Torque distribution method and device for electric vehicle and electric vehicle

Abstract

The application 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 used for driving the electric vehicle. The torque distribution method comprises the following steps: obtaining a desired torque of the electric vehicle, a first rotating speed of the first motor and a second rotating speed of the second motor; substituting the desired 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 a corresponding relationship between the desired torque, the first rotating speed, the second rotating speed and the torque distribution coefficient when the first motor and the second motor are in an efficiency optimum state; the torque distribution table is obtained through a genetic algorithm; 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 desired torque. The application can quickly distribute the torque of the double motors, the total efficiency of the double motors is the highest, and the vehicle endurance is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the torque control technology field of an electric vehicle, in particular to a torque distribution method and device of an electric vehicle and the electric vehicle. BACKGROUND

[0002] With the enhancement of environmental awareness and the transformation of energy structure, electric vehicles as representatives of clean energy transportation tools have been widely concerned and developed in recent years. In order to improve the power performance and endurance of electric vehicles, many electric vehicles adopt a double-motor driving system, that is, two motors are equipped to jointly drive the vehicle. This design not only can provide stronger power output, but also can optimize the working state of the motor through reasonable torque distribution, so as to improve the overall energy efficiency.

[0003] However, the double-motor driving system also brings new challenges, especially in torque distribution. How to ensure the smoothness of vehicle power output and the maximization of energy efficiency has become a problem to be solved. SUMMARY

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

[0005] The application provides a torque distribution method of an electric vehicle, the electric vehicle comprising a first motor and a second motor, the first motor and the second motor being used to jointly drive the electric vehicle, the torque distribution method comprising:

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

[0007] substituting the desired 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 a corresponding relationship between the desired torque, the first rotating speed, the second rotating speed and the torque distribution coefficient when the first motor and the second motor are in the most efficient state; the torque distribution table is obtained through a genetic algorithm;

[0008] 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 desired torque.

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

[0010] determining a population size, a termination condition, a selection strategy, a crossover type, a crossover rate, and a mutation rate, wherein the selection strategy is determined according to efficiencies, maximum output torque, maximum output speed of the first motor and the second motor, maximum output power of the power battery, and a bias factor;

[0011] generating individuals in accordance with the population size, each individual including data of a set of expected torque, first speed, second speed, and torque distribution coefficient;

[0012] determining superior individuals in accordance with the selection strategy, and deleting other individuals from the population;

[0013] crossing and mutating the superior individuals in accordance with the crossover type, the crossover rate, and the mutation rate to obtain new individuals;

[0014] adding the new individuals to the population, and repeating the steps of determining superior individuals and obtaining new individuals until the termination condition is reached, and determining the data of expected torque, first speed, second speed, and torque distribution coefficient included in the individuals in the population as the torque distribution table.

[0015] Optionally, the selection strategy includes: selecting a preset number of individuals with high fitness values in linear arrangement of all individuals from high to low as the superior individuals, wherein the fitness values are determined according to a fitness function, and the fitness function is determined according to the efficiencies, maximum output torque, maximum output speed of the first motor and the second motor, the maximum output power of the power battery, and the bias factor, and the bias factor is determined according to current operating parameters of the first motor and the second motor.

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

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

[0018] injecting a plurality of different current adjustment amounts into the quadrature-axis currents and the direct-axis currents to obtain a plurality of different incremental quadrature-axis currents and incremental direct-axis currents;

[0019] determining operating parameter data of the first motor and the second motor corresponding to each group of incremental quadrature-axis currents and incremental direct-axis currents, wherein the operating parameter data includes flux linkage and inductance;

[0020] finding incremental quadrature-axis currents and incremental direct-axis currents with minimum active power from the plurality of incremental quadrature-axis currents, incremental direct-axis currents, and the operating parameter data as effective incremental quadrature-axis currents and effective incremental direct-axis currents;

[0021] determine the biasing factor 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 lookup quantity.

[0023] The looking up of the incremental quadrature axis current and the incremental direct axis current at which the active power is minimum in the plurality of sets of incremental quadrature axis current, incremental direct axis current and the operating parameter data as the effective incremental quadrature axis current and the effective incremental direct axis current comprises:

[0024] The looking up of the lookup quantity at which the active power is minimum in the plurality of sets of incremental quadrature axis current, incremental direct axis current and the operating parameter data of the first motor and the plurality of sets of incremental quadrature axis current, incremental direct axis current and the operating parameter data of the second motor respectively according to the descending direction of the gradient of the direction quantity as the first effective lookup quantity of the first motor and the second effective lookup quantity of the second motor.

[0025] The determining of the biasing 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 lookup quantity is the incremental quadrature axis current, the biasing factor is determined according to the quadrature axis current of the first motor and the second motor, the first effective lookup quantity and the second effective lookup quantity.

[0027] If the lookup quantity is the incremental direct axis current, the biasing factor is determined according to the direct axis current of the first motor and the second motor, the first effective lookup quantity and the second effective lookup quantity.

[0028] Optionally, the termination condition comprises that the fitness value of the individual no longer improves.

[0029] Optionally, the selection strategy comprises that the probability of each individual is determined according to a set probability selection mechanism, and a preset number of individuals in front of the probability in a descending order of the probability are selected as the better individuals.

[0030] Optionally, the termination condition comprises that iteration reaches a preset iteration number.

[0031] The application provides a torque distribution device of an electric vehicle, comprising one or more processors, which are used to implement the torque distribution method of the electric vehicle described in any one of the above.

[0032] The 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 with the first motor and the second motor.

[0035] In some embodiments, a torque distribution table representing the corresponding relationship between the expected torque, the first rotation speed, the second rotation speed and the torque distribution coefficient when the first motor and the second motor are most efficient is determined in advance by a genetic algorithm. During actual operation of the vehicle, the obtained expected torque, first rotation speed and second rotation 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-motor can be quickly distributed, while the total efficiency of the dual-motor is the highest, and the vehicle's endurance is improved.

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

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

[0038] Figure 1 A structural schematic diagram of one embodiment of the electric vehicle of the present application is shown.

[0039] Figure 2 A flow chart of one embodiment of the torque distribution method of the present application is shown.

[0040] Figure 3 A timing chart of one embodiment of injecting different groups of current adjustment amounts into the quadrature-axis current and the direct-axis current is shown.

[0041] Figure 4 A schematic diagram of one embodiment of the torque distribution method of the present application is shown.

[0042] Figure 5 A structural block diagram of one embodiment of the torque distribution device of the electric vehicle of the present application is shown. DETAILED DESCRIPTION

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

[0044] Figure 1Fig. 1 shows a structural schematic diagram of an embodiment of the electric vehicle 10 of the present application. As shown in Fig. 1, the electric vehicle 10 comprises a first electric motor 11 and a second electric motor 12. The first electric motor 11 and the second electric motor 12 are used together to drive the electric vehicle 10. Figure 1

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

[0046] The electric vehicle 10 further comprises a torque distribution device provided by the present application, which is electrically connected with the first electric motor 11 and the second electric motor 12. The torque distribution device is used to perform the torque distribution method provided by the present application.

[0047] The torque distribution device is used to distribute the torque for the first electric motor 11 and the second electric motor 12 according to the required torque of the electric vehicle 10, to determine the torque required to be output by the first electric motor 11 and the second electric motor 12 respectively, so that the first electric motor 11 and the second electric motor 12 can work in a higher efficiency interval, thereby improving the efficiency of the whole vehicle and the endurance of the vehicle.

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

[0049] Figure 2 Fig. 2 shows 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 comprises steps 21-23.

[0051] In step 21, the expected torque of the electric vehicle 10, the first rotational speed of the first electric motor 11, and the second rotational speed of the second electric motor 12 are obtained.

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

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

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

[0055] The torque distribution table is the corresponding relationship between the desired torque, the first rotational speed, the second rotational speed and the torque distribution coefficient when the first motor 11 and the second motor 12 are most efficient. The torque distribution table is obtained by a genetic algorithm.

[0056] The torque distribution coefficient represents 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, then 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 makes the total efficiency of the first motor 11 and the second motor 12 highest under different combinations of desired torque, rotational speed can be obtained.

[0058] The desired torque, the first rotational speed and the second rotational speed obtained in step 21 are substituted into the pre-generated torque distribution table to obtain the torque distribution coefficient corresponding to the desired torque, the first rotational speed and the second rotational 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 highest under the conditions of the desired torque, the first rotational speed and the second rotational speed.

[0059] Step 23, according to the current torque distribution coefficient and the desired torque, the first torque of the first motor and the second torque of the second motor are determined.

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

[0061] In some embodiments, a torque distribution table representing the correspondence between the desired torque, the first rotation speed, the second rotation speed and the torque distribution coefficient when the first motor 11 and the second motor 12 are most efficient is determined in advance by a genetic algorithm. When the vehicle is actually running, the obtained desired torque, first rotation speed and second rotation speed are substituted into the torque distribution table to obtain the corresponding torque distribution coefficient as the current torque distribution coefficient. 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-motor can be quickly distributed, and at the same time, the total efficiency of the dual-motor is the highest, and the vehicle endurance is improved.

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

[0063] The population size, termination condition, selection strategy, crossover type, crossover rate and mutation rate are determined. The selection strategy is determined according to the efficiency, maximum output torque, maximum output rotation speed, maximum output power of the power battery and the bias factor of the first motor 11 and the second motor 12.

[0064] Individuals conforming to the population size are generated, each individual including a set of data of desired torque, first rotation speed, second rotation speed and torque distribution coefficient;

[0065] According to the selection strategy, the better individual is determined, and other individuals are deleted from the population;

[0066] According to the crossover type, the crossover rate and the mutation rate, the better individual is crossed and mutated to obtain a new individual;

[0067] 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. The desired torque, first rotation speed, second rotation speed and torque distribution coefficient included in the individual in the population are determined as the torque distribution table.

[0068] The population size refers to the number of candidate solutions (individuals) considered simultaneously in the genetic algorithm. Setting a suitable population size can increase the possibility of finding the global optimal solution, while balancing the computational complexity. The population size can be determined by trial or experience.

[0069] The termination condition is the condition for stopping the operation of the genetic algorithm. In some embodiments, the termination condition includes that the iteration reaches a preset number of iterations. The number of iterations can be determined by trial or experience. Setting a suitable number of iterations can prevent the algorithm from failing to converge and improve the effectiveness of the algorithm.

[0070] The termination condition can also be that a solution satisfying a certain condition is found or the solutions in the population no longer improve significantly.

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

[0072] Under the conditions of the maximum output torque, maximum output speed of the first motor 11 and the second motor 12, the maximum output power of the power battery, the individual that makes the efficiency of the first motor 11 and the second motor 12 higher is preferentially selected.

[0073] The crossover type is an operation in genetic algorithms used 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 the frequency of performing the crossover operation in the population. A suitable crossover rate can increase the diversity of the population while avoiding the destruction of the gene combination of excellent individuals.

[0075] The mutation rate refers to the probability of introducing randomness in the genetic algorithm operation. Mutation allows individuals to undergo minor genetic changes during reproduction. The mutation rate determines the probability of mutation.

[0076] After the above parameters are determined, individuals that meet the population size are generated, each individual representing a torque distribution strategy. Each individual is composed of a set of data, including the expected 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, the 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] The selected better individuals are subjected to a crossover operation, and new individuals are generated according to the crossover type and the crossover rate.

[0079] The new individuals are subjected to a mutation operation, and random changes are introduced according to the mutation rate.

[0080] The new individuals are added to the population, and the above steps of determining better individuals, crossover, and mutation are repeated.

[0081] The iteration process continues until the termination condition is reached. At this time, the expected torque, speed, and torque distribution coefficient contained in the individuals in the population will be determined as the final torque distribution table.

[0082] Through the genetic algorithm, it is possible to effectively avoid falling into local optima, and at the same time, the torque distribution table can be obtained relatively quickly.

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

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

[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 superior 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 number. The selected superior individuals are used as parents to participate in subsequent crossover and mutation operations.

[0087] The probability selector assigns the probability of the individual to select the superior individual, so that the selection probability of the individual with a higher fitness value is large, and at the same time, the individual with a lower fitness value also has a certain probability of being selected, thereby maintaining the diversity of the population and helping to avoid the algorithm converging to a local optimal solution too early. At the same time, it has a certain robustness to noise and inaccurate data. The probability selector avoids the requirement for accurate calculation of the fitness value of the individual by assigning the selection probability, thereby reducing the sensitivity of the algorithm to noisy data.

[0088] In some embodiments, the selection strategy comprises: selecting a preset number of individuals with higher fitness values in a linear arrangement of all individuals from high to low, as superior individuals; the fitness value is determined according to a fitness function; the fitness function is determined according to the efficiency of the first motor 11 and the second motor 12, the maximum output torque, the maximum output speed, the maximum output power of the power battery, and the bias factor; 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] where T1 is the first torque of the first motor 11, T2 is the second torque of the second motor 12, T req is the expected torque, η is the torque distribution coefficient, η = T1 / (T1+T2), T max1 is the maximum output torque of the first motor 11, T max2Tmax is the maximum output torque of the second motor 12, n max1 ωmax1 is the maximum output rotational speed of the first motor 11, n max2 ωmax2 is the maximum output rotational speed of the second motor 12, P maxbattery Pmax is the maximum output power of the power battery, P1 is an efficiency function of the first motor 11, P2 is an efficiency function of the second motor 12, and k is a bias factor.

[0092] The fitness function determined by 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 in the actual use of the motor. The current operating parameters include the voltage, current, flux linkage, inductance, etc. of the motor. As the motor is used, its efficiency also changes. The introduction of the bias factor k in the fitness function makes the fitness value change with the actual use of the motor, and is more in line with the actual situation of the motor.

[0094] Through the above method selection strategy, a torque distribution table that conforms to the actual use of the motor can be obtained, so that when the torque is allocated to the motor, the error caused by the deviation of the nonlinear characteristics 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 that the fitness value of the individual no longer improves.

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

[0097] The termination condition is set according to the change in the fitness value of the individual, which can balance the optimization effect and the calculation cost of the algorithm. According to the change in the fitness value, the optimal solution can be accurately found, and at the same time, the algorithm running time will not be too long, and the calculation resources will not be wasted, which helps to ensure the effectiveness and efficiency of the algorithm.

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

[0099] 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 determined respectively;

[0100] A plurality of different current adjustment amounts are injected into the quadrature-axis current and the direct-axis current to obtain a plurality of different incremental quadrature-axis currents and incremental direct-axis currents;

[0101] determining operation 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 operation parameter data including flux linkage and inductance;

[0102] In the multiple groups of incremental quadrature-axis current, incremental direct-axis current and operation parameter data, the incremental quadrature-axis current and the incremental direct-axis current at the minimum active power are found as the effective incremental quadrature-axis current and the effective incremental direct-axis current.

[0103] According to the quadrature-axis current, the direct-axis current, the effective incremental quadrature-axis current and the effective incremental direct-axis current, a bias factor is determined.

[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 trend of motor performance, a plurality of different current adjustment amounts are 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 slight current adjustment, the corresponding changes of motor performance (such as power, efficiency, etc.) are observed.

[0106] Figure 3 The timing diagram of one embodiment of injecting multiple groups of different current adjustment amounts into the quadrature-axis current and the direct-axis current is shown.

[0107] The timing diagram of the current adjustment amount can be determined according to experiments or experience. Figure 3 Taking the first motor 11 as an example, as shown in the figure, Figure 3 On the basis of the direct-axis current and the quadrature-axis current [i d1a ,i q1a ] of the first motor 11, two groups of steady-state currents are inserted to obtain [i d1b ,i q1b ] and [i d1c ,i q1c ], and the incremental direct-axis current and the incremental quadrature-axis current are [△i d1b ,△i q1b ] and [△i d1c ,△i q1c ] respectively.

[0108] The transition section current is added between the steady-state currents, and the distributed transition is adopted, so that the phase of the incremental direct-axis current is in front of the phase of the incremental quadrature-axis current, so as to reduce the influence of steady-state current fluctuation on 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, including flux (a physical quantity reflecting the strength of the magnetic field) and inductance (a physical quantity reflecting the self-inductance and mutual inductance characteristics of the motor winding), are measured and recorded. The above-mentioned operating parameter data is crucial for understanding the performance of the motor under specific current conditions.

[0110] Active power is the power actually done by the motor, and reducing the 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, the set of incremental quadrature-axis current and incremental direct-axis current that minimizes the total active power of the first motor 11 and the second motor 12 is found. Under this set of incremental quadrature-axis current and incremental direct-axis current, the total active power of the first motor 11 and the second motor 12 is minimized, and the total operating efficiency is maximized.

[0111] Based on the original quadrature-axis current, direct-axis current, and the found effective incremental quadrature-axis current and effective incremental direct-axis current, a bias factor is determined. The bias factor thus obtained represents the relationship between the effective incremental quadrature-axis current, the effective incremental direct-axis current, and the original quadrature-axis current, direct-axis current when the total operating efficiency of the motor is the highest. The original quadrature-axis current, direct-axis current is optimized by the bias factor, so that the motor operates efficiently.

[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] Said finding the incremental quadrature-axis current and the incremental direct-axis current with the minimum active power among the multiple sets of incremental quadrature-axis current, incremental direct-axis current and operating parameter data as the effective incremental quadrature-axis current and the effective incremental direct-axis current, comprises:

[0114] In 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, the search quantity is found as the first effective search quantity of the first motor 11 and the second effective search quantity of the second motor 12, respectively according to the direction of the gradient of the direction quantity.

[0115] Said determining the bias 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:

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

[0117] If the search quantity is the incremental direct-axis current, the bias factor is determined according to the direct-axis currents of the first motor 11 and the second motor 12, the first effective search quantity and the second effective search quantity.

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

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

[0120] Taking the first motor 11 as an example, after injecting the current time sequence as shown in FIG. 2, the voltage change obtained according to the injected current is as formula (2) and formula (3): Figure 3

[0121]

[0122]

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

[0124] Further, the reactive power model is as formula (4) and formula (5):

[0125]

[0126] By the least square method, the motor can be parameter identified to obtain the apparent flux linkage and the incremental inductance.

[0127] Further, the matrix of the multiple sets of incremental quadrature-axis current, incremental direct-axis current and operating parameter data is as shown below:

[0128]

[0129] wherein,​ 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 variable and the incremental quadrature-axis current is used as the search variable. The iterative search model is shown in formulas (6) and (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 is used 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 and a first effective incremental quadrature-axis current of the second motor 12 .

[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 taken as the direction quantity, the incremental direct-axis current is taken as the search quantity, and the weight factor k is determined by formula (9).

[0140]

[0141] wherein, △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 motor 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 the point with the minimum active power, and the weight factor is determined according to the optimal current increment, so that the weight factor at the highest motor operation efficiency can be obtained.

[0143] Figure 4 Fig. 1 shows a schematic diagram of one 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 and the direct-axis current of the first motor 11 and the second motor 12 and the current adjustment amount, 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 at the minimum active power are searched by iterative search. The weight factor is determined according to the effective incremental quadrature-axis current and the effective incremental direct-axis current, and the weight 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 can ensure that the operation efficiency of the first motor 11 and the second motor 12 is optimal.

[0145] Figure 5 Fig. 2 shows a structure block diagram of one embodiment of the torque distribution device of the electric vehicle of the present application.

[0146] As Figure 5 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 callable 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 depending on the actual application.

[0148] The computer-readable storage medium 32 of the embodiment of the present application stores a program thereon, which, when executed by the processor 31 , is used to implement the torque distribution method 20 for the electric vehicle as 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. Computer-readable storage media 32 include permanent and non-permanent, removable and non-removable media, and may implement information storage by any method or technology. The information may be computer-readable instructions, data structures, program modules, 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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices, 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 by, The electric vehicle includes a power battery, 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 includes: Obtaining a desired torque of the electric vehicle, a first speed of the first motor, and a second 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 the 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; the torque distribution table is obtained by the following steps: Determining a population size, termination conditions, a selection strategy, a crossover type, a crossover rate, and a mutation rate; the selection strategy is determined based on the efficiency, maximum output torque, maximum output speed of the first motor and the second motor, the maximum output power of the power battery, and a weight bias factor; Generating individuals that meet the population size, each individual including a set of data on desired torque, first speed, second speed, and torque distribution coefficient; According to the selection strategy, the better individuals are identified and the others are removed from the population; Performing crossover and mutation on the superior individuals according to the crossover type, the crossover rate, and the mutation rate to obtain new individuals; Adding the new individual to the population, and repeating the steps of determining the better individual and obtaining the new individual until the termination condition is reached, and determining the expected torque, first speed, second speed, and torque distribution coefficient included in the individuals in the population as the torque distribution table; 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 for an electric vehicle according to claim 1, characterized by, The selection strategy includes: among all individuals arranged linearly from high to low according to fitness values, selecting a preset number of individuals with higher fitness values ​​as the 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 and the second motor; the bias factor is determined according to the current operating parameters of the first motor and the second motor.

3. The torque distribution method for an electric vehicle according to claim 1 or 2, characterized by, The bias factor is determined by the following steps: determining, according to the first torque and the second torque, a quadrature-axis current and a direct-axis current of the first motor and the second motor, respectively; Injecting multiple sets of different current adjustment amounts into the quadrature-axis current and the direct-axis current to obtain multiple sets of different incremental quadrature-axis currents and incremental direct-axis currents; Determining operating parameter data of the first motor and the second motor corresponding to each set of incremental quadrature-axis current and incremental direct-axis current, the operating parameter data including flux linkage and inductance; The incremental quadrature-axis current and the incremental direct-axis current at the minimum active power are found from the multiple sets of incremental quadrature-axis current, incremental direct-axis current and the operation parameter data as the effective incremental quadrature-axis current and the effective incremental direct-axis current; The bias 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.

4. The torque distribution method for an electric vehicle according to claim 3, characterized by, One of the incremental quadrature-axis current and the incremental direct-axis current is a direction quantity, and the other is a finding quantity; The finding of the incremental quadrature-axis current and the incremental direct-axis current at the minimum active power from the multiple sets of incremental quadrature-axis current, incremental direct-axis current and the operation parameter data as the effective incremental quadrature-axis current and the effective incremental direct-axis current comprises: The finding of the incremental quadrature-axis current and the incremental direct-axis current at the minimum active power from the multiple sets of incremental quadrature-axis current, incremental direct-axis current and the operation parameter data as the effective incremental quadrature-axis current and the effective incremental direct-axis current comprises: The determination of the bias 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 finding quantity is the incremental quadrature-axis current, the bias factor is determined according to the quadrature-axis current of the first motor and the second motor, the first effective finding quantity and the second effective finding quantity; If the finding quantity is the incremental direct-axis current, the bias factor is determined according to the direct-axis current of the first motor and the second motor, the first effective finding quantity and the second effective finding quantity.

5. The torque distribution method for an electric vehicle according to claim 2, characterized by, The termination condition comprises that the fitness value of the individual no longer improves.

6. The torque distribution method for an electric vehicle according to claim 1, characterized by, The selection strategy comprises that the probability of each individual is determined according to a set probability selection mechanism, and a preset number of individuals with high probability are selected as the better individuals in the arrangement according to the probability from high to low.

7. The torque distribution method for an electric vehicle according to claim 1, characterized by, The termination condition comprises that the iteration reaches a preset iteration number.

8. A torque distribution device for an electric vehicle, characterized by comprising: The torque distribution device comprises one or more processors for implementing the torque distribution method of the electric vehicle.

9. An electric vehicle, characterized by The torque distribution device comprises: a power battery; a first motor and a second motor; and The torque distribution device of claim 8 is electrically connected with the first motor and the second motor.

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