CN113435061B - Method for quickly constructing reliability target load of electric drive system - Google Patents

Method for quickly constructing reliability target load of electric drive system Download PDF

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CN113435061B
CN113435061B CN202110808043.2A CN202110808043A CN113435061B CN 113435061 B CN113435061 B CN 113435061B CN 202110808043 A CN202110808043 A CN 202110808043A CN 113435061 B CN113435061 B CN 113435061B
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CN113435061A (en
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赵礼辉
邓思城
王震
周驰
冯金芝
郑松林
高大威
翁硕
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University of Shanghai for Science and Technology
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Abstract

The invention discloses a method for quickly constructing a reliability target load of an electric drive system, which comprises the following specific steps: s1, collecting parameters of an electric drive system, and constructing a complete vehicle dynamics simulation model; s2, obtaining function fitting of the damage and equivalent parameters based on the whole vehicle dynamics simulation model; s3, obtaining a user optimization model and result analysis by combining the dynamic balance calculation according to the function fitting of the damage and the equivalent parameters; s4, obtaining an optimization model with unified parameters of the target function; and S5, carrying out optimization solution on the optimization model with unified parameters to obtain equivalent parameters of the optimal solution, carrying out simulation treatment on the equivalent parameters of the optimal solution to obtain a reliability target load of simulation construction, and analyzing the reliability target load. The reliability target load of the electric drive system covering a certain user use strength is constructed, comparison verification is carried out by combining subsequent operation data of the user, and a foundation is laid for fast and accurately carrying out reliability evaluation and service life prediction on the electric drive system.

Description

Method for quickly constructing reliability target load of electric drive system
Technical Field
The invention relates to the technical field of reliability analysis of an electric drive system, in particular to a method for quickly constructing a reliability target load of the electric drive system.
Background
In an automobile research and development system, reliability design, analysis and test based on vehicle running load are important ways for guaranteeing the reliability level of the vehicle under the use condition of a user, wherein the working condition load capable of reproducing performance degradation in the running process of vehicles of different users is the premise for carrying out the work. The construction of the reliability target load needs to be based on a large amount of user sample data. The method for directly collecting the load through the user vehicle has high cost and long period, and the sample size of the user is often low; the simulation method is low in cost and rapid in load acquisition, and is an important mode for acquiring the vehicle load. However, the road gradient and the whole vehicle mass are used as main parameters influencing the construction precision of the reliability target load, and the problems that accurate acquisition is difficult, the data difference is large under different user working conditions and the like exist in the vehicle running process, so that equivalent parameters representing the average level of the road gradient and the whole vehicle mass are difficult to accurately obtain, and the reliability level of the simulation load of the electric drive system is influenced. At present, research focuses on estimating running states such as the gradient of a running road of a vehicle and the mass of the whole vehicle through running data such as speed, driving force, longitudinal acceleration and the like, and reverse research is rarely carried out. Therefore, the method and the device have the advantages that the optimal equivalent parameters are obtained through a multi-objective optimization method on the basis of the user historical operation vehicle speed data which are relatively easy to obtain, the purpose of quickly constructing the reliability target load of the electric drive system is achieved, and technical support is provided for the reliability evaluation of the electric drive system.
Disclosure of Invention
The invention aims to provide a method for quickly constructing a reliability target load of an electric drive system, which aims to solve the problems in the prior art, provide technical support for evaluating the reliability of the electric drive system, provide reference for constructing the reliability target load of a whole vehicle and other systems and realize quick construction of the reliability target load of the electric drive system.
In order to achieve the purpose, the invention provides the following scheme:
the invention provides a method for quickly constructing a reliability target load of an electric drive system, which comprises the following specific steps:
s1, collecting parameters of an electric drive system, and constructing a complete vehicle dynamics simulation model by combining dynamic balance calculation;
s2, calculating damage by combining an S-N curve and a Miner linear damage accumulation theory based on the whole vehicle dynamics simulation model to obtain function fitting of the damage and equivalent parameters;
s3, obtaining a user optimization model and result analysis by combining the dynamic balance calculation according to the function fitting of the damage and the equivalent parameters;
s4, performing damage domain analysis and time domain analysis on the user optimization model to obtain an optimization model with uniform target function construction parameters;
and S5, acquiring operation data, performing optimization solution on the optimization model with unified parameters to obtain equivalent parameters of an optimal solution, performing simulation processing on the equivalent parameters of the optimal solution to obtain a reliability target load constructed by simulation, and analyzing the reliability target load.
Preferably, the user optimization model in S3 is an optimized mathematical model that targets the damage domain and the time domain of the load.
Preferably, the specific steps of the process of obtaining the user optimization model in S3 include:
step one, obtaining an absolute value f of a difference of a functional relation between actual damage and fitting damage1j(m，α)，f1j(m, α) is defined as the optimal objective function of torque impairment:
f1j(m,α)＝|dj-dacj|
wherein m and alpha are equivalent vehicle mass and equivalent driving gradient in the driving process of the vehicle; f. of1j、dj、dacjRespectively a damage target function, a damage mathematical model and an actual damage value of the jth user;
step two, obtaining an absolute value of the difference between the simulation and the actual load at any moment as a time domain optimization objective function, and summing the absolute value of the difference of the actual driving positive torque at each moment, wherein the absolute value is expressed by the following expression:
Figure BDA0003167287250000031
wherein n isjThe total number of times of the drive torque of the jth user; t isi、TaciThe simulated and actual drive torques for the ith occurrence of the user, respectively;
step three, obtaining a torque time domain distribution optimization objective function f of the jth user2j(m, alpha), calculating and summing the collected historical service data,
Figure BDA0003167287250000032
wherein i1Eta is the total transmission ratio and the transmission efficiency; r is the tire radius; m is the equivalent mass of the whole vehicle; f is the tire rolling resistance coefficient; cDIs the air resistance coefficient; a is the windward area; alpha is the equivalent gradient; forming a vehicle body model; v. ofi、aiRespectively the automobile speed and the acceleration when the driving torque appears at the ith time;
step four, taking the road gradient and the whole vehicle mass as variables, and taking the normal value interval of the road gradient and the whole vehicle mass as a constraint condition, specifically:
Figure BDA0003167287250000033
obtaining the multi-objective optimization of the simulation torque of the jth user, which specifically comprises the following steps:
minfj(m,α)＝{f1j,f2j}
Figure BDA0003167287250000034
preferably, in S3, the analysis of the result is that the simulation and the actual damage are subject to a model of log-positive distribution.
Preferably, the objective function obtained by the damage domain analysis in S4 is:
Figure BDA0003167287250000041
Figure BDA0003167287250000042
F3＝|(1.6449σ+μ)-(1.6449σac+μac)|；
the objective function obtained by the time domain analysis in S4 is:
Figure BDA0003167287250000043
preferably, based on the objective function obtained by the damage domain analysis and the objective function obtained by the time domain analysis, an optimization model with unified parameters is obtained through a variable and constraint algorithm:
Figure BDA0003167287250000044
preferably, the equivalent parameters of the optimal solution in S5 include: equivalent grade and equivalent mass.
The invention discloses the following technical effects:
the invention provides a method for constructing a reliability target load of an electric drive system based on historical vehicle speed data of vehicles of different users. Based on a complete vehicle dynamics simulation model, the simulation load is consistent with the actual load damage and frequency distribution in the long-term use process of a user through parameter optimization of equivalent mass and equivalent gradient; and further combining the accumulated distribution of the damage of the whole user, constructing the reliability target load of the electric drive system covering certain user use strength by unifying parameters under the percentile damage level, and performing comparison verification by combining subsequent operation data of the user, thereby laying a foundation for rapidly and accurately performing reliability evaluation and service life prediction of the electric drive system.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is an overall flow chart of an embodiment of the present invention;
FIG. 2 is a diagram of a vehicle dynamics model of an electric vehicle according to an embodiment of the present invention;
FIG. 3 is a histogram of simulated load damage distribution at various levels of parameters according to an embodiment of the present invention;
FIG. 4 is a histogram of damage fit errors at various levels of parameters for an embodiment of the present invention;
FIG. 5 is a schematic diagram of travel time and mileage for each user in accordance with an embodiment of the present invention;
FIG. 6 is a graph of user actual versus simulated load damage and error for an embodiment of the present invention;
FIG. 7 is a graph of cumulative probability of user actual and simulated load damage according to an embodiment of the present invention;
FIG. 8 is a graph of a normalized comparison of torque versus time history for an embodiment of the present invention;
FIG. 9 is a graph comparing the normalized distribution of the magnitude of the load for an embodiment of the present invention;
FIG. 10 is a graph comparing the amplitude normalized distribution of the load for an embodiment of the present invention;
FIG. 11 is a first year user actual and simulated load damage cumulative distribution plot of an embodiment of the present invention;
FIG. 12 is a cumulative distribution graph of actual and simulated load damage for a user in a second year in accordance with an embodiment of the present invention;
FIG. 13 is a graph comparing the normalized distribution of the magnitude of the load for an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to various exemplary embodiments of the invention, the detailed description should not be construed as limiting the invention but as a more detailed description of certain aspects, features and embodiments of the invention.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Further, for numerical ranges in this disclosure, it is understood that each intervening value, between the upper and lower limit of that range, is also specifically disclosed. Every smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in a stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.
It will be apparent to those skilled in the art that various modifications and variations can be made in the specific embodiments of the present disclosure without departing from the scope or spirit of the disclosure. Other embodiments will be apparent to those skilled in the art from consideration of the specification. The specification and examples are exemplary only.
As used herein, the terms "comprising," "including," "having," "containing," and the like are open-ended terms that mean including, but not limited to.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
The general implementation scheme flow of the invention is shown in fig. 1, and comprises the steps of establishing a complete vehicle dynamics simulation model, performing function fitting of load damage and equivalent parameters, establishing a user optimization model, performing simulation load damage and time domain analysis under optimized parameters, establishing an optimization model with unified parameters, and performing reliability target load verification analysis of simulation construction. The specific implementation steps are as follows:
step 1: the invention takes the torque of an electric drive system as an example, and the reliability target load is constructed. Based on basic parameters of a certain brand of new energy automobile, the whole automobile dynamic balance equation is combined, all mathematical models are connected, and a whole automobile simulation model is constructed as shown in fig. 2.
The mathematical model in this embodiment is established based on a stress balance equation during the driving process of the vehicle, as follows:
Figure BDA0003167287250000071
in formula 1, TtqIs the motor shaft torque; i.e. i1Eta are respectively the total transmission ratio and the transmission efficiency; r is the tire radius; m is the equivalent mass of the whole vehicle; f is the tire rolling resistance coefficient; cDIs the air resistance coefficient; a is the windward area; alpha is the equivalent gradient; v, a are the speed and acceleration of the vehicle.
And 2, step: load data under parameters of all levels are obtained through simulation based on historical operating data of 40 users in one year, and the damage is calculated by combining an S-N curve and a Miner linear damage accumulation theory so as to obtain an approximate function relation of the damage and equivalent parameters through fitting.
In this embodiment, an optimization function with reliability as a target is established, and a functional relationship between the damage and the equivalent parameter is fitted to serve as a construction basis for the damage optimization target function. The invention takes the historical operating data of a single random user as an example for analysis. The maximum load of the vehicle is 500 Kg; the influence of the uphill road section on the vehicle torque is much larger than that of the downhill, and the slope of most roads is less than 12%. The mass of the 1500Kg-2000Kg whole vehicle is simulated at equal intervals of 100Kg and the gradient is simulated at equal intervals of 1% from 0% to 12%, and the damage of the simulated load under each grade parameter is calculated, and the result is shown in figure 3. The functional relationship between the simulated load damage and the equivalent parameter is fitted, and the functional relationship is as follows:
d＝4.079×10-15m2-6.935×10-12m+4.472×10-14m2α-9.848×10-11mα+6.234×10-8α+2.818×10-9 (2)
in the formula (2), m and alpha are the equivalent whole vehicle mass and the equivalent running gradient in the running process of the vehicle.
The whole vehicle mass and gradient value interval considering the actual vehicle state is as follows: m is more than or equal to 1500kg and less than or equal to 2000kg, and alpha is more than or equal to 0 and less than or equal to 12 percent. Using this functional relation, damage calculation was performed for each grade of mass and slope, and the damage fitting error was about 3% at the maximum, as shown in fig. 4.
And step 3: and establishing an optimized mathematical model taking the damage domain and the time domain of the load as targets by combining a complete vehicle dynamics balance equation according to the damage fitting function. Wherein, the establishment of the model comprises the following substeps:
step 3-1, defining the absolute value of the difference of the functional relation between the actual damage and the fitting damage as an optimized objective function of the torque damage:
f1j(m,α)＝|dj-dacj| (3)
in the formula (3), f1j、dj、dacjRespectively is the damage objective function, the damage mathematical model and the actual damage value of the j-th user.
Step 3-2, when the absolute value of the difference between the simulation at any moment and the actual load is taken as a time domain optimization objective function, a large number of equations are introduced to obtain a complex solution; the solution of summing the load difference values at all the moments is provided, so that the calculated amount can be effectively reduced, and a unified solution can be obtained. Since the load is affected by the braking control strategy when the vehicle brakes, the absolute value of the difference between the simulation and the actual driving positive torque at each moment is summed, and the following can be obtained:
Figure BDA0003167287250000081
in the formula (4), njThe total number of times of the drive torque of the jth user; t isi、TaciThe simulated and actual drive torques for the ith occurrence of the user, respectively;
the maximum gradient value is 12%, and within a small range, the following steps can be performed:
mgfcosα≈mgf (5)
from the triangle inequality:
Figure BDA0003167287250000091
according to equations (5) and (6), the time-domain torque distribution optimization objective function for the ith user is defined as:
Figure BDA0003167287250000092
and calculating and summing the collected historical service data to obtain the coefficients of the formula.
And 3-3, taking the road gradient and the whole vehicle mass as variables, and taking a normal value interval of the road gradient and the whole vehicle mass as a constraint condition, wherein the method specifically comprises the following steps:
Figure BDA0003167287250000093
step 3-4, according to the formula (3), the formula (7) and the formula (8), the simulation torque multi-objective optimization problem of the jth user can be described as follows:
Figure BDA0003167287250000094
and (3) performing parameter optimization solution on different users by adopting an NSGA-II improved genetic algorithm by combining an optimization model as a typical multivariable and multi-objective optimization model. And obtaining an equivalent slope and equivalent mass Pareto optimal solution through 200 iterations of each user parameter. The damage precision analysis is carried out on the selected 10 random users, the driving mileage and the time of each user are shown in fig. 5, the corresponding optimal solutions are shown in table 1, wherein the slope solution of the user 5 is the largest, the whole vehicle mass solution of the user 7 is the largest, the slope and the mass solution of the user No. 9 are both in a higher level, and the unit damage strength is high. The maximum error of the simulation load damage and the actual load damage ratio based on parameter optimization is about 4% as shown in fig. 6. The group user simulation and the actual damage are subjected to a logarithm positive distribution model, the cumulative probability distribution is shown in figure 7, and the 95% expected damage levels of the group user simulation and the actual damage are basically consistent.
The user (number 5) with the largest damage in table 1 is taken as an example for explanation, and the frequency distribution of the simulation load under the optimal equivalent parameter is compared with the frequency distribution of the simulation load under the no-load no-gradient state parameter. The relative change of the torque in the two time periods is shown in fig. 8, and the matching degree of the optimized parameter simulation load extreme value and the actual load extreme value is higher. The amplitude and amplitude distribution after the torque normalization processing are shown in fig. 9 and 10, the amplitude distribution of the optimized parameter simulation load is closer to the actual load, the distribution is obvious when the amplitude is larger, and the forward amplitude frequency is obviously improved and is close to the actual amplitude distribution.
TABLE 1
Figure BDA0003167287250000101
And 4, based on the characteristic that the user load damage obeys log normal distribution, defining the average value and the standard deviation of the logarithms obtained by group user simulation and actual torque damage and the absolute value of the difference of the corresponding 95 percent damage as a target function:
Figure BDA0003167287250000111
Figure BDA0003167287250000112
F3＝|(1.6449σ+μ)-(1.6449σac+μac)| (12)
according to the formula 8, the time domain distribution optimization objective functions of the torques of the users are summed to obtain a group time domain distribution as an objective function:
Figure BDA0003167287250000113
and (3) constructing an optimized mathematical model with the variables and the constraints being the same as the formula (9):
minF(m,α)＝{F1,F2,F3,F4}
Figure BDA0003167287250000114
and 5, carrying out optimization solution on the group user model parameters based on historical operating data to obtain a uniform optimal solution for all user equivalent parameters: the equivalent gradient and the equivalent mass are 1779kg and 5.23 percent respectively. The damage of the simulated load and the actual load under the parameter obeys log-normal distribution, the cumulative probability distribution is shown in figure 11, and the 95% horizontal error of the damage of the simulated load and the actual load is 0.94%. On the basis, comparison and verification are carried out by combining with the user operation data in the second year, the target load obtained by the parameter simulation also meets the expected level of 95% of actual load damage, and the error is 1.6%, as shown in FIG. 12.
As shown in fig. 13, the optimal parameters for user No. 5 in step 3 are compared with the simulated torque for the uniform parameters. The actual torque amplitude is in normalized distribution, the distribution of the simulated torque with unified parameters is higher than the actual goodness of fit when the amplitude is less than 0.8 time of the maximum amplitude, and is opposite to the actual goodness of fit when the amplitude is more than 0.8 time of the maximum amplitude.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.

Claims (7)

1. A method for quickly constructing a reliability target load of an electric drive system is characterized by comprising the following steps: the method comprises the following steps:
s1, collecting parameters of an electric drive system, and constructing a complete vehicle dynamics simulation model by combining dynamics balance calculation;
s2, calculating damage by combining an S-N curve and a Miner linear damage accumulation theory based on the whole vehicle dynamics simulation model to obtain function fitting of the damage and equivalent parameters;
the fitting of the function of the simulated load damage and the equivalent parameters comprises: fitting the functional relation between the simulated load damage and the equivalent parameters,
d＝4.079×10-15m2-6.935×10-12m+4.472×10-14m2α-9.848×10-11mα+6.234×10-8α+2.818×10-9
in the formula, m and alpha are equivalent whole vehicle mass and equivalent running gradient in the running process of the vehicle;
s3, obtaining a user optimization model and result analysis by combining the dynamic balance calculation according to the function fitting of the damage and the equivalent parameters;
s4, performing damage domain analysis and time domain analysis on the user optimization model to obtain an optimization model with uniform target function construction parameters;
and S5, acquiring operation data, performing optimization solution on the optimization model with unified parameters to obtain equivalent parameters of an optimal solution, performing simulation processing on the equivalent parameters of the optimal solution to obtain a reliability target load constructed by simulation, and analyzing the reliability target load.
2. The electric drive system reliability target load rapid construction method according to claim 1, characterized in that: the user optimization model in S3 is an optimized mathematical model that targets the damage domain and the time domain of the load.
3. The electric drive system reliability target load rapid construction method according to claim 2, characterized in that: the specific steps of the process of obtaining the user optimization model in S3 include:
step one, obtaining an absolute value f of a difference of a functional relation between actual damage and fitting damage1j(m，α)，f1j(m, α) is defined as the optimal objective function of torque impairment:
f1j(m,α)＝|dj-dacj|
wherein m and alpha are the equivalent whole vehicle mass and the equivalent running gradient in the running process of the vehicle; f. of1j、dj、dacjRespectively a damage target function, a damage mathematical model and an actual damage value of the jth user;
step two, obtaining an absolute value of the difference between the simulation and the actual load at any moment as a time domain optimization objective function, and summing the absolute value of the difference of the actual driving positive torque at each moment, wherein the absolute value is expressed by the following expression:
Figure FDA0003607758570000021
wherein n isjThe total number of times of the drive torque of the jth user; t is a unit ofi、TaciThe simulated and actual driving torques occurring at the ith time of the user are respectively;
step three, obtaining a torque time domain distribution optimization objective function f of the jth user2j(m, alpha), calculating and summing the collected historical service data,
Figure FDA0003607758570000022
wherein i1Eta is the total transmission ratio and the transmission efficiency; r is the tire radius; m is the equivalent mass of the whole vehicle; f is the tire rolling resistance coefficient; cDIs air resistanceA coefficient; a is the windward area; alpha is the equivalent gradient; forming a vehicle body model; v. ofi、aiRespectively the speed and the acceleration of the automobile when the driving torque appears at the ith time;
step four, taking the road gradient and the whole vehicle mass as variables, and taking the normal value interval of the road gradient and the whole vehicle mass as a constraint condition, specifically:
Figure FDA0003607758570000023
obtaining the multi-objective optimization of the simulation torque of the jth user, which specifically comprises the following steps:
min fj(m,α)＝{f1j,f2j}
Figure FDA0003607758570000031
4. the electric drive system reliability target load rapid construction method according to claim 1, characterized in that: in S3, the analysis of the results is that the simulation and the actual damage are subject to a log-positive distribution model.
5. The method for rapidly building reliability target load of electric drive system according to claim 1, wherein: the objective function obtained by analyzing the damage domain in S4 is:
Figure FDA0003607758570000032
Figure FDA0003607758570000033
F3＝|(1.6449σ+μ)-(1.6449σac+μac)|；
the objective function obtained by the time domain analysis in S4 is:
Figure FDA0003607758570000034
6. the method for rapidly building reliability target load of electric drive system according to claim 5, wherein: based on the objective function obtained by the damage domain analysis and the objective function obtained by the time domain analysis, obtaining an optimization model with unified parameters through a variable and constraint algorithm:
min F(m,α)＝{F1,F2,F3,F4}
Figure FDA0003607758570000035
7. the electric drive system reliability target load rapid construction method according to claim 1, characterized in that: the equivalent parameters of the optimal solution in S5 include: equivalent grade and equivalent mass.
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