Torque distribution method and device of electric automobile and electric automobile

Abstract

The invention provides a torque distribution method and device of an electric vehicle and the electric vehicle. The electric vehicle comprises a first motor and a second motor, and the first motor and the second motor are jointly used for driving the electric vehicle. The torque distribution method comprises the steps that the expected torque of the electric vehicle, the first rotating speed of a first motor and the second rotating speed of a second motor are obtained; substituting the expected torque, the first rotating speed and the second rotating speed into a torque distribution table to obtain a corresponding torque distribution coefficient as a current torque distribution coefficient; the torque distribution table is the corresponding relation among the expected torque, the first rotating speed, the second rotating speed and the torque distribution coefficient when the efficiency of the first motor and the second motor is optimal; the torque distribution table is obtained through a genetic algorithm; and determining a first torque of the first motor and a second torque of the second motor according to the current torque distribution coefficient and the expected torque. Torque distribution can be rapidly carried out on the double motors, meanwhile, the total efficiency of the double motors is the highest, and the cruising ability of the vehicle is improved.

Description

Technical Field

[0001] The present application relates to the technical field of torque control of electric vehicles, and in particular to a torque distribution method and device for electric vehicles and an electric vehicle. Background Art

[0002] With the increasing awareness of environmental protection and the transformation of energy structure, electric vehicles, as a representative of clean energy transportation, have received extensive attention and development in recent years. In order to improve the power performance and endurance of electric vehicles, many electric vehicles use a dual-motor drive system, that is, two motors are equipped to drive the vehicle together. This design can not only provide stronger power output, but also optimize the working state of the motor through reasonable torque distribution, thereby improving overall energy efficiency.

[0003] However, the dual-motor drive system also brings new challenges, especially in terms of torque distribution. How to ensure the smoothness of vehicle power output and maximize energy efficiency has become an issue that needs to be solved urgently. Summary of the invention

[0004] The present application provides a torque distribution method and device for an electric vehicle with high efficiency, and the electric vehicle.

[0005] The present application provides a torque distribution method for an electric vehicle, wherein the electric vehicle comprises a first motor and a second motor, wherein the first motor and the second motor are used together to drive the electric vehicle, and the torque distribution method comprises:

[0006] Acquire a desired torque of the electric vehicle, a first rotation speed of the first motor, and a second rotation speed of the second motor;

[0007] Substituting the desired torque, the first speed and the second speed into a torque distribution table to obtain a corresponding torque distribution coefficient as a current torque distribution coefficient; the torque distribution table is a correspondence between the desired torque, the first speed, the second speed and the torque distribution coefficient when the efficiency of the first motor and the second motor is optimal; the torque distribution table is obtained by a genetic algorithm;

[0008] A first torque of the first motor and a second torque of the second motor are determined according to the current torque distribution coefficient and the desired torque.

[0009] Optionally, the electric vehicle includes a power battery, and the torque distribution table is obtained by the following steps:

[0010] Determine a population size, a termination condition, a selection strategy, a crossover type, a crossover rate, and a mutation rate; the selection strategy is determined based on the efficiency, the maximum output torque, the maximum output speed, the maximum output power of the power battery, and the bias factor of the first motor and the second motor;

[0011] Generate individuals that meet the population size, each individual including a set of data on expected torque, first speed, second speed, and torque distribution coefficient;

[0012] According to the selection strategy, the better individuals are determined and other individuals are deleted from the population;

[0013] According to the crossover type, the crossover rate and the mutation rate, crossover and mutation are performed on the better individuals to obtain new individuals;

[0014] The new individual is added to the population, and the steps of determining the better individual and obtaining the new individual are repeated until the termination condition is reached, and the expected torque, first speed, second speed and torque distribution coefficient included in the individuals in the population are determined as the torque distribution table.

[0015] Optionally, the selection strategy includes: among all individuals linearly arranged from high to low according to fitness values, selecting a preset number of individuals with top fitness values ​​as the better individuals; the fitness value is determined according to a fitness function; the fitness function is determined according to the efficiency, maximum output torque, maximum output speed, maximum output power of the power battery and a weight bias factor of the first motor and the second motor; the weight bias factor is determined according to the current operating parameters of the first motor and the second motor.

[0016] Optionally, the bias factor is determined by the following steps:

[0017] Determine the quadrature-axis current and the direct-axis current of the first motor and the second motor respectively according to the first torque and the second torque;

[0018] Injecting multiple groups of different current adjustment amounts into the quadrature-axis current and the direct-axis current to obtain multiple groups of different incremental quadrature-axis currents and incremental direct-axis currents;

[0019] Determine operating parameter data of the first motor and the second motor corresponding to each group of incremental quadrature-axis current and incremental direct-axis current, the operating parameter data including flux linkage and inductance;

[0020] Finding the incremental quadrature-axis current and the incremental direct-axis current with the minimum active power in the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents and the operating parameter data as the effective incremental quadrature-axis current and the effective incremental direct-axis current;

[0021] The weighting factor is determined according to the quadrature-axis current, the direct-axis current, the effective incremental quadrature-axis current, and the effective incremental direct-axis current.

[0022] Optionally, one of the incremental quadrature-axis current and the incremental direct-axis current is a direction quantity, and the other is a search quantity;

[0023] The step of searching, from among the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents and the operating parameter data, the incremental quadrature-axis current and the incremental direct-axis current with the minimum active power as the effective incremental quadrature-axis current and the effective incremental direct-axis current comprises:

[0024] Among the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents, and the operating parameter data of the first motor, and the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents, and the operating parameter data of the second motor, respectively searching for the search amount with the minimum active power according to the direction of the gradient descent of the direction amount, as the first effective search amount of the first motor and the second effective search amount of the second motor;

[0025] The step of determining the weighting factor according to the quadrature-axis current, the direct-axis current, the effective incremental quadrature-axis current, and the effective incremental direct-axis current comprises:

[0026] If the search amount is the incremental quadrature-axis current, the biasing factor is determined according to the quadrature-axis currents of the first motor and the second motor, the first effective search amount, and the second effective search amount;

[0027] If the search amount is the incremental direct-axis current, the biasing factor is determined based on the direct-axis currents of the first motor and the second motor, the first effective search amount, and the second effective search amount.

[0028] Optionally, the termination condition includes: the fitness value of the individual no longer increases.

[0029] Optionally, the selection strategy includes: determining the probability of each individual according to a set probability selection mechanism, and selecting a preset number of individuals with higher probabilities in the order of probability from high to low as the better individuals.

[0030] Optionally, the termination condition includes: the iteration reaches a preset number of iterations.

[0031] The present application provides a torque distribution device for an electric vehicle, comprising one or more processors, for implementing any of the torque distribution methods for an electric vehicle described above.

[0032] The present application also provides an electric vehicle, comprising:

[0033] a first motor and a second motor; and

[0034] The torque distribution device as described above is electrically connected to the first motor and the second motor.

[0035] In some embodiments, a torque distribution table is pre-determined by a genetic algorithm to indicate the correspondence between the expected torque, the first speed, the second speed, and the torque distribution coefficient when the efficiency of the first motor and the second motor is optimal. When the vehicle is actually running, the obtained expected torque, the first speed, and the second speed are substituted into the torque distribution table to obtain the corresponding torque distribution coefficient as the current torque distribution coefficient; and the first torque of the first motor and the second torque of the second motor are determined according to the current torque distribution coefficient and the expected torque. Through the torque distribution table, the torque of the dual motors can be quickly distributed, while the total efficiency of the dual motors is maximized, thereby improving the vehicle's endurance.

[0036] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] Figure 1 Shown is a schematic structural diagram of an embodiment of an electric vehicle of the present application.

[0039] Figure 2 Shown is a flow chart of an embodiment of the torque distribution method of the present application.

[0040] Figure 3 FIG. 1 is a timing diagram of an embodiment of injecting multiple sets of different current regulation amounts into the quadrature-axis current and the direct-axis current.

[0041] Figure 4 Shown is a schematic diagram of an embodiment of the torque distribution method of the present application.

[0042] Figure 5 Shown is a structural block diagram of an embodiment of a torque distribution device for an electric vehicle of the present application. DETAILED DESCRIPTION

[0043] The present application provides a torque distribution method and device for an electric vehicle and an electric vehicle. The torque distribution method and device for an electric vehicle and an electric vehicle of the present application are described in detail below in conjunction with the accompanying drawings. The features of the following embodiments and implementations can be combined with each other without conflict.

[0044] Figure 1FIG. 1 is a schematic diagram of the structure of an embodiment of an electric vehicle 10 of the present application. Figure 1 As shown, the electric vehicle 10 includes a first motor 11 and a second motor 12. The first motor 11 and the second motor 12 are used together to drive the electric vehicle 10.

[0045] The first motor 11 is arranged on the front axle of the electric vehicle 10, and the second motor 12 is arranged on the rear axle of the electric vehicle 10. The first motor 11 and the second motor 12 jointly provide the torque required by the electric vehicle 10 to drive the electric vehicle 10 to run. In order to adapt to different driving conditions, the first motor 11 and the second motor 12 are usually different motors with different efficiency characteristics.

[0046] The electric vehicle 10 further includes a torque distribution device provided in the present application, and the torque distribution device is electrically connected to the first motor 11 and the second motor 12. The torque distribution device is used to execute the torque distribution method provided in the present application.

[0047] The torque distribution device is used to distribute torque to the first motor 11 and the second motor 12 according to the torque required by the electric vehicle 10, and determine the torque that the first motor 11 and the second motor 12 need to output respectively, so that the first motor 11 and the second motor 12 can operate in a higher efficiency range, thereby improving the efficiency of the entire vehicle and improving the vehicle's endurance.

[0048] The electric vehicle 10 further includes a power battery for supplying power to the first motor 11 and the second motor 12 .

[0049] Figure 2 FIG. 1 is a flow chart of an embodiment of the torque distribution method 20 of the present application.

[0050] The torque distribution method 20 of the electric vehicle includes: step 21 to step 23 .

[0051] Step 21 , obtaining the desired torque of the electric vehicle 10 , the first rotation speed of the first motor 11 , and the second rotation speed of the second motor 12 .

[0052] The expected torque is the total driving torque currently required by the electric vehicle 10, which is usually determined by the vehicle's acceleration, deceleration, cruising state, road condition information, etc. The expected speed of the vehicle can be obtained first, and then the expected torque of the vehicle can be determined based on the expected speed.

[0053] The first speed and the second speed are respectively the current speeds of the first motor 11 and the second motor 12. The speed of the motor is closely related to its working efficiency and output torque. The speed of the motor can be obtained through the instrument panel, the on-board diagnostic system (OBD), the speed sensor, etc.

[0054] Step 22, substituting the desired torque, the first speed and the second speed into the torque distribution table to obtain the corresponding torque distribution coefficient as the current torque distribution coefficient.

[0055] The torque distribution table is a correspondence between the expected torque, the first speed, the second speed and the torque distribution coefficient when the efficiency of the first motor 11 and the second motor 12 is optimal. The torque distribution table is obtained by a genetic algorithm.

[0056] The torque distribution coefficient indicates the distribution ratio of the desired torque between the first motor 11 and the second motor 12. For example, if the torque distribution coefficient is 0.6, the first motor 11 will bear 60% of the desired torque, and the second motor 12 will bear the remaining 40% of the desired torque.

[0057] The genetic algorithm is a search algorithm that simulates natural selection and genetic mechanisms. Through the genetic algorithm, the torque distribution coefficient that maximizes the total efficiency of the first motor 11 and the second motor 12 under different combinations of desired torques and speeds can be obtained.

[0058] Substitute the desired torque, the first speed, and the second speed obtained in step 21 into the pre-generated torque distribution table to obtain a torque distribution coefficient corresponding to the desired torque, the first speed, and the second speed as the current torque distribution coefficient. The current torque distribution coefficient represents the torque distribution between the first motor 11 and the second motor 12 when the total efficiency of the first motor 11 and the second motor 12 is the highest under the conditions of the desired torque, the first speed, and the second speed.

[0059] Step 23, determining a first torque of the first motor and a second torque of the second motor according to the current torque distribution coefficient and the expected torque.

[0060] The first torque and the second torque respectively represent the torques that the first motor 11 and the second motor 12 need to output under the current working conditions. The first torque and the second torque are obtained by multiplying the desired torque by the corresponding torque distribution coefficient. For example, if the desired torque is 100Nm and the torque distribution coefficient is 0.6, then the first torque of the first motor 11 is 60Nm, and the second torque of the second motor 12 is 40Nm. The first torque and the second torque are the torques output by the first motor 11 and the second motor 12 respectively when the total efficiency of the first motor 11 and the second motor 12 is the highest under the conditions of the desired torque, the first speed and the second speed.

[0061] In some embodiments, a torque distribution table is pre-determined by a genetic algorithm to indicate the correspondence between the desired torque, the first speed, the second speed, and the torque distribution coefficient when the efficiency of the first motor 11 and the second motor 12 is optimal. When the vehicle is actually running, the desired torque, the first speed, and the second speed are substituted into the torque distribution table to obtain the corresponding torque distribution coefficient as the current torque distribution coefficient; and the first torque of the first motor 11 and the second torque of the second motor 12 are determined according to the current torque distribution coefficient and the desired torque. Through the torque distribution table, the torque of the dual motors can be quickly distributed, while the total efficiency of the dual motors is maximized, thereby improving the vehicle's endurance.

[0062] In some embodiments, the torque distribution table is obtained by the following steps:

[0063] Determine the population size, termination conditions, selection strategy, crossover type, crossover rate, and mutation rate; the selection strategy is determined based on the efficiency, maximum output torque, maximum output speed, maximum output power of the power battery, and bias factor of the first motor 11 and the second motor 12;

[0064] Generate individuals that meet the population size, each individual including a set of data of expected torque, first speed, second speed and torque distribution coefficient;

[0065] According to the selection strategy, the better individuals are identified and other individuals are removed from the population;

[0066] According to the crossover type, crossover rate and mutation rate, crossover and mutation are performed on the better individuals to obtain new individuals;

[0067] Add the new individual to the population, and repeat the steps of determining the better individual and obtaining the new individual until the termination condition is reached, and determine the expected torque, first speed, second speed and torque distribution coefficient included in the individuals in the population as a torque distribution table.

[0068] The population size refers to the number of candidate solutions (individuals) considered simultaneously in a genetic algorithm. Setting an appropriate population size can increase the likelihood of finding a global optimal solution while balancing computational complexity. The population size can be determined experimentally or empirically.

[0069] The termination condition is the condition under which the genetic algorithm stops running. In some embodiments, the termination condition includes: the iteration reaches a preset number of iterations. The number of iterations can be determined by experiment or experience. Setting an appropriate number of iterations can prevent the algorithm from failing to converge and improve the effectiveness of the algorithm.

[0070] The termination condition may also be: finding a solution that meets certain conditions or the solution in the population no longer significantly improves, etc.

[0071] The selection strategy is used to determine which individuals will be used to reproduce the next generation. The selection strategy is determined based on the efficiency, maximum output torque, maximum output speed, maximum output power of the power battery, and the bias factor of the first motor 11 and the second motor 12.

[0072] Under the conditions of the maximum output torque, the maximum output speed, and the maximum output power of the power battery of the first motor 11 and the second motor 12, it is preferred to select individuals that make the first motor 11 and the second motor 12 more efficient.

[0073] Crossover type is the operation used in genetic algorithms to generate new individuals. The crossover type determines how the genes of two parent individuals are combined to produce offspring individuals.

[0074] The crossover rate determines how often the crossover operation is performed in the population. An appropriate crossover rate can increase the diversity of the population while avoiding destroying the genetic combination of excellent individuals.

[0075] The mutation rate is the probability of introducing randomness into the genetic algorithm. Mutation allows individuals to undergo small genetic changes during reproduction. The mutation rate determines the probability of a mutation occurring.

[0076] After determining the above parameters, individuals that meet the population size are generated, each of which represents a torque distribution strategy. Each individual consists of a set of data, including the desired torque, the first speed of the first motor 11, the second speed of the second motor 12, and the torque distribution coefficient.

[0077] According to the selection strategy, better individuals are selected from the population and other individuals are deleted from the population to reduce the computational complexity and focus on individuals that are more likely to produce excellent solutions.

[0078] Perform a crossover operation on the selected better individuals and generate new individuals based on the crossover type and crossover rate.

[0079] Perform mutation operations on new individuals, introducing random changes according to the mutation rate.

[0080] Add new individuals to the population and repeat the above steps of determining better individuals, crossover and mutation.

[0081] The iteration process continues until the termination condition is reached, at which point the expected torque, speed, and torque distribution coefficients contained in the individuals in the population will be determined as the final torque distribution table.

[0082] Through genetic algorithms, we can effectively avoid falling into local optimality and obtain the torque distribution table relatively quickly.

[0083] In some embodiments, the selection strategy includes: determining the probability of each individual according to a set probability selection mechanism, and selecting a preset number of individuals with higher probabilities as the better individuals in the order of probability from high to low.

[0084] First, a fitness function is defined to evaluate the quality of each individual in the population. According to the fitness function, the fitness value of each individual, that is, the total efficiency of the first motor 11 and the second motor 12, can be determined.

[0085] According to the fitness of the individual, the probability of each individual being selected is calculated through a certain probability selection mechanism (such as roulette selection, tournament selection, etc.). The probability of each individual can be proportional to the fitness of the individual, that is, the higher the fitness of the individual, the greater the probability of being selected.

[0086] After calculating the selection probability of each individual, a preset number of individuals are selected from the population as better individuals according to the probability from high to low. The preset number is pre-set, and the preset number can be half of the population. The selected better individuals participate in the subsequent crossover and mutation operations as parents.

[0087] The probability selector allocates the probability of individuals to select better individuals, so that individuals with higher fitness values ​​have a higher probability of selection, while individuals with lower fitness values ​​also have a certain probability of being selected, thereby maintaining the diversity of the population and helping to prevent the algorithm from converging to a local optimal solution too early. At the same time, it has a certain robustness to noise and imprecise data. The probability selector avoids the requirement for precise calculation of individual fitness values ​​by allocating selection probabilities, thereby reducing the sensitivity of the algorithm to noisy data.

[0088] In some embodiments, the selection strategy includes: among all individuals arranged linearly from high to low according to fitness values, a preset number of individuals with higher fitness values ​​are selected as better individuals; the fitness value is determined according to the fitness function; the fitness function is determined according to the efficiency, maximum output torque, maximum output speed, maximum output power of the power battery and the bias factor of the first motor 11 and the second motor 12; the bias factor is determined according to the current operating parameters of the first motor 11 and the second motor 12.

[0089] The fitness function is determined according to formula (1):

[0090]

[0091] Among them, T 1 is the first torque of the first motor 11, T 2 is the second torque of the second motor 12, T req is the desired torque, η is the torque distribution coefficient, η=T 1 / (T 1+T 2 ), T max1 is the maximum output torque of the first motor 11, T max2 is the maximum output torque of the second motor 12; n max1 is the maximum output speed of the first motor 11, n max2 is the maximum output speed of the second motor 12, P maxbattery is the maximum output power of the power battery, P 1 is the efficiency function of the first motor 11, P 2 is the efficiency function of the second motor 12, and k is the bias factor.

[0092] The fitness function determined by the formula (1) represents the maximization of the sum of the efficiencies of the first motor 11 and the second motor 12 .

[0093] The bias factor k is determined according to the current operating parameters of the first motor 11 and the second motor 12, and reflects the performance changes of the motors during actual use. The current operating parameters include the voltage, current, flux, inductance, etc. of the motors. As the motors are used, their efficiency will also change. The bias factor k is introduced into the fitness function so that the fitness value can change with the actual use of the motors, which is more in line with the actual situation of the motors.

[0094] By selecting the strategy through the above method, a torque distribution table that meets the actual use of the motor can be obtained, so that when distributing torque to the motor, the error caused by the nonlinear characteristic offset during the use of the motor can be eliminated, and a more efficient torque distribution can be obtained.

[0095] In some embodiments, the termination condition includes: the fitness value of the individual no longer increases.

[0096] The fitness function is used to evaluate the quality of individuals, and selection and reproduction are performed based on the fitness value. If after a certain number of iterations, the highest fitness value or average fitness value of individuals in the population no longer increases significantly (that is, the change in the highest fitness value or average fitness value is less than the preset threshold), it can be considered that the algorithm has converged to the optimal solution and the iteration is stopped.

[0097] Setting the termination condition according to the change of individual fitness values ​​can balance the optimization effect and computational cost of the algorithm. According to the change of fitness value, the optimal solution can be found more accurately, while the algorithm will not run too long and waste computing resources, which helps to ensure the effectiveness and efficiency of the algorithm.

[0098] In some embodiments, the bias factor is determined by:

[0099] Determine the quadrature-axis current and the direct-axis current of the first motor 11 and the second motor 12 respectively according to the first torque and the second torque;

[0100] Injecting multiple groups of different current adjustment amounts into the quadrature-axis current and the direct-axis current to obtain multiple groups of different incremental quadrature-axis currents and incremental direct-axis currents;

[0101] Determine operating parameter data of the first motor 11 and the second motor 12 corresponding to each group of incremental quadrature-axis current and incremental direct-axis current, the operating parameter data including flux linkage and inductance;

[0102] Among the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents and operation parameter data, find the incremental quadrature-axis current and the incremental direct-axis current with the minimum active power as the effective incremental quadrature-axis current and the effective incremental direct-axis current;

[0103] The weighting factor is determined based on the quadrature-axis current, the direct-axis current, the effective incremental quadrature-axis current and the effective incremental direct-axis current.

[0104] According to the first torque and the second torque, the quadrature-axis current and the direct-axis current of the first motor 11 and the second motor 12 are respectively calculated by a motor control algorithm.

[0105] In order to explore the changing trend of motor performance, multiple groups of different current adjustment amounts are respectively injected into the quadrature-axis current and the direct-axis current of the first motor 11 and the second motor 12 to form multiple groups of incremental quadrature-axis current and incremental direct-axis current. Through small current adjustments, the corresponding changes in motor performance (such as power, efficiency, etc.) are observed.

[0106] Figure 3 FIG. 1 is a timing diagram of an embodiment of injecting multiple sets of different current regulation amounts into the quadrature-axis current and the direct-axis current.

[0107] The timing diagram of the current regulation amount can be determined based on experiments or experience. Figure 3 Taking the first motor 11 as an example, Figure 3 As shown, in the direct-axis current and quadrature-axis current of the first motor 11 [i d1a ,i q1a ], insert two groups of steady-state currents and obtain [i d1b ,i q1b ] and [i d1c ,i q1c ], the incremental direct-axis current and incremental quadrature-axis current are [△i d1b ,△i q1b ] and [△i d1c ,△i q1c ].

[0108] A transition current is added between steady-state currents, and a distributed transition method is used to make the incremental direct-axis current phase ahead of the incremental quadrature-axis current phase, so as to reduce the influence of steady-state current fluctuations on the incremental inductance.

[0109] For each set of incremental quadrature-axis current and incremental direct-axis current, the operating parameter data of the first motor 11 and the second motor 12 are measured and recorded, including flux linkage (a physical quantity reflecting the magnetic field strength) and inductance (a physical quantity reflecting the self-inductance and mutual inductance characteristics of the motor winding). The above operating parameter data are crucial for understanding the performance of the motor under specific current conditions.

[0110] Active power is the power of the motor actually doing work, and reducing active power means improving the efficiency of the motor system. Among all the incremental quadrature-axis currents, incremental direct-axis currents, and corresponding operating parameter data, find the set of incremental quadrature-axis currents and incremental direct-axis currents that minimize the total active power of the first motor 11 and the second motor 12. Under this set of incremental quadrature-axis currents and incremental direct-axis currents, the total active power of the first motor 11 and the second motor 12 is the smallest, and the overall operating efficiency is the highest.

[0111] Based on the original quadrature-axis current, direct-axis current, and the effective incremental quadrature-axis current and effective incremental direct-axis current found, a bias factor is determined. The bias factor obtained in this way represents the relationship between the effective incremental quadrature-axis current, the effective incremental direct-axis current, and the original quadrature-axis current and the direct-axis current when the overall operating efficiency of the motor is the highest. The original quadrature-axis current and the direct-axis current are optimized by the bias factor, so that the motor has high operating efficiency.

[0112] In some embodiments, one of the incremental quadrature-axis current and the incremental direct-axis current is a direction quantity, and the other is a search quantity;

[0113] The step of searching, from among the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents and operating parameter data, the incremental quadrature-axis current and the incremental direct-axis current with the minimum active power as the effective incremental quadrature-axis current and the effective incremental direct-axis current comprises:

[0114] Among the multiple sets of incremental quadrature-axis current, incremental direct-axis current and operating parameter data of the first motor 11, and the multiple sets of incremental quadrature-axis current, incremental direct-axis current and operating parameter data of the second motor 12, respectively, according to the direction of the gradient descent of the direction quantity, the search quantity with the minimum active power is searched as the first effective search quantity of the first motor 11 and the second effective search quantity of the second motor 12;

[0115] Determining the weighting factor according to the quadrature-axis current, the direct-axis current, the effective incremental quadrature-axis current and the effective incremental direct-axis current includes:

[0116] If the search amount is the incremental quadrature-axis current, the bias factor is determined according to the quadrature-axis currents of the first motor 11 and the second motor 12, the first effective search amount and the second effective search amount;

[0117] If the search amount is an incremental direct-axis current, the bias factor is determined based on the direct-axis currents of the first motor 11 and the second motor 12, the first effective search amount, and the second effective search amount.

[0118] One of the incremental quadrature-axis current and the incremental direct-axis current is designated as the direction variable, and the other is the search variable. The direction variable is used to determine the search direction, and the search variable is used to find the optimal solution in that direction. In this way, while keeping one direction unchanged, the incremental quadrature-axis current and the incremental direct-axis current when the active power is minimized are found by adjusting the other variable.

[0119] By comparing multiple sets of incremental quadrature-axis current, incremental direct-axis current and operating parameter data, the incremental current value when the active power is minimum is found.

[0120] Taking the first motor 11 as an example, inject Figure 3 After the current sequence shown, the voltage changes obtained according to the injected current are as shown in formula (2) and formula (3):

[0121]

[0122]

[0123] Among them, i d1 、i q1 、u d1 、u q1 are the direct-axis current, quadrature-axis current, direct-axis voltage, and quadrature-axis voltage of the first motor 11, respectively, and are determined by the first torque; Δu d1 , Δu q1 , Δi d1 , Δi q1 They are incremental direct-axis voltage, incremental quadrature-axis voltage, incremental direct-axis current, and incremental quadrature-axis current respectively; and L d1 , L q1 are the apparent flux linkage and incremental inductance of the direct axis and quadrature axis respectively.

[0124] Furthermore, the reactive power model can be obtained as shown in formula (4) and formula (5):

[0125]

[0126] Through the least squares method, the motor parameters can be identified to obtain the apparent flux and incremental inductance.

[0127] Furthermore, the obtained matrices of multiple sets of incremental quadrature-axis currents, incremental direct-axis currents, and operating parameter data are shown below:

[0128]

[0129] in, is the direct axis flux, is the cross-axis flux, L d1 is the direct-axis inductance, L d1 Quadrature-axis inductance.

[0130] The incremental direct-axis current is used as the direction quantity and the incremental quadrature-axis current is used as the search quantity. The iterative search model is shown in formula (6) and formula (7).

[0131] △I d1 = -γ(P b -P a ) / (i d1b -△i d1a ) Formula (6)

[0132]

[0133] Among them, △I d1 is the incremental direct-axis current, △I q1 is the incremental quadrature axis current, γ is the iteration rate, which is used to balance the iteration speed and accuracy and is the test experience value. b and P a for Figure 3 The current active power of phase b and phase a of the current injection point shown is calculated as P = 1.5 (u d i d +u q i q ).

[0134] The search is performed in the descending direction of the gradient of the incremental direct-axis current until the incremental quadrature-axis current corresponding to the point with the minimum active power is found, which serves as the first effective search quantity of the first motor 11, that is, the first effective incremental quadrature-axis current.

[0135] For the second motor 12 , the same steps are repeated to obtain a second effective search quantity of the second motor 12 , namely, a first effective incremental quadrature-axis current.

[0136] The bias factor k is determined by formula (8).

[0137]

[0138] Among them, △I q1 is the first effective incremental quadrature-axis current of the first motor 11, ΔI q2 is the second effective incremental quadrature-axis current of the second motor 12, I q1 is the quadrature axis current of the first motor 11 determined according to the first torque, I q2 is the quadrature-axis current of the second electric machine 12 determined according to the second torque.

[0139] If the incremental quadrature-axis current is used as the direction quantity and the incremental direct-axis current is used as the search quantity, the weighting factor k is determined by formula (9).

[0140]

[0141] Among them, △I d1 is the first effective incremental direct-axis current of the first motor 11, △I d2 is the second effective incremental direct-axis current of the second motor 12, I d1 is the direct-axis current of the first motor 11 determined according to the first torque, I d2 is the direct-axis current of the second electric machine 12 determined according to the second torque.

[0142] Based on the incremental quadrature-axis current and the incremental direct-axis current, the optimal current increment is determined by searching for the point with the minimum active power, and the weighting factor is determined according to the optimal current increment, so that the weighting factor when the motor operates with the highest efficiency can be obtained.

[0143] Figure 4 Shown is a schematic diagram of an embodiment of the torque distribution method of the present application.

[0144] The desired torque is input into the torque distribution table to obtain the first torque and the second torque, and the quadrature-axis current and the direct-axis current when the first motor 11 and the second motor 12 output the first torque and the second torque respectively are determined. According to the quadrature-axis current, the direct-axis current and the current adjustment amount of the first motor 11 and the second motor 12, the parameters of the first motor 11 and the second motor 12 are identified, and the effective incremental quadrature-axis current and the effective incremental direct-axis current when the active power is minimum are found through iterative search. According to the effective incremental quadrature-axis current and the effective incremental direct-axis current, the bias factor is determined, and the bias factor is updated to the torque distribution table, so that the torque distribution of the torque distribution table is more in line with the current operation of the motor, and the torque distribution obtained according to the torque distribution table is ensured, so that the operation efficiency of the first motor 11 and the second motor 12 can be optimized.

[0145] Figure 5 Shown is a structural block diagram of an embodiment of a torque distribution device for an electric vehicle of the present application.

[0146] like Figure 5 As shown, the torque distribution device of the electric vehicle includes one or more processors 31 for implementing the torque distribution method 20 of the electric vehicle as described above.

[0147] In some embodiments, the torque distribution device of the electric vehicle may include a computer-readable storage medium 32, which may store a program that can be called by the processor 31 and may include a non-volatile storage medium. In some embodiments, the torque distribution device of the electric vehicle may include a memory 33 and an interface 34. In some embodiments, the torque distribution device of the electric vehicle may also include other hardware according to actual applications.

[0148] The computer-readable storage medium 32 of the embodiment of the present application stores a program thereon, and when the program is executed by the processor 31, it is used to implement the torque distribution method 20 of the electric vehicle described above.

[0149] The present application may take the form of a computer program product implemented on one or more computer-readable storage media 32 (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. The computer-readable storage medium 32 includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be a computer-readable instruction, a data structure, a module of a program, or other data. Examples of computer-readable storage media 32 include but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassette, tape disk storage or other magnetic storage device or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

Claims

1. A torque distribution method for an electric vehicle, characterized in that: The electric vehicle comprises a first motor and a second motor, the first motor and the second motor are used together to drive the electric vehicle, and the torque distribution method comprises: Acquire a desired torque of the electric vehicle, a first rotation speed of the first motor, and a second rotation speed of the second motor; Substituting the desired torque, the first speed and the second speed into a torque distribution table to obtain a corresponding torque distribution coefficient as a current torque distribution coefficient; the torque distribution table is a correspondence between the desired torque, the first speed, the second speed and the torque distribution coefficient when the efficiency of the first motor and the second motor is optimal; the torque distribution table is obtained by a genetic algorithm; A first torque of the first motor and a second torque of the second motor are determined according to the current torque distribution coefficient and the desired torque.

2. The torque distribution method of an electric vehicle according to claim 1, characterized in that: The electric vehicle includes a power battery, and the torque distribution table is obtained by the following steps: Determine a population size, a termination condition, a selection strategy, a crossover type, a crossover rate, and a mutation rate; the selection strategy is determined based on the efficiency, the maximum output torque, the maximum output speed, the maximum output power of the power battery, and the bias factor of the first motor and the second motor; Generate individuals that meet the population size, each individual including a set of data on expected torque, first speed, second speed, and torque distribution coefficient; According to the selection strategy, the better individuals are determined and other individuals are deleted from the population; According to the crossover type, the crossover rate and the mutation rate, crossover and mutation are performed on the better individuals to obtain new individuals; The new individual is added to the population, and the steps of determining the better individual and obtaining the new individual are repeated until the termination condition is reached, and the expected torque, first speed, second speed and torque distribution coefficient included in the individuals in the population are determined as the torque distribution table.

3. The torque distribution method of an electric vehicle according to claim 2, characterized in that: The selection strategy includes: among all individuals arranged linearly from high to low according to fitness values, a preset number of individuals with top fitness values ​​are selected as the better individuals; the fitness value is determined according to a fitness function; the fitness function is determined according to the efficiency, maximum output torque, maximum output speed, maximum output power of the power battery and a weight bias factor of the first motor and the second motor; the weight bias factor is determined according to the current operating parameters of the first motor and the second motor.

4. The torque distribution method of an electric vehicle according to claim 2 or 3, characterized in that: The bias factor is determined by the following steps: Determine the quadrature-axis current and the direct-axis current of the first motor and the second motor respectively according to the first torque and the second torque; Injecting multiple groups of different current adjustment amounts into the quadrature-axis current and the direct-axis current to obtain multiple groups of different incremental quadrature-axis currents and incremental direct-axis currents; Determine operating parameter data of the first motor and the second motor corresponding to each group of incremental quadrature-axis current and incremental direct-axis current, the operating parameter data including flux linkage and inductance; Finding the incremental quadrature-axis current and the incremental direct-axis current with the minimum active power in the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents and the operating parameter data as the effective incremental quadrature-axis current and the effective incremental direct-axis current; The weighting factor is determined according to the quadrature-axis current, the direct-axis current, the effective incremental quadrature-axis current, and the effective incremental direct-axis current.

5. The torque distribution method of an electric vehicle according to claim 4, characterized in that: One of the incremental quadrature-axis current and the incremental direct-axis current is a direction quantity, and the other is a search quantity; The step of searching, from among the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents and the operating parameter data, the incremental quadrature-axis current and the incremental direct-axis current with the minimum active power as the effective incremental quadrature-axis current and the effective incremental direct-axis current comprises: Among the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents, and the operating parameter data of the first motor, and the multiple groups of incremental quadrature-axis currents, incremental direct-axis currents, and the operating parameter data of the second motor, respectively searching for the search amount with the minimum active power according to the direction of the gradient descent of the direction amount, as the first effective search amount of the first motor and the second effective search amount of the second motor; The step of determining the weighting factor according to the quadrature-axis current, the direct-axis current, the effective incremental quadrature-axis current, and the effective incremental direct-axis current comprises: If the search amount is the incremental quadrature-axis current, the biasing factor is determined according to the quadrature-axis currents of the first motor and the second motor, the first effective search amount, and the second effective search amount; If the search amount is the incremental direct-axis current, the biasing factor is determined according to the direct-axis currents of the first motor and the second motor, the first effective search amount, and the second effective search amount.

6. The torque distribution method of an electric vehicle according to claim 3, characterized in that: The termination condition includes: the fitness value of the individual no longer increases.

7. The torque distribution method of an electric vehicle according to claim 2, characterized in that: The selection strategy includes: determining the probability of each individual according to a set probability selection mechanism, and selecting a preset number of individuals with higher probabilities in the order from high to low probability as the better individuals.

8. The torque distribution method of an electric vehicle according to claim 2, characterized in that: The termination condition includes: the iteration reaches a preset number of iterations.

9. A torque distribution device for an electric vehicle, characterized in that: The invention comprises one or more processors for implementing the torque distribution method of the electric vehicle according to any one of claims 1 to 8.

10. An electric vehicle, characterized in that: include: a first motor and a second motor; and The torque distribution device of claim 9, electrically connected to the first motor and the second motor.

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