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

Double-motor coupling driving system integration optimization method for electric bus Download PDF

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CN113326572B
CN113326572B CN202110711757.1A CN202110711757A CN113326572B CN 113326572 B CN113326572 B CN 113326572B CN 202110711757 A CN202110711757 A CN 202110711757A CN 113326572 B CN113326572 B CN 113326572B
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loss
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CN113326572A (en
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张硕
宿玉康
张承宁
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Beijing Institute of Technology BIT
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
    • B60K1/00—Arrangement or mounting of electrical propulsion units
    • B60K1/02—Arrangement or mounting of electrical propulsion units comprising more than one electric motor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/25—Design optimisation, verification or simulation using particle-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/06—Power analysis or power optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Abstract

The invention provides an integrated optimization method for a double-motor coupling driving system of an electric bus, which respectively constructs an upper-layer algorithm and a lower-layer algorithm by utilizing a particle swarm algorithm and a dynamic programming algorithm. The particle swarm optimization takes the particle coordinates as system parameters to be optimized, a dynamic programming algorithm is used as a control strategy optimization algorithm under each particle, the control strategy of the coupling driving system is optimized by taking the system power loss as a target function, each particle is guaranteed to operate by the optimal control strategy, the obtained particle can reach the minimum power loss, the power loss and the power level are subjected to weighted summation to serve as the target function of the particle swarm optimization, and the particle swarm optimization seeks the particle coordinates corresponding to the minimum value of the target function to be the optimal system parameters.

Description

Double-motor coupling driving system integration optimization method for electric bus
Technical Field
The invention belongs to the technical field of parameter matching of a double-motor coupling driving system, and particularly relates to integrated optimization of system parameters and a control strategy of the double-motor coupling driving system.
Background
The dual-motor coupling driving system is a novel driving system mainly oriented to large-scale load or passenger-carrying electric vehicles with large loads and high requirements on output power, and has the main advantages that the load rate of the motor in the whole operation working condition can be improved by properly adjusting the working mode, and further the driving efficiency is improved. The double-motor coupling driving system has various topological structure forms, parameters such as motor power, transmission gear transmission ratio and the like have important positions in different topological structures, and the performance of the coupling device efficiency is influenced to a great extent by selecting the topological structures and system parameters.
The currently common parameter matching methods include: the method is simplest, but the influence of system parameter matching on the efficiency of the coupling device is not considered, and the vehicle driving system which has low requirement on the system dynamic property and higher requirement on the system economy is not suitable for the electric bus; the optimization method is based on energy management and carries out matching optimization on system parameters, although the optimization method considers the influence of the system parameters of the coupling device on the efficiency of the driving system, the optimization method does not consider the influence of a control strategy on the efficiency, and the experimental result and the actual result have larger difference. Therefore, how to comprehensively consider the parameter matching and control strategy influence factors and further improve the efficiency of the dual-motor coupling driving system is a technical problem to be solved in the field.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a double-motor coupling driving system integrated optimization method for an electric bus, which specifically comprises the following steps:
selecting parameters of a system to be optimized in a driving system as particle coordinates, including respective rated powers of two motors and transmission ratios of transmission gears, and setting corresponding value ranges to complete initialization of a particle swarm algorithm;
step two, verifying whether the selected particles meet the requirements of the dynamic property of the automobile, reserving the particles meeting the requirements, entering a dynamic programming algorithm, skipping the dynamic programming algorithm for the particles not meeting the requirements, inputting the particles into a particle swarm algorithm, and taking the particle swarm objective function value as a preset value;
taking the power distribution proportion of the two motors as a state parameter and a gear shifting decision and power distribution proportion variable quantity as decision parameters during the working mode and coupling driving of the driving system, taking the minimum total power loss of the motors, the belt rows and the gears considering efficiency in the whole operating condition as a dynamic planning algorithm target, optimizing a control strategy of the reserved particles, and calculating the corresponding total power loss;
step four, weighted summation of the total power loss obtained by calculation in the step three and the power level determined by the two motors is taken as a target function of the particle swarm algorithm, iterative calculation is carried out on a particle swarm composed of the particles optimized in the step three and the particles input in the step two, and an optimal solution is output when the particle swarm algorithm meets a termination condition; the driving system provides an optimal control strategy based on the rated power of the two motors corresponding to the optimal solution and the gear transmission ratio corresponding to the topological structure of the coupling device;
and step five, inputting the optimal solution obtained in the step four into the step two regularly, and updating the optimal solution.
Furthermore, the coordinates of the particles and the parameters to be optimized have the same dimension, and the initial value of each coordinate randomly takes a value within the value range of the parameters to be optimized;
further, in the second step, whether the selected particles meet the requirements of the dynamic performance of the vehicle is verified, the maximum output torque of the coupling device at each vehicle speed can be solved according to the external characteristic curve of the motor, and the maximum output torque of the coupling device at each vehicle speed is compared with the output torque requirement of the coupling device required by the maximum climbing gradient of the vehicle and the output torque requirement of the coupling device required by the maximum vehicle speed of the vehicle to judge.
Further, the dynamic programming algorithm in step three specifically adopts a retrograde optimization iteration, and the calculation process is as follows:
mode(n)＝mode(n+1)+shift(n)
k(n)＝k(n+1)+Δk(n)
wherein, mode (n +1) is an operating mode state parameter of the nth state and the (n +1) th state respectively, k (n), k (n +1) is a power distribution proportion state parameter of the nth state and the (n +1) th state, shift (n) is a shift decision parameter between the nth state and the (n +1) th state, and Δ k (n) is a power distribution proportion variable quantity between the nth state and the (n +1) th state;
for the nth state, the objective function of the dynamic programming algorithm is that the total power loss of the whole coupling device in the operation process from the nth state to the last state is the objective function, namely:
JDP(mode(n),k(n))＝min(JDP(mode(n+1),k(n+1))+loss(shift(n),Δk(n)))
in the formula, JDPFor the minimum power loss corresponding to the corresponding state, loss (shift (n), Δ k (n)) is the power loss generated by the decision process from the (n +1) th state to the nth state.
Further, the efficiency-considered motor, belt row and gear power losses in step three are calculated by:
motor power loss is calculated based on Willans linear modelmotor：
pme＝e·pma-pmloss，e＝e0-e1·pma
e0＝e00+e01·cm+e02·cm 2，e1＝e10+e11·cm
pmloss＝pmloss0+pmloss1·cm+pmloss2·cm 2，
Figure GDA0003642666590000021
Figure GDA0003642666590000022
Figure GDA0003642666590000023
Wherein p ismeIs the mean effective pressure of the rotor surface of the machine, cmIs the surface linear velocity, eta, of the motor rotormotorTo the efficiency of the motor, e0,e00,e01,e02,e10,e11,pmloss0,pmloss1,pmloss2Can be obtained by multiple linear regression of experimental data, u and i respectively represent voltage and current, TeRated torque of the motor, VrRepresenting the effective volume of the motor, pmaTo average effective pressure, it can be calculated by the following formula;
Figure GDA0003642666590000024
calculating the loss of power of the strip line loss based on the following formulaclu：
Figure GDA0003642666590000031
Figure GDA0003642666590000032
In the formula TcluFor belt-row torque due to oil film shear stress, ncluFor the relative rotational speed of the driving and driven parts of the belt row, h0Thickness of the oil film, num is the number of friction plates of the clutch, epsilon is the viscosity of the oil film of the clutch, R0Is the outer diameter of the clutch, R1Is the effective inner diameter of the band row;
Figure GDA0003642666590000033
where ρ is the lubricant density, Q is the lubricant flow, β0Is the contact angle, α is the surface tension coefficient;
calculating the loss of parallel shaft gear based on the following formulagear：
Figure GDA0003642666590000034
lossgear＝Tgearωgear(1-ηgear)
In the formula etagearFor parallel axis gear efficiency, z1，z2Number of teeth of drive gear, Tgear，ωgearThe transmission torque and the rotating speed of the gear;
regarding the planetary row, the planetary row is regarded as a conversion mechanism with a fixed planet carrier, and the power loss of the planetary row is calculatedpai：
Figure GDA0003642666590000035
While driving
Figure GDA0003642666590000036
During braking
losspai＝Tpaiωpai(1-ηpai)
In the formula etapaiIn order to be efficient for the planetary row,
Figure GDA0003642666590000037
loss of power of the conversion means, PinFor input power, #cTo be the power loss factor of the conversion means,
Figure GDA0003642666590000038
for the conversion of the transmission efficiency of the mechanism from sun gear to ring gear, ipIs the gear ratio of the planetary gear set, Ts，ωsTorque and rotational speed, T, of the sun gear, respectivelyr，ωrTorque and speed of rotation of the ring gear, Tc，ωcThe torque and the rotational speed of the planet carrier are respectively.
Further, the objective function of the particle swarm algorithm in step four specifically adopts the following form:
JPSO(c,d)＝(1-λ)·JDP(c,d)+λ·Pzon(c,d)
JPSO(c, d) is the objective function value of the c particle after the d iteration, wherein lambda isWeighting factors for power loss and power level, JDP(c, d) is the optimization result of the dynamic programming algorithm for the c particle of the d iteration, Pzon(c, d) is the power level of the c particle for the d iteration, which is given by:
Pzon(c,d)＝Pe1(c,d)+Pe2(c,d)
in the formula, Pe1(c,d)，Pe2(c, d) rated power of two motors of the c particle of the d iteration respectively,
obtaining the optimal particle coordinate, namely the optimal parameter matching result according to the following relation:
Figure GDA0003642666590000041
g(d)＝F-1(JPSO.i(d))
in the formula JPSO.i(d) An objective function value for the optimal particle for the d-th iteration, g (d) a position for the optimal particle for the d-th iteration, F-1() Is the correspondence between the value of the objective function and the position of the particle.
According to the method provided by the invention, the particle swarm algorithm and the dynamic programming algorithm are utilized to respectively construct the upper layer algorithm and the lower layer algorithm. The particle swarm optimization takes the particle coordinates as system parameters to be optimized, a dynamic programming algorithm is used as a control strategy optimization algorithm under each particle, the control strategy of the coupling driving system is optimized by taking the system power loss as a target function, each particle is guaranteed to operate by the optimal control strategy, the obtained particle can reach the minimum power loss, the power loss and the power level are subjected to weighted summation to serve as the target function of the particle swarm optimization, and the particle swarm optimization seeks the particle coordinates corresponding to the minimum value of the target function to be the optimal system parameters. Compared with the prior art, the invention can at least provide the following beneficial effects:
1. the method has high optimization speed and can effectively avoid falling into local optimum.
2. By using the dynamic programming algorithm as the control strategy optimization algorithm, the optimal control strategy can be obtained, each group of system parameters can be compared under the optimal control effect, and the optimization result can provide a basis for the actual control strategy of the coupling device.
3. Because the dynamic property of the automobile is introduced as a constraint condition, the system parameters participating in optimization can meet the dynamic property requirement of the automobile.
4. The optimization process simultaneously considers the influence of system parameters and control strategies on the efficiency exertion of the driving system, so that the optimization result has more practical application significance.
5. The particle swarm algorithm objective function simultaneously considers the power loss and the power grade, and simultaneously considers the influence of the efficiency and the cost of the driving system for optimization.
Drawings
FIG. 1 is a schematic flow diagram of the overall process of the method of the present invention;
FIG. 2 is a graph illustrating the effect of system optimization on torque coupling based on the present invention;
fig. 3 illustrates the effect of system optimization on the slew rate coupling based on the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a double-motor coupling driving system integration optimization method for an electric bus, which specifically comprises the following steps as shown in figure 1:
selecting parameters of a system to be optimized in a driving system as particle coordinates, including respective rated powers of two motors and transmission ratios of transmission gears, and setting corresponding value ranges to complete initialization of a particle swarm algorithm;
step two, verifying whether the selected particles meet the requirements of the dynamic property of the automobile, reserving the particles meeting the requirements, entering a dynamic programming algorithm, skipping the dynamic programming algorithm for the particles not meeting the requirements, inputting the particles into a particle swarm algorithm, and taking the particle swarm objective function value as a preset value;
taking the power distribution proportion of the two motors as a state parameter and the gear shifting decision and power distribution proportion variable quantity as decision parameters during the working mode and coupling driving of the driving system, taking the minimum total power loss of the motors, the belt rows and the gears considering the efficiency in the whole operating condition as a dynamic planning algorithm target, optimizing a control strategy of the reserved particles, and calculating the corresponding total power loss;
step four, weighted summation of the total power loss obtained by calculation in the step three and the power level determined by the two motors is taken as a target function of the particle swarm algorithm, iterative calculation is carried out on a particle swarm composed of the particles optimized in the step three and the particles input in the step two, and an optimal solution is output when the particle swarm algorithm meets a termination condition; the driving system provides an optimal control strategy based on the rated power of the two motors corresponding to the optimal solution and the gear transmission ratio corresponding to the topological structure of the coupling device;
and step five, inputting the optimal solution obtained in the step four into the step two regularly, and updating 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 valued in the value range of the parameters to be optimized;
the dynamic constraint is taken as an important factor considered in the algorithm of the invention, and particularly refers to the constraint of vehicle dynamic indexes on the matching of system parameters of a coupling driving system, namely the matched system parameters must meet the requirement that the power output of a coupling device can meet the dynamic performance of an automobile.
Dynamic demand of automobile is characterized by dynamic index and relevant dynamic equation of vehicle
Figure GDA0003642666590000051
FD＝Fα+FA+Ff+Fδ
Figure GDA0003642666590000052
Figure GDA0003642666590000053
Figure GDA0003642666590000054
Figure GDA0003642666590000055
In the formula
Figure GDA0003642666590000056
The driving torque is transmitted to the motor by the driving motor,
Figure GDA0003642666590000057
for tractive effort, RtireIs the wheel radius, TOUT(v) For the output of torque, i, of the coupling devicegIs the main reducer gear ratio, etagFor efficiency of coupling box output to wheel, FαAs slope resistance, FAAs air resistance, FfTo rolling resistance, FδFor acceleration resistance, M is the overall weight of the vehicle, g is the gravitational acceleration,
Figure GDA0003642666590000058
is the slope of the climbing slope, rho is the air density, A is the windward area of the vehicle, CDIs the wind resistance coefficient, v is the vehicle speed, f is the rolling resistance coefficient, and δ is the mass conversion coefficient.
The dynamic indexes to be considered comprise the highest vehicle speed and the maximum climbing gradient, wherein the highest vehicle speed can represent the requirement of a high vehicle speed working condition on the output of the coupling device, and the maximum climbing gradient represents the requirement of a low vehicle speed working condition on the output of the coupling device.
Maximum climbing gradient:
Figure GDA0003642666590000061
v＝v0
maximum vehicle speed:
Figure GDA0003642666590000062
in the formula
Figure GDA0003642666590000063
In order to satisfy the driving force required for the maximum climbing gradient,
Figure GDA0003642666590000064
in order to satisfy the driving force required for the maximum vehicle speed requirement,
Figure GDA0003642666590000065
at the maximum climbing gradient, v0，vmOne representative vehicle speed, which is a low vehicle speed and a high vehicle speed, respectively, may be selected as the lowest threshold vehicle speed and the highest vehicle speed.
The maximum output torque of the coupling device is related to the topology of the coupling device. According to the coupling device at two representative vehicle speeds v0，vmThe lower output torque and the required drive torque for the maximum grade and maximum vehicle speed of the vehicle may establish the dynamic constraints. For the particles whose output torque of the coupling device cannot meet the driving requirement of the vehicle, the objective function of the particle swarm optimization is directly set to be 1000000 in the execution of the step two, which indicates that the particles are eliminated particles, and the dynamic programming process is skipped.
Under the system parameters defined by a group of coordinates of each particle, the control strategy of the whole operation condition is optimized, the vehicle operation condition is divided into N small segments, each segment point is used as a state point, namely N +1 state points, the state of each state point consists of two state parameters, namely a working mode and a power distribution proportion, and the decision between every two state points consists of two decision parameters, namely a gear shifting strategy and a power distribution proportion change value. Different working modes and different power distribution proportions form various state parameters, and different gear shifting strategies and power distribution proportion change values form various decision parameters. The objective of the dynamic programming algorithm is to find an optimal control strategy to minimize the power loss of the whole system.
The number of operating modes of the coupling device is limited and discrete; the power division ratio is a continuous quantity because the power division ratio of the coupling device is continuously variable within a certain range.
Based on such consideration, in a preferred embodiment of the present invention, the dynamic programming algorithm in step three specifically employs a retrograde optimization iteration, and the calculation process is as follows:
mode(n)＝mode(n+1)+shift(n)
k(n)＝k(n+1)+Δk(n)
wherein, mode (n +1) is an operating mode state parameter of the nth state and the (n +1) th state respectively, k (n), k (n +1) is a power distribution proportion state parameter of the nth state and the (n +1) th state, shift (n) is a shift decision parameter between the nth state and the (n +1) th state, and Δ k (n) is a power distribution proportion variable quantity between the nth state and the (n +1) th state;
for the nth state, the objective function of the dynamic programming algorithm is the total power loss of the whole coupling device during the operation from the nth state to the last state, namely:
JDP(mode(n),k(n))＝min(JDP(mode(n+1),k(n+1))+loss(shift(n),Δk(n)))
in the formula, JDPAnd for the minimum power loss corresponding to the corresponding state, loss (shift (n), Δ k (n)) is the power loss generated in the decision process between the state parameters of the (n +1) th state and the nth state.
In a preferred embodiment of the invention, the efficiency-considered motor, belt row and gear power losses in step three are calculated by:
motor power loss is calculated based on Willans linear modelmotor：
pme＝e·pma-pmloss，e＝e0-e1·pma
e0＝e00+e01·cm+e02·cm 2，e1＝e10+e11·cm
pmloss＝pmloss0+pmloss1·cm+pmloss2·cm 2，
Figure GDA0003642666590000071
Figure GDA0003642666590000072
Figure GDA0003642666590000073
Wherein p ismeIs the mean effective pressure of the rotor surface of the machine, cmIs the surface linear velocity, eta, of the motor rotormotorTo the efficiency of the motor, e0,e00,e01,e02,e10,e11,pmloss0,pmloss1,pmloss2Can be obtained by multiple linear regression of experimental data, u and i respectively represent voltage and current, TeRated torque of the motor, VrRepresenting the effective volume of the motor, pmaTo average effective pressure, it can be calculated by the following formula;
Figure GDA0003642666590000074
calculating the loss of power of the strip line loss based on the following formulaclu：
Figure GDA0003642666590000075
Figure GDA0003642666590000076
In the formula TcluFor belt-row torque due to oil film shear stress, ncluFor the relative rotational speed of the driving and driven parts of the belt row, h0Thickness of the oil film, num is the number of friction plates of the clutch, epsilon is the viscosity of the oil film of the clutch, R0Is the outer diameter of the clutch, R1Is the effective inner diameter of the band row;
Figure GDA0003642666590000077
where ρ is the density of the lubricant, Q is the flow of the lubricant, and β0Is the contact angle, α is the surface tension coefficient;
calculating the loss of parallel shaft gear based on the following formulagear：
Figure GDA0003642666590000078
lossgear＝Tgearωgear(1-ηgear)
In the formula etagearFor parallel axis gear efficiency, z1，z2Number of teeth of drive gear, Tgear，ωgearThe transmission torque and the rotating speed of the gear;
regarding the planetary row, the planetary row is regarded as a conversion mechanism with a fixed planet carrier, and the power loss of the planetary row is calculatedpai：
Figure GDA0003642666590000081
While driving
Figure GDA0003642666590000082
During braking
losspai＝Tpaiωpai(1-ηpai)
In the formula etapaiIn order to be efficient for the planetary row,
Figure GDA0003642666590000083
loss of power of the conversion means, PinFor input power, #cTo be the power loss factor of the conversion means,
Figure GDA0003642666590000084
for the conversion of the transmission efficiency of the mechanism from sun gear to ring gear, ipIs the gear ratio of the planetary gear set, Ts，ωsTorque and rotational speed, T, of the sun gear, respectivelyr，ωrTorque and speed of rotation of the ring gear, Tc，ωcThe torque and the rotational speed of the planet carrier are respectively.
After the reverse optimization is completed, the optimal state parameters of each state and the decision parameters between the states are determined, forward calculation can be carried out according to an efficiency model, the power loss value of a coupling driving system is controlled according to the optimal decision track under the whole operation condition, and the power loss value is used as the optimal objective function value J of the final dynamic programming algorithmDP。
In a preferred embodiment of the present invention, the objective function of the particle swarm algorithm in step four is specifically in the form of:
JPSO(c,d)＝(1-λ)·JDP(c,d)+λ·Pzon(c,d)
JPSO(c, d) is the objective function value for the c particle after the d iteration, where λ is the weighting factor for the power loss and power level, JDP(c, d) is the optimization result of the dynamic programming algorithm for the c particle of the d iteration, Pzon(c, d) is the power level of the c particle of the d iteration, which is given by:
Pzon(c,d)＝Pe1(c,d)+Pe2(c,d)
in the formula, Pe1(c,d)，Pe2(c, d) the sum of two motors for the c particle of the d iteration respectivelyThe power is fixed at a constant rate,
computing globally optimal particles
Figure GDA0003642666590000085
g(d)＝F-1(JPSO.i(d))
In the formula, JPSO.i(d) An objective function value of the global optimal particle for the d-th iteration, and g (d) a position of the optimal particle for the d-th iteration;
obtaining the optimal particles on the particle track, namely the optimal rated power of the motor according to the following relation:
Figure GDA0003642666590000086
p(c,d)＝F-1(JPSO.p(c,d))
in the formula, JPSO.p(c, d) represents the minimum of the objective function values that occur for the c-th particle after d iterations, and p (c, d) is JPSO.p(c, d) the corresponding particle position, F-1() Is the correspondence between the value of the objective function and the coordinates of the particles.
In a preferred embodiment of the invention, an iteration of the particle velocity can be performed from the particle coordinate position:
Figure GDA0003642666590000091
wherein x (c, d) is the position coordinate of the c-th particle after the d-th iteration, and v (c, d) is the speed of the c-th particle after the d-th iteration; phi represents an inertia coefficient and represents the influence degree of the speed of the previous cycle on the speed of the current cycle; alpha is alpha1，α2Representing an acceleration coefficient, representing the influence of the attraction of the particles by p (c, d) and g (d) on the speed; γ (d), γ (d) being two [0,1 ]]A random number within;
in order to further improve the performance of the particle swarm optimization, phi in the formula is set to be linearly reduced and then kept constant. The initial design value is larger to perform rough global sweep as soon as possible in the early stage, and the general position of the optimal value is determined, and the later inertia coefficient is smaller to ensure that the particles can be concentrated quickly, which satisfies the following formula:
Figure GDA0003642666590000092
in the formula0，φmThe coefficients of inertia at the beginning and end of the linear variation interval, l0Is the end position of the linear variation interval.
Fig. 2 and 3 show the optimization effect achieved by running the algorithm of the invention for a torque coupling driving system and a rotating speed coupling driving system respectively, and it can be seen that the invention can provide satisfactory optimization strategies for different topological structures.
It should be understood that, the sequence numbers of the steps in the embodiments of the present invention do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (4)

1. A double-motor coupling driving system integration optimization method for an electric bus is characterized by comprising the following steps: the method specifically comprises the following steps:
selecting parameters of a system to be optimized in a driving system as particle coordinates, including respective rated powers of two motors and transmission ratios of transmission gears, and setting corresponding value ranges to complete initialization of a particle swarm algorithm;
step two, verifying whether the selected particles meet the requirements of the dynamic property of the automobile, reserving the particles meeting the requirements, entering a dynamic programming algorithm, skipping the dynamic programming algorithm for the particles not meeting the requirements, inputting the particles into a particle swarm algorithm, and taking the particle swarm objective function value as a preset value;
taking the power distribution proportion of the two motors as a state parameter and the gear shifting decision and the power distribution proportion variable quantity as decision parameters during the working mode and the coupling driving of the driving system, taking the minimum total power loss of the motors, the belt rows and the gears considering the efficiency in the whole operating condition as a dynamic planning algorithm target, optimizing a control strategy of the reserved particles, and calculating the corresponding total power loss, wherein the calculation process is as follows:
mode(n)＝mode(n+1)+shift(n)
k(n)＝k(n+1)+Δk(n)
wherein, mode (n +1) is an operating mode state parameter of the nth state and the (n +1) th state respectively, k (n), k (n +1) is a power distribution proportion state parameter of the nth state and the (n +1) th state, shift (n) is a shift decision parameter between the nth state and the (n +1) th state, and Δ k (n) is a power distribution proportion variable quantity between the nth state and the (n +1) th state;
for the nth state, the objective function of the dynamic programming algorithm is that the total power loss of the whole coupling device in the operation process from the nth state to the last state is the objective function, namely:
JDP(mode(n),k(n))＝min(JDP(mode(n+1),k(n+1))+loss(shift(n),Δk(n)))
in the formula, JDPThe loss (shift (n), Δ k (n)) is the power loss generated in the decision process from the (n +1) th state to the nth state;
step four, weighted summation of the total power loss obtained by calculation in the step three and the power level determined by the two motors is taken as a target function of the particle swarm algorithm, iterative calculation is carried out on a particle swarm composed of the particles optimized in the step three and the particles input in the step two, and an optimal solution is output when the particle swarm algorithm meets a termination condition; the driving system provides an optimal control strategy based on the rated power of the two motors corresponding to the optimal solution and the gear transmission ratio corresponding to the topological structure of the coupling device; the objective function of the particle swarm algorithm specifically adopts the following form:
JPSO(c,d)＝(1-λ)·JDP(c,d)+λ·Pzon(c,d)
JPSO(c, d) is the objective function value for the c particle after the d iteration, where λ is the weighting factor for the power loss and power level, JDP(c, d) is the optimization result of the dynamic programming algorithm for the c particle of the d iteration, Pzon(c, d) is the power level of the c particle of the d iteration, which is given by:
Pzon(c,d)＝Pe1(c,d)+Pe2(c,d)
in the formula, Pe1(c,d)，Pe2(c, d) rated power of two motors of the c particle of the d iteration respectively,
obtaining the optimal particle coordinate, namely the optimal parameter matching result according to the following relation:
Figure FDA0003642666580000021
g(d)＝F-1(JPSO.i(d))
in the formula, JPSO.i(d) An objective function value for the optimal particle for the d-th iteration, g (d) a position for the optimal particle for the d-th iteration, F-1() Is the corresponding relation between the objective function value and the particle position;
and step five, inputting the optimal solution obtained in the step four into the step two regularly, and updating the optimal solution.
2. The method of claim 1, wherein: the coordinates of the particles and the parameters to be optimized have the same dimensionality, and the initial value of each coordinate randomly takes a value in the value range of the parameters to be optimized.
3. The method of claim 1, wherein: and step two, verifying whether the selected particles meet the requirements of the dynamic property of the automobile or not, solving the maximum output torque of the coupling device at each speed according to the external characteristic curve of the motor, and comparing the maximum output torque with the coupling device output torque required by the maximum climbing gradient of the automobile and the coupling device output torque required by the maximum speed of the automobile to judge.
4. The method of claim 1, wherein: the efficiency-considered motor, belt row and gear power losses in step three are calculated by:
motor power loss calculated based on Willans linear modelmotor：
pme＝e·pma-pmloss，e＝e0-e1·pma
e0＝e00+e01·cm+e02·cm 2，e1＝e10+e11·cm
pmloss＝pmloss0+pmloss1·cm+pmloss2·cm 2，
Figure FDA0003642666580000022
Figure FDA0003642666580000023
Figure FDA0003642666580000024
Wherein p ismeIs the mean effective pressure of the rotor surface of the machine, cmIs the surface linear velocity, eta, of the motor rotormotorTo the efficiency of the motor, e0,e00,e01,e02,e10,e11,pmloss0,pmloss1,pmloss2Can be obtained by multiple linear regression of experimental dataTo u, i represent voltage and current, respectively, TeRated torque of the motor, VrRepresenting the effective volume of the motor, pmaTo average effective pressure, it can be calculated by the following formula;
Figure FDA0003642666580000025
calculating the loss of power of the strip line loss based on the following formulaclu：
Figure FDA0003642666580000031
Figure FDA0003642666580000032
In the formula, TcluFor belt-row torque due to oil film shear stress, ncluFor the relative rotational speed of the driving and driven parts of the belt row, h0Thickness of the oil film, num is the number of friction plates of the clutch, epsilon is the viscosity of the oil film of the clutch, R0Is the clutch outer diameter, R1The effective inner diameter of the belt row, and omega is the rotating speed;
Figure FDA0003642666580000033
where ρ is the lubricant density, Q is the lubricant flow, β0Is the contact angle, α is the surface tension coefficient;
calculating the parallel shaft gear power loss based on the following formulagear：
Figure FDA0003642666580000034
lossgear＝Tgearωgear(1-ηgear)
In the formula etagearFor parallel axis gear efficiency, z1，z2Number of teeth of drive gear, Tgear，ωgearThe transmission torque and the rotating speed of the gear;
regarding the planetary row, the planetary row is regarded as a conversion mechanism with a fixed planet carrier, and the power loss of the planetary row is calculatedpai：
Figure FDA0003642666580000035
While driving
Figure FDA0003642666580000036
During braking
losspai＝Tpaiωpai(1-ηpai)
In the formula etapaiEfficiency of the planetary gear set, Pf cLoss of power of the conversion means, PinFor input power, #cTo be the power loss factor of the conversion means,
Figure FDA0003642666580000037
for the conversion of the transmission efficiency of the mechanism from sun gear to ring gear, ipIs the gear ratio of the planetary gear set, Ts、ωsTorque and rotational speed, T, of the sun gear, respectivelyr、ωrTorque and speed of rotation of the ring gear, Tc、ωcThe torque and the rotational speed of the planet carrier are respectively.
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