step (k+1) and step k, respectively, and α(k) represents the acceleration in step k. Based on the vehicle's overall traction force function and the aforementioned velocity and acceleration formulas, and combined with the vehicle's road conditions, the vehicle's overall traction forward function is calculated; specifically, the vehicle's overall traction forward function is: In the formula, F t (k) represents the vehicle's traction force, F W (k) represents air resistance, F i (k) represents the ramp resistance, F f (k) represents rolling resistance, δ is the vehicle rotational mass conversion factor, and M is the total vehicle mass.

5. The torque distribution method for a distributed dual-motor vehicle according to claim 1, characterized in that, The preset constraints are specifically as follows: v min ≤v(k)≤v max ; n m,min ≤n m (k)≤n m,max ; T m,min (n m (k))≤T m (n m (k))≤T m,max (n m (k)); In the formula, v min v max Let n represent the minimum and maximum permissible vehicle speeds, respectively. m,min n m,max T represents the minimum and maximum values ​​of the motor speed, respectively. m,min (n m (k)), T m,max (n m (k) represents the electrode rotation speed n. m (k) indicates the minimum and maximum torque output by the electrode.

6. The torque distribution method for a distributed dual-motor vehicle according to claim 1, characterized in that, The rolling prediction optimization problem is specifically as follows: In the formula, P·Δt represents the energy efficiency index, and v d This represents the set reference vehicle speed, κ1 and κ2 represent weighting coefficients in kg, and (v(k)-v d ) 2 This represents the speed tracking performance index, (v(N)-v d ) 2 Let F represent the terminal penalty index, Δt represent the time interval from step k to step (k+1), s(k+1) and s(k) represent the distance traveled in steps (k+1) and k, respectively, and v(k+1) and v(k) represent the speeds in steps (k+1) and k, respectively. t (k) represents the vehicle's traction force, F W (k) represents air resistance, F i (k) represents the ramp resistance, F f (k) represents rolling resistance, δ is the vehicle rotational mass conversion factor, and M is the total vehicle mass.

7. The torque distribution method for a distributed dual-motor vehicle according to claim 4 or 6, characterized in that, F w (k)=C D Aρ·v 2 (k) / 2,F i (k)=Mgsinα(k),F f (k)=Mgf·cosθ(k); In the formula, C D Let A represent the air resistance coefficient, ρ represent the frontal area, g represent the gravitational acceleration, f represent the wheel rolling resistance coefficient, and θ(k) represent the road slope at the k-th step.

8. The torque distribution method for a distributed dual-motor vehicle according to claim 1, characterized in that, The optimal control quantities include at least the wheel rolling resistance coefficient, frontal area, reduction ratio between the front and rear axle motors and the wheels, and power transmission efficiency.

9. A torque distribution system for a distributed dual-motor vehicle, employing the method described in claim 1, characterized in that, The system includes: The setting module is used to set the range of the torque distribution coefficient λ of the distributed dual-motor vehicle, λ∈[a,b], and 0<a<b<1; The calculation module is used to divide the value range into k steps and calculate the increment of the torque distribution coefficient in each step m = (ba) / k, so as to obtain the discrete value of the torque distribution coefficient in each step within the value range. A module is established to build a longitudinal dynamics model of the vehicle based on the distributed dual-motor drive mode; The definition module is used to define the objective function based on the longitudinal dynamics model; The optimization module is used to optimize the energy-efficient rolling prediction optimization problem by combining preset constraints and the objective function. The solution module is used to solve the rolling prediction optimization problem based on the Pontryagin minimum principle and the bisection iteration method to obtain the optimal control quantity related to energy consumption calculation. The selection module is used to calculate the energy consumption value under each discrete value based on the optimal control quantity, and select the target discrete value corresponding to the minimum energy consumption value, which is the optimal torque distribution coefficient.

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