CN107133374A - A kind of construction method of mode operating mode - Google Patents

Abstract

The invention discloses a method for constructing modal working conditions. The method is based on the results of short-stroke division and cluster analysis of data, adopts multi-objective optimization to solve various modal working condition models, and develops a modal cycle test tool. condition. Through the formulation of the shift schedule, the application range of the test condition is expanded, so that the modal condition can be used not only for the optimization test of the traditional vehicle with transmission, but also for the test simulation of the vehicle without transmission. The errors between the construction working conditions and ECE working conditions and the overall sampling data are analyzed, and the simulation analysis is done on the modal working conditions developed by selecting the real vehicle pair.

Description

A construction method of modal working condition

technical field

The invention belongs to the field of optimization testing of various vehicles in actual road working conditions, and in particular relates to a method for constructing modal working conditions.

Background technique

Vehicle driving conditions reflect the kinematics characteristics of vehicles running in a specific environment, and take the speed-time process as a characteristic form of expression. On the basis of a large amount of measured data, using the principles of mathematical statistics, the driving conditions are classified and sorted out, the complex real driving conditions are simplified as modal conditions, and a modal test cycle with shift points is developed. The main purpose of the modal test cycle is to be used for vehicle optimization testing and simulation, to determine vehicle emissions, fuel consumption, control strategies and parameter matching, etc., and it is a common core technology for automotive research.

Data processing and classification of working conditions are the prerequisites for the construction of modal working conditions. Principal component analysis in multivariate analysis is used to solve multivariate complexity and multivariate correlation problems. Principal component analysis is to construct a series of linear combinations of the original variables, so that each linear combination can reflect as much information as possible of the original variables on the premise that they are not related to each other, even if the variance is the largest. According to different needs, the linear combination that reflects more than 80% or 90% of the original variable information can be used. The cluster analysis method is used to classify the overall segment samples, the purpose of which is to classify the segments with the same traffic characteristics into one category, and then analyze the segments representing different types of roads, and then analyze the driving conditions on the corresponding types of roads.

Contents of the invention

The purpose of the present invention is to overcome the above disadvantages and provide a method for constructing a modal working condition. The modal working condition of the present invention has regular shape, simple shape, good test operability and repeatability, and is convenient for development. The advantages of launching a bench test and conducting real vehicle verification.

In order to achieve the above object, the present invention comprises the following steps:

Step 1, obtaining vehicle road driving test data through a GPS device;

Step 2: Carry out short-stroke division and cluster analysis based on the collected test data;

The third step is to propose simplified models of various modal working conditions. The simplified model of modal working conditions includes the first type of working condition model and is simplified into a low-speed working condition and a high-speed working condition segment. The high-speed working condition performs an upshift acceleration process; the second The second type of working condition simplifies a low-speed working condition and a high-speed working condition with two upshift accelerations; the third type of working condition is simplified as a low-speed working condition and a high-speed working condition with three upshift accelerations and one downshift deceleration;

Step 4: Using the multi-objective optimization theory, optimize the modal working condition model and develop the city bus modal driving condition; select the idle speed ratio F _Pmi , the acceleration ratio F _Pma , the constant speed ratio F _Pmc , the deceleration ratio F _Pmd , The average vehicle speed F _vm , the average running speed F _vmr , the average acceleration F _am and the average deceleration F _dm are used as the optimization calculation of the objective function, through computer programming, using the Matlab optimization toolbox to solve various working conditions, and rounding the time to get The optimized solution of each parameter of each type of modal working condition, according to the optimization result, according to various time ratios, synthesizes the modal driving comprehensive test working condition;

Step 5: Considering the power switching comprehensively, develop a modal test cycle with shift points.

In the first step, the data of the road test of the vehicle obtained through the GPS device includes time t, vehicle speed V(t), and acceleration A(t), wherein the unit of vehicle speed V(t) is km/h, and V(t)≥ 0, the unit of t is s, and t∈N.

In the second step, on the basis of ensuring the accuracy of the original data, the collected data is corrected and eliminated, and based on the principal component analysis and cluster analysis method, three types of representatives of the target area are analyzed: congestion, relatively unobstructed, and unobstructed. Working conditions, and calculate the eigenvalues of various working conditions.

The characteristic values of various working conditions include maximum vehicle speed V _max , minimum vehicle speed V _mean , average vehicle speed V _mr , maximum acceleration A _max , minimum acceleration A _mean , maximum deceleration D _max , minimum deceleration D _mean , 0-10km /h idle speed ratio P0-10, 10-20km/h idle speed ratio P10-20, 20-30km/h idle speed ratio P20-30, 30-40km/h idle speed ratio P30-40, 40-50km/h idle speed ratio P40- 50, 50-60km/h idle speed ratio P50-60, 60-70km/h idle speed ratio P60-70, acceleration ratio P _a , deceleration ratio P _d , constant speed ratio P _c , idle speed ratio P _i .

In the third step, the power switching delay time ts=2s, and the power switching of a transmissionless vehicle is defined as the running time at a constant speed.

After the fourth step is completed, the rationality of the method is analyzed through error analysis and simulation comparison.

Compared with the prior art, the present invention is based on the use of principal component analysis and cluster analysis and other mathematical statistical methods to divide the collected data into three categories, thereby reasonably dealing with multivariate complexity and correlation problems, and integrating the characteristic values of various working conditions A simplified model of various modal conditions is proposed, and the multi-objective optimization theory is used to optimize the calculation of the modal condition model, and a more realistic modal driving condition of the vehicle in the target area is developed to restore the actual driving condition. In order to increase the operability and applicability of the developed modal test conditions, this method takes into account the characteristics of new energy vehicles and traditional vehicles, and comprehensively considers the dynamic switching (gear switching), develops a shift point The modal test cycle is designed to make the developed modal cycle not only meet the test research of new energy vehicles, but also meet the test research of traditional vehicles with transmissions, and provide a theoretical and data basis for subsequent vehicle control strategy optimization and parameter matching.

Description of drawings

Fig. 1 is a flowchart of the present invention.

Figure 2 is the distribution diagram of the actual high and low speed driving conditions of the car and its acceleration inflection points; wherein, the arrows point to high-speed, low-speed segments and acceleration inflection points;

Fig. 3 is a simplified model diagram of the first type of congestion working condition;

Fig. 4 is a simplified model diagram of the second general working condition;

Fig. 5 is a simplified model diagram of the third type of unobstructed working condition;

Fig. 6 is the first kind of congested working condition map after multi-objective optimization;

Fig. 7 is the second type of general operating condition diagram after multi-objective optimization;

Fig. 8 is the diagram of the third type of unobstructed condition after multi-objective optimization;

Fig. 9 is a city bus modal comprehensive test working condition diagram;

Fig. 10 is a city bus modal cycle test working condition diagram of adding shifting rules;

Figure 11 is a comparison chart of emission performance between XA-Mode10 working condition and other typical modal working conditions;

Figure 12 is a comparison chart of fuel consumption per 100 kilometers equivalent between the XA-Mode10 working condition and other typical modal working conditions.

detailed description

The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

Example:

The data collected in this paper are the actual operation data of hybrid electric buses in a certain city. The short-distance division method, principal component analysis, and cluster analysis are used to classify and organize the data. Calculate the proportion of time occupied by various working conditions, construct simplified models of various working conditions, use multi-objective optimization theory, multi-objective weighting to obtain a single objective, solve the optimal solution of the objective function, and construct various modal working conditions, according to the time ratio Synthesize a complete urban modal working condition, take into account the dynamic switching, add the shift schedule, and obtain the modal working condition test cycle history. Carry out driving verification: error analysis, simulation comparison, analysis and explanation of the rationality of building modal conditions.

Referring to Figure 1, the detailed steps are described below:

Step 1: Data collection and sorting.

The vehicle road driving test data is obtained through the GPS device, including time t, vehicle speed V(t), and acceleration A(t). Using principal component analysis and cluster analysis to classify and arrange, the eigenvalues of various working conditions are shown in Table 1.

Step 2: Establishment of the modal working condition optimization model.

Cars running on actual roads can usually be simplified into four working conditions: acceleration, deceleration, constant speed and parking (idling speed). Usually, a car does not perform power switching when driving under low-speed conditions, but generally needs to perform power switching under high-speed conditions. Through the analysis of transient driving conditions, high-speed and low-speed segments are distinguished, and the acceleration inflection point of high-speed conditions is marked, as shown in Figure 2.

It can be seen from Fig. 2 that the low-speed running section of a city bus can be simplified into four working conditions: acceleration, deceleration, constant speed and idling (stopping); when driving at high speed, acceleration changes and power demand changes caused by power switching should be considered (Fig. 2 shows acceleration inflection point). Combined with Table 1, taking into account the actual driving characteristics of various working conditions and the influence of the stop (idling speed) ratio caused by traffic lights and getting on and off at passenger stations, this paper simplifies the model of the first type of working condition (congestion working condition) into a low-speed working condition condition and a segment of high-speed working condition, an upshift acceleration process is performed under high-speed working condition, as shown in Figure 3. The second type of working condition (general working condition) simplifies a low-speed working condition and a high-speed working condition with two upshifts, as shown in Figure 4. The third type of working condition (unobstructed working condition) is simplified as a low-speed working condition and a high-speed working condition with three upshifts for acceleration and one downshift for deceleration, as shown in Figure 5. In order to facilitate the experimental operation under the modal cycle test conditions, and refer to the ECE standard cycle, the power switching delay time (or shift time) is set as ts=2s (the power switching of the car without transmission is defined as the running time at a constant speed).

Step 3. Using the multi-objective optimization theory, the modal working condition model is optimized and calculated, and the urban bus modal driving working condition is developed;

Based on the above-mentioned models of various working conditions, the optimization variables, objective functions and constraints of the above three types of working conditions are analyzed as follows:

The first type of working condition: For the first type of working condition, it is necessary to optimize the running time and running speed of each operation segment, a total of 12 variables. In this way, the optimization variables can be expressed as: Idle ratio (F _Pmi ), acceleration ratio (F _Pma ), constant speed ratio (F _Pmc ), deceleration ratio (F _Pmd ), average vehicle speed (F _vm ), average running vehicle speed (F _vmr ), the average acceleration (F _am ), and the average deceleration (F _dm ) are 8 statistically important indicators that are optimized and calculated as the objective function. According to the geometric meaning, the graphic area in Figure 3 is the running distance Sc, so the objective function is expressed as:

According to the collected maximum vehicle speed, the maximum vehicle speed is defined as not exceeding 70km/h, and the maximum vehicle speed of the low-speed working condition of each working condition section is lower than the maximum vehicle speed of the high-speed section. According to Table 1, it is defined that the maximum acceleration and deceleration of each working condition of the first type of working condition shall not exceed 3m/s2, and the acceleration acceleration of the upshift is reduced, and the speed of the deceleration part of the downshift is reduced. It is defined that each speed should be greater than 0, and the other defined The maximum speed does not exceed 40km/h, and each time is not less than 2s. , so that the constraints are obtained:

The second type of working condition: there are 14 optimization variables, which can be expressed as: X _n = (T _n1 ,T _n2 ,...,T _n10 ,V _n1 ,V _n2 ,...,V _n4 ) ₁₄ , the objective function for:

According to Table 1, it is defined that the maximum acceleration and deceleration of the second type of working condition shall not exceed 3m/s2, and the acceleration acceleration of upshifts shall be reduced. All speeds shall be greater than 0, and the maximum vehicle speed shall not exceed 55km/h. The duration of the condition is not less than 2s, so the constraints are obtained:

The third type of working condition: there are 18 optimization variables, which can be expressed as X _s = (T _s1 ,T _s2 ,...,T _s12 ,V _s1 ,V _s2 ,...,V _s6 ) ₁₈

The objective function is:

According to Table 1, it is defined that the maximum acceleration and deceleration of the third type of working condition is not more than 3m/s2, and the acceleration acceleration of upshift is reduced, and the deceleration of downshift is increased. It is defined that each vehicle speed should be greater than 0, and its maximum speed is defined as not Over 70km/h, each time is not less than 2s. , so that the constraints are obtained:

In the above equations, T _ji , T _ja , T _jc , Tj _d , V _jm , V _jmr , A _jm , D _jm (j=c,n,s) respectively denote the converted experimental data congestion working condition (Congestion), general working condition Average idle time, acceleration time, constant speed time, deceleration time, average vehicle speed, average running speed, average acceleration and average deceleration in Normal and Smooth conditions.

Now solve the above three types of objective variables. Since the above eight objective functions reflect the important characteristics of the vehicle driving conditions and have a parallel relationship, this paper uses the weighting method to transform the multi-objective into a single-objective solution. The conversion comprehensive optimization target F method is as follows:

F＝ω ₁ F _pmi +ω ₂ F _pma +ω ₃ F _pmc +ω ₄ F _pmd +ω ₅ F _vm +ω ₆ F _vmr +ω ₇ F _am +ω ₈ F _dm

Among them, ω1-ω8 are the weight factors of the above eight sub-objectives. Since the above eight sub-objectives describe the developed working conditions from the aspects of velocity, velocity distribution and acceleration, this paper considers them to be quite important. For convenience, this paper Take their weight factors to be equal, that is, take:

ω ₁ =ω ₂ =ω ₃ =ω ₄ =ω ₅ =ω ₆ =ω ₇ =ω ₈ =0.125

Through computer programming, use the Matlab optimization toolbox to solve the various working conditions in Table 1, and round the time to get the optimal solution of each parameter of each type of modal working condition. According to the optimization results, various working conditions are shown in Figure 6-Figure 8, and according to various time ratios, a comprehensive modal driving test working condition is synthesized, named XA-Mode10, as shown in Figure 9. The eigenvalues of the working conditions of the modal driving comprehensive test are shown in Table 2.

The optimized solution vector is: X _c = (68,6,6,10,68,7,11,11,11,14.07,14.98,38.60)

X _n = (35,37,33,36,35,5,4,3,2,5,30.37,35.53,48.6,55);

Xs = ( _{24,38,41,38,24,6,5,5,2,2,5,2,27.54,27.54,49.82,66.4,70,18.06} );

Step 4. Considering the dynamic switching comprehensively, develop a modal test cycle with shift points;

Set the shift schedule for the above modal conditions. Considering the characteristics of various working conditions, it can be seen from Fig. 6 that the overall congestion of the first working condition is relatively large, and the acceleration and deceleration speeds are relatively large, so it is set to accelerate at the first gear. It can be seen from Figure 7 and Figure 8 that the second and third types of working conditions are relatively smooth, each containing a cycle of low-speed segments and a dynamic test segment of rapid acceleration and deceleration, and the overall acceleration of the low-speed section is flat, and the high-speed section The overall acceleration is relatively large, so it is set as the second and third types of working conditions to accelerate from the second gear in the low-speed section, and start to accelerate in the first gear in the high-speed section. In this way, the modal driving test cycle is obtained, as shown in Table 3, and the modal test working condition with the shift rule is shown in Figure 10.

In order to evaluate the driving conditions of urban buses in a certain city, select V _mean , V _mr , _{V sd} , A _mean , D _mean , A _sd , P _a , P _d , P _c , and Pi in total 10 A characteristic parameter with a relatively large influence and statistical significance is used for error evaluation. The calculation formulas of each index error δ _i , the total cumulative error δ and the average error are as follows:

It can be seen from Table 4 that the average error of the XA-MBUS working condition is only 10.8%, and the difference between the ECE working condition and the urban bus working condition of a certain city is relatively large, and the average error is as high as 24.79%. This shows that the modal condition construction method used in this paper can more truly reflect the actual driving characteristics of the vehicle.

In order to further analyze the differences between the XA-MBUS working condition and the ECE working condition, ECE+EUDC, and Japan10-15 three modal working conditions, the Higer KLQ6129GCHEV hybrid prototype vehicle was selected for simulation analysis based on ADVISOR. Simulation analysis of vehicle emissions and energy consumption under several working conditions is shown in Figure 11 and Figure 12, respectively. It can be seen from the figure that the HC, CO and NOx emissions of the prototype vehicle in the ECE working condition are higher than other typical working conditions, and the XA-MBUS emission is closer to the Japanese working condition. The equivalent fuel consumption per 100 kilometers of the prototype vehicle is 24.7L under the XA-MBUS working condition (slope is set to 0), which is higher than the other three typical modal working conditions. This shows that the modal conditions constructed by this method are reasonable in terms of vehicle emissions and energy consumption.

Table 1 Comparison of various average characteristic values of urban buses in a certain city

Table 2 The eigenvalues of the urban bus modal test conditions in a certain city

T/(S) T _a /(s) T _d /(s) T _c /(s) T _i /(s) S/(km) _Vmax V _m V _mr 1000 242 200 218 358 4.18 70 15.05 23.43 V _{sd Amax} A _{mean Dmax} D _mean A _sd P _0-10 P _{10-20 P20-30} 16.51 1.64 0.494 -3.06 -0.598 0.556 0.128 0.146 0.235 _{P30-40 P40-50 P50-60 P60-70 Pa} P _d P _c P _i 0.065 0.024 0.018 0.027 0.241 0.200 0.211 0.358

Table 3 modal working condition test cycle history table

The I, II, III, and IV gears marked in brackets are all for passenger cars with transmissions. For some hybrid electric passenger cars or pure electric passenger cars without transmissions, the shift points are regarded as driving at a constant speed.

Table 4 Comparison of construction working conditions and ECE working conditions with urban bus sampling data errors in a certain city

Claims (6)

1. a kind of construction method of mode operating mode, it is characterised in that comprise the following steps：

Step one, road vehicle running test data are obtained by GPS device；

Step 2, short stroke division and clustering are carried out based on the test data collected；

Step 3, proposes all kinds of mode operating mode simplified models, and mode operating mode simplified model is reduced to including first kind condition model One speed operation and a high-speed working condition fragment, high-speed working condition carry out a upshift accelerator；Equations of The Second Kind operating mode simplifies one Individual speed operation and the high-speed working condition of a upshift acceleration twice；3rd class operating mode be reduced to a speed operation and one three times The high-speed working condition that upshift accelerates and a downshift is slowed down；

Step 4, using multi-objective optimization theory, calculating is optimized to mode condition model, develops city bus mode row Sail operating mode；Choose idling ratio F_Pmi, accelerate ratio F_Pma, at the uniform velocity ratio F_Pmc, deceleration ratio F_Pmd, average speed F_vm, average fortune Speed of driving a vehicle F_vmr, average acceleration F_amWith average retardation rate F_dmCalculate, by computer programming, use as objective function optimization Matlab Optimization Toolboxes, to all kinds of operating modes solve, and time rounding is obtained every class mode operating mode parameters optimization solution, According to optimum results, according to all kinds of time scales, synthesize mode traveling integration test operating mode；

Step 5, considers dynamic property switching, develops the mould measurement circulation of shifting points.

2. the construction method of a kind of mode operating mode according to claim 1, it is characterised in that in the step one, pass through The data that GPS device obtains road vehicle running test include time t, vehicle velocity V (t), acceleration A (t), and wherein vehicle velocity V (t) is single Position is km/h, and V (t) >=0, t unit is s, and t ∈ N.

3. the construction method of a kind of mode operating mode according to claim 1, it is characterised in that in the step 2, for Gathered data carries out abnormal data amendment and rejecting, based on principal component analysis on the basis of initial data accuracy is ensured And clustering methodology, parse that target area congestion, comparison be unobstructed, unobstructed three class represents operating mode, and calculates all kinds of operating modes Characteristic value.

4. a kind of construction method of mode operating mode according to claim 3, it is characterised in that the feature of all kinds of operating modes Value includes max. speed V_max, minimum vehicle velocity V_mean, average speed V_mr, peak acceleration A_max, minimum acceleration A_mean, maximum subtracts Speed D_max, minimum deceleration degree D_mean, 0-10km/h idling ratio P0-10,10-20km/h idling ratios P10-20,20-30km/ H idling ratio P20-30,30-40km/h idling ratio P30-40,40-50km/h idling ratio P40-50,50-60km/h idling Ratio P50-60,60-70km/h idling ratio P60-70, acceleration ratio P_a, deceleration ratio P_d, at the uniform velocity ratio P_c, idling ratio P_i。

5. the construction method of a kind of mode operating mode according to claim 1, it is characterised in that in the step 3, power Property switching delay time ts=2s, the switching of no speed changer automobile power is defined as the time of traveling at the uniform speed.

6. the construction method of a kind of mode operating mode according to claim 1, it is characterised in that after the completion of the step 4, Reasonability by error analysis and emulation relatively to method is analyzed.