Multi-motor cooperative driving and torque distribution method and system

Abstract

The invention relates to the technical field of motor driving control, in particular to a multi-motor cooperative driving and torque distribution method and system, and the method comprises the steps: collecting motor rotating speed, current, battery SOC and whole vehicle driving condition data in real time, carrying out the preprocessing of the collected multi-source data, eliminating the noise interference, and carrying out the signal feature extraction; a torque demand model is established based on vehicle driving condition classification, and the target torque of the whole vehicle is calculated; constructing a torque distribution model fusing working condition weights, combining motor efficiency characteristics, a battery SOC state and road condition resistance, and dynamically distributing the torque of each motor through an improved particle swarm optimization algorithm; and the state of the motor is monitored in real time in the driving process, torque distribution data of this time is input into a database after the vehicle is driven, and the torque demand model and torque distribution model parameters are optimized. Through working condition identification, intelligent algorithm distribution and dynamic optimization, the driving safety of the electro-tricycle and the driving efficiency of the motor are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor drive control, in particular to a multi-motor cooperative driving and torque distribution method and system. BACKGROUND

[0002] Electric tricycles are widely used in short-distance freight transportation, urban and rural transportation and other scenarios due to their low cost and high flexibility. With the increasing demand for heavy loads, the traditional single-motor drive system has the problems of insufficient power and easy damage under heavy load, and multi-motor drive has become the mainstream solution, such as 2-4 motors. However, the existing multi-motor drive torque distribution method has obvious shortcomings in the electric tricycle scenario: Torque distribution is fixed: average distribution or proportional distribution according to speed is adopted, without considering the complex working conditions of electric tricycles, such as large torque required for starting and climbing, and differentiated torque required for turning. Average torque distribution during climbing will cause simultaneous overload of the motors, and no differentiated distribution during turning will increase the turning resistance and reduce the controllability; High energy consumption and uneven load: the efficiency characteristics of the motors are ignored, and the real-time efficiency curve of the motors is not combined during distribution, resulting in operation of some motors in the low efficiency range, an increase of 15-20% in the energy consumption of the whole vehicle, and easy occurrence of full load of some motors and low load of some motors, with large difference in the service life of the motors, the service life of the low load motors being 2-3 times that of the full load motors; No dynamic adaptation: the battery SOC state is not considered, such as high torque distribution when SOC is less than or equal to 20%, acceleration of the battery, and change of the working condition, such as no compensation when the load suddenly increases, poor adaptability; Weak fault response: when one motor is overloaded or overheated, there is no emergency torque transfer strategy, and the drive is directly cut off, resulting in a stranded vehicle in the middle of the road, which greatly affects the users who rely on electric tricycles for transportation and easily causes delay of goods.

[0003] In view of the above problems, it is urgent to develop a multi-motor torque distribution method that adapts to the working condition of electric tricycles and takes into account the power, energy consumption and safety. SUMMARY

[0004] The present application provides a multi-motor cooperative driving and torque distribution method and system to solve the problems of uneven torque distribution, high energy consumption and weak fault response of the existing multi-motor drive of electric tricycles.

[0005] The present application is implemented by the following technical solutions: A multi-motor cooperative driving and torque distribution method is provided, which comprises the following steps: Step S10: Constructing the electric tricycle multi-motor drive architecture, and collecting the motor speed, current, battery SOC and vehicle driving condition data in real time, including vehicle starting, climbing and turning, preprocessing the collected multi-source data, eliminating noise interference and extracting signal features; Step S20: Establishing a torque demand model based on vehicle driving condition classification, and calculating the vehicle target torque; Step S30: Constructing a torque distribution model that integrates condition weights, combining motor efficiency characteristics, battery SOC state and road resistance to dynamically distribute motor torque, using an improved particle swarm optimization algorithm to obtain an optimal torque distribution scheme and output the target distribution torque of each motor; Step S40: In the driving process, the motor controller monitors the motor state in real time, and when overload and blockage occur, the torque emergency redistribution strategy is triggered. After the vehicle finishes driving, the torque distribution data of this time is entered into the database, and the torque demand model and torque distribution model parameters are optimized; Preferably, the step of constructing the electric tricycle multi-motor drive architecture in step S10 comprises: Motor arrangement: 2 motors are symmetrically arranged on the left and right wheels of the electric tricycle rear drive wheels, or 4 motors are arranged on the front and rear drive wheels, one on each wheel, and the motors are connected to the wheels through a reducer; Controller selection: Select a motor controller based on a DSP chip, support CAN bus communication, realize multi-motor synchronous control; Sensor installation: Install the Hall speed sensor on the motor output shaft coaxially with the shaft, and the Hall current sensor in series with the motor stator winding. The inclination sensor is installed at the center of gravity of the vehicle frame, and the steering angle sensor is integrated on the steering shaft of the handlebar; Power supply design: The battery pack uses lithium iron phosphate batteries, and the sensors and controllers are powered by a DC-DC converter to ensure stable data collection.

[0006] Preferably, the step of establishing a torque demand model based on vehicle driving condition classification and calculating the vehicle target torque in step S20 comprises: Starting condition: According to the motor starting torque characteristics, increase the starting compensation coefficient k1 based on the total torque T_total of the vehicle, and the formula for calculating the starting condition demand torque is T_start=k1×T_total, where T_start is the starting condition demand torque, to avoid starting without power; Constant speed condition: Introduce the energy consumption optimization coefficient k2, and the formula is T_const=k2×T_total, where T_const is the constant speed condition demand torque, which reduces redundant torque and energy consumption; Climbing condition: increase the slope compensation coefficient k3, the calculation formula is T_climb=k3×T_total, wherein T_climb is the climbing condition demand torque, and the climbing power is ensured to be sufficient; Turning condition: calculate the turning compensation coefficient k4 according to the turning angle α, k4=1-0.01×α, the turning condition demand torque T_turn=k4×T_total, and meanwhile, the outer side wheel torque is distributed to the inner side drive wheel, and the turning resistance is reduced.

[0007] Preferably, the step of constructing the torque distribution model of the fusion condition weight in the step S30 comprises: Distribution condition weight coefficient: according to the use scene statistics of the electric tricycle, the weights of four conditions are set, which are used to reflect the influence degree of the conditions on the distribution, including the starting condition 0.3, the uniform speed condition 0.2, the climbing condition 0.3 and the turning condition 0.2, the sum of the weights of the four conditions is 1, and the weights can be updated according to the actual driving data later; Fitting motor efficiency curve: collect the motor efficiency η under different torque and speed through bench test, and fit the efficiency curve η=a×T 2 +b×T+c, wherein a, b and c are fitting coefficients, T is the fitting torque, and the calculation formula is T=0.3×T_start+0.2×T_const+0.3×T_climb+0.2×T_turn; Monitoring battery SOC state: the battery SOC state reflects the ratio of the current capacity to the rated capacity of the battery, which is collected and corrected in real time through the battery management system BMS, when SOC≤20%, the torque is preferentially distributed to the high-efficiency motor to reduce the battery energy consumption, and when SOC≥80%, the upper limit of the torque distribution can be appropriately increased to ensure the power; Setting constraint condition: the distribution torque T_i of each motor is less than or equal to the rated torque, and the speed n_i is less than or equal to the rated speed, so as to avoid overloading of the motor.

[0008] Preferably, the step of obtaining the optimal torque distribution scheme by using the improved particle swarm optimization algorithm in the step S30 and outputting the target distribution torque of each motor comprises: Particle coding and population initialization: the to-be-distributed torque of each motor is taken as the dimension parameter of the particle, initial population containing a set number of feasible schemes is generated by combining the optimal torque distribution data under the same condition in history, and the particles are uniformly distributed in the torque constraint range, thereby laying a foundation for subsequent search; Fitness function design: according to the set condition weight, the fitness function is constructed by fusing three types of indexes including motor efficiency, battery SOC balance degree and power response speed, the function value is smaller when the efficiency is higher, the SOC is more balanced and the power response is more timely, and a better distribution scheme is corresponded; Improved PSO Iterative Optimization: Adaptive inertia weights are used to adjust the particle search step size. The range is expanded in the early stages of iteration and search. In the later stages of iteration, the optimal solution for torque distribution is finely searched. Particles that violate constraints such as motor torque and battery power are penalized and forced to converge to the feasible region. The optimal solution is approximated through multiple iterations. Optimal torque determination: When the fitness value changes less than a set threshold in multiple consecutive iterations, the iteration is considered to have converged. The torque value of each motor corresponding to the optimal particle at this time is extracted as the final target allocated torque to ensure that the current working condition requirements are met.

[0009] Preferably, the step of triggering the emergency torque reallocation strategy in step S40 includes: Fault determination: If the motor controller detects overload, stall, or overheating for three consecutive sampling cycles, it is determined to be a fault, and the torque output of the faulty motor is locked. Torque transfer calculation: The maximum acceptable torque of a normal motor is T_max = 0.8 × T_rated, where T_rated is the rated torque of the normal motor. The torque to be transferred is T_trans = T_fault, where T_fault is the original torque allocated to the faulty motor. When T_trans ≤ T_max, all of T_trans is transferred to one normal motor. When T_trans > T_max, it is proportionally allocated to a set number of normal motors. Vehicle torque adjustment: The target torque of the vehicle is reduced to T_total'=0.8-0.9×T_total, while the driving speed is limited to ≤20km / h to avoid overloading of the normal motor; Warning push: Sends fault codes to the electric tricycle's dashboard via CAN bus and illuminates the fault indicator light, prompting the driver to reduce the load or stop for inspection.

[0010] Preferably, the step S40, which involves recording the torque distribution data into the database after the vehicle has finished driving, and optimizing the parameters of the torque demand model and torque distribution model, includes: Data filtering: Valid driving data within the past month is filtered from the database, i.e., data on driving under faults and idling is removed. Each set of data includes the operating condition type, torque distribution value, and energy consumption value. Weighting coefficient update: Calculate the percentage of driving time for each working condition and adjust the weighting coefficient of the working condition according to the percentage; Efficiency curve update: The new motor efficiency curve is fitted using the least squares method, and the calculation formula is the same as in step S30; Verify the update effect: Select 10-20 sets of new driving data from the valid driving data as verification data, and use the updated model to simulate torque distribution on the verification data. When the deviation between the calculated energy consumption value and the actual value is ≤8%, the model update takes effect; otherwise, the parameters are refitted.

[0011] Furthermore, to achieve the above objectives, the present invention also proposes a multi-motor cooperative drive and torque distribution system, wherein the multi-motor cooperative drive and torque distribution system includes: Data acquisition and preprocessing module: used to build a multi-motor drive architecture for electric tricycles and collect motor speed, current, battery SOC and vehicle driving condition data in real time, including vehicle start-up, climbing and steering. The module preprocesses the collected multi-source data to eliminate noise interference and extract signal features. Operating condition identification and torque demand calculation module: used to establish a torque demand model based on vehicle driving condition classification and calculate the target torque of the whole vehicle; Torque distribution module: Used to build a torque distribution model with integrated operating condition weights, dynamically distribute the torque of each motor by combining motor efficiency characteristics, battery SOC state and road resistance, and obtain the optimal torque distribution scheme by using an improved particle swarm optimization algorithm, and output the target distributed torque of each motor. Status monitoring and model update module: It is used to monitor the motor status in real time through the motor controller during driving. When abnormalities such as overload and stall occur, it triggers the torque emergency reallocation strategy. After the vehicle finishes driving, the torque allocation data is entered into the database to optimize the torque demand model and torque allocation model parameters.

[0012] Furthermore, to achieve the above objectives, the present invention also proposes a multi-motor cooperative drive and torque distribution device, the device comprising: a memory, a processor, and programs such as a multi-motor cooperative drive and torque distribution algorithm stored in the memory and executable on the processor, wherein the multi-motor cooperative drive and torque distribution algorithm and other programs are steps for implementing the multi-motor cooperative drive and torque distribution method described above.

[0013] In addition, to achieve the above objectives, the present invention also provides a computer program product, which includes programs such as multi-motor cooperative drive and torque distribution algorithm. When the multi-motor cooperative drive and torque distribution algorithm is executed by a processor, it implements a multi-motor cooperative drive and torque distribution method as described above.

[0014] The advantages and effects of this invention are: This invention addresses the issues of insufficient power and cumbersome steering caused by traditional fixed-distribution systems by identifying operating conditions, using intelligent algorithms, and dynamically optimizing the torque demand model and weight allocation for typical operating conditions of electric tricycles, such as starting, climbing, and turning. Simultaneously, by improving the particle swarm optimization algorithm and combining it with motor efficiency curves, the invention reduces overall vehicle energy consumption and extends the overall lifespan of the motor. Furthermore, by incorporating a battery SOC state adjustment strategy, the invention adapts to battery aging and load changes, and updates model parameters monthly to ensure long-term compatibility with real-world usage scenarios. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a multi-motor cooperative drive and torque distribution method according to the present invention.

[0017] Figure 2 This is a schematic diagram of a multi-motor cooperative drive and torque distribution system according to the present invention.

[0018] Figure 3 This is a schematic block diagram of a multi-motor cooperative drive and torque distribution electronic device according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, in one embodiment of the present invention, a multi-motor cooperative drive and torque distribution method includes the following steps: Step S10: Construct a multi-motor drive architecture for the electric tricycle and collect real-time data on motor speed, current, battery SOC, and overall vehicle driving conditions, including vehicle start-up, hill climbing, and steering. The multi-motor drive architecture for the electric tricycle includes 2-4 permanent magnet synchronous motors with a rated power of 1-3kW, a motor controller, a battery pack with a voltage of 48-72V, and a set of operating condition sensors, including Hall effect speed sensors, current sensors, tilt sensors, and steering angle sensors. The collected data includes the speed of each motor, stator current, battery SOC, driving gradient, and steering angle. The collected multi-source data is preprocessed to eliminate noise interference and extract signal features.

[0021] Specifically, the steps in step S10 for constructing the multi-motor drive architecture for the electric tricycle include: Motor arrangement: Two motors are symmetrically arranged on the left and right rear drive wheels of the electric tricycle, one each, or four motors are arranged on the front and rear drive wheels, one on each wheel. The motors are connected to the wheels through a reducer with a transmission ratio of 5-10:1. Controller selection: Select a motor controller based on a DSP chip, such as the TMS320F28335 DSP chip, which supports CAN bus communication, has a transmission rate of 250kbps, realizes synchronous control of multiple motors, and has a control delay of ≤10ms. Sensor installation: The Hall speed sensor is installed on the motor output shaft, coaxial with the shaft; the Hall current sensor is connected in series with the motor stator winding; the tilt sensor is installed at the center of gravity of the frame; and the steering angle sensor is integrated into the steering shaft of the handlebar. Power supply design: The battery pack uses lithium iron phosphate batteries and supplies power to the sensors and controller through a DC-DC converter. The output voltage is stable at 12V±0.5V to ensure stable data acquisition.

[0022] Step S20: Establish a torque demand model based on vehicle driving condition classification, and calculate the target torque of the whole vehicle. The vehicle driving conditions are divided into starting condition, constant speed condition, climbing condition, and turning condition. Among them, in the starting condition, the motor speed is ≤500rpm and the vehicle acceleration is ≥0.5m / s². 2 Under constant speed conditions, the motor speed is 500-2000 rpm and the vehicle acceleration is ±0.2 m / s². 2 In the climbing condition, the driving gradient is ≥5° and the motor current is ≥80% of the rated current. In the steering condition, the vehicle steering angle is ≥15°. The target torque T_total of the whole vehicle is calculated using the formula: T_total=(m×g×f×v+m×g×sinθ×v+0.5×C_d×ρ×A×v) 3 ) / ω, where m is the vehicle mass, g is the gravitational acceleration, f is the rolling resistance coefficient, θ is the slope, v is the driving speed, C_d is the air resistance coefficient, ρ is the air density, A is the frontal area, and ω is the average angular velocity of the motor.

[0023] Specifically, step S20, which involves establishing a torque demand model based on vehicle driving condition classification and calculating the target torque of the entire vehicle, includes: Starting condition: Based on the motor starting torque characteristics, the starting compensation coefficient k1 is added to the total torque required by the whole vehicle T_total. The value of k1 is in the range of [1.2, 1.5]. The formula for calculating the torque required under the starting condition is T_start = k1 × T_total, where T_start is the torque required under the starting condition, to avoid weak start-up. Uniform speed operation: Introduce energy consumption optimization coefficient k2, with a value range of [0.8, 0.95]. The calculation formula is T_const=k2×T_total, where T_const is the torque required under uniform speed operation, which reduces redundant torque and energy consumption. Climbing condition: Increase the slope compensation coefficient k3, with a value range of [1.1, 1.3]. For every 5° increase in slope, k3 increases by 0.1. The calculation formula is T_climb=k3×T_total, where T_climb is the torque required for climbing condition, ensuring sufficient climbing power. Steering condition: Calculate the steering compensation coefficient k4 based on the steering angle α, k4 = 1 - 0.01 × α, and the required torque for steering condition T_turn = k4 × T_total. At the same time, distribute 80-90% of the torque of the outer wheel to the inner drive wheel to reduce steering resistance.

[0024] Step S30: Construct a torque distribution model with integrated operating condition weights, dynamically distribute the torque of each motor by combining motor efficiency characteristics, battery SOC state and road resistance, obtain the optimal torque distribution scheme by using an improved particle swarm optimization algorithm, and output the target distributed torque of each motor.

[0025] Specifically, step S30, which involves constructing the torque distribution model with integrated operating condition weights, includes: Weighting coefficients for different operating conditions: Based on statistics of the usage scenarios of electric tricycles, weights are set for four types of operating conditions to reflect the degree of influence of the operating conditions on the allocation, including starting condition 0.3, constant speed condition 0.2, climbing condition 0.3 and turning condition 0.2. The sum of the weights of the four types of operating conditions is 1. The weights can be updated later based on actual driving data. Fitting the motor efficiency curve: The motor efficiency η is collected under different torques and speeds through bench tests. For example, when the torque is 5 N•m and the speed is 1000 rpm, η = 88%. The fitted efficiency curve is η = a × T. 2 +b×T+c, where a, b, and c are fitting coefficients, and T is the fitting torque. The calculation formula is T=0.3×T_start+0.2×T_const+0.3×T_climb+0.2×T_turn, with an error ≤3%. Monitoring battery SOC status: The battery SOC status reflects the ratio of the battery's current capacity to its rated capacity. It is collected and corrected in real time by the battery management system (BMS). When SOC ≤ 20%, torque is preferentially allocated to the high-efficiency motor to reduce battery energy consumption. When SOC ≥ 80%, the upper limit of torque allocation can be appropriately increased to ensure power. Set constraints: the distributed torque T_i of each motor ≤ rated torque, the speed n_i ≤ rated speed, the rated torque is 10-30 N•m, and the rated speed is 3000 rpm, to avoid motor overload.

[0026] Specifically, step S30, which uses an improved particle swarm optimization algorithm to obtain the optimal torque distribution scheme and outputs the target distributed torque for each motor, includes the following steps: Particle encoding and population initialization: The torque to be allocated for each motor is used as the dimension parameter of the particles. Combined with the optimal torque allocation data under the same historical working conditions, an initial population containing a set number of feasible solutions is generated to ensure that the particles are evenly distributed within the torque constraint range, laying the foundation for subsequent searches. Fitness function design: Based on the set operating condition weights, a fitness function is constructed by integrating three types of indicators: motor efficiency, battery SOC balance, and power response speed. The higher the efficiency, the more balanced the SOC, and the more timely the power response, the smaller the function value, which corresponds to a better allocation scheme. Improved PSO Iterative Optimization: Adaptive inertia weights are used to adjust the particle search step size. The range is expanded in the early stages of iteration and search. In the later stages of iteration, the optimal solution for torque distribution is finely searched. Particles that violate constraints such as motor torque and battery power are penalized and forced to converge to the feasible region. The optimal solution is approximated through multiple iterations. Optimal torque determination: When the fitness value changes less than a set threshold in multiple consecutive iterations, the iteration is considered to have converged. The torque value of each motor corresponding to the optimal particle at this time is extracted as the final target allocated torque to ensure that the current working condition requirements are met.

[0027] Step S40: During driving, the motor status is monitored in real time by the motor controller. When abnormalities such as overload and stall occur, the torque emergency reallocation strategy is triggered. After the vehicle finishes driving, the torque distribution data is entered into the database to optimize the torque demand model and torque distribution model parameters.

[0028] Specifically, the steps in step S40 that trigger the emergency torque reallocation strategy include: Fault determination: If the motor controller detects overload, stall, or overheating for three consecutive sampling cycles, it is determined to be a fault, and the torque output of the faulty motor is locked and set to 0. Torque transfer calculation: The maximum acceptable torque of a normal motor is T_max = 0.8 × T_rated, where T_rated is the rated torque of the normal motor. The torque to be transferred is T_trans = T_fault, where T_fault is the original torque allocated to the faulty motor. When T_trans ≤ T_max, all of T_trans is transferred to one normal motor. When T_trans > T_max, it is proportionally allocated to a set number of normal motors. For example, for two normal motors, each is allocated T_trans / 2. Vehicle torque adjustment: The target torque of the vehicle is reduced to T_total'=0.8-0.9×T_total, while the driving speed is limited to ≤20km / h to avoid overloading of the normal motor; Warning push: Sends fault codes, such as "Motor 1 overload", to the electric tricycle's dashboard via CAN bus and illuminates the fault indicator light, prompting the driver to reduce the load or stop the vehicle for inspection.

[0029] Specifically, in step S40, after the vehicle has finished driving, the torque distribution data is entered into the database, and the parameters of the torque demand model and torque distribution model are optimized. Data filtering: Valid driving data within the past month is filtered from the database, i.e., data on driving under faults and idling is removed. Each set of data includes the operating condition type, torque distribution value, and energy consumption value. Weighting coefficient update: Calculate the percentage of driving time for each working condition and adjust the weighting coefficient of the working condition according to the percentage. For example, if the percentage of driving time for the climbing condition increases from 10% to 15%, the weighting coefficient will increase from 0.3 to 0.35. Efficiency curve update: The least squares method is used to fit the new motor efficiency curve. The calculation formula is the same as in step S30, ensuring that the fitting error is ≤3%. Verify the update effect: Select 10-20 sets of new driving data from the valid driving data as verification data, and use the updated model to simulate torque distribution on the verification data. When the deviation between the calculated energy consumption value and the actual value is ≤8%, the model update takes effect; otherwise, the parameters are refitted.

[0030] In addition, such as Figure 2 As shown, in one embodiment of the present invention, a multi-motor cooperative drive and torque distribution system is proposed, the system comprising: Data acquisition and preprocessing module: used to build a multi-motor drive architecture for electric tricycles and collect motor speed, current, battery SOC and vehicle driving condition data in real time, including vehicle start-up, climbing and steering. The module preprocesses the collected multi-source data to eliminate noise interference and extract signal features. Operating condition identification and torque demand calculation module: used to establish a torque demand model based on vehicle driving condition classification and calculate the target torque of the whole vehicle; Torque distribution module: Used to build a torque distribution model with integrated operating condition weights, dynamically distribute the torque of each motor by combining motor efficiency characteristics, battery SOC state and road resistance, and obtain the optimal torque distribution scheme by using an improved particle swarm optimization algorithm, and output the target distributed torque of each motor. Status monitoring and model update module: It is used to monitor the motor status in real time through the motor controller during driving. When abnormalities such as overload and stall occur, it triggers the torque emergency reallocation strategy. After the vehicle finishes driving, the torque allocation data is entered into the database to optimize the torque demand model and torque allocation model parameters.

[0031] This application provides a multi-motor cooperative drive and torque distribution system, employing a multi-motor cooperative drive and torque distribution method as described in the above embodiments. This system addresses the technical problems of uneven torque distribution, high energy consumption, and weak fault response in existing multi-motor drives of electric tricycles. Compared to the prior art, the beneficial effects of the multi-motor cooperative drive and torque distribution system provided in this application are the same as those of the multi-motor cooperative drive and torque distribution method provided in the above embodiments. Furthermore, other technical features of the multi-motor cooperative drive and torque distribution system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0032] This application provides a multi-motor cooperative drive and torque distribution device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the multi-motor cooperative drive and torque distribution method in the above embodiment 1.

[0033] like Figure 3 As shown in the illustration, in one embodiment of the present invention, a structural schematic diagram of a multi-motor cooperative drive and torque distribution device suitable for implementing the embodiments of this application is presented. The multi-motor cooperative drive and torque distribution device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The multi-motor cooperative drive and torque distribution device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0034] Figure 3The multi-motor cooperative drive and torque distribution device shown may include a processor 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage unit 1003 into a machine-readable storage medium (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the multi-motor cooperative drive and torque distribution device. The processor 1001, the read-only memory 1002, and the machine-readable storage medium 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage units 1003 including, for example, magnetic tapes, hard disks, etc.; and communication units 1009. Communication unit 1009 allows a multi-motor cooperative drive and torque distribution device to exchange data with other devices wirelessly or via wired communication. Although a multi-motor cooperative drive and torque distribution device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0035] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication system, or installed from storage unit 1003, or installed from read-only memory 1002. When the computer program is executed by processor 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0036] This application provides a multi-motor cooperative drive and torque distribution device, employing a multi-motor cooperative drive and torque distribution method as described in the above embodiments. This addresses the technical problems of uneven torque distribution, high energy consumption, and weak fault response in existing multi-motor drives of electric tricycles. Compared to the prior art, the beneficial effects of the multi-motor cooperative drive and torque distribution device provided in this application are the same as those of the multi-motor cooperative drive and torque distribution method provided in the above embodiments. Furthermore, other technical features of this multi-motor cooperative drive and torque distribution device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0037] The various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0038] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the multi-motor cooperative drive and torque distribution method described above.

[0039] The computer program product provided in this application can solve the technical problems of uneven torque distribution, high energy consumption, and weak fault response in existing multi-motor drives of electric tricycles. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the multi-motor cooperative drive and torque distribution method provided in the above embodiments, and will not be repeated here.

[0040] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for multi-motor cooperative drive and torque distribution, characterized in that, The method includes the following steps: Step S10: Construct a multi-motor drive architecture for the electric tricycle and collect real-time data on motor speed, current, battery SOC, and vehicle driving conditions. Preprocess the collected multi-source data to eliminate noise interference and extract signal features. Step S20: Establish a torque demand model based on vehicle driving condition classification and calculate the target torque of the whole vehicle; Step S30: Construct a torque distribution model with integrated operating condition weights, dynamically distribute the torque of each motor by combining motor efficiency characteristics, battery SOC state and road resistance, use an improved particle swarm optimization algorithm to obtain the optimal torque distribution scheme, and output the target distributed torque of each motor. Step S40: During driving, the motor status is monitored in real time by the motor controller. When overload or stall occurs, the torque emergency reallocation strategy is triggered. After the vehicle finishes driving, the torque distribution data is entered into the database to optimize the torque demand model and torque distribution model parameters.

2. The multi-motor cooperative drive and torque distribution method according to claim 1, characterized in that, The steps in step S10 of constructing the multi-motor drive architecture for the electric tricycle include: Motor arrangement: Two motors are symmetrically arranged on the left and right rear drive wheels of the electric tricycle, one each, or four motors are arranged on the front and rear drive wheels, one on each wheel. The motors are connected to the wheels through a reducer. Controller selection: A motor controller based on a DSP chip is selected, which supports CAN bus communication and enables synchronous control of multiple motors; Sensor installation: The Hall speed sensor is installed on the coaxial motor output shaft, the Hall current sensor is connected in series with the motor stator winding, the tilt sensor is installed at the center of gravity of the frame, and the steering angle sensor is integrated into the steering shaft of the handlebar. Power supply design: The battery pack uses lithium iron phosphate batteries and supplies power to the sensors and controllers through a DC-DC converter.

3. The multi-motor cooperative drive and torque distribution method according to claim 1, characterized in that, The step S20, which involves establishing a torque demand model based on vehicle driving condition classification and calculating the target torque of the entire vehicle, includes the following steps: Starting condition: Based on the motor starting torque characteristics, the starting compensation coefficient k1 is added to the total torque required by the whole vehicle T_total. The formula for calculating the torque required under the starting condition is T_start=k1×T_total, where T_start is the torque required under the starting condition. Uniform speed operation: Introduce energy consumption optimization coefficient k2, the calculation formula is T_const=k2×T_total, where T_const is the torque required under uniform speed operation; Climbing condition: Increase the slope compensation coefficient k3, and the calculation formula is T_climb=k3×T_total, where T_climb is the torque required for climbing condition; Steering condition: Calculate the steering compensation coefficient k4 based on the steering angle α, k4 = 1 - 0.01 × α, and the required torque for steering condition T_turn = k4 × T_total. At the same time, distribute the torque of the outer wheel to the inner drive wheel to reduce steering resistance.

4. The multi-motor cooperative drive and torque distribution method according to claim 1, characterized in that, The steps in step S30 of constructing the torque distribution model with integrated operating condition weights include: Weighting coefficients for different working conditions: Based on statistics of the usage scenarios of electric tricycles, weights are set for four types of working conditions, including starting condition 0.3, constant speed condition 0.2, climbing condition 0.3 and turning condition 0.

2. The sum of the weights of the four types of working conditions is 1. Fitting the motor efficiency curve: The motor efficiency η under different torques and speeds was collected through bench tests, and the efficiency curve η=a×T was fitted. 2 +b×T+c, where a, b, and c are fitting coefficients, and T is the fitting torque. The calculation formula is T=0.3×T_start+0.2×T_const+0.3×T_climb+0.2×T_turn, where T_start is the torque required for starting, T_const is the torque required for constant speed, T_climb is the torque required for climbing, and T_turn is the torque required for turning. Monitoring battery SOC status: The battery SOC status reflects the ratio of the battery's current capacity to its rated capacity. It is collected and corrected in real time by the battery management system (BMS). When SOC ≤ 20%, torque is preferentially allocated to the high-efficiency motor. When SOC ≥ 80%, the upper limit of torque allocation is increased. Set constraints: the distributed torque T_i of each motor ≤ rated torque, and the speed n_i ≤ rated speed, to avoid motor overload.

5. The multi-motor cooperative drive and torque distribution method according to claim 1, characterized in that, The step S30, which uses an improved particle swarm optimization algorithm to obtain the optimal torque allocation scheme and outputs the target allocated torque for each motor, includes the following steps: Particle encoding and population initialization: The torque to be allocated by each motor is used as the dimension parameter of the particles. Combined with the optimal torque allocation data under the same historical working conditions, an initial population containing a set number of feasible solutions is generated to ensure that the particles are evenly distributed within the torque constraint range. Fitness function design: Based on the set operating condition weights, a fitness function is constructed by integrating three types of indicators: motor efficiency, battery SOC balance, and power response speed. Improved PSO Iterative Optimization: Adaptive inertia weight is used to adjust the particle search step size. The range is expanded in the early stage of iteration and search. In the later stage of iteration, the optimal solution for torque distribution is finely searched. Particles that violate the constraints of motor torque and battery power are penalized and forced to converge to the feasible region. The optimal solution is approximated through multiple rounds of iteration. Optimal torque determination: When the fitness value changes less than a set threshold in multiple consecutive iterations, the iteration is considered to have converged. The torque values ​​of each motor corresponding to the optimal particle at this time are extracted as the final target allocated torque.

6. The multi-motor cooperative drive and torque distribution method according to claim 1, characterized in that, The step of triggering the emergency torque reallocation strategy in step S40 includes: Fault determination: If the motor controller detects overload, stall, or overheating for three consecutive sampling cycles, it is determined to be a fault, and the torque output of the faulty motor is locked. Torque transfer calculation: The maximum acceptable torque of a normal motor is T_max = 0.8 × T_rated, where T_rated is the rated torque of the normal motor. The torque to be transferred is T_trans = T_fault, where T_fault is the original torque allocated to the faulty motor. When T_trans ≤ T_max, all of T_trans is transferred to one normal motor. When T_trans > T_max, it is proportionally allocated to a set number of normal motors. Vehicle torque adjustment: The target torque of the vehicle is reduced to T_total'=0.8-0.9×T_total, where T_total is the total torque required by the vehicle and T_total' is the target torque of the vehicle. At the same time, the driving speed is limited to ≤20km / h to avoid overloading of the normal motor. Warning push: Sends fault codes to the electric tricycle's dashboard via CAN bus and illuminates the fault indicator light.

7. The multi-motor cooperative drive and torque distribution method according to claim 1, characterized in that, The steps in step S40, which involve recording the torque distribution data into the database after the vehicle has finished driving and optimizing the parameters of the torque demand model and torque distribution model, include: Data filtering: Valid driving data within the past month is filtered from the database. Each set of data includes the operating condition type, torque distribution value, and energy consumption value. Weighting coefficient update: Calculate the percentage of driving time for each working condition and adjust the weighting coefficient of the working condition according to the percentage; Efficiency curve update: The new motor efficiency curve is fitted using the least squares method, and the calculation formula is the same as in step S30; Verify the update effect: Select 10-20 sets of new driving data from the valid driving data as verification data, and use the updated model to simulate torque distribution on the verification data. When the deviation between the calculated energy consumption value and the actual value is ≤8%, the model update takes effect; otherwise, the parameters are refitted.

8. A multi-motor cooperative drive and torque distribution system, characterized in that, The multi-motor cooperative drive and torque distribution method as described in claim 1 includes: Data acquisition and preprocessing module: used to build a multi-motor drive architecture for electric tricycles and collect motor speed, current, battery SOC and vehicle driving condition data in real time. It preprocesses the collected multi-source data to eliminate noise interference and extract signal features. Operating condition identification and torque demand calculation module: used to establish a torque demand model based on vehicle driving condition classification and calculate the target torque of the whole vehicle; Torque distribution module: Used to build a torque distribution model with integrated operating condition weights, dynamically distribute the torque of each motor by combining motor efficiency characteristics, battery SOC state and road resistance, and obtain the optimal torque distribution scheme by using an improved particle swarm optimization algorithm, and output the target distributed torque of each motor. Status monitoring and model update module: It is used to monitor the motor status in real time through the motor controller during driving. When overload and stalling occur, it triggers the emergency torque reallocation strategy. After the vehicle finishes driving, the torque allocation data is entered into the database to optimize the torque demand model and torque allocation model parameters.

9. A multi-motor cooperative drive and torque distribution device, characterized in that, The multi-motor cooperative drive and torque distribution device includes: The system includes a memory, a processor, and a multi-motor cooperative drive and torque distribution program stored in the memory and executable on the processor. When the multi-motor cooperative drive and torque distribution program is executed by the processor, it implements a multi-motor cooperative drive and torque distribution method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a multi-motor cooperative drive and torque distribution program, which, when executed by a processor, implements a multi-motor cooperative drive and torque distribution method as described in any one of claims 1 to 7.

Images