CN113326572B - Double-motor coupling driving system integration optimization method for electric bus - Google Patents

Abstract

The invention provides an integrated optimization method for a dual-motor coupling drive system for an electric bus, and uses particle swarm algorithm and dynamic programming algorithm to construct upper-layer and lower-layer algorithms respectively. Among them, the particle swarm algorithm uses the particle coordinates to represent the system parameters to be optimized, and uses the dynamic programming algorithm as the control strategy optimization algorithm under each particle to optimize the control strategy of the coupled drive system with the system power loss as the objective function. Each particle runs with the optimal control strategy, so that the minimum power loss that the obtained particle can achieve, and the weighted summation of the power loss and the power level is used as the objective function of the particle swarm optimization algorithm, and the particle swarm optimization algorithm seeks its objective function. The particle coordinate corresponding to the minimum value is the optimal system parameter.

Description

An integrated optimization method for dual-motor coupling drive system for electric buses

technical field

The invention belongs to the technical field of parameter matching of a dual-motor coupling drive system, in particular to the integrated optimization of system parameters and control strategies of the dual-motor coupling drive system.

Background technique

The dual-motor coupling drive system is an emerging drive system mainly for large-scale heavy-duty or passenger-carrying electric vehicles with large load and high output power requirements. Its main advantage is that by adjusting the working mode appropriately, it can improve the The load rate of the motor in the entire operating condition, thereby improving the drive efficiency. The dual-motor coupling drive system has a variety of topology structures. Parameters such as motor power and transmission gear ratio have a relatively important position in different topology structures. Whether the topology structure and system parameters are appropriate will greatly affect the efficiency of the coupling device. play.

At present, the common parameter matching methods include: matching the motor power and gear ratio according to the requirements of vehicle dynamics, and designing the coupling device by taking the value from the matching parameter value range. This method is the simplest, but does not consider the system parameters. The influence of matching on the efficiency of the coupling device is not suitable for the electric bus, which does not require high system dynamics but requires higher system economy. The system parameters are matched and optimized based on energy management. Although this optimization method considers the influence of the coupling device system parameters on the efficiency of the drive system, it does not consider the influence of the control strategy on the efficiency, which will lead to a large discrepancy between the experimental results and the actual results. Therefore, how to comprehensively consider the parameters matching and the influencing factors of the control strategy to further improve the efficiency of the dual-motor coupling drive system is a technical problem to be solved urgently in the field.

SUMMARY OF THE INVENTION

In view of the technical problems existing in the above-mentioned field, the present invention provides a method for integrating and optimizing a dual-motor coupling drive system for an electric bus, which specifically includes the following steps:

Step 1: Select the parameters of the system to be optimized in the drive system as the particle coordinates, including the respective rated power of the two motors and the transmission ratio of the transmission gear, and set the corresponding value range to complete the initialization of the particle swarm algorithm;

Step 2: Verify whether the selected particles meet the requirements of vehicle dynamics, retain the particles that meet the requirements and enter the dynamic programming algorithm, and skip the dynamic programming algorithm and enter the particle swarm algorithm for the particles that do not meet the requirements. The objective function value is taken as a predetermined value;

Step 3. Take the working mode of the drive system and the power distribution ratio of the two motors during coupled drive as the state parameter, the shift decision and the change in the power distribution ratio as the decision parameter, and take the motor, belt-exhaust, etc. considering the efficiency in the entire operating condition as the state parameter. The goal of the dynamic programming algorithm is to minimize the total power loss of the gear and the gear, optimize the control strategy for the retained particles, and calculate the corresponding total power loss;

Step 4. Use the weighted summation of the total power loss calculated in step 3 and the power levels determined by the two motors as the objective function of the particle swarm algorithm, and compare the particles optimized in step 3 and the particles input in step 2. The particle swarm is iteratively calculated, and the optimal solution is output when the particle swarm algorithm meets the termination conditions; the drive system provides an optimal control strategy based on the rated power of the two motors corresponding to the optimal solution and the gear ratio corresponding to the topology of the coupling device;

Step 5. Regularly input the optimal solution obtained in Step 4 into Step 2 to update the optimal solution.

Further, the coordinates of the particles and the parameters to be optimized have the same dimension, and the initial value of each coordinate is randomly selected within the value range of the parameters to be optimized;

Further, in step 2, to verify whether the selected particles meet the requirements of the vehicle dynamic performance, the maximum output torque of the coupling device at each vehicle speed can be calculated according to the external characteristic curve of the motor, and the output torque of the coupling device required by the maximum grade of the vehicle can be compared with the output torque of the coupling device. It is judged by comparing the output torque demand of the coupling device with the demand and the maximum speed demand of the vehicle.

Further, in step 3, the dynamic programming algorithm specifically adopts retrograde optimization iteration, and the calculation process is as follows:

mode(n)=mode(n+1)+shift(n)

k(n)=k(n+1)+Δk(n)

Among them, mode(n), mode(n+1) are the working mode state parameters of the nth state and the n+1th state, respectively, and k(n), k(n+1) are the nth state and the nth state. The power distribution proportional state parameter of n+1 states, shift(n) is the shift decision parameter between the nth state and the n+1th state, Δk(n) is the nth state and the n+1th state The amount of change in the power distribution ratio between the states;

For the nth state, the objective function of the dynamic programming algorithm is the total power loss of the entire coupling device during the operation from the nth state to the last state as the objective function, namely:

J _DP (mode(n),k(n))=min(J _DP (mode(n+1),k(n+1))+loss(shift(n),Δk(n)))

In the formula, J _DP is the minimum power loss corresponding to the corresponding state, and loss(shift(n), Δk(n)) is the power loss generated by the decision-making process from the n+1th state to the nth state.

Further, the motor, belt row and gear power losses considering the efficiency described in step 3 are calculated in the following ways:

Calculate the motor power loss loss _motor based on the Willans linear model:

p _me =e·p _ma -p _mloss , e=e ₀ -e ₁ ·p _ma

e ₀ =e ₀₀ +e ₀₁ · _cm +e ₀₂ · _cm ² , e ₁ =e ₁₀ +e ₁₁ · _cm

p _mloss =p _mloss0 +p _mloss1 ·c _m +p _mloss2 ·c _m ² ,

Among them, p _me is the average effective pressure on the surface of the motor rotor, cm is the linear speed of the motor rotor surface, η _motor is the motor efficiency, e ₀ ,e ₀₀ ,e ₀₁ ,e ₀₂ ,e ₁₀ ,e ₁₁ , _{p mloss0} ,p _mloss1 and p _mloss2 can be obtained from the experimental data through multiple linear regression, u and i represent voltage and current respectively, T _e is the rated torque of the motor, V _r is the effective volume of the motor, and p _ma is the average effective pressure, which can be obtained by the following formula calculate;

Calculate the belt row power loss loss _clu based on the following formula:

In the formula, T _clu is the belt displacement torque caused by the shear stress of the oil film, n _clu is the relative speed of the driving part and the driven part of the belt displacement, h ₀ is the thickness of the belt displacement oil film, num is the number of clutch friction plates, and ε is the Clutch oil film viscosity, R ₀ is the outer diameter of the clutch, R ₁ is the effective inner diameter of the belt row;

where ρ is the lubricating oil density, Q is the lubricating oil flow rate, β ₀ is the contact angle, and α is the surface tension coefficient;

Calculate the parallel shaft gear power loss loss _gear based on the following formula:

loss _gear =T _gear ω _gear (1-η _gear )

In the formula, η _gear is the transmission efficiency of the parallel shaft gear, z ₁ and z ₂ are the number of teeth of the transmission gear, and T _gear and ω _gear are the transmission torque and speed of the gear;

For the planetary row, it is regarded as a planetary carrier fixed transformation mechanism and its power loss loss _pai is calculated:

驱动时

when driving

制动时

when braking

loss _pai =T _pai ω _pai (1-η _pai )

转化机构的功率损失，P_in为输入功率，ψ^c为转化机构的功率损失系数，

为转化机构从太阳轮到齿圈的传动效率，i_p为行星排的传动比，T_s，ω_s分别为太阳轮的转矩和转速，T_r，ω_r为齿圈的转矩和转速，T_c，ω_c分别为行星架的转矩和转速。where η _pai is the efficiency of the planetary row,

The power loss of the conversion mechanism, P _in is the input power, ψ ^c is the power loss coefficient of the conversion mechanism,

is the transmission efficiency of the conversion mechanism from the sun gear to the ring gear, i _p is the transmission ratio of the planetary row, T _s , ω _s are the torque and rotational speed of the sun gear, respectively, T _r , ω _r are the torque and rotational speed of the ring gear , T _c , ω _c are the torque and rotational speed of the planet carrier, respectively.

Further, the objective function of the particle swarm algorithm in step 4 specifically adopts the following form:

J _PSO (c,d)=(1-λ)·J _DP (c,d)+λ·P _zon (c,d)

J _PSO (c, d) is the objective function value of the c-th particle after the d-th iteration, where λ is the weighting factor of power loss and power level, and J _DP (c, d) is the c-th particle of the d-th iteration The optimization result of the dynamic programming algorithm of , P _zon (c, d) is the power level of the c-th particle in the d-th iteration, which can be obtained by the following formula:

P _zon (c,d)=P _e1 (c,d)+P _e2 (c,d)

In the formula, P _e1 (c, d) and P _e2 (c, d) are the rated powers of the two motors of the c-th particle in the d-th iteration, respectively,

The optimal particle coordinates, that is, the optimal parameter matching result, are obtained according to the following relationship:

g(d)=F ^-1 (J _PSO.i (d))

where J _PSO.i (d) is the objective function value of the global optimal particle in the d-th iteration, g(d) is the position of the optimal particle in the d-th iteration, and F ^-1 () is the objective function value and particle position Correspondence between.

The above-mentioned method provided by the present invention utilizes the particle swarm algorithm and the dynamic programming algorithm to construct the upper-layer and lower-layer algorithms respectively. Among them, the particle swarm algorithm uses the particle coordinates to represent the system parameters to be optimized, and uses the dynamic programming algorithm as the control strategy optimization algorithm under each particle to optimize the control strategy of the coupled drive system with the system power loss as the objective function, ensuring that Each particle runs with the optimal control strategy, so that the minimum power loss that the obtained particle can achieve, and the weighted summation of the power loss and the power level is used as the objective function of the particle swarm optimization algorithm, and the particle swarm optimization algorithm seeks its objective function. The particle coordinate corresponding to the minimum value is the optimal system parameter. Compared with the prior art, the present invention can at least provide the following beneficial effects:

1. The method of the present invention has a fast optimization speed and can effectively avoid falling into a local optimum.

2. By using the dynamic programming algorithm as the control strategy optimization algorithm, the optimal control strategy can be obtained, ensuring that each group of system parameters can be compared under the optimal control effect, and the optimization result can be the actual control strategy of the coupling device Provide evidence.

3. The introduction of vehicle dynamics as a constraint ensures that the parameters of the system participating in the optimization can meet the vehicle dynamics requirements.

4. The optimization process takes into account the influence of system parameters and control strategies on the efficiency of the drive system, making the optimization results more meaningful for practical applications.

5. The adopted particle swarm optimization objective function considers both power loss and power level, and at the same time optimizes the drive system efficiency and cost.

Description of drawings

Fig. 1 is the overall flow schematic diagram of the method provided by the present invention;

Fig. 2 is the effect of system optimization of torque coupling based on the present invention;

FIG. 3 shows the effect of system optimization of rotational speed coupling based on the present invention.

Detailed ways

The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The integrated optimization method for a dual-motor coupling drive system for an electric bus provided by the present invention, as shown in Figure 1, specifically includes the following steps:

Step 1: Select the parameters of the system to be optimized in the drive system as the particle coordinates, including the respective rated power of the two motors and the transmission ratio of the transmission gear, and set the corresponding value range to complete the initialization of the particle swarm algorithm;

Step 2: Verify whether the selected particles meet the requirements of vehicle dynamics, retain the particles that meet the requirements and enter the dynamic programming algorithm, and skip the dynamic programming algorithm and enter the particle swarm algorithm for the particles that do not meet the requirements. The objective function value is taken as a predetermined value;

Step 3. Take the working mode of the drive system and the power distribution ratio of the two motors during coupled drive as the state parameter, the shift decision and the change in the power distribution ratio as the decision parameter, and take the motor, belt-exhaust, etc. considering the efficiency in the entire operating condition as the state parameter. The goal of the dynamic programming algorithm is to minimize the total power loss of the gear and the gear, optimize the control strategy for the retained particles, and calculate the corresponding total power loss;

Step 4. Use the weighted summation of the total power loss calculated in step 3 and the power levels determined by the two motors as the objective function of the particle swarm algorithm, and compare the particles optimized in step 3 and the particles input in step 2. The particle swarm is iteratively calculated, and the optimal solution is output when the particle swarm algorithm meets the termination conditions; the drive system provides an optimal control strategy based on the rated power of the two motors corresponding to the optimal solution and the gear ratio corresponding to the topology of the coupling device;

Step 5. Regularly input the optimal solution obtained in Step 4 into Step 2 to update the optimal solution.

The coordinates of the particles have the same dimension as the parameters to be optimized, and the initial value of each coordinate is randomly selected within the value range of the parameters to be optimized;

The dynamic constraint is an important factor considered in the algorithm of the present invention, and specifically refers to the constraint of the vehicle dynamic index on the parameter matching of the coupling drive system, that is, the matched system parameters must satisfy the power output of the coupling device to achieve the vehicle dynamic performance. demand.

The power requirement of the car is characterized by the vehicle power index and the related power equation

F _D =F _α +F _A +F _f +F _δ

驱动转矩，

为牵引力，R_tire为车轮半径，T_OUT(v)为耦合装置输出转矩，i_g为主减速器齿轮传动比，η_g为耦合箱输出到车轮的效率，F_α为坡度阻力，F_A为空气阻力，F_f为滚动阻力，F_δ为加速阻力，M为车辆总体重量，g为重力加速度，

为爬坡坡度，ρ为空气密度，A为车辆迎风面积，C_D为风阻系数，v为车速，f为滚动阻力系数，δ为质量转换系数。in the formula

drive torque,

is the traction force, R _tire is the wheel radius, T _OUT (v) is the output torque of the coupling device, i _g is the gear ratio of the main reducer, η _g is the output efficiency of the coupling box to the wheel, F _α is the slope resistance, F _A is the air resistance, F _f is the rolling resistance, F _δ is the acceleration resistance, M is the overall weight of the vehicle, g is the gravitational acceleration,

is the climbing gradient, ρ is the air density, A is the windward area of the vehicle, C _D is the wind resistance coefficient, v is the vehicle speed, f is the rolling resistance coefficient, and δ is the mass conversion coefficient.

The dynamic indicators that need to be considered are the maximum speed and the maximum grade, in which the maximum speed can represent the output requirements of the coupling device in high speed conditions, and the maximum grade can represent the output requirements of the coupling device in low speed conditions.

v＝v₀ Maximum grade:

v=v ₀

Maximum speed:

为满足最大爬坡度要求所需的驱动力，

为满足最高车速要求所需的驱动力，

为最大爬坡度，v₀，v_m分别为低车速和高车速的一个代表车速，可选择为最低临界车速和最高车速。in the formula

The driving force required to meet the maximum grade requirement,

The driving force required to meet the maximum speed requirement,

is the maximum grade, v ₀ , v _m are respectively a representative vehicle speed of low vehicle speed and high vehicle speed, and can be selected as the minimum critical vehicle speed and the maximum vehicle speed.

The maximum output torque of the coupling is related to the topology of the coupling. Dynamic constraints can be established based on the output torque of the coupling device at two representative vehicle speeds v ₀ , _vm and the required drive torque for the vehicle's maximum gradeability and maximum vehicle speed. For the particles whose output torque of the coupling device cannot meet the driving requirements of the vehicle, the objective function of the particle swarm algorithm can be directly set to 1,000,000 in the execution of step 2, indicating that they are eliminated particles, and the dynamic programming process is skipped.

Under the system parameters defined by a set of coordinates of each particle, the control strategy of the entire operating condition is optimized, and the vehicle operating condition is divided into N subsections, and each subsection point is used as a state point, that is, there are N +1 state point, the state of each state point is composed of two state parameters - working mode and power distribution ratio, and the decision between each two state points is composed of two decision parameters - shift strategy and power distribution ratio change value composition. Different working modes and different power distribution ratios will constitute a variety of state parameters, and different shifting strategies and power distribution ratio changes will constitute a variety of decision parameters. The purpose of dynamic programming algorithm is to find the optimal control strategy to minimize the power loss of the whole system.

The working mode is a finite discrete parameter, because the number of working modes of the coupling device is limited and discrete; the power distribution ratio is a continuous quantity, because the power distribution ratio of the coupling device can be continuously changed within a certain range.

Based on this consideration, in a preferred embodiment of the present invention, the dynamic programming algorithm in step 3 specifically adopts reverse optimization iteration, and the calculation process is as follows:

mode(n)=mode(n+1)+shift(n)

k(n)=k(n+1)+Δk(n)

Among them, mode(n), mode(n+1) are the working mode state parameters of the nth state and the n+1th state, respectively, and k(n), k(n+1) are the nth state and the nth state. The power distribution proportional state parameter of n+1 states, shift(n) is the shift decision parameter between the nth state and the n+1th state, Δk(n) is the nth state and the n+1th state The amount of change in the power distribution ratio between the states;

For the nth state, the objective function of the dynamic programming algorithm is the total power loss of the entire coupling device during the operation from the nth state to the last state, namely:

J _DP (mode(n),k(n))=min(J _DP (mode(n+1),k(n+1))+loss(shift(n),Δk(n)))

In the formula, J _DP is the minimum power loss corresponding to the corresponding state, and loss(shift(n), Δk(n)) is the power generated by the decision-making process between the state parameters from the n+1th state to the nth state. loss.

In a preferred embodiment of the present invention, the motor, belt row and gear power losses considering the efficiency in step 3 are calculated in the following ways:

Calculate the motor power loss loss _motor based on the Willans linear model:

p _me =e·p _ma -p _mloss , e=e ₀ -e ₁ ·p _ma

e ₀ =e ₀₀ +e ₀₁ · _cm +e ₀₂ · _cm ² , e ₁ =e ₁₀ +e ₁₁ · _cm

p _mloss =p _mloss0 +p _mloss1 ·c _m +p _mloss2 ·c _m ² ,

Among them, p _me is the average effective pressure on the surface of the motor rotor, cm is the linear speed of the motor rotor surface, η _motor is the motor efficiency, e ₀ ,e ₀₀ ,e ₀₁ ,e ₀₂ ,e ₁₀ ,e ₁₁ , _{p mloss0} ,p _mloss1 and p _mloss2 can be obtained from the experimental data through multiple linear regression, u and i represent voltage and current respectively, T _e is the rated torque of the motor, V _r is the effective volume of the motor, and p _ma is the average effective pressure, which can be obtained by the following formula calculate;

Calculate the belt row power loss loss _clu based on the following formula:

In the formula, T _clu is the belt displacement torque caused by the shear stress of the oil film, n _clu is the relative speed of the driving part and the driven part of the belt displacement, h ₀ is the thickness of the belt displacement oil film, num is the number of clutch friction plates, and ε is the Clutch oil film viscosity, R ₀ is the outer diameter of the clutch, R ₁ is the effective inner diameter of the belt row;

where ρ is the lubricating oil density, Q is the lubricating oil flow rate, β ₀ is the contact angle, and α is the surface tension coefficient;

Calculate the parallel shaft gear power loss loss _gear based on the following formula:

loss _gear =T _gear ω _gear (1-η _gear )

In the formula, η _gear is the transmission efficiency of the parallel shaft gear, z ₁ and z ₂ are the number of teeth of the transmission gear, and T _gear and ω _gear are the transmission torque and speed of the gear;

For the planetary row, it is regarded as a planetary carrier fixed transformation mechanism and its power loss loss _pai is calculated:

驱动时

when driving

制动时

when braking

loss _pai =T _pai ω _pai (1-η _pai )

转化机构的功率损失，P_in为输入功率，ψ^c为转化机构的功率损失系数，

为转化机构从太阳轮到齿圈的传动效率，i_p为行星排的传动比，T_s，ω_s分别为太阳轮的转矩和转速，T_r，ω_r为齿圈的转矩和转速，T_c，ω_c分别为行星架的转矩和转速。where η _pai is the efficiency of the planetary row,

The power loss of the conversion mechanism, P _in is the input power, ψ ^c is the power loss coefficient of the conversion mechanism,

is the transmission efficiency of the conversion mechanism from the sun gear to the ring gear, i _p is the transmission ratio of the planetary row, T _s , ω _s are the torque and rotational speed of the sun gear, respectively, T _r , ω _r are the torque and rotational speed of the ring gear , T _c , ω _c are the torque and rotational speed of the planet carrier, respectively.

After the reverse optimization is completed, the optimal state parameters of each state and the decision parameters between each state have been determined, and the forward calculation can be performed according to the efficiency model. The decision trajectory is used for control, coupling the power loss value of the drive system, and taking it as the optimal objective function value J _DP of the final dynamic programming algorithm.

In a preferred embodiment of the present invention, the objective function of the particle swarm algorithm in step 4 specifically adopts the following form:

J _PSO (c,d)=(1-λ)·J _DP (c,d)+λ·P _zon (c,d)

J _PSO (c, d) is the objective function value of the c-th particle after the d-th iteration, where λ is the weighting factor of power loss and power level, and J _DP (c, d) is the c-th particle of the d-th iteration The optimization result of the dynamic programming algorithm of , P _zon (c, d) is the power level of the c-th particle in the d-th iteration, which can be obtained by the following formula:

P _zon (c,d)=P _e1 (c,d)+P _e2 (c,d)

In the formula, P _e1 (c, d) and P _e2 (c, d) are the rated powers of the two motors of the c-th particle in the d-th iteration, respectively,

Calculate the global optimal particle

g(d)=F ^-1 (J _PSO.i (d))

In the formula, J _PSO.i (d) is the objective function value of the global optimal particle in the d-th iteration, and g(d) is the position of the optimal particle in the d-th iteration;

The optimal particle on the particle trajectory, that is, the optimal motor rated power, is obtained according to the following relationship:

p(c,d)=F ^-1 (J _PSO.p (c,d))

In the formula, J _PSO.p (c, d) represents the minimum value of the objective function value that the c-th particle has appeared after d iterations, and p(c, d) is J _PSO.p (c, d) ) corresponds to the particle position, and F ^-1 ( ) is the correspondence between the objective function value and the particle coordinates.

In a preferred embodiment of the present invention, the particle velocity iteration can be performed according to the particle coordinate position:

In the formula, x(c, d) is the position coordinate of the c-th particle after the d-th iteration, where v(c, d) is the velocity of the c-th particle after the d-th iteration; φ represents the inertia coefficient , representing the influence of the speed of the previous cycle on the speed of the current cycle; α ₁ , α ₂ represent the acceleration coefficient, representing the influence of the particle on the speed caused by the attraction of p(c,d) and g(d); γ(d ), γ(d) is a random number within two [0,1];

In order to further improve the performance of particle swarm optimization, the present invention sets φ in the formula to linearly decrease first and then keep constant. The initial design value is relatively large in order to perform a rough global sweep as soon as possible in the early stage to determine the general position of the optimal value, and the small inertia coefficient in the later stage is to ensure that the particles can be concentrated quickly, which satisfies the following formula:

where φ ₀ and φ _m are the inertia coefficients at the start and end of the linear change interval, respectively, and l ₀ is the end position of the linear change interval.

Fig. 2 and Fig. 3 show the optimization effect achieved by running the algorithm of the present invention for a torque coupled drive system and a rotational speed coupled drive system respectively. It can be seen that the present invention can provide satisfactory performance for different topology structures Optimization Strategy.

It should be understood that the size of the sequence numbers of the steps in the embodiments of the present invention does not imply the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention .

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, and substitutions can be made in these embodiments without departing from the principle and spirit of the invention and modifications, the scope of the present invention is defined by the appended claims and their equivalents.

Claims (4)

1. a dual-motor coupling drive system integration optimization method for electric bus, is characterized in that: specifically comprise the following steps: Step 1: Select the parameters of the system to be optimized in the drive system as the particle coordinates, including the respective rated power of the two motors and the transmission ratio of the transmission gear, and set the corresponding value range to complete the initialization of the particle swarm algorithm; Step 2: Verify whether the selected particles meet the requirements of vehicle dynamics, retain the particles that meet the requirements and enter the dynamic programming algorithm, and skip the dynamic programming algorithm and enter the particle swarm algorithm for the particles that do not meet the requirements. The objective function value is taken as a predetermined value; Step 3. Take the working mode of the drive system and the power distribution ratio of the two motors during coupled drive as the state parameter, the shift decision and the change in the power distribution ratio as the decision parameter, and take the motor, belt-exhaust, etc. considering the efficiency in the entire operating condition as the state parameter. The goal of the dynamic programming algorithm is to minimize the total power loss of the gear and the remaining particles. The control strategy is optimized, and the corresponding total power loss is calculated. The calculation process is as follows: mode(n)=mode(n+1)+shift(n) k(n)=k(n+1)+Δk(n) Among them, mode(n), mode(n+1) are the working mode state parameters of the nth state and the n+1th state, respectively, and k(n), k(n+1) are the nth state and the nth state. The power distribution ratio state parameter of n+1 states, shift(n) is the shift decision parameter between the nth state and the n+1th state, Δk(n) is the nth state and the n+1th state The amount of change in the power distribution ratio between the states; For the nth state, the objective function of the dynamic programming algorithm is the total power loss of the entire coupling device during the operation from the nth state to the last state as the objective function, namely: J _DP (mode(n),k(n))=min(J _DP (mode(n+1),k(n+1))+loss(shift(n),Δk(n))) In the formula, J _DP is the minimum power loss corresponding to the corresponding state, and loss(shift(n), Δk(n)) is the power loss generated by the decision-making process from the n+1th state to the nth state; Step 4. Use the weighted summation of the total power loss calculated in step 3 and the power levels determined by the two motors as the objective function of the particle swarm algorithm, and compare the particles optimized in step 3 and the particles input in step 2. The particle swarm performs iterative calculation and outputs the optimal solution when the particle swarm algorithm meets the termination conditions; the drive system provides an optimal control strategy based on the rated power of the two motors corresponding to the optimal solution and the gear ratio corresponding to the topology of the coupling device; The objective function of the swarm algorithm takes the following form: J _PSO (c,d)=(1-λ)·J _DP (c,d)+λ·P _zon (c,d) J _PSO (c, d) is the objective function value of the c-th particle after the d-th iteration, where λ is the weighting factor of power loss and power level, and J _DP (c, d) is the c-th particle of the d-th iteration The optimization result of the dynamic programming algorithm of , P _zon (c, d) is the power level of the c-th particle in the d-th iteration, which can be obtained by the following formula: P _zon (c,d)=P _e1 (c,d)+P _e2 (c,d) In the formula, P _e1 (c, d) and P _e2 (c, d) are the rated powers of the two motors of the c-th particle in the d-th iteration, respectively, The optimal particle coordinates, that is, the optimal parameter matching result, are obtained according to the following relationship:

g(d)=F ^-1 (J _PSO.i (d)) In the formula, J _PSO.i (d) is the objective function value of the global optimal particle in the d-th iteration, g(d) is the position of the optimal particle in the d-th iteration, and F ^-1 () is the objective function value and particle Correspondence between positions; Step 5. Regularly input the optimal solution obtained in Step 4 into Step 2 to update the optimal solution. 2 . The method according to claim 1 , wherein the coordinates of the particles and the parameters to be optimized have the same dimension, and the initial value of each coordinate is randomly selected within the value range of the parameters to be optimized. 3 . 3. method as claimed in claim 1 is characterized in that: in step 2, whether the selected particles are verified to meet the requirements of automobile power and can be solved according to the external characteristic curve of the motor to solve the maximum output torque of the coupling device at each vehicle speed, and its It is judged by comparing it with the output torque demand of the coupling device required by the maximum grade of the vehicle and the output torque demand of the coupling device required by the maximum speed of the vehicle. 4. The method of claim 1 , wherein the motor, belt row and gear power losses considering the efficiency in step 3 are calculated by the following methods: Calculate the motor power loss loss _motor based on the Willans linear model: p _me =e·p _ma -p _mloss , e=e ₀ -e ₁ ·p _ma e ₀ =e ₀₀ +e ₀₁ · _cm +e ₀₂ · _cm ² , e ₁ =e ₁₀ +e ₁₁ · _cm p _mloss =p _mloss0 +p _mloss1 ·c _m +p _mloss2 ·c _m ² ,

Among them, p _me is the average effective pressure on the surface of the motor rotor, cm is the linear speed of the motor rotor surface, η _motor is the motor efficiency, e ₀ ,e ₀₀ ,e ₀₁ ,e ₀₂ ,e ₁₀ ,e ₁₁ , _{p mloss0} ,p _mloss1 and p _mloss2 can be obtained from the experimental data through multiple linear regression, u and i represent voltage and current respectively, T _e is the rated torque of the motor, V _r is the effective volume of the motor, and p _ma is the average effective pressure, which can be obtained by the following formula calculate;

Calculate the belt row power loss loss _clu based on the following formula:

In the formula, T _clu is the torque of the belt discharge caused by the shear stress of the oil film, n _clu is the relative speed of the driving part and the driven part of the belt discharge, h ₀ is the thickness of the oil film of the belt discharge, num is the number of clutch friction plates, ε is the viscosity of the clutch oil film, R ₀ is the outer diameter of the clutch, R ₁ is the effective inner diameter of the belt row, and ω is the rotational speed;

In the formula, ρ is the lubricating oil density, Q is the lubricating oil flow rate, β ₀ is the contact angle, and α is the surface tension coefficient; Calculate the parallel shaft gear power loss loss _gear based on the following formula:

loss _gear =T _gear ω _gear (1-η _gear ) In the formula, η _gear is the transmission efficiency of the parallel shaft gear, z ₁ and z ₂ are the number of teeth of the transmission gear, and T _gear and ω _gear are the transmission torque and speed of the gear; For the planetary row, it is regarded as a planetary carrier fixed transformation mechanism and its power loss loss _pai is calculated:

when driving

when braking loss _pai =T _pai ω _pai (1-η _pai ) where η _pai is the efficiency of the planetary row, P _f ^c is the power loss of the conversion mechanism, P _in is the input power, ψ ^c is the power loss coefficient of the conversion mechanism,

For the transmission efficiency of the conversion mechanism from the sun gear to the ring gear, i _p is the transmission ratio of the planetary row, T _s and ω _s are the torque and speed of the sun gear, respectively, and T _r and ω _r are the torque and speed of the ring gear. , T _c and ω _c are the torque and rotational speed of the planet carrier, respectively.

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