A Torque Optimization Allocation Control Method and System for an Electrically Driven Multi-Axle Vehicle

Abstract

The present invention discloses a torque optimization distribution control method and system for an electric drive multi-axle vehicle, which relates to the technical field of energy consumption control of electric drive multi-axle vehicles, and includes the steps of: solving the optimal number of drive axles and the drive torque on each drive axle of the electric drive multi-axle vehicle through a first objective function; optimizing the maximum copper loss sequence within the motor safety temperature rise threshold by using a second objective function, and further obtaining the maximum torque that can be provided within the motor temperature rise safety threshold; the distribution strategy of the present invention can delay the motor temperature rise rate and improve the efficiency of the electric drive system. When the temperature of a certain axle motor is too high, it can adjust the number of drive axles and the torque distribution on each drive axle in real time, and limit the motor temperature rise by dynamically adjusting the motor output torque, so as to balance the temperatures of the motors on each axle, not only improving the economy of the electric drive system, but also reducing the probability of motor thermal failures.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption control of electric drive multi-axle vehicles, and particularly relates to a torque optimization distribution control method and system for electric drive multi-axle vehicles. Background Art

[0002] Electric drive multi-axle vehicles have the characteristics of high control flexibility, short transmission chain, compact structure, high transmission efficiency, high utilization rate of space layout, etc., and have significant advantages in stability control, active safety control and energy-saving control, and are widely used in fields such as freight transportation, construction transportation, field operations, national defense and military. However, due to the characteristics of over-drive and multi-constraints of electric drive multi-axle vehicles, and their large self-weight and strong load-bearing capacity, there are still theoretical and technical challenges in the driving economy control of electric drive multi-axle vehicles.

[0003] Regarding the energy consumption problem of electric drive multi-axle vehicles, an effective solution is to control the number of drive axles and the working torque of each drive motor, so as to improve the efficiency of each drive motor and reduce the energy consumption of the vehicle's electric drive system.

[0004] Currently, the torque distribution control strategies for electric drive multi-axle vehicles mainly include methods such as torque average distribution control strategy and torque optimal average distribution control strategy based on the motor efficiency Map. Among them, the torque average distribution control strategy distributes the current vehicle demand torque evenly to each drive motor according to the current vehicle demand torque. This strategy is simple and convenient to design, but the efficiency of the electric drive system is low and the energy consumption is high; the torque optimal average distribution control strategy based on the motor efficiency Map takes the total energy consumption of the electric drive system under different numbers of drive axles as the objective function, solves the corresponding number of drive axles, takes it as the optimal drive mode, and then distributes the vehicle demand torque evenly to each motor participating in the drive. This strategy can effectively improve the efficiency of the electric drive system, but in some working conditions where the vehicle demand torque is small, in order to improve the efficiency of the electric drive system, usually one or several drive motors will continuously work in the high-efficiency region close to the peak torque, and this control strategy is the average distribution of the inter-axle drive torque, resulting in a higher motor temperature rise and unable to provide the maximum efficiency of the electric drive system, and can only slightly reduce the energy consumption of the electric drive system. Summary of the Invention

[0005] The purpose of the present invention is to provide a torque optimization distribution control method and system for electric drive multi-axle vehicles in view of the above-mentioned deficiencies of the prior art, so as to solve the problems in the prior art.

[0006] The present invention specifically provides the following technical solutions:

[0007] A torque optimization distribution control method for an electric drive multi-axle vehicle, comprising:

[0008] Construct a first objective function based on the instantaneous total energy consumption of the electric drive system, with the maximum torque that the motor can provide as a constraint condition, minimize the instantaneous total energy consumption of the electric drive system, and preferentially select the axles with an axle load ratio greater than the threshold as drive axles according to the vertical loads of each axle of the electric multi-axle vehicle, and obtain the optimal control variables of the first objective function at the current moment. The control variables of the first objective function include the number of drive axles of the electric multi-axle vehicle, the drive torque and drive speed on each drive axle;

[0009] Obtain the difference between the predicted sequence of the motor stator winding temperature and the stator winding reference temperature output within the prediction time domain, and use the sum of the difference and the copper loss change rate within the prediction time domain as the second objective function. With the copper loss and the copper loss change rate as constraint conditions, minimize the second objective function, and obtain the optimal control variable sequence of the second objective function through the minimized second objective function. The optimal control variable sequence of the second objective function includes the maximum copper loss sequence within the motor safe temperature rise threshold;

[0010] According to the maximum copper loss sequence within the motor safe temperature rise threshold, obtain the maximum effective current value sequence within the motor safe temperature rise threshold, and based on the optimal control variables of the first objective function and the maximum effective current value sequence, obtain the maximum torque that each axle motor can provide within the motor temperature rise safety threshold according to the three-dimensional look-up table of current - speed - torque.

[0011] Preferably, before constructing the first objective function based on the instantaneous total energy consumption of the electric drive system, the instantaneous total energy consumption of the electric drive system is further corrected, including:

[0012] Characterize the torque distribution correction factor by the relationship among the motor speed, working torque, rated torque and peak torque. The specific expression is:

[0013] ;

[0014] Wherein, is the torque distribution correction factor, is the motor working torque, is the rated torque at the current motor speed, is the peak torque at the current motor speed, is the motor speed, is the i th drive axle, is the left motor, is the right motor;

[0015] Correct the working torque through the correction factor, and obtain the corrected instantaneous total energy consumption of the electric drive system through the corrected torque.

[0016] Preferably, in the construction of the first objective function through the instantaneous total energy consumption of the electric drive system, the specific expression of the first objective function is:

[0017] ;

[0018] where, is the first objective function, N is the number of drive axles, is the total power of the electric drive system after correction, is the left or right motor power of the th axle in the dynamic models of the components of the power system, is the maximum number of drive axles, is the th axle left or right motor angular velocity, is the th axle left or right motor efficiency.

[0019] Preferably, in the minimization of the instantaneous total energy consumption of the electric drive system with the maximum torque that the motor can provide as the constraint condition, the specific expression for minimizing the instantaneous total energy consumption of the electric drive system is:

[0020] ;

[0021] where, is the slip ratio; is the vertical load of the th axle, i.e., the normal reaction force; is the maximum torque that the motor can provide within the safe temperature rise threshold; is the power of the energy source; is the current of the energy source; is the voltage of the energy source; is the state of charge of the energy source; the subscripts min and max represent the minimum and maximum values of the corresponding variables respectively.

[0022] Preferably, the selection of the axle with an axle load ratio greater than the threshold as the drive axle according to the vertical load of each axle of the electric drive multi-axle vehicle includes:

[0023] Obtain the axle load ratio of each axle of the electric drive multi-axle vehicle, and the specific expression is:

[0024] ;

[0025] where, is the axle load ratio of the th axle, i.e., the ratio of the normal reaction force on the th axle to the gravity of the electric drive multi-axle vehicle; m is the mass of the electric drive multi-axle vehicle, and g is the acceleration due to gravity;

[0026] By preferentially selecting the axles with an axle load ratio greater than the threshold as drive axles, and distributing the drive torque to the motors on the drive axles in descending order of the axle load ratio.

[0027] Preferably, obtaining the difference between the predicted sequence of the motor stator winding temperature output within the prediction horizon and the reference temperature of the stator winding, and taking the sum of the difference and the copper loss change rate within the prediction horizon as the second objective function. With the copper loss and the copper loss change rate as the constraint conditions, minimizing the second objective function, and obtaining the optimal control variable sequence of the second objective function through the minimized second objective function, including:

[0028] Using the linear parameter-varying model predictive control method, with the copper loss as the control variable, and the stator effective winding temperature , the end winding temperature , the stator core temperature , the rotor temperature , the permanent magnet temperature as the state variables and output variables, and the stator yoke iron loss , the stator tooth iron loss , the rotor iron loss , the permanent magnet eddy current loss , the ambient temperature , the coolant temperature in the cooling pipe as the disturbance variables, and with the copper loss , the copper loss change rate as the constraint conditions of the second objective function, constructing a state space equation that separates the copper loss from other losses, specifically:

[0029] Select as the state variable, as the disturbance variable, as the control variable, and constructing the state space equation, the specific expression is:

[0030] ;

[0031] Discretize the state space equation, the specific expression is:

[0032] ;

[0033] Among them, is the state matrix, is the disturbance matrix, is the input matrix; is the discretized state variable, is the discretized disturbance variable, is the discretized control variable, is the discretized state matrix, is the discretized disturbance matrix, is the discretized input matrix;

[0034] According to the motor insulation class, set the maximum insulation temperature of the motor stator winding as the reference temperature of the stator winding;

[0035] Use the discretized state - space equation to obtain the predicted temperature sequences of the stator windings of each driving motor output for controlling the copper loss within the prediction horizon, and obtain the difference between each predicted temperature sequence of the stator windings of the driving motors and the reference temperature of the stator winding. Take the sum of the difference and the copper loss change rate within the prediction horizon as the optimization objective of the second objective function. The specific expression is:

[0036] ;

[0037] where, is the second objective function, is the prediction horizon; is the control horizon; is the temperature sequence of the motor stator winding within the prediction horizon; is the set reference temperature of the motor stator winding; is the weight coefficient of the motor temperature rise cost, is the sequence of the change amount of the motor copper loss within the control horizon, is the weight coefficient of the motor copper loss change rate, represents calculated at the variable at time ;

[0038] Take the copper loss and the copper loss change rate as the constraint conditions of the second objective function, including:

[0039] ;

[0040] Convert the discretized state - space equation and perform matrix transformation, convert the optimization objective of the second objective function into a standard quadratic programming problem, and use the linear quadratic programming algorithm to minimize the optimization objective of the second objective function to obtain the optimal control variable sequence of the second objective function within the prediction horizon, that is, the maximum copper loss sequence within the motor safe temperature rise threshold.

[0041] Preferably, according to the maximum copper loss sequence within the motor safe temperature rise threshold, obtain the maximum effective current value sequence within the motor safe temperature rise threshold, and based on the optimal control variable of the first objective function and the maximum effective current value sequence, obtain the maximum torque that each axis motor can provide within the motor temperature rise safety threshold according to the three - dimensional look - up table of current - speed - torque, including:

[0042] Obtain the maximum effective current value sequence within the motor safe temperature rise threshold through the maximum copper loss sequence within the motor safe temperature rise threshold. The specific expression is:

[0043] ;

[0044] Wherein, is the number of phases of the PMSM winding; is the copper loss corresponding to the maximum effective value of the winding phase current; is the effective resistance value of each phase winding, and its resistance value is related to the temperature characteristics of the material;

[0045] The corresponding torque is obtained by looking up a three-dimensional table of current - speed - torque, and the specific expression is:

[0046] ;

[0047] Wherein, is the maximum torque that the left or right motor of the axis can provide within the safety threshold of the stator winding temperature rise, is the left motor, is the left or right motor current of the is the left or right motor speed of the

[0048] The present invention provides a torque optimization distribution control method for an electric - drive multi - axis vehicle, including:

[0049] A torque optimization distribution module, configured to construct a first objective function through the instantaneous total energy consumption of the electric drive system, use the maximum torque that the motor can provide as a constraint condition, minimize the instantaneous total energy consumption of the electric drive system, and preferentially select the axle with an axle load ratio greater than a threshold as the drive axle according to the vertical load of each axle of the electric - drive multi - axis vehicle, and obtain the optimal control variables of the first objective function at the current moment. The control variables of the first objective function include the number of drive axles of the electric - drive multi - axis vehicle, the drive torque and drive speed on each drive axle;

[0050] A temperature rise limitation module, configured to obtain the difference between the predicted sequence of the motor stator winding temperature output within the prediction time domain and the reference temperature of the stator winding, and use the sum of the difference and the copper loss change rate within the prediction time domain as a second objective function, use the copper loss and copper loss change rate as constraint conditions, minimize the second objective function, and obtain the optimal control variable sequence of the second objective function through the minimized second objective function. The optimal control variable sequence of the second objective function includes the maximum copper loss sequence within the motor safety temperature rise threshold;

[0051] The maximum torque distribution module is used to obtain the sequence of the maximum effective current values within the motor safety temperature rise threshold based on the sequence of the maximum copper losses within the motor safety temperature rise threshold, and based on the optimal control variables of the first objective function and the sequence of the maximum effective current values, obtain the maximum torque that each axis motor can provide within the motor temperature rise safety threshold according to the three-dimensional look-up table of current - speed - torque.

[0052] Compared with the prior art, the present invention has the following remarkable advantages:

[0053] The present invention constructs the first objective function through the instantaneous total energy consumption of the electric drive system, minimizes the first objective function with the maximum torque as the constraint condition, preliminarily limits the motor temperature rise by minimizing the instantaneous total energy consumption of the electric drive system, and realizes the optimal control variables of the first objective function by preferentially selecting the axle with an axle load ratio greater than the threshold as the drive axle, further limits the motor temperature rise by reasonably allocating the number of drive axles and drive axles, and obtains the sequence of the maximum copper losses within the motor safety temperature rise threshold and the sequence of the maximum effective current values within the motor safety temperature rise threshold by minimizing the second objective function with the copper loss and the copper loss change rate, and finally obtains the maximum torque that each axis motor can provide within the motor temperature rise safety threshold according to the three-dimensional look-up table of current - speed - torque, actively limits the maximum torque of the motors of the electric drive multi-axle vehicle by the solved maximum torque, realizes the problem of limiting the motor temperature rise by limiting the maximum torque, reasonably allocating the number of drive axles and drive axles, solves the problem of the temperature rise caused by the long-term operation of the motor, prevents the wheel-side motor from overheating, reduces the probability of motor thermal faults, and further improves the efficiency of the electric drive system to the greatest extent. Description of the Drawings

[0054] Figure 1 is a schematic diagram of the model structure;

[0055] Figure 2 is a flowchart of a torque optimization distribution control method for an electric drive multi-axle vehicle in the present invention. Detailed Embodiments

[0056] The following combines the drawings in the present invention to clearly and completely describe the technical solutions of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] As Figures 1 to 2 shown, the following describes a torque optimization distribution control method for an electric drive multi-axle vehicle provided by the present invention, which specifically includes the following steps:

[0058] Step S1: Obtain the parameters of each drive motor in the electric drive multi-axle vehicle.

[0059] According to the parameters of the electric drive multi-axle vehicle, a longitudinal dynamics model of the electric drive multi-axle vehicle is established to obtain the vehicle speed, and the specific expression is:

[0060] ;

[0061] Among them, is the reduction ratio of the main reducer; is the transmission efficiency of the main reducer; is the number of drive axles; is the th drive axle; is the left motor, is the right motor; is the output torque of the left or right drive motor of the th axle; is the wheel rolling radius; is the braking force; is the air density; is the wind resistance coefficient; is the frontal area of the electric drive multi-axle vehicle; is the vehicle speed; is the vehicle weight; is the gravity coefficient; is the road surface rolling resistance coefficient; is the road slope.

[0062] Furthermore, the motor speed is obtained, and the specific expression is:

[0063] ;

[0064] Among them, is the wheel rolling radius, is the reduction ratio of the main reducer.

[0065] The dynamic models of each component of the power system are established to obtain the motor efficiency, the energy source current, and the state of charge (SOC), and the specific expressions are:

[0066] ;

[0067] ;

[0068] ;

[0069] Among them, is the power of the left or right motor of the th axle; is the angular velocity of the left or right motor of the th axle; is the The efficiency of the left or right motor of the shaft is obtained by querying the universal motor characteristic diagram. The charging or discharging current of the energy source; The open-circuit voltage of the battery pack; The charging or discharging internal resistance of the battery pack; The power of the battery pack, The initial value, The maximum capacity of the energy source.

[0070] According to the motor parameters of the electric drive multi-axis vehicle, a lumped-parameter thermal network model LPTN (Lumped-parameter Thermal Network) model considering the water cooling of the drive motor is constructed.

[0071] To improve the real-time performance, the motor thermal nodes are simplified into 5 nodes: effective winding, end winding, stator core, rotor core, and permanent magnet. To improve the accuracy of motor temperature estimation, the motor cooling system is considered, and its cooling process is equivalent to two steps: (1) the heat exchange between the coolant and the cooling pipe wall; (2) the heat exchange between the cooling pipe wall and the external environment.

[0072] ;

[0073] ;

[0074] Among them, is the net heat flow rate; is the convective part of the heat flow rate at non-zero flow velocity; is the thermal conductivity of the coolant in the pipe; is the hydraulic diameter of the pipe; is the surface area of the pipe wall, equal to the product of the pipe circumference and length; is the temperature of the pipe wall, is the heat flow; is the convective heat transfer coefficient; is the surface area; and are the temperatures of two objects, here representing the temperature of the pipe wall and the ambient temperature respectively.

[0075] Step S2: Take the number of drive axles and the drive torque on each drive axle as the control variables of the first objective function. Construct the first objective function through the corrected instantaneous total energy consumption of the electric drive system. Use the power limit of the energy source, the external characteristic limit of the motor, the adhesion limit, and the maximum torque limit that the motor can provide within the motor safety temperature rise threshold as the constraint conditions of the first objective function. Under the constraint conditions of the first objective function, minimize the optimization objective (instantaneous total energy consumption of the electric drive system) of the first objective function, and preferentially select the axles with an axle load ratio greater than the threshold as drive axles according to the vertical load of each axle of the electric drive multi-axle vehicle to obtain the optimal control variables of the first objective function at the current moment. The control variables of the first objective function include the number of drive axles of the electric drive multi-axle vehicle, the drive torque and drive speed on each drive axle.

[0076] Before constructing the first objective function through the instantaneous total energy consumption of the electric drive system, the instantaneous total energy consumption of the electric drive system is also corrected, including:

[0077] ;

[0078] Among them, is the torque distribution correction factor, is the working torque of the motor, is the rated torque of the motor at the current speed, is the peak torque of the motor at the current speed; is the motor speed, is the i th drive axle, is the left motor, is the right motor.

[0079] Correct the working torque through the correction factor, and calculate the corrected instantaneous total energy consumption of the electric drive system through the corrected torque.

[0080] In constructing the first objective function through the instantaneous total energy consumption of the electric drive system, the specific expression of the first objective function is:

[0081] ;

[0082] Among them, is the first objective function, N is the number of drive axles, is the total power of the corrected electric drive system, is the power of the left or right motor of the th axle in the dynamic models of the components of the power system, is the maximum number of drive axles, is the th axle left or right motor angular velocity, is the th axle left or right motor efficiency.

[0083] With the maximum torque that the motor can provide as the constraint condition, in minimizing the instantaneous total energy consumption of the electric drive system, the specific expression for minimizing the instantaneous total energy consumption of the electric drive system is:

[0084] ;

[0085] where is the slip ratio; is the vertical load of the th axle, that is, the normal reaction force; is the maximum torque that the motor can provide within the safe temperature rise threshold; is the power of the energy source; is the current of the energy source; is the voltage of the energy source; is the state of charge of the energy source; the subscripts min and max represent the minimum and maximum values of the corresponding variables respectively.

[0086] where, according to the vertical loads of each axle of the electric drive multi-axle vehicle, axles with an axle load ratio greater than the threshold are preferentially selected as drive axles, including:

[0087] Obtain the axle load ratio of each axle of the electric drive multi-axle vehicle, and the specific expression is:

[0088] ;

[0089] where is the axle load ratio of the th axle, that is, the ratio of the normal reaction force on the th axle to the gravity of the electric drive multi-axle vehicle; m is the mass of the electric drive multi-axle vehicle, and g is the acceleration due to gravity.

[0090] By preferentially selecting axles with an axle load ratio greater than the threshold as drive axles, and distributing the drive torque to the motors on the drive axles in descending order of the axle load ratio.

[0091] Step S3: Use the linear parameter varying model predictive control (LPV-MPC) method. Take the copper loss as the control variable, the stator effective winding temperature, the end winding temperature, the stator core temperature, the rotor temperature, and the permanent magnet temperature in each drive motor parameter as the state variables and output variables, and the iron loss, the permanent magnet eddy current loss, the ambient temperature, and the coolant temperature in the cooling pipe as the disturbance variables. Obtain the difference between the predicted sequence of the stator winding temperature of each drive motor when controlling the copper loss and the reference temperature of the stator winding within the prediction time domain, and take the sum of the difference and the copper loss change rate within the prediction time domain as the optimization objective of the second objective function. With the copper loss and the copper loss change rate as the constraint conditions of the second objective function, under the constraint conditions of the second objective function, use the linear quadratic programming algorithm to minimize the optimization objective of the second objective function, and obtain the optimal control variable sequence of the second objective function within the prediction time domain through the minimized second objective function. The optimal control variable sequence of the second objective function includes the maximum copper loss sequence within the motor safety temperature rise threshold.

[0092] Use the linear parameter varying model predictive control method. Take the copper loss as the control variable, and the stator effective winding temperature in each drive motor parameter , the end winding temperature , the stator core temperature , the rotor temperature , and the permanent magnet temperature as the state variables and output variables, and the stator yoke iron loss , the stator tooth iron loss , the rotor iron loss , the permanent magnet eddy current loss , the ambient temperature , and the coolant temperature in the cooling pipe as the disturbance variables. With the copper loss , and the copper loss change rate as the constraint conditions of the second objective function, construct a state space equation that separates the copper loss from other losses, specifically:

[0093] Select as the state variable, as the disturbance variable, as the control variable, and construct a state space equation. The specific expression is:

[0094] ;

[0095] Discretize the state space equation. The specific expression is:

[0096] ;

[0097] Among them, is the state matrix, is the disturbance matrix, is the input matrix; is the discretized state variable, is the discretized disturbance quantity, is the discretized control variable, is the discretized state matrix, is the discretized disturbance matrix, is the discretized input matrix.

[0098] In model predictive control (MPC), it is necessary to discretize the state - space equation because the MPC algorithm is usually implemented in discrete - time cases. The following are the main reasons:

[0099] Computer implementation: When a computer executes a control algorithm, it operates at discrete time steps. By discretizing a continuous - time system, numerical algorithms can be used to predict and optimize the future behavior of the system.

[0100] Prediction and optimization: The MPC algorithm needs to predict a series of future control inputs and the states of the system. This requires multi - step prediction at discrete time points to ensure the feasibility and efficiency of the calculation.

[0101] Real - time application: In practical applications, the measurements of the control system and the actions of the actuators are carried out at discrete time points (such as the sampling of sensor data and the execution of control signals). Discretization enables MPC to calculate and apply new control inputs within each sampling period.

[0102] Through discretization, the continuous - time dynamic equation can be converted into a discrete - time equation, which is more suitable for implementation in a digital control system, thus ensuring the accuracy and real - time performance of the control algorithm.

[0103] Set the maximum insulation temperature of the motor stator winding as the reference temperature according to the motor insulation class.

[0104] Use the discretized state - space equation to obtain the prediction sequence of the stator winding temperature of each drive motor output by controlling the copper loss within the prediction horizon, and obtain the difference between the prediction sequence of the stator winding temperature of each drive motor and the reference temperature of the stator winding. Take the sum of the difference and the change rate of copper loss within the prediction horizon as the second objective function. The specific expression is:

[0105] ;

[0106] where, is the second objective function, is the prediction horizon; is the control horizon; is the temperature sequence of the motor stator winding within the prediction horizon; is the set reference temperature of the motor stator winding; is the weight coefficient of the motor temperature rise cost, is the sequence of the change amount of the motor copper loss within the control time domain, is the weight coefficient of the motor copper loss change rate, represents at the variable calculated at the moment at the moment.

[0107] Establish the constraint conditions with the copper loss and the copper loss change rate as the second objective function, including:

[0108] ;

[0109] Convert the discretized state space equation and perform matrix transformation, and then convert the optimization objective of the second objective function into a standard quadratic programming problem in the form of Adopt the linear quadratic programming algorithm to minimize the optimization objective of the second objective function, and obtain the optimal control variable sequence of the second objective function within the prediction time domain, that is, the maximum copper loss sequence within the motor safe temperature rise threshold. Among them, is the control variable increment, is the quadratic term coefficient matrix, is the linear term coefficient vector.

[0110] Step S4: According to the maximum copper loss sequence within the motor safe temperature rise threshold, obtain the maximum effective current value sequence within the motor safe temperature rise threshold, and based on the optimal control variable of the first objective function and the maximum effective current value sequence, obtain the maximum torque that each axis motor can provide within the motor temperature rise safety threshold according to the current-speed-torque three-dimensional look-up table.

[0111] Obtain the maximum effective current value sequence within the motor safe temperature rise threshold through the maximum copper loss sequence within the motor safe temperature rise threshold. The specific expression is:

[0112] ;

[0113] Among them, is the number of phases of the PMSM winding; is the copper loss corresponding to the maximum effective value of the winding phase current; is the effective resistance value of each phase winding, and its resistance value is related to the temperature characteristics of the material.

[0114] Obtain the corresponding torque through the current-speed-torque three-dimensional look-up table. The specific expression is:

[0115] ;

[0116] Among them, is the The maximum torque that the left or right motor of the shaft can provide within the safety threshold of the stator winding temperature rise is the left motor, is the right motor; is the current of the left or right motor of the shaft, is the speed of the left or right motor of the shaft.

[0117] In the torque distribution control strategy proposed by the present invention, the estimation of the motor temperature does not require the arrangement of sensors to estimate and control the temperature rise of the motor. Based on the mechanical stability, temperature rise stability, and operation reliability of the motor near the rated external characteristic curve, by restricting the motor operating point near the motor external characteristic curve, the temperature rise of the motor is reduced, the working efficiency of the motor is improved, and the electric power of the vehicle electric drive system is reduced. It focuses on achieving the optimal economy of distributed multi-axis heavy-duty electric drive multi-axis vehicles and the safety of electric drive multi-axis vehicles.

[0118] Based on the above method, the present invention provides a distributed electric drive multi-axis vehicle optimal torque distribution control system, including: a torque optimization distribution module, a temperature rise limit module, and a maximum torque distribution module.

[0119] Among them, the torque optimization distribution module is used to construct a first objective function through the instantaneous total energy consumption of the electric drive system, with the maximum torque that the motor can provide as a constraint condition, minimize the instantaneous total energy consumption of the electric drive system, and preferentially select the axle with an axle load ratio greater than the threshold as the drive axle according to the vertical load of each axle of the electric drive multi-axis vehicle, and obtain the optimal control variables of the first objective function at the current moment. The control variables of the first objective function include the number of drive axles of the electric drive multi-axis vehicle, the drive torque and drive speed on each drive axle; the temperature rise limit module is used to obtain the difference between the predicted sequence of the motor stator winding temperature output within the prediction time domain and the reference temperature of the stator winding, and use the sum of the difference and the copper loss change rate within the prediction time domain as the second objective function, with the copper loss and copper loss change rate as constraint conditions, minimize the second objective function, and obtain the optimal control variable sequence of the second objective function through the minimized second objective function. The optimal control variable sequence of the second objective function includes the maximum copper loss sequence within the motor safety temperature rise threshold; the maximum torque distribution module is used to obtain the maximum effective current value sequence within the motor safety temperature rise threshold based on the maximum copper loss sequence within the motor safety temperature rise threshold, and based on the optimal control variables of the first objective function and the maximum effective current value sequence, obtain the maximum torque that each axle motor can provide within the motor temperature rise safety threshold according to the three-dimensional look-up table of current - speed - torque.

[0120] Among them, as Figure 1As shown, in the model structure, the electric drive multi-axis vehicle obtains the motor speed and the vehicle's required torque through the accelerator / brake pedal opening degree and a calculation model. The vehicle's required torque is input into the torque optimization distribution module based on instantaneous energy consumption optimization, which considers the motor torque distribution correction factor, so as to obtain the drive torque commands for each axis and send them to the motor controller through CAN communication. Otherwise, the drive motor torques are sent to the motor thermal management module, and the motor speed is also sent to the motor thermal management module. The motor thermal management module sends the temperatures and losses of each node to the motor temperature rise limit module based on linear parameter variable and model predictive control (LPV-MPC). The maximum torque that the motor can provide within the safe temperature rise threshold is sent to the torque optimization distribution module and then sent to the motor controller again to achieve control.

[0121] According to the disclosed embodiments, the embedded vehicle controller can communicate with one or more external devices (such as sensors, motor controllers, battery management systems), or communicate with any device (such as Ethernet, CAN bus, etc.) that enables the controller to communicate with one or more other controllers or devices.

[0122] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should all be regarded as belonging to the protection scope of the present invention.

Claims

1. A torque optimization distribution control method for an electric drive multi-axle vehicle, characterized in that: include: The first objective function is constructed through the instantaneous total energy consumption of the electric drive system, the instantaneous total energy consumption of the electric drive system is minimized with the maximum torque that the motor can provide as a constraint condition, and the axle with an axle load ratio greater than a threshold is preferentially selected as the drive axle according to the vertical load of each axle of the electric drive multi-axle vehicle, and the optimal control variable of the first objective function at the current moment is obtained, and the optimal control variable of the first objective function includes the number of drive axles of the electric drive multi-axle vehicle, the drive torque on each drive axle, and the drive speed; Obtain the difference between the motor stator winding temperature prediction sequence output in the prediction time domain and the stator winding reference temperature, and use the sum of the difference and the copper loss change rate in the prediction time domain as the second objective function, minimize the second objective function with the copper loss and the copper loss change rate as constraints, and obtain the optimal control variable sequence of the second objective function through the minimized second objective function, wherein the optimal control variable sequence of the second objective function includes the maximum copper loss sequence within the motor safety temperature rise threshold; According to the maximum copper loss sequence within the motor safety temperature rise threshold, the maximum current effective value sequence within the motor safety temperature rise threshold is obtained, and based on the optimal control variable of the first objective function and the maximum current effective value sequence, the maximum torque that each axis motor can provide within the motor temperature rise safety threshold is obtained according to the current-speed-torque three-dimensional table lookup; specifically: The maximum current effective value sequence within the motor safety temperature rise threshold is obtained by the maximum copper loss sequence within the motor safety temperature rise threshold. The specific expression is: Where, m is the number of phases of the PMSM winding; Copper loss P Cu_max The corresponding maximum effective value of the winding phase current; R s is the effective resistance of each phase winding, and its resistance is related to the temperature characteristics of the material; The corresponding torque is obtained by looking up the current-speed-torque three-dimensional table. The specific expression is: in, is the maximum torque that the left or right motor of the i-th axis can provide within the stator winding temperature rise safety threshold, j=1 is the left motor, j=2 is the right motor; is the left or right motor current of the i-th axis, is the left or right motor speed of the i-th axis; The torque distribution correction factor is represented by the relationship between the motor speed, working torque, rated torque and peak torque. The specific expression is: Where λ is the torque distribution correction factor, is the motor working torque, is the rated torque of the motor at the current speed, is the peak torque at the current motor speed, is the motor speed, i is the i-th drive shaft, j=1 is the left motor, j=2 is the right motor; The working torque is corrected by a correction factor, and a corrected instantaneous total energy consumption of the electric drive system is obtained by the corrected torque.

2. The method for optimizing torque distribution control of an electric drive multi-axle vehicle according to claim 1, characterized in that: In the construction of the first objective function by the instantaneous total energy consumption of the electric drive system, the specific expression of the first objective function is: Among them, J1 is the first objective function, N is the number of drive shafts, P total (N,λ) is the total power of the electric drive system after correction, is the power of the left or right motor of the i-th axis in the dynamic model of each component of the power system, a is the maximum number of drive axes, is the angular velocity of the left or right motor of the i-th axis, is the left or right motor efficiency of the i-th axis.

3. The method for optimizing torque distribution control of an electric multi-axle vehicle according to claim 2, characterized in that: In the above-mentioned process of minimizing the instantaneous total energy consumption of the electric drive system with the maximum torque that the motor can provide as a constraint condition, the specific expression for minimizing the instantaneous total energy consumption of the electric drive system is: in, is the slip rate; is the vertical load on the i-th axis, i.e., the normal reaction force; P is the maximum torque that the motor can provide within the safe temperature rise threshold; b is the energy source power; I b is the energy source current; U b is the energy source voltage; SOC is the state of charge of the energy source; the subscripts min and max represent the minimum and maximum values ​​of the corresponding variables, respectively.

4. The method for optimizing torque distribution control of an electric multi-axle vehicle according to claim 3, characterized in that: The method of preferentially selecting an axle having an axle load ratio greater than a threshold as a driving axle according to the vertical load of each axle of the electric drive multi-axle vehicle comprises: Obtain the axle load ratio of each axle of the electric drive multi-axle vehicle. The specific expression is: Among them, q i is the axle load ratio of the i-th axis, that is, the ratio of the normal reaction force on the i-th axis to the gravity of the electric multi-axle vehicle; m is the mass of the electric multi-axle vehicle, and g is the acceleration due to gravity; The axle with an axle load ratio greater than a threshold is preferentially selected as the drive axle, and the drive torque is distributed to the motors on the drive axle in descending order of the axle load ratio.

5. The method for optimizing torque distribution control of an electric drive multi-axle vehicle according to claim 1, characterized in that: The method comprises obtaining a difference between a motor stator winding temperature prediction sequence output in a prediction time domain and a stator winding reference temperature, and taking the sum of the difference and the copper loss change rate in the prediction time domain as a second objective function, minimizing the second objective function with the copper loss and the copper loss change rate as constraints, and obtaining an optimal control variable sequence of the second objective function through the minimized second objective function, including: The linear variable parameter model predictive control method is used, copper loss is used as the control quantity, and the stator effective winding temperature T W , end winding temperature T EW , stator core temperature T sat , rotor temperature T rot , permanent magnet temperature T pm As the state quantity and output quantity, the stator yoke iron loss P fesyoke , stator tooth iron loss P festooth 、Rotor iron loss P fer , permanent magnet eddy current loss P mag 、Ambient temperature T E , Coolant temperature in cooling pipe T C As the interference quantity, copper loss P cu , the copper loss change rate is the constraint condition of the second objective function, and the state space equation that separates copper loss from other losses is constructed, which is specifically: Select is the state variable, is the interference amount, [P cu ] is the control variable, and the state space equation is constructed. The specific expression is: Discretize the state space equation, the specific expression is: Among them, A is the state matrix, B u is the interference matrix, B d is the input matrix; is the discretized state variable, is the discretized disturbance quantity, is the discretized control variable, A d is the discretized state matrix, B u_d is the discretized interference matrix, B d_d is the discretized input matrix; According to the motor insulation level, set the maximum insulation temperature of the motor stator winding as the stator winding reference temperature; The discretized state space equation is used to obtain the predicted temperature sequence of each drive motor stator winding output by controlling the copper loss in the prediction time domain, and the difference between the predicted temperature sequence of each drive motor stator winding and the stator winding reference temperature is obtained. The sum of the difference and the copper loss change rate in the prediction time domain is used as the second objective function. The specific expression is: Among them, J2 is the second objective function, N p is the prediction time domain; N c To control the time domain; is the temperature series of the motor stator winding in the prediction time domain; is the set reference temperature of the motor stator winding; ψ is the weight coefficient of the motor temperature rise cost, is the sequence of the change of motor copper loss in the control time domain, ζ is the weight coefficient of the change rate of motor copper loss, (k+j|k) represents the variable at time k+j calculated at time k; The copper loss and copper loss change rate are used as constraints for the second objective function, including: The discretized state space equation is transformed into a matrix, the optimization target of the second objective function is converted into a standard quadratic programming problem, and a linear quadratic programming algorithm is used to minimize the optimization target of the second objective function to obtain the optimal control variable sequence of the second objective function in the prediction time domain, that is, the maximum copper loss sequence within the safe temperature rise threshold of the motor.

6. A torque optimization distribution control system for an electric drive multi-axle vehicle, characterized in that: include: a torque optimization distribution module, for constructing a first objective function through the instantaneous total energy consumption of the electric drive system, minimizing the instantaneous total energy consumption of the electric drive system with the maximum torque that the motor can provide as a constraint, and preferentially selecting an axle with an axle load ratio greater than a threshold as a drive axle according to the vertical load of each axle of the electric drive multi-axle vehicle, and obtaining the optimal control variable of the first objective function at the current moment, wherein the optimal control variable of the first objective function includes the number of drive axles of the electric drive multi-axle vehicle, the drive torque on each drive axle, and the drive speed; A temperature rise limiting module is used to obtain the difference between the motor stator winding temperature prediction sequence output in the prediction time domain and the stator winding reference temperature, and take the sum of the difference and the copper loss change rate in the prediction time domain as the second objective function, and minimize the second objective function with the copper loss and the copper loss change rate as constraints, and obtain the optimal control variable sequence of the second objective function through the minimized second objective function, wherein the optimal control variable sequence of the second objective function includes the maximum copper loss sequence within the motor safety temperature rise threshold; The maximum torque distribution module is used to obtain the maximum current effective value sequence within the motor safety temperature rise threshold according to the maximum copper loss sequence within the motor safety temperature rise threshold, and based on the optimal control variable of the first objective function and the maximum current effective value sequence, obtain the maximum torque that each axis motor can provide within the motor temperature rise safety threshold according to the current-speed-torque three-dimensional table lookup; specifically: The maximum current effective value sequence within the motor safety temperature rise threshold is obtained by the maximum copper loss sequence within the motor safety temperature rise threshold. The specific expression is: Where, m is the number of phases of the PMSM winding; Copper loss P Cu_max The corresponding maximum effective value of the winding phase current; R s is the effective resistance of each phase winding, and its resistance is related to the temperature characteristics of the material; The corresponding torque is obtained by looking up the current-speed-torque three-dimensional table. The specific expression is: in, is the maximum torque that the left or right motor of the i-th axis can provide within the stator winding temperature rise safety threshold, j=1 is the left motor, j=2 is the right motor; is the left or right motor current of the i-th axis, is the left or right motor speed of the i-th axis; The torque distribution correction factor is represented by the relationship between the motor speed, working torque, rated torque and peak torque. The specific expression is: Where λ is the torque distribution correction factor, is the motor working torque, is the rated torque of the motor at the current speed, is the peak torque at the current motor speed, is the motor speed, i is the i-th drive shaft, j=1 is the left motor, j=2 is the right motor; The working torque is corrected by a correction factor, and a corrected instantaneous total energy consumption of the electric drive system is obtained by the corrected torque.

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