CN106599439A - Energy consumption-oriented parameter optimization and matching method for dual-motor power system of pure electric vehicle - Google Patents
Energy consumption-oriented parameter optimization and matching method for dual-motor power system of pure electric vehicle Download PDFInfo
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Abstract
The invention aims to seek optimal dual-motor power parameters by considering contradiction between power performance and economical efficiency, and improve the power performance and endurance of a pure electric vehicle by reasonably selecting the dual-motor power parameters, and discloses an energy consumption-oriented parameter optimization and matching method for a dual-motor power system of the pure electric vehicle. The parameter optimization and matching method comprises the following steps of performing preliminary parameter matching on a dual-motor torque and rotary speed coupling structure, which is researched previously and has four working modes, based on an NEDC working condition and the like; next, on the basis of comprehensive consideration of the power performance and the economical efficiency of the electric vehicle, establishing a dual-motor power system parameter optimization model which takes acceleration time in 100 kilometers and specific energy consumption as a target, takes motor power, rotary speed, and transmission ratio as optimization variables, and takes power performance as constraint; and finally, solving the optimization model by a crossed particle swarm optimization, and performing comparison and analysis on an optimization result.
Description
Technical Field
The invention relates to the field of mechanical transmission, in particular to parameter optimization and matching of a dual-motor power system of a pure electric vehicle.
Background
In order to improve the energy utilization rate of the electric automobile in the driving process, the scheme of the dual-motor power system is gradually developed, but the driving range of the electric automobile is restricted by the immaturity of the battery technology.
Some researchers have conducted research into optimizing certain component parameters in a powertrain system with the goal of reducing the energy consumption of an electric vehicle. The Huminghui and the like are used for matching the parameters of the motor and the transmission system of the electric automobile by combining the standard working condition with the aim of taking the dynamic requirement of the whole automobile as constraint and economy as targets; wang's invention matches parameters of a pure electric vehicle transmission system in mechanical drive arrangement and optimizes the speed ratio of the transmission system; the Zhangkang et al provides a parameter matching method of a pure electric vehicle power system based on multi-objective optimization, optimization analysis is carried out on a transmission ratio, and the effectiveness of parameter matching is verified through a drum test. Zhuyu et al propose a design scheme for matching parameters of electric vehicles based on cycle conditions, and optimally match capacity parameters and transmission ratios of storage batteries.
In addition, in order to improve the dynamic performance and the economical efficiency of the electric automobile, some scholars research on the cooperative optimization matching of the parameters of the motor, the battery and the speed reducer in the power assembly. Li and the like optimize the motor power, the number of battery packs and the transmission ratio by taking energy consumption, cost and acceleration time as targets; gao and the like optimize the motor power, the number of battery packs and the transmission ratio by taking the energy consumption of the whole vehicle as a target and taking the dynamic index as a constraint; wu Xue optimizes the parameters of a motor and a reducer of a driving system of the electric automobile by taking the mass of the whole automobile and the energy consumption of hundreds of kilometers as targets; gao et al have optimized engine, motor and battery parameters for hybrid vehicles with a view to power and economy.
The research is mainly to carry out optimization matching on parameters of a single motor power system, and is less developed on double motor power systems. Due to the fact that the torque and the rotating speed of the two motors are distributed under different working modes, the parameter optimization matching of the dual-motor power system is more complex. ZHANG et al proposed a dual-mode power driving system with multiple working modes, and matched and optimized the parameters based on genetic algorithm, the results show that the system has higher efficiency than single-motor two-gear variable speed driving systems and dual-motor independent driving systems. Wang et al propose a rotational speed torque coupling dual drive system that optimizes power system parameters using quantum genetic algorithm with hundred kilometers of acceleration time and driving range as targets. Wang feng et al propose a power transmission device including a speed-regulating motor and a planetary reduction gear, which performs matching calculation on parameters of the motor and the transmission device, and optimizes the transmission ratio by comprehensively considering the dynamic property and the economical property of the electric automobile. However, the above research fails to fully consider the distribution problem of the rotation speeds and torques of the two motors in different operation modes in the optimization of the power parameters of the double motors, so that the optimization result is inaccurate. Therefore, it is of great research significance to fully consider the influence of the motor and the drive train parameters on the hundred kilometer acceleration time and the energy consumption of the electric vehicle in different working modes and consider the contradiction between the dynamic property and the economical property so as to search for the optimal dynamic parameter.
Disclosure of Invention
The invention mainly aims to solve a parameter optimization model of a dual-motor power system on the basis of comprehensively considering the dynamic property and the economical efficiency of an electric automobile, obtain the optimal power system parameter and improve the dynamic property and the cruising ability of the pure electric automobile.
The technical scheme adopted for achieving the purpose of the invention is that the energy consumption-oriented parameter optimization matching method for the double-motor power system of the pure electric vehicle comprises the following steps:
step 1: based on the information such as the NEDC working condition, the parameters of the double-motor torque and rotating speed coupling structure with four working modes are preliminarily matched so as to meet the power performance index of the electric automobile in the running process.
Step 2: on the basis of comprehensively considering the dynamic property and the economical efficiency of the electric automobile, the torque, the rotating speed and the power distribution of the dual-motor torque and rotating speed coupling mechanism in different working modes are considered, and a parameter optimization model of the dual-motor power system is established, wherein the parameter optimization model takes hundred kilometer acceleration time and specific energy consumption as targets, the motor power, the rotating speed and the transmission ratio as optimization variables, and the dynamic property is constraint.
And step 3: and solving the double-motor power system parameter optimization model by using a cross particle swarm algorithm to obtain the optimally matched double-motor power driving system parameters.
Preferably, in step 1, the preliminary parameter matching process for the dual-motor torque-rotation speed coupling structure with four operation modes includes:
(1) motor parameter matching
The four working modes of the double-motor power system are as follows: m1The working condition of single working and low speed and large torque; m2Working independently, and working at medium speed and small torque; torque coupling, medium-speed large-torque working condition; rotating speed coupling and high-speed working conditions.
The selection principle of the peak power of the motor is as follows:
Pmax≥max(Pv，Pα，Pt)
in the formula, PvFor the power determined from the highest vehicle speed,wherein m is the maximum total mass of the automobile, g is the gravity acceleration, f is the rolling resistance coefficient, vmaxAt the maximum vehicle speed, CDIs the wind resistance coefficient, A is the windward area; pαIs the power of the automobile when climbing a slope,Imaxat the maximum climbing gradient, v0For overcoming the maximum climbing gradient when the vehicle is fully loaded ηTFor transmission efficiency; ptFor the power determined on the basis of the acceleration time,among these are the rotating mass coefficients. The peak power P of the motor is selected by considering the margin of 15%max。
And obtaining the maximum value of the motor power in the low-speed interval of 0-35km/h according to the NEDC working condition information of the graph. In order to ensure that the electric automobile runs in an efficient interval as much as possible and improve the energy utilization rate of the electric automobile, a motor M is arranged1The rated power of the motor M is determined as the maximum value of the power in the low-speed interval, and the motor M1、M2The sum of the peak powers of is Pmax. Selecting the highest rotating speed of the permanent magnet synchronous motor according to the characteristic that the motor is low in high-speed efficiency; and considering that the electric automobile has better climbing performance, and selecting the rated rotating speed of the motor.
(2) Driveline parameter matching
The transmission ratio is an important performance parameter of an automobile transmission system, and has important influence on the maximum speed, the maximum climbing gradient and the acceleration performance of an automobile. In addition, the reasonable setting of the transmission ratio can effectively adjust the working range of the motor, so that the motor can work in a range with higher efficiency to the maximum extent, and the energy efficiency of the electric automobile can be improved. The single-motor single-stage reduction transmission ratio i of the traditional electric automobile needs to meet the following requirements:
wherein v ismaxAt the highest speed of time, nmaxThe maximum rotation speed of the motor, r is the radius of the wheel, TmaxIs the maximum output torque of the motor, ImaxThe maximum climbing gradient of the electric automobile.
The maximum climbing gradient is known by the motor M according to the characteristics of the planetary gear mechanism1When operating alone, the transmission ratio i satisfies the above relationship. Simultaneously, for making electric automobile have higher energy efficiency, the drive ratio matches and needs to satisfy:
(a) when the motor M1When the motor is driven independently and the speed per hour is 35km/h, the motor works within 4500r/min as much as possible (the efficiency is relatively high).
(b) When the motor M2When the motor is driven by single drive or double-motor torque coupling drive and the speed per hour covers 35-70km/h, in order to enable the motor to work in a high-efficiency region, the motor is distributed as much as 2000-4500r/min when the motor is in a working rotating speed range.
(3) Dual-motor power system performance analysis
In order to verify the validity of the parameter matching, the variation relations of the electric automobile flat road resistance, the ramp resistance, the driving force and the like along with the automobile speed per hour are analyzed according to the matched parameters of the dual-motor power system, and a dynamic resistance balance analysis chart is obtained.
Preferably, in step 2, the double-motor power system parameter optimization model with target of hundred kilometers acceleration time and specific energy consumption, optimized variables of motor power, rotating speed and transmission ratio, and constraint of dynamic property is as follows:
wherein a istFor a one hundred kilometers acceleration time of a dual-motor power system, atThe following relationship is satisfied:
wherein v is1Indicating the motor M1Vehicle speed, v, at which individual drive and torque-coupled drive forces are equal2Representing the maximum vehicle speed achieved by the torque-coupled drive,indicating the motor M1The maximum driving force converted to the wheels as the vehicle speed changes,representing the maximum driving force on the torque-coupled wheels,indicating the maximum driving force on the wheel when the rotational speed is coupled. Then there is
When the rotating speed coupling driving mode works, the gear ring needs to overcome larger torque, so that the maximum driving force needs to be according to the motor M at the moment2The driving force that can be provided. For M to let2Keep a larger driving force and can adjust the motor M1Speed of rotation makes motor M2The driving force is larger when the motor works in a constant torque interval, namely within a rated rotating speed.
Wherein,and v is the vehicle speed.
ECRepresenting the energy consumption of the complete operating mode NEDC, LCRepresents the mileage of the operating condition NEDC, wherein:
wherein eta (t) is the efficiency of the motor at the time t, v (t) is the speed per hour of the automobile at the time t, the unit km/h and F represent the driving force corresponding to the automobile at the working condition time t, and for the whole working condition, the resistance of the automobile comprises the rolling resistance of wheels, the air resistance of the automobile and the acceleration resistance.
The working efficiency eta (t) of the motor at any point in the working interval is
WhereinPiRepresents the power of a certain operating point; ploss-iThe total loss of a certain working point of the motor is represented, and the total loss mainly comprises copper loss P of the working pointCu-iEddy current loss Pe-iHysteresis loss Ph-iMechanical loss Pm-iAnd additional loss Ps-i. Wherein, PCu-i、Pe-iAnd Ph-iCan pass through the copper loss P of the peak point respectivelyCuEddy current loss PeHysteresis loss PhAnd the operating point speed niPower PiTo obtain the mechanical loss Pm-iAnd additional loss Ps-iCan be obtained by empirical formulas. Namely, it is
Wherein, the total loss P of the peak pointlossSatisfy the requirement of
Wherein h represents a heat dissipation coefficient, W/m2K; t is the temperature rise limit, DEG C; a represents the surface area of the motor core in mm2。
Copper loss PCuAnd iron loss PFeRespectively account for 0.59 and 0.22 of the total loss, PeAnd PhEach accounting for half of the core loss.
And obtaining a maximum equivalent driving moment diagram of the dual-motor power system according to the dynamic resistance balance analysis diagram, and dividing working intervals of four working modes of the dual-motor power system according to the maximum equivalent driving moment diagram.
Preferably, in step 2, the torque, the rotating speed and the power distribution process of the dual-motor torque-rotating speed coupling mechanism in different operating modes are as follows:
in the dual-motor power system, the two motors work together in a rotating speed coupling and torque coupling working mode, and the energy consumption at the moment is the sum of the energy consumption of the two motors. In order to enable the two motors to work at a high-efficiency working point, the torque, the rotating speed and the power of the two motors in the two modes are reasonably distributed. The torques of the two motors need to be distributed in the torque coupling working mode, namely, the equal proportion distribution of power is carried out on the torque coupling modes of the two motors according to the motor similarity principle, so that the efficiency of the two motors is relatively similar and relatively high. Under the torque coupling driving mode, the rotating speeds of the double motors are kept consistent, so that the torque distribution meets the requirement
In the process of coupling the rotating speeds of the two motors, the rotating speeds of the two motors need to be distributed according to the working condition information. Through the analysis of the coupling characteristics of the rotating speeds of the double motors, the torque on the gear ring is large, and if the power is distributed in equal proportion, the motor M is caused1The rotational speed exceeds the limit. Therefore, the maximum rotating speed under the working torque is distributed, the efficiency maximization of the two motors can be met, and the specific distribution principle is as follows:
in the formula TiThe working torque of the motor is represented and can be calculated through the torque required by the wheels, v is the speed per hour of the automobile, and k is a rotating speed proportionality coefficient. When the highest rotating speed under the working torque exceeds the highest rotating speed of the motor, the maximum rotating speed is recorded as 6000 r/min. So that the rotation speed distribution of the two motors is satisfied
n1＝knmax1i
n2＝knmax2i
Preferably, in step 3, the solving process of the double-motor power system parameter optimization model by using the cross particle swarm optimization algorithm is as follows:
the particle swarm optimization algorithm has the characteristics of high quality of non-inferior solutions and good robustness, and is often used for parameter optimization of multi-objective problems. In view of this, the particle swarm optimization is adopted to optimize the parameters of the dual-motor power system. Each particle in the particle swarm represents a combination scheme of parameters of the dual-motor power driving system, and is represented by three indexes of position, speed and fitness. Wherein the decision variables of the model are considered to mainly comprise the motor M1、M2Parameters, planetary gear characteristic parameters, secondary fixed transmission ratio and the like, wherein each particle position is a seven-dimensional vector Xi＝[Pmaxli，PN1i，nN，PN2i，Pmax2i，αi，i0i](ii) a The particle velocity represents the maximum flight distance of each particle during each iteration; the fitness function is an optimized objective function, the hundred kilometers acceleration time and the specific energy consumption under the working condition, namely minF (X) ═ min EC，min t]. The particles get the fitness value through the objective function, and then the best position P experienced is evaluatediWhile the particles also know the optimal position P of the particles in all populationsgbBased on this, the direction of the next generation particle evolution can be determined.
In the formula, omega is an inertia weight factor; r is1And r2Is [0, 1 ]]A random number in between; c. C1And c2Representing a learning factor.
In addition, in order to solve the defects that the particle swarm algorithm is easy to generate premature convergence and low in later iteration efficiency, a crossed particle swarm algorithm is adopted. The method adopts self-adaptive inertia weight and introduces selective cross operation in the genetic algorithm, so that the global and local searching capability of the algorithm can be effectively improved, and the iterative convergence speed is accelerated.
Through the algorithm process, the optimized parameters of the dual-motor power system can be obtained.
Compared with the prior art, the invention has the beneficial effects that:
the method firstly provides a power parameter matching principle based on different working modes on the basis of different working modes of the dual-motor power assembly system, performs primary parameter matching on the power assembly parameters based on the NEDC working condition, and verifies the rationality of the parameter matching through dynamic resistance balance analysis of the electric automobile. Secondly, a multi-target optimization model which takes the working condition specific energy consumption primarily divided based on the double-motor working mode and the hundred kilometers of acceleration time under the multi-mode combined driving as objective functions and takes the motor rated power, the rated rotating speed and the transmission ratio as optimization variables is established, and the solution is carried out by utilizing a cross particle swarm optimization, and the result shows that the optimized power system parameters have better power performance and economic performance. The invention provides theoretical support for improving the comprehensive performance of the pure electric vehicle powered by double motors, and is beneficial to further development of the pure electric vehicle industry.
Drawings
FIG. 1 is a power coupling system of a dual motor
FIG. 2 is a general flow chart of parameter optimization and matching of dual-motor power system
FIG. 3 NEDC operating conditions and power demand
FIG. 4 is a dynamic resistance balance diagram of a dual-motor power system
FIG. 5 electric machine powertrain maximum equivalent drive torque
FIG. 6 hundred kilometers acceleration contrast analysis
FIG. 7 specific energy consumption comparison analysis
FIG. 8 equivalent driving force comparison diagram
FIG. 9 Motor efficiency comparison analysis
Detailed Description
The present invention will be further described with reference to the accompanying drawings and examples, but it should not be construed that the scope of the above-described subject matter is limited to the examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
In the embodiment, a pure electric vehicle of a certain model of a Chongqing automobile company Limited is used as a research object, and the dynamic indexes of the pure electric vehicle are as follows: maximum vehicle speed vmax120km/h, acceleration time a of hundred kilometerstIs 18s, maximum climbing gradient ImaxThe content was 30%. The whole basic parameters of the electric automobile are shown in table 1.
TABLE 1 Whole vehicle parameters
The NEDC operating conditions and the required power are shown in fig. 3.
According to Pmax≥max(Pv，Pα，Pt) The motor is selected by considering the margin of 15 percentPeak power Pmax＝45kW。
Motor M1The rated power is 12kW, and the peak power is 27 kW; motor M2The rated power is 8kW, and the peak power is 18 kW. The maximum rotating speed of the permanent magnet synchronous motor is 6000r/min, and the rated rotating speed is 2400 r/min.
Maximum climbing gradient driven by motor M1When the gear is solely operated, the transmission ratio satisfies i > 10.3.
The transmission ratio matching needs to meet the following requirements:
(a) when the motor M1When the motor is driven independently and the speed per hour is 35km/h, the motor works within 4500r/min as much as possible (the efficiency is relatively high), so that the transmission ratio i when the sun wheel is input is obtained113.7, the transmission ratio i1The value is 13.5.
(b) When the motor M2When the motor is driven by single drive or double-motor torque coupling drive and the speed per hour covers 35-70km/h, in order to enable the motor to work in a high-efficiency region, when the motor is in a working rotating speed range, the working rotating speed range is distributed as much as possible at 2000-4500r/min, and the transmission ratio i is at the moment2Satisfies i is more than or equal to 6.112Less than or equal to 6.88, comprehensively considering the dynamic property and the economical efficiency of the electric automobile, and selecting a transmission ratio i2It was 6.75.
Therefore, according to the planetary gear characteristics, the planetary mechanism characteristic parameter α is 2, and the two-stage fixed transmission ratio i is obtainedo4.5. Sun input reduction ratio 13.5, ring input reduction ratio 6.75.
The dynamic resistance balance diagram of the dual-motor power system of the electric automobile is shown in figure 4.
As shown by a dynamic resistance balance diagram, the maximum climbing gradient of the dual-motor power driving system exceeds 30 percent, the requirement of climbing gradient is met, and the requirement of 120km/h maximum speed per hour can be met through the coupling of the rotating speeds of the dual motors.
According to the dynamic resistance balance diagram, the working modes of the dual-motor power system are as follows: first 25km/h motor M1Single drive, 25-96km/h double-motor torque coupling drive, 96km/h-1And 00km/h is rotating speed coupling driving. The available hundred kilometers acceleration time is:
wherein,andrespectively represent the motor M1The maximum driving force on the independent driving hour wheel, the maximum driving force on the torque coupling wheel and the maximum driving force on the rotating speed coupling hour wheel.
The acceleration time is counted by adopting MATLAB, and the result shows that the hundred-kilometer acceleration time of the electric automobile adopting the double-motor power system provided by the invention is 15.8s, so that the requirement on the acceleration performance is met.
The maximum equivalent driving torque of the motor-driven system is shown in fig. 5.
Preliminarily dividing working intervals of four working modes of the dual-motor power system according to an equivalent driving moment diagram: the motor M in the interval of 0-24km/h1Working alone, the motor M is in the interval of 24km/h-70km/h and equivalent driving torque T < 400Nm2The motor works independently, the motor is driven by double-motor torque coupling in the range of 24km/h-70km/h and the equivalent driving torque T is more than 400Nm, and the motor is driven by double-motor rotating speed coupling more than 70 km/h.
The boundary conditions are constrained to be:
Pmax＝Pmax1+Pmax2＝45kW，20kW＜Pmax1＜35kW
8kW＜PN1＜15kW，5kW＜PN2＜12kW
basic parameters of the cross particle swarm algorithm are set as follows: inertial weight factor omegamax＝0.6，ωmin0.2; the learning factor satisfies c1＝c21 is ═ 1; the number of the populations is 50; the number of iterations is 100; particle flight velocity vmax＝1.5，vmin＝-1.5。
Through the algorithm process, the optimized parameters of the dual-motor power system can be obtained, the comparison result with the original parameters is shown in table 2, and the optimized comparison result of the performance of the electric automobile is shown in table 3:
TABLE 2 comparison of parameters before and after optimization
TABLE 3 comparison of Performance before and after optimization
Therefore, according to the optimization results, after the parameters of the dual-motor power system are optimized by adopting the cross particle swarm optimization, the power performance and the economic performance of the electric automobile are improved to a certain extent, and the design requirements are met.
FIG. 6 shows the comparison before and after the optimization of the hundred kilometers acceleration time of the electric vehicle, and it can be seen from the graph that the hundred kilometers acceleration performance is obviously improved after the optimization; fig. 7 shows comparison between the electric vehicle before and after the optimization of the specific energy consumption under the NEDC cycle condition, which shows that the specific energy consumption is significantly reduced after the optimization, that is, the driving range of the electric vehicle can be increased by the optimized power parameters under the same battery capacity.
As can be seen from FIG. 8Motor M1The peak driving force is obviously increased when the motor works alone compared with the peak driving force before optimization, so that the motor M can be driven1The acceleration time is reduced when the motor is driven alone; although the driving force in the torque coupling stage is not obviously improved compared with that before optimization, the motor M is required for accelerating the electric automobile to 100km/h before optimization1The method comprises three stages of independent driving, torque coupling driving and rotating speed coupling driving, and only the motor M is needed after optimization1The driving method comprises two stages of independent driving and torque coupling driving, wherein the torque coupling stage is larger than the driving force of the rotating speed coupling. Therefore, the hundred-kilometer acceleration time of the electric automobile is shortened by 1.6 seconds compared with that before optimization.
Fig. 9 is a graph showing a comparison of efficiencies of two optimized motors before and after the electric vehicle is driven under the NEDC condition. The blue line represents the efficiency of the motor at each working point before parameter optimization, and the red line represents the efficiency of the motor at each working point after parameter optimization. The energy consumption of the electric automobile is mainly influenced by the efficiency of the working point of the motor, and the working range of the motor can be effectively adjusted by optimizing the parameters of the motor and the speed reducer of the power assembly, so that more motors can work in a high-efficiency area, and the loss of the motor is reduced. As can be seen from fig. 9, the efficiency of the two motors at each operating point of the NEDC operating condition after the parameter optimization is improved to a certain extent.
Claims (5)
1. The energy consumption-oriented parameter optimization matching method for the dual-motor power system of the pure electric vehicle is characterized by comprising the following steps of:
step 1: based on the information such as the NEDC working condition, the parameters of the double-motor torque and rotating speed coupling structure with four working modes are preliminarily matched so as to meet the power performance index of the electric automobile in the running process.
Step 2: on the basis of comprehensively considering the dynamic property and the economical efficiency of the electric automobile, the torque, the rotating speed and the power distribution of the dual-motor torque and rotating speed coupling mechanism in different working modes are considered, and a parameter optimization model of the dual-motor power system is established, wherein the parameter optimization model takes hundred kilometer acceleration time and specific energy consumption as targets, the motor power, the rotating speed and the transmission ratio as optimization variables, and the dynamic property is constraint.
And step 3: and solving the double-motor power system parameter optimization model by using a cross particle swarm algorithm to obtain the optimally matched double-motor power driving system parameters.
2. The energy consumption-oriented pure electric vehicle dual-motor power system parameter optimization matching method as claimed in claim 1, characterized in that: in the step 1, the process of performing parameter preliminary matching on the double-motor torque and rotating speed coupling mechanism comprises the following steps:
(1) motor parameter matching
The peak power selection principle is as follows:
Pmax≥max(Pv，Pα，Pt)
in the formula, PvFor the power determined from the highest vehicle speed,wherein m is the maximum total mass of the automobile, g is the gravity acceleration, f is the rolling resistance coefficient, vmaxAt the maximum vehicle speed, CDIs the wind resistance coefficient, A is the windward area; pαIs the power of the automobile when climbing a slope,Imaxat the maximum climbing gradient, v0For overcoming the maximum climbing gradient when the vehicle is fully loaded ηTFor transmission efficiency; ptFor the power determined on the basis of the acceleration time,among these are the rotating mass coefficients. The peak power P of the motor is selected by considering the margin of 15%max。
The four working modes of the dual-motor power system are as follows: m1The working condition of single working and low speed and large torque; m2Working independently, and working at medium speed and small torque; the double motors work simultaneously, the torque is coupled, and the working conditions of medium speed and large torque are met; the double motors work simultaneously, the rotating speeds are coupled, and the working conditions are high.
The four working modes of the dual-motor power system and the running condition of the automobile are comprehensively considered, and the whole speed per hour range is divided into 3 intervals. Wherein 0-35km/h is a low-speed region, and the motor M1Working independently; the motor M is in a medium-speed period of 35-70km/h2Working alone or torque-coupled; 70-120km/h is a high-speed interval, and a motor M1And motor M2The rotational speed coupling works. Obtaining the maximum value of the motor power in a low-speed range of 0-35km/h according to the statistical information of the NEDC working conditions, and determining the value as the motor M1Rated power, redistributed to the motor M1Peak power of and motor M2Rated power and peak power. The method is characterized in that the high-speed efficiency of the motor is low, the highest rotating speed of the permanent magnet synchronous motor is selected, the electric automobile has good climbing performance, and the rated rotating speed of the motor is selected.
(2) Driveline parameter matching
The single-motor single-stage reduction transmission ratio i of the traditional electric automobile needs to meet the following requirements:
wherein v ismaxAt the highest speed of time, nmaxThe maximum rotation speed of the motor, r is the radius of the wheel, TmaxIs the maximum output torque of the motor, ImaxThe maximum climbing gradient of the electric automobile.
The maximum climbing gradient is known by the motor M according to the characteristics of the planetary gear mechanism1Determined when working alone. Meanwhile, in order to enable the electric automobile to have higher energy efficiency, the following conditions need to be met in the transmission ratio matching:
(a) when the motor M1When the motor is driven independently and the speed per hour is 35km/h, the motor works within 4500r/min as much as possible (the efficiency is relatively high).
(b) When the motor M2The single drive or the double-motor torque coupling drive is adopted, and the speed per hour covers 35-70km/h, so that the motor worksIn the high-efficiency interval, the working rotating speed range of the motor is distributed as much as 2000-4500 r/min.
3. The energy consumption-oriented pure electric vehicle dual-motor power system parameter optimization matching method as claimed in claim 1, characterized in that: in step 2, aiming at hundred kilometers of acceleration time and specific energy consumption, the motor power, the rotating speed and the transmission ratio are optimized variables, and the parameter optimization model of the dual-motor power system with dynamic property as constraint is as follows:
in the formula, atThe acceleration time is hundred kilometers of a double-motor power system, and the formula is satisfied:
wherein v is1Indicating the motor M1Vehicle speed, v, at which individual drive and torque-coupled drive forces are equal2Representing the maximum vehicle speed achieved by the torque-coupled drive,indicating the motor M1The maximum driving force converted to the wheels as the vehicle speed changes,representing the maximum driving force on the torque-coupled wheels,indicating the maximum driving force on the wheel when the rotational speed is coupled.
LCIndicating the mileage of the operating mode NEDC, ECIndicating complete operationEnergy consumption of the NEDC, among others,
eta (t) is the efficiency of the motor at the time t, v (t) is the speed per hour of the automobile at the time t, the unit km/h and F represent the driving force corresponding to the automobile at the working condition time t, and for the whole working condition, the resistance of the automobile comprises wheel rolling resistance, automobile air resistance and acceleration resistance.
4. The energy consumption-oriented pure electric vehicle dual-motor power system parameter optimization matching method as claimed in claim 1, characterized in that: in step 2, the torque, rotating speed and power distribution process of the dual-motor torque and rotating speed coupling mechanism in different working modes is as follows:
the torques of the two motors need to be distributed in the torque coupling working mode, namely, the equal proportion distribution of power is carried out on the torque coupling modes of the two motors according to the motor similarity principle, so that the efficiency of the two motors is relatively similar and relatively high. Under the torque coupling drive mode, the rotational speeds of the double motors are kept consistent, so that the torque distribution meets the following requirements:
in the process of coupling the rotating speeds of the two motors, the rotating speeds of the two motors need to be distributed according to the working condition information. Through the analysis of the coupling characteristics of the rotating speeds of the double motors, the torque on the gear ring is large, and if the power is distributed in equal proportion, the motor M is caused1The rotational speed exceeds the limit. Therefore, the distribution is performed based on the maximum rotating speed under the working torque, and the efficiency maximization of the two motors can be met, and the specific distribution principle is as follows:
in the formula TiThe working torque of the motor is represented and can be calculated through the torque required by the wheels, v is the speed per hour of the automobile, and k is a rotating speed proportionality coefficient. When the highest rotating speed under the working torque exceeds the highest rotating speed of the motor, the maximum rotating speed is recorded as 6000 r/min. So that the rotation speed distribution of the two motors is satisfied
n1＝knmax1i
n2＝knmax2i。
5. The energy consumption-oriented pure electric vehicle dual-motor power system parameter optimization matching method as claimed in claim 1, characterized in that: and 3, solving the parameter optimization model of the dual-motor power system by adopting a particle swarm optimization algorithm based on a cross method.
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