A multi-drive motor cooperative control method and system for new energy special vehicles

Abstract

A multi-drive motor cooperative control method and system for new energy special vehicles, which relates to the field of vehicle control technology; constructing a digital twin model of a special vehicle and simulating it, using an LSTM neural network to construct a road condition prediction model to output predicted road condition parameters, obtaining the drive anti-skid index, yaw rate deviation rate, and comprehensive stability coefficient of the special vehicle, obtaining the joint acceleration index, power maintenance degree, and comprehensive power coefficient of the special vehicle, setting the desired stability coefficient and desired power coefficient, obtaining the corresponding cooperative control parameters in the digital twin model, and constructing a cooperative control model of the special vehicle to perform cooperative control on the special vehicle; which is beneficial to providing targeted motor control parameters according to different road conditions and can ensure the stability and power performance of vehicle driving.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control, and in particular to a multi-drive motor cooperative control method and system for new energy special vehicles. Background Technique

[0002] Cooperatively controlling multiple drive motors of new energy special vehicles is a highly integrated and complex technology. Through an advanced hardware architecture, intelligent control algorithms, reliable communication mechanisms, and comprehensive safety designs, the vehicle's efficient, stable, and dynamic performance under various working conditions is achieved;

[0003] Different from the drive modes of traditional special vehicles, new energy special vehicles generally adopt a multi-motor drive control mode. This drive mode will lead to a large uncertainty between the longitudinal and lateral stabilities of the vehicle. How to achieve vehicle stability and power control under multi-motor drive conditions is one of the technical problems in the current control of multi-motor drive vehicles;

[0004] In the prior art, there is a lack of a control method that can simultaneously take into account the driving stability and overall power performance of special vehicles, and the existing technical means often ignore the influence of road conditions on motor drive, resulting in the inability to provide targeted motor control parameters. In view of the deficiencies of the prior art, the present invention provides a multi-drive motor cooperative control method and system for new energy special vehicles. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-drive motor cooperative control method and system for new energy special vehicles.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A multi-drive motor cooperative control system for new energy special vehicles includes the following modules:

[0007] A data acquisition module, which is used to obtain the basic information of the special vehicle, construct a corresponding digital twin model, and perform simulation on the digital twin model;

[0008] A road condition prediction module, which is used to construct a three-dimensional point cloud model of the road condition and obtain corresponding road condition parameters, and use an LSTM neural network to construct a road condition prediction model to output predicted road condition parameters;

[0009] A data evaluation module, which is used to obtain the drive anti-skid index and yaw rate deviation rate of the special vehicle, and obtain the corresponding comprehensive stability coefficient, and is also used to obtain the joint acceleration index and power maintenance degree of the special vehicle, and obtain the corresponding comprehensive power coefficient;

[0010] A vehicle control module is used to set the desired stability coefficient and the desired dynamic coefficient, obtain corresponding cooperative control parameters by combining different predicted road condition parameters in the digital twin model, and is also used to construct a cooperative control model for special vehicles and perform cooperative control on special vehicles using the cooperative control model.

[0011] Further, obtaining the basic information of the special vehicle and constructing a corresponding digital twin model, the process of simulating the digital twin model includes:

[0012] The basic information includes the geometric parameters, mass parameters, power structure parameters, transmission structure parameters, suspension and steering parameters, and tire and ground parameters of the special vehicle. The digital twin model of the special vehicle is constructed using digital twin technology based on the obtained basic information;

[0013] Using simulation software to simulate the constructed digital twin model to obtain the theoretical operation data of the special vehicle during operation. The theoretical operation data refers to all the data generated when the special vehicle operates in the digital twin model.

[0014] Further, constructing a three-dimensional point cloud model of the road condition and obtaining corresponding road condition parameters, the process of constructing a road condition prediction model using an LSTM neural network to output predicted road condition parameters includes:

[0015] A laser scanning unit and a visual acquisition unit are respectively set on the special vehicle to obtain the point cloud data of the road in the forward direction of the special vehicle. The ICP algorithm is used to perform point cloud registration on the point cloud data, and the voxelization algorithm is used to convert the point cloud data into a three-dimensional point cloud model. Corresponding road condition parameters are obtained in the three-dimensional point cloud model, including road surface slope, friction coefficient, and obstacle distribution;

[0016] Obtaining a road condition prediction set containing the point cloud data and its road condition parameters of common roads in different mines and construction sites, and dividing the obtained road condition prediction set into a first training set and a first test set;

[0017] Taking the point cloud data of common roads in different mines and construction sites in the first training set as the input data of the LSTM neural network, and taking its corresponding road condition parameters as the output data of the LSTM neural network, and training the LSTM neural network to obtain an initial LSTM neural network;

[0018] Using the first test set to verify the initial LSTM neural network model, and outputting the initial LSTM neural network with an error less than or equal to the preset first test error threshold as the road condition prediction model, and continuously inputting the point cloud data obtained by the laser scanning unit and the visual acquisition unit into the road condition prediction model to output corresponding predicted road condition parameters.

[0019] Further, the process of obtaining the drive slip index and yaw rate deviation rate of the special vehicle and obtaining the corresponding comprehensive stability coefficient includes:

[0020] Denote the friction coefficient in the predicted road condition parameters as , and denote the maximum friction coefficient of the special vehicle on its driving road as ;

[0021] Obtain the actual operation data of the special vehicle. The actual operation data refers to all data generated when the special vehicle operates in the actual application scenario. Denote the average angular velocity of each wheel in the actual operation data as ;

[0022] In the digital twin model, simulate the operation process of the special vehicle by combining the predicted road condition parameters to obtain the theoretical operation data. Denote the average theoretical angular velocity of each wheel in the theoretical operation data as , and obtain the drive slip index T of the special vehicle at the corresponding moment c ;

[0023]

[0024] Denote the longitudinal vehicle speed, turning radius, and yaw rate of the special vehicle in the actual operation data as , R, , and obtain the yaw rate deviation rate Y of the special vehicle at the corresponding moment r ;

[0025]

[0026] is the preset expected yaw rate, and for the drive slip index T c and the yaw rate deviation rate Y r respectively set the weight values Q1 and Q2, and obtain the comprehensive stability coefficient S of the special vehicle at the corresponding moment w ;

[0027]

[0028] Further, the process of obtaining the joint acceleration index and power maintenance degree of the special vehicle and obtaining the corresponding comprehensive power coefficient includes:

[0029] Number the drive motors of the special vehicle, denoted as i, i = 1, 2,..., n, where n is the number of drive motors. Denote the actual acceleration of the i-th drive motor in the actual operation data as , and obtain the acceleration variance of each drive motor at the same moment ;

[0030]

[0031] Denote the theoretical maximum acceleration of the drive motor of the special vehicle as and obtain the combined acceleration index J of the special vehicle at the corresponding moment a , which is a preset coordination parameter;

[0032]

[0033] Denote the actual power and real-time temperature of the i-th drive motor in the actual operation data as P i and W i , and denote the rated power of the drive motor of the special vehicle as P r , and obtain the power maintenance degree P of the special vehicle at the corresponding moment s , which is a preset temperature decay parameter, and W b is the preset reference temperature;

[0034]

[0035] is the combined acceleration index J a and the power maintenance degree P s are respectively set with weight values Q3 and Q4, and the comprehensive power coefficient S of the special vehicle at the corresponding moment is obtained d , J ref is the combined acceleration index under ideal conditions;

[0036]

[0037] Furthermore, setting the expected stability coefficient and the expected power coefficient, the process of obtaining the corresponding cooperative control parameters by combining different predicted road condition parameters in the digital twin model includes:

[0038] The weight values of the drive slip index, yaw rate deviation rate, combined acceleration index, and power maintenance degree are all adjustable. Adjust the weight values of each item according to actual needs to obtain the corresponding expected stability coefficient and expected power coefficient;

[0039] In the digital twin model, simulate the special vehicle by combining different predicted road condition parameters to obtain the drive parameters of each drive motor when the expected stability coefficient and expected power coefficient are reached, including speed, torque, and direction, and use the drive parameters of each drive motor as the corresponding cooperative control parameters.

[0040] Furthermore, the process of constructing a cooperative control model for the special vehicle and using the cooperative control model to perform cooperative control on the special vehicle includes:

[0041] Generate a collaborative control set according to the collaborative control parameters of the special vehicle under different expected stability coefficients, expected dynamic coefficients, and predicted road condition parameters, and divide the obtained collaborative control set into a second training set and a second test set;

[0042] Construct a convolutional neural network. Use different expected stability coefficients, expected dynamic coefficients, and predicted road condition parameters in the second training set as the input data of the convolutional neural network, and use the corresponding collaborative control parameters as the output data of the convolutional neural network. Train the convolutional neural network to obtain an initial convolutional neural network;

[0043] Use the second test set to verify the model of the initial convolutional neural network, and output the initial convolutional neural network whose error is less than or equal to the preset second test error threshold as the collaborative control model;

[0044] Use the predicted road condition parameters of the current special vehicle on its driving road as real-time road condition parameters, and combine the set expected stability coefficient and expected dynamic coefficient to input into the collaborative control model to output the corresponding real-time control parameters, and control each drive motor of the special vehicle according to the real-time control parameters.

[0045] A multi-drive motor collaborative control method for new energy special vehicles includes the following steps:

[0046] Step S1: Obtain the basic information of the special vehicle, construct a corresponding digital twin model, and perform simulation on the digital twin model;

[0047] Step S2: Construct a three-dimensional point cloud model of the road condition and obtain the corresponding road condition parameters, and use the LSTM neural network to construct a road condition prediction model to output the predicted road condition parameters;

[0048] Step S3: Obtain the drive slip index and yaw rate deviation rate of the special vehicle, and obtain the corresponding comprehensive stability coefficient, and obtain the joint acceleration index and power maintenance degree of the special vehicle, and obtain the corresponding comprehensive dynamic coefficient;

[0049] Step S4: Set the expected stability coefficient and expected dynamic coefficient, obtain the corresponding collaborative control parameters by combining different predicted road condition parameters in the digital twin model, construct a collaborative control model for the special vehicle, and use the collaborative control model to perform collaborative control on the special vehicle.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] By constructing a digital twin model of the special vehicle and performing simulation on it, the present invention can simulate the operation data of the special vehicle under ideal conditions. Using the LSTM neural network to construct a road condition prediction model can directly output the corresponding predicted road condition parameters according to the point cloud data of the road;

[0052] By obtaining the drive anti-skid index, yaw rate deviation rate, combined acceleration index, and power maintenance degree of special vehicles, the driving stability and overall power performance of special vehicles can be comprehensively evaluated. Set the expected stability coefficient and expected power coefficient in the digital twin model, combine different predicted road condition parameters to obtain corresponding cooperative control parameters, and construct a cooperative control model;

[0053] It can effectively combine user needs and real-time road conditions to output the drive parameters of each drive motor, and control it to operate according to the obtained real-time control parameters, which is beneficial to providing targeted motor control parameters according to different road conditions and can ensure the driving stability and power performance of the vehicle. Brief Description of the Drawings

[0054] Figure 1 It is the schematic diagram of the present invention. Detailed Embodiment

[0055] As Figure 1 shown, a multi-drive motor cooperative control system for new energy special vehicles includes the following modules:

[0056] The data acquisition module is used to obtain the basic information of special vehicles, construct a corresponding digital twin model, and perform simulation on the digital twin model;

[0057] The road condition prediction module is used to construct a three-dimensional point cloud model of road conditions and obtain corresponding road condition parameters, and use the LSTM neural network to construct a road condition prediction model to output predicted road condition parameters;

[0058] The data evaluation module is used to obtain the drive anti-skid index, yaw rate deviation rate of special vehicles, and obtain corresponding comprehensive stability coefficients, and is also used to obtain the combined acceleration index, power maintenance degree of special vehicles, and obtain corresponding comprehensive power coefficients;

[0059] The vehicle control module is used to set the expected stability coefficient and expected power coefficient, obtain corresponding cooperative control parameters by combining different predicted road condition parameters in the digital twin model, and is also used to construct a cooperative control model of special vehicles and perform cooperative control on special vehicles by using the cooperative control model.

[0060] It should be further noted that in the specific implementation process, the process of obtaining the basic information of special vehicles, constructing a corresponding digital twin model, and performing simulation on the digital twin model includes:

[0061] The special vehicle refers to a vehicle that is specially designed and modified to meet specific work requirements and perform special tasks. It usually has unique functions, structures, and performance characteristics and is applied in technical fields such as military, engineering construction, medical and health, fire rescue, and transportation;

[0062] The basic information refers to the various data necessary for constructing the digital twin model of the special vehicle, including the geometric parameters, mass parameters, power structure parameters, transmission structure parameters, suspension and steering parameters, tire and ground parameters of the special vehicle. The digital twin model of the special vehicle is constructed using digital twin technology based on the obtained basic information;

[0063] At this time, the digital twin model can only reflect the physical structure of the special vehicle. On this basis, the constructed digital twin model is simulated using simulation software. At this time, the digital twin model can obtain the theoretical operation data of the special vehicle during operation. The theoretical operation data refers to all the data generated when the special vehicle operates in the digital twin model.

[0064] It should be further noted that in the specific implementation process, the process of constructing the three-dimensional point cloud model of the road condition and obtaining the corresponding road condition parameters, and using the LSTM neural network to construct the road condition prediction model to output the predicted road condition parameters includes:

[0065] A laser scanning unit and a visual acquisition unit are respectively set on the special vehicle. The laser scanning unit and the visual acquisition unit are used to respectively obtain the point cloud data of the road in the forward direction of the special vehicle, and the ICP algorithm is used to perform point cloud registration on the point cloud data obtained by both;

[0066] Feature extraction is performed on the various feature information in the point cloud data after point cloud registration, including but not limited to surface normal, edge, geometric structure, etc. Modeling is performed based on the point cloud data and the extracted feature information. The voxelization algorithm is used to convert the point cloud data into a three-dimensional point cloud model, and the corresponding road condition parameters are obtained in the three-dimensional point cloud model, including road surface slope, friction coefficient, obstacle distribution, etc.;

[0067] A road condition prediction set is obtained. The road condition prediction set contains the point cloud data of common roads in different mines and construction sites and their corresponding road condition parameters. The obtained road condition prediction set is divided into a first training set and a first test set;

[0068] The point cloud data of common roads in different mines and construction sites in the first training set is used as the input data of the LSTM neural network, and their corresponding road condition parameters are used as the output data of the LSTM neural network. The LSTM neural network is trained to obtain an initial LSTM neural network;

[0069] The initial LSTM neural network is verified using the first test set, and the initial LSTM neural network with an output less than or equal to the preset first test error threshold is used as the corresponding road condition prediction model. The point cloud data obtained by the laser scanning unit and the vision acquisition unit is continuously input into the road condition prediction model to output the corresponding predicted road condition parameters.

[0070] It should be further noted that in the specific implementation process, the process of obtaining the drive anti-slip index and yaw rate deviation rate of the special vehicle and obtaining the corresponding comprehensive stability coefficient includes:

[0071] In the embodiment of the present invention, the drive anti-slip index and yaw rate deviation rate of the special vehicle during its operation are respectively evaluated, and then the driving stability of the special vehicle is comprehensively evaluated according to the above two parameters;

[0072] The drive anti-slip index is used to evaluate the ability of the special vehicle to prevent wheel slip. Denote the friction coefficient in the predicted road condition parameters as and denote the maximum friction coefficient obtained by the special vehicle on its driving road as ;

[0073] Obtain the actual operation data of the special vehicle. The actual operation data refers to all data generated when the special vehicle operates in the actual application scenario. Denote the average angular velocity of each wheel in the actual operation data as ;

[0074] In the digital twin model, the operation process of the special vehicle is simulated by combining the predicted road condition parameters to obtain the theoretical operation data. Denote the average theoretical angular velocity of each wheel in the theoretical operation data as , and obtain the drive anti-slip index of the special vehicle at the corresponding moment, denoted as T c ;

[0075]

[0076] The yaw rate deviation rate is used to evaluate the stability of the special vehicle during turning. The yaw rate refers to the rotation speed of the special vehicle around the vertical axis, denoted as . Denote the longitudinal vehicle speed and turning radius of the special vehicle in the actual operation data as and R respectively, and obtain the yaw rate deviation rate of the special vehicle at the corresponding moment, denoted as Y r ;

[0077]

[0078] where is the preset expected yaw rate;

[0079] For the obtained anti-skid driving index T c and the yaw rate deviation Y r respectively set the corresponding weight values, denoted as Q1 and Q2, and obtain the comprehensive stability coefficient S of the special vehicle at the corresponding moment w ;

[0080]

[0081] It should be further noted that in the specific implementation process, the process of obtaining the combined acceleration index, power maintenance degree of the special vehicle, and obtaining the corresponding comprehensive power coefficient includes:

[0082] In the embodiment of the present invention, by evaluating the combined acceleration index, power maintenance degree, and maximum climbing angle dynamic margin of the special vehicle during its operation, and then comprehensively evaluating the overall power performance of the special vehicle according to the above three parameters;

[0083] The combined acceleration index is used to evaluate the comprehensive acceleration performance of each drive motor when working together. The drive motors of the special vehicle are numbered, denoted as i, i = 1, 2,..., n, where n is the number of drive motors. Denote the actual acceleration of the ith drive motor in the actual operation data as , and obtain the acceleration variance of each drive motor at the same moment, denoted as ;

[0084]

[0085] Obtain the theoretical maximum acceleration of the drive motor of the special vehicle. Assume that each drive motor of the same special vehicle is of the same model, and its theoretical maximum acceleration is also the same, denoted as , and obtain the combined acceleration index of the special vehicle at the corresponding moment, denoted as J a ;

[0086]

[0087] Among them, is a preset coordination parameter, and its value range is 0.5 - 1.2;

[0088] The power maintenance degree is used to evaluate the ability of each drive motor to maintain stable power output under load changes. Denote the actual power and real-time temperature of the ith drive motor in the actual operation data as P i and W i , obtain the rated power of the drive motor of the special vehicle, denoted as P r , and obtain the power maintenance degree of the special vehicle at the corresponding moment, denoted as P s ;

[0089]

[0090] Among them, is a preset temperature decay parameter, and its value range is 0.02 - 0.05, W b is the reference temperature, and its value is 25 degrees Celsius;

[0091] is the obtained combined acceleration index J a and the power maintenance degree P s respectively set corresponding weight values, denoted as Q3 and Q4, and obtain the comprehensive power coefficient S of the special vehicle at the corresponding moment d ;

[0092]

[0093] Among them, J ref is the combined acceleration index under ideal conditions.

[0094] It should be further noted that in the specific implementation process, setting the expected stability coefficient and the expected power coefficient, and the process of obtaining the corresponding cooperative control parameters by combining different predicted road condition parameters in the digital twin model includes:

[0095] In the process of obtaining the comprehensive stability coefficient, the weight values corresponding to the two parameters included therein are both adjustable. If the application scenario of the special vehicle is a highway, the weight value of the drive slip index is increased. If the application scenario of the special vehicle is a mountain road, the weight value of the yaw rate deviation rate is increased. The sum of the two weight values is always equal to 1;

[0096] In the process of obtaining the comprehensive power coefficient, the weight values corresponding to the two parameters included therein are also both adjustable. If the user expects the special vehicle to have strong power, the weight value of the combined acceleration index is increased. If the user expects the special vehicle to have a long endurance, the weight value of the power maintenance degree is increased. The sum of the two weight values is always equal to 1;

[0097] The user adjusts the weight values of each item according to actual needs, and uses the obtained comprehensive stability coefficient and comprehensive power coefficient after adjusting the weight values as the expected stability coefficient and expected power coefficient of the special vehicle respectively, with the goal of achieving the expected stability coefficient and expected power coefficient in the digital twin model;

[0098] Combining different predicted road condition parameters to conduct simulation on the special vehicle to obtain the drive parameters of each drive motor when the expected stability coefficient and expected power coefficient are achieved. The drive parameters include speed, torque, direction, etc., and use the drive parameters of each drive motor as the corresponding cooperative control parameters.

[0099] It should be further noted that in the specific implementation process, the process of constructing a cooperative control model for special vehicles and using the cooperative control model to perform cooperative control on special vehicles includes:

[0100] Generating a corresponding cooperative control set according to the cooperative control parameters of the special vehicle under different expected stability coefficients, expected dynamic coefficients, and predicted road condition parameters, and dividing the obtained cooperative control set into a second training set and a second test set;

[0101] Constructing a convolutional neural network, using different expected stability coefficients, expected dynamic coefficients, and predicted road condition parameters in the second training set as the input data of the convolutional neural network, and using the corresponding cooperative control parameters as the output data of the convolutional neural network, and training the convolutional neural network to obtain an initial convolutional neural network;

[0102] Using the second test set to perform model verification on the initial convolutional neural network, and outputting the initial convolutional neural network with an error less than or equal to a preset second test error threshold as the corresponding cooperative control model;

[0103] Taking the predicted road condition parameters of the current special vehicle on its driving road as real-time road condition parameters, combining the expected stability coefficient and expected dynamic coefficient set by the user and inputting them into the cooperative control model, and outputting the corresponding real-time control parameters, and controlling each drive motor of the special vehicle according to the real-time control parameters.

[0104] The embodiment of the present invention further includes a cooperative control method for multiple drive motors of a new energy special vehicle, including the following steps:

[0105] Step S1: Obtaining the basic information of the special vehicle, constructing a corresponding digital twin model, and performing simulation on the digital twin model;

[0106] Step S2: Constructing a three-dimensional point cloud model of the road condition and obtaining the corresponding road condition parameters, and using an LSTM neural network to construct a road condition prediction model to output predicted road condition parameters;

[0107] Step S3: Obtaining the drive slip index and yaw rate deviation rate of the special vehicle, and obtaining the corresponding comprehensive stability coefficient, and obtaining the joint acceleration index and power maintenance degree of the special vehicle, and obtaining the corresponding comprehensive dynamic coefficient;

[0108] Step S4: Setting the expected stability coefficient and expected dynamic coefficient, obtaining the corresponding cooperative control parameters by combining different predicted road condition parameters in the digital twin model, constructing a cooperative control model for the special vehicle, and using the cooperative control model to perform cooperative control on the special vehicle.

[0109] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A new energy special vehicle multi-drive motor coordinated control system, characterized in that: Includes the following modules: The data acquisition module is used to obtain the basic information of special vehicles, build the corresponding digital twin model, and simulate the digital twin model; The road condition prediction module is used to construct a three-dimensional point cloud model of the road condition and obtain the corresponding road condition parameters, and use the LSTM neural network to build a road condition prediction model to output the predicted road condition parameters; The data evaluation module is used to obtain the driving anti-skid index and yaw rate deviation rate of the special vehicle, and obtain the corresponding comprehensive stability coefficient, and is also used to obtain the joint acceleration index and power maintenance of the special vehicle, and obtain the corresponding comprehensive power coefficient; The vehicle control module is used to set the expected stability coefficient and the expected power coefficient, obtain the corresponding collaborative control parameters in the digital twin model by combining different predicted road condition parameters, and is also used to build a collaborative control model for special vehicles and use the collaborative control model to perform collaborative control on special vehicles; The process of obtaining collaborative control parameters in the digital twin model includes: The weight values ​​of the driving anti-skid index, yaw rate deviation rate, combined acceleration index, and power maintenance degree are all adjustable. The weight values ​​of each item are adjusted according to actual needs, and the comprehensive stability coefficient and comprehensive power coefficient obtained after adjusting the weight values ​​are used as the expected stability coefficient and expected power coefficient of the special vehicle respectively; In the digital twin model, the special vehicle is simulated in combination with different predicted road condition parameters to obtain the driving parameters of each drive motor when the expected stability coefficient and the expected power coefficient are achieved, including speed, torque, and direction, and the driving parameters of each drive motor are used as corresponding coordinated control parameters.

2. A new energy special vehicle multi-drive motor coordinated control system according to claim 1, characterized in that: The process of building a digital twin model of a special vehicle and simulating it includes: The basic information includes geometric parameters, mass parameters, power structure parameters, transmission structure parameters, suspension and steering parameters, tire and ground parameters of the special vehicle. The digital twin model of the special vehicle is constructed based on the acquired basic information using digital twin technology; The constructed digital twin model is simulated using simulation software to obtain the theoretical operating data of the special vehicle during operation. The theoretical operating data refers to all data generated when the special vehicle is running in the digital twin model.

3. A new energy special vehicle multi-drive motor coordinated control system according to claim 2, characterized in that: The process of building a traffic condition prediction model to output predicted traffic condition parameters includes: A laser scanning unit and a visual acquisition unit are respectively set on the special vehicle to obtain point cloud data of the road in the direction of the special vehicle's advance, the point cloud data is registered using the ICP algorithm, and the point cloud data is converted into a three-dimensional point cloud model using a voxelization algorithm, and the corresponding road condition parameters are obtained in the three-dimensional point cloud model, including road slope, friction coefficient, and obstacle distribution; Acquire a road condition prediction set including point cloud data of common roads in different mines and construction sites and road condition parameters thereof, and divide the acquired road condition prediction set into a first training set and a first test set; The point cloud data of common roads in different mines and construction sites in the first training set are used as input data of the LSTM neural network, and the corresponding road condition parameters are used as output data of the LSTM neural network, and the LSTM neural network is trained to obtain an initial LSTM neural network; The initial LSTM neural network is verified using the first test set, and the initial LSTM neural network with a first test error threshold that is less than or equal to the preset value is output as a road condition prediction model. The point cloud data obtained by the laser scanning unit and the visual acquisition unit are continuously input into the road condition prediction model to output the corresponding predicted road condition parameters.

4. A new energy special vehicle multi-drive motor coordinated control system according to claim 3, characterized in that: The process of obtaining the comprehensive stability coefficient of special vehicles includes: The friction coefficient in the predicted road condition parameters is recorded as , the highest friction coefficient of the special vehicle on its driving road is recorded as ; The actual operation data of the special vehicle is obtained. The actual operation data refers to all the data generated when the special vehicle is running in the actual application scenario. The average angular velocity of each wheel in the actual operation data is recorded as ; In the digital twin model, the operation process of the special vehicle is simulated in combination with the predicted road condition parameters to obtain the theoretical operation data, and the theoretical angular velocity mean of each wheel in the theoretical operation data is recorded as , obtain the driving anti-skid index T of the special vehicle at the corresponding time c ; The longitudinal speed, turning radius, and yaw rate of the special vehicle in the actual operation data are recorded as , R, , obtain the yaw rate deviation rate Y of the special vehicle at the corresponding moment r ; is the preset expected yaw rate, is the driving anti-slip index T c and yaw rate deviation rate Y r Set the weight values ​​Q1 and Q2 respectively to obtain the comprehensive stability coefficient S of the special vehicle at the corresponding time w ; 5. A new energy special vehicle multi-drive motor coordinated control system according to claim 4, characterized in that: The process of obtaining the comprehensive power coefficient of special vehicles includes: The drive motors of special vehicles are numbered as i, i=1, 2, ..., n, where n is the number of drive motors. The actual acceleration of the i-th drive motor in the actual operation data is recorded as , get the acceleration variance of each drive motor at the same time ; The theoretical maximum acceleration of the drive motor of a special vehicle is recorded as , obtain the joint acceleration index J of special vehicles at the corresponding time a , is the preset coordination parameter; The actual power and real-time temperature of the i-th drive motor in the actual operation data are recorded as P i and W i , the rated power of the drive motor of the special vehicle is recorded as P r , obtain the power maintenance degree P of the special vehicle at the corresponding moment s , is the preset temperature attenuation parameter, W b is the preset reference temperature; is the joint acceleration index J a And power maintenance P s Set the weight values ​​Q3 and Q4 respectively to obtain the comprehensive power coefficient S of the special vehicle at the corresponding moment d , J ref is the joint acceleration index under ideal conditions; 6. A new energy special vehicle multi-drive motor coordinated control system according to claim 5, characterized in that: The process of using the collaborative control model to collaboratively control special vehicles includes: Generate a cooperative control set according to cooperative control parameters of the special vehicle under different expected stability coefficients, expected power coefficients, and predicted road condition parameters, and divide the obtained cooperative control set into a second training set and a second test set; Constructing a convolutional neural network, taking different expected stability coefficients, expected power coefficients, and predicted road condition parameters in the second training set as input data of the convolutional neural network, taking corresponding cooperative control parameters as output data of the convolutional neural network, and training the convolutional neural network to obtain an initial convolutional neural network; Using the second test set to perform model verification on the initial convolutional neural network, and outputting an initial convolutional neural network that is less than or equal to a preset second test error threshold as a collaborative control model; The predicted road condition parameters of the current special vehicle on its driving road are used as real-time road condition parameters, combined with the set expected stability coefficient and expected power coefficient, and input into the collaborative control model to output corresponding real-time control parameters, and each drive motor of the special vehicle is controlled according to the real-time control parameters.

7. A method for cooperative control of multiple drive motors of a new energy special vehicle, which is implemented based on the cooperative control system of multiple drive motors of a new energy special vehicle according to any one of claims 1 to 6, characterized in that: The method comprises: Step S1: Obtain basic information of special vehicles, build a corresponding digital twin model, and simulate the digital twin model; Step S2: construct a three-dimensional point cloud model of the road condition and obtain the corresponding road condition parameters, and use the LSTM neural network to construct a road condition prediction model to output the predicted road condition parameters; Step S3: obtaining the driving anti-skid index and yaw rate deviation rate of the special vehicle, and obtaining the corresponding comprehensive stability coefficient, and obtaining the joint acceleration index and power maintenance degree of the special vehicle, and obtaining the corresponding comprehensive power coefficient; Step S4: Set the expected stability coefficient and the expected power coefficient, obtain the corresponding collaborative control parameters by combining different predicted road condition parameters in the digital twin model, and build a collaborative control model for special vehicles, and use the collaborative control model to perform collaborative control on special vehicles.

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