Automobile air conditioning system performance evaluation method based on data envelope analysis

Abstract

The invention discloses an automobile air conditioning system performance evaluation method based on data envelope analysis. The method comprises the steps of 1, grouping test sample automobiles according to a consistency principle; 2, collecting and sorting performance test data of the air-conditioning system to form a performance evaluation index system of the air-conditioning system of the test sample car; and 3, evaluating the relative efficiency of the performance of the air-conditioning system of the test sample car based on data envelopment analysis (DEA), analyzing the redundancy of each input index and output index of the performance of the air-conditioning system, calculating the redundancy proportion, and giving targeted improvement suggestions. According to the method, the performance of the automobile air-conditioning system is evaluated based on the analysis thought of a multi-input and multi-output system, the relative efficiency and redundancy proportion of the performance of the air-conditioning system of the test sample automobile are calculated according to the DEA-BCC model, the short board of the performance of the air-conditioning system can be analyzed, and a direction is pointed out for improving measures.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile air-conditioning system performance evaluation, and in particular, relates to an automobile air-conditioning system performance evaluation method based on data envelopment analysis (DEA). Background Art

[0002] With the rapid development of the automobile industry and the improvement of people's living standards, car air conditioners are gaining more and more attention from consumers. As the air conditioning device in the passenger compartment of a car, car air conditioners can control the temperature, humidity, cleanliness and flow speed of the air in the passenger compartment, provide a comfortable environment for the driver and passengers, reduce travel fatigue and improve driving safety.

[0003] At present, automobile OEMs generally conduct automobile air-conditioning system performance tests based on QC / T 658 "Automobile Air Conditioning Vehicle Cooling Performance Test Method", but this method does not make clear instructions on how to evaluate the performance of automobile air-conditioning systems. Most OEMs only use the average facial temperature of occupants after each test stage as an indicator for evaluating the performance of air-conditioning systems. This evaluation method is one-sided and limited, and cannot objectively and comprehensively reflect the performance of automobile air-conditioning systems.

[0004] The performance evaluation of automobile air-conditioning systems should include many evaluation indicators. In addition to the average temperature of the occupants' faces, there should also be the average temperature of the air-conditioning outlets, the average wind speed at the air-conditioning outlets, the air tightness of the entire vehicle, etc. These evaluation indicators jointly affect the performance of the automobile air-conditioning system.

[0005] Data envelopment analysis (DEA) is a method used to evaluate the relative effectiveness of a multi-input and multi-output indicator system. The evaluation indicators do not need to have the same measurement unit and properties, nor do they need to confirm the weights of each evaluation indicator. Therefore, the use of data envelopment analysis (DEA) can more objectively, comprehensively and simply evaluate the performance of automobile air-conditioning systems.

[0006] By using data envelopment analysis (DEA), the balance of the air-conditioning system performance of the test sample vehicle can be calculated and analyzed. Combined with the specific conditions of the redundant variables and redundant ratios of each evaluation indicator, the shortcomings of the air-conditioning system performance can be analyzed, which points out the direction for improving the performance of the air-conditioning system. Summary of the invention

[0007] Therefore, the purpose of the present invention is to provide a method for evaluating the performance of an automobile air-conditioning system based on data envelopment analysis. This method can be used to obtain the relative efficiency of the air-conditioning system performance of a test sample vehicle. Combined with the redundant variables and redundant ratios of various evaluation indicators, the improvement potential of each evaluation indicator can be obtained, thereby providing an improvement direction for improving the balance of the performance of the automobile air-conditioning system.

[0008] The technical solution of the invention is as follows:

[0009] A method for evaluating the performance of an automobile air conditioning system based on data envelopment analysis, characterized in that it comprises the following steps:

[0010] Step 1: Group the test vehicles according to the wheelbase, body type, air outlet type and refrigeration principle consistency principle;

[0011] Step 2: Collect and organize basic vehicle information and air conditioning system performance test data of the same group of test vehicles to form an air conditioning system performance evaluation index system for the test vehicles;

[0012] Among them, the evaluation indicators include 4 items: average temperature of occupants’ faces, average temperature of air outlets, average wind speed at air outlets, and air tightness of the whole vehicle;

[0013] Step 3: Select the test data of the same group of test vehicles under the same driving conditions as the original evaluation data, and evaluate the relative efficiency of the air-conditioning system performance of the test vehicles based on data envelopment analysis.

[0014] Furthermore, the average facial temperature of the occupants and the average temperature of the air outlet in step 2 are used as input indicators of the data envelopment analysis model, and the average wind speed at the air outlet and the air tightness of the whole vehicle are used as output indicators of the data envelopment analysis model, and each indicator is quantified:

[0015] (1) Average occupant facial temperature refers to the average facial temperature of all occupants of the test vehicle, in degrees Celsius;

[0016] (2) Average air outlet temperature refers to the average temperature of all air-conditioning outlets of the test vehicle, in degrees Celsius;

[0017] (3) Average air velocity at the air outlet refers to the average air velocity at all air-conditioning outlets of the test vehicle, in m / s;

[0018] (4) The air tightness of the whole vehicle refers to the air leakage of the passenger compartment when the internal and external pressure difference is 250 Pa, 1000÷1000. The result is a constant value without units.

[0019] Further, step 3 includes the following steps:

[0020] (1) Model building

[0021] Using the DEA-BCC model with a non-Archimedean infinitesimal ε, the planning equation of the model is:

[0022]

[0023] Where:

[0024] θ is the distance from the test vehicle, i.e. the decision-making unit, to the effective boundary, and is also the relative efficiency index based on the data envelope.

[0025] ε is a non-Archimedean infinitesimal,

[0026] n is the number of decision making units,

[0027] s - is a redundant variable of m input indicators. In the present invention, the average temperature of the occupant's face and the average temperature of the air outlet are set as input indicators.

[0028] s + is a redundant variable of the s-item output index. In the present invention, the average wind speed at the air outlet and the air tightness of the whole vehicle are defined as output indicators.

[0029] x j is the input index value of the decision-making unit, x0 is the initial value of the input index,

[0030] y j is the output index value of the decision-making unit, y0 is the initial value of the output index,

[0031] λ j is the weight coefficient;

[0032] (2) Determination and statistics of DEA effectiveness of air conditioning system performance of test vehicle

[0033] Solve the DEA-BCC model planning equation and get the decision unit j i The optimal solutions are θ i ,s i - ,s i + ,

[0034] If θ i =1, then decision unit j i It is effective for DEA;

[0035] If θ i <1, then decision unit j i Invalid for DEA;

[0036] DEA's effective decision-making units indicate that the air-conditioning system has balanced performance, while DEA's invalid decision-making units indicate that the air-conditioning system has redundant performance.

[0037] Furthermore, for the test sample vehicle with redundant air-conditioning system performance, the redundancy of each input index and output index of the air-conditioning system performance is analyzed, and the redundancy ratio is calculated:

[0038] (1) Calculate the redundancy ratio of the test vehicle input index according to the following formula:

[0039] I mi =s - mi / xmi

[0040] Among them, s - mi is the redundant variable of the mth input index of the i-th test vehicle, x mi is the value of the mth input index of the i-th test vehicle, I mi That is, the redundancy ratio of the mth input index of the i-th test vehicle;

[0041] (2) Calculate the redundant ratio of the test vehicle output index according to the following formula:

[0042] I si =s + si / y si

[0043] Among them, s + si is the redundant variable of the sth output index of the i-th test vehicle, y si is the value of the sth output index of the i-th test vehicle, I si That is, the redundancy ratio of the sth output index of the i-th test vehicle;

[0044] The redundant proportions of input indicators and output indicators of the DEA invalid test sample vehicles are sorted and counted respectively, and targeted improvement suggestions are given.

[0045] The beneficial technical effect embodied by the present invention is: the present invention provides an objective and comprehensive automobile air-conditioning system performance evaluation method. The mathematical model based on data envelopment described in the present invention obtains a judgment on the effectiveness of DEA by calculating and analyzing the input index and output index of the air-conditioning system performance of the test sample vehicle; and gives targeted suggestions based on the redundant ratio of the input and output indicators of the DEA invalid test sample vehicle, so as to quickly improve the air-conditioning system performance of the test sample vehicle, make the air-conditioning system performance more balanced, effectively improve the research and development efficiency, and avoid the insufficiency and redundancy of evaluation indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention is a flow chart of automobile air conditioning system performance evaluation. DETAILED DESCRIPTION

[0047] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings and specific embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The present invention provides a method for evaluating the performance of an automobile air conditioning system based on data envelopment analysis (DEA). Figure 1 As shown, the following steps are included:

[0049] Step 1: Group the test vehicles according to wheelbase, body type, air outlet type and refrigeration principle. For example, the wheelbase should be within a certain range (such as all A-class vehicles or B-class vehicles), the body type should be consistent (such as all hatchbacks or sedans), the air outlet type should be consistent (such as traditional air outlets), and the refrigeration principle should be consistent (such as all mechanical fixed-frequency compressors or electric compressors) to ensure that the test vehicles are comparable.

[0050] Step 2: Collect and organize the basic vehicle information and air conditioning system performance test data of the same group of test vehicles, quantify the data, and form an air conditioning system performance evaluation index system for the test vehicles. The evaluation index includes four items: average face temperature of occupants, average temperature of air outlets, average wind speed at air outlets, and air tightness of the whole vehicle;

[0051] Step 3: According to the provisions of QC / T 658 "Automobile Air Conditioning Cooling Performance Test Method", the test data of the same group of test vehicles at the end of the 100km / h driving condition are selected as the evaluation original data, and the relative efficiency of the test vehicle air conditioning system performance is evaluated based on data envelopment analysis (DEA);

[0052] Step 4: Based on data envelopment analysis (DEA), analyze the redundancy of each input index and output index of the air-conditioning system performance, calculate the redundancy ratio, and give targeted improvement suggestions.

[0053] In the above step 1, for example, a group of test vehicles are selected according to the following criteria:

[0054]

[0055] In step 2 above, the indicator quantification is as follows:

[0056] 1) Average facial temperature of occupants X1, which refers to the average facial temperature of all occupants of the test vehicle, in °C;

[0057] 2) Average air outlet temperature X2, which refers to the average temperature of all air-conditioning outlets of the test vehicle, in °C;

[0058] 3) Average air velocity at the air outlet Y1, which refers to the average air velocity at all air-conditioning outlets of the test vehicle, in m / s;

[0059] 4) The whole vehicle air tightness Y2 refers to the whole vehicle air tightness of the test sample vehicle, specifically 1000÷passenger compartment air leakage (when the internal and external pressure difference is 250pa). The result is taken as a constant value without unit.

[0060] In step 3 above, the method for evaluating the relative efficiency of the air conditioning system performance of the test vehicle based on data envelopment analysis (DEA) is:

[0061] (1) Model building

[0062] Using the DEA-BCC model (with non-Archimedean infinitesimal ε), the model planning equation is:

[0063]

[0064] In this article:

[0065] θ is the distance from the test vehicle to be evaluated (i.e., decision-making unit) to the effective boundary, which is also the relative efficiency index based on the data envelope. The effective boundary can be understood as the boundary formed by the most efficient points.

[0066] n is the number of decision-making units;

[0067] ε is a non-Archimedean infinitesimal;

[0068] s - is a redundant variable for m input indicators, s + It is a redundant variable of s output indicators. According to the basic principle of data envelopment analysis method, the indicator that is negatively correlated with the performance level of the air conditioning system of the test sample vehicle is used as the input indicator, and the indicator that is positively correlated with the performance level of the air conditioning system of the test sample vehicle is used as the output indicator. Combined with specific test data, that is, the average temperature of the occupant's face and the average temperature of the air outlet are input indicators, and the average wind speed at the air outlet and the air tightness of the whole vehicle are output indicators, the redundant variables are obtained by the ready-made software DEAP software used for data envelopment analysis;

[0069] x j is the input index value of the decision-making unit, and x0 is the initial value of the input index;

[0070] y j is the output index value of the decision-making unit, and y0 is the initial value of the output index;

[0071] λ j is the weight coefficient.

[0072] (2) Determination and statistics of DEA effectiveness of air conditioning system performance of test vehicle

[0073] Solving the DEA-BCC model planning equation, we can get the decision unit j i The optimal solutions are θ i ,s i - ,s j + , statistics of θ,s of each test vehicle- ,s + ;

[0074] If θ i =1, then decision unit j i It is effective for DEA;

[0075] If θ i <1, then decision unit j i Not valid for DEA.

[0076] For example: According to the DEA-BCC model planning equation, 20 test vehicles that meet the vehicle parameter requirements are selected, among which the basic data of the average occupant face temperature X1, the average air outlet temperature X2, the average air outlet wind speed Y1 and the vehicle air tightness Y2 are shown in Table 1:

[0077] Table 1 Basic data of performance evaluation index of air conditioning system of test sample vehicle

[0078]

[0079]

[0080] Using the DEA-BCC model, the software DEAP1.2 was used to obtain the relative efficiency index θ of the air conditioning system performance of each test vehicle and the redundant variables s of each input index. - And each output index redundant variable s + , as shown in Table 2:

[0081] Table 2 Relative efficiency index and index redundancy variables of the air conditioning system performance of the test vehicle

[0082]

[0083]

[0084] It can be seen from Table 2 that the relative efficiency index of the air-conditioning system performance of 7 test vehicles, namely 1#, 3#, 6#, 11#, 12#, 16# and 18#, is 1, that is, DEA is effective, and the relative efficiency index of the air-conditioning system performance of the remaining 13 test vehicles is less than 1, that is, DEA is invalid.

[0085] In step 4, assuming that the data of the above 20 test vehicles are taken as an example, 13 test vehicles with invalid air conditioning system performance DEA are taken as research objects, and the input index redundancy ratio and output index redundancy ratio (excluding indicators with redundant variables of 0) are calculated according to the following formula:

[0086] I mi =s - mi / x mi

[0087] I si =s + si / y si

[0088] Among them, s - mi is the redundant variable of the mth input index of the i-th test vehicle, x mi is the value of the mth input index of the i-th test vehicle, I mi It is the redundancy ratio of the mth input index of the ith test vehicle. For the test vehicle with effective air conditioning system performance DEA, the redundancy ratio of its input index is 0. For the test vehicle with invalid air conditioning system performance DEA, at least one of its input index redundancy ratios is not 0.

[0089] Among them, s + si is the redundant variable of the sth output index of the i-th test vehicle, y si is the value of the sth output index of the i-th test vehicle, I si It is the redundancy ratio of the sth output index of the i-th test sample. For the test sample with effective air-conditioning system performance DEA, its output index redundancy ratio is 0. For the test sample with invalid air-conditioning system performance DEA, at least one of its output index redundancy ratios is not 0.

[0090] The calculation results are sorted according to the specific evaluation indicators, as shown in Table 3, Table 4, Table 5, and Table 6:

[0091] Table 3 Ranking of the results of the average facial temperature of the test sample vehicle occupants X1 redundancy ratio

[0092]

[0093]

[0094] For example: The redundant variable of the average facial temperature of the occupants of the 14# test sample car is 3.424, and the average facial temperature of the occupants is 22.12. The redundant ratio = 3.424 / 22.12*100% = 15.48%.

[0095] Table 4 Ranking of test sample vehicle air outlet average temperature X2 redundancy ratio results

[0096]

[0097] For example: The redundant variable of the average temperature of the air outlet of the 8# test sample car is 1.876, and its average air outlet temperature is 7.69, and its redundant ratio = 1.876 / 7.69*100% = 24.40%.

[0098] Table 5 Ranking of the results of the average wind speed Y1 redundancy ratio at the air outlet of the test vehicle

[0099]

[0100]

[0101] For example: The redundant variable of the average wind speed at the air outlet of the 20# test sample car is 0.352, and its average wind speed at the air outlet is 7.1, and its redundancy ratio = 0.352 / 7.1*100% = 4.96%.

[0102] Table 6 Ranking of test sample vehicle air tightness Y2 redundancy ratio results

[0103]

[0104] For example: The redundant variable of the air tightness of the 5# test sample vehicle is 0.486, and the air tightness of the vehicle is 4.57, and its redundancy ratio = 0.486 / 4.57*100% = 10.63%.

[0105] Result analysis:

[0106] (1) The relative efficiency of the air-conditioning system performance of 7 out of 20 test vehicles (1#, 3#, 6#, 11#, 12#, 16#, and 18#) reached DEA effectiveness, indicating that the air-conditioning system of these 7 test vehicles has high efficiency and good balance, and there is no redundancy in the input and output indicators.

[0107] (2) For the test sample vehicle where the DEA of air conditioning system performance relative efficiency is invalid, corresponding improvement suggestions are given according to the redundancy ratio of each input index and output index. The details are as follows:

[0108] a) The X1 redundancy ratio of the average facial temperature of the occupants in the 14# test sample car is the highest, and this indicator has the largest room for improvement. The performance of the air conditioning system can be further improved by optimizing the occupant human-machine model and improving the layout of the air conditioning outlets.

[0109] b) The average temperature X2 redundancy ratio of the air outlet of the 8# test sample car is the highest, and this indicator has the largest room for improvement. The performance of the air conditioning system can be further improved by optimizing the air conditioning system pipeline layout, increasing the compressor power, and increasing the evaporator area.

[0110] c) The average wind speed Y1 redundancy ratio of the 20# test sample car outlet is the highest, and this indicator has the largest room for improvement. The performance of the air conditioning system can be further improved by increasing the blower power and optimizing the air duct layout;

[0111] d) The 5# test vehicle has the highest Y2 redundancy ratio of vehicle air tightness, and this indicator has the largest room for improvement. The performance of the air-conditioning system can be further improved by replacing sealing strips and other sealing parts, improving the vehicle assembly process, etc.

Claims

1. A method for evaluating automobile air conditioning system performance based on data envelopment analysis, characterized in that: The steps include: Step 1: Group the test vehicles according to the wheelbase, body type, air outlet type and refrigeration principle consistency principle; Step 2: Collect and organize basic vehicle information and air conditioning system performance test data of the same group of test vehicles to form an air conditioning system performance evaluation index system for the test vehicles; Among them, the evaluation indicators include 4 items: average temperature of occupants’ faces, average temperature of air outlets, average wind speed at air outlets, and air tightness of the whole vehicle; Step 3: Select the test data of the same group of test vehicles under the same driving conditions as the original evaluation data, and evaluate the relative efficiency of the air-conditioning system performance of the test vehicles based on data envelopment analysis.

2. The automobile air conditioning system performance evaluation method according to claim 1, characterized in that: In step 2, the average facial temperature of the occupants and the average temperature of the air outlet are used as input indicators of the data envelopment analysis model, and the average wind speed at the air outlet and the air tightness of the whole vehicle are used as output indicators of the data envelopment analysis model. Quantify each indicator: (1) Average occupant facial temperature refers to the average facial temperature of all occupants of the test vehicle, in degrees Celsius; (2) Average air outlet temperature refers to the average temperature of all air-conditioning outlets of the test vehicle, in degrees Celsius; (3) Average air velocity at the air outlet refers to the average air velocity at all air-conditioning outlets of the test vehicle, in m / s; (4) The air tightness of the whole vehicle refers to the air leakage of the passenger compartment when the internal and external pressure difference is 250 Pa, 1000÷1000. The result is a constant value without units.

3. The automobile air conditioning system performance evaluation method according to claim 1, characterized in that: Step 3 includes the following steps: (1) Model building Using the DEA-BCC model with a non-Archimedean infinitesimal ε, the planning equation of the model is: Where: θ is the distance from the test vehicle, i.e. the decision-making unit, to the effective boundary, and is also the relative efficiency index based on the data envelope. ε is a non-Archimedean infinitesimal, n is the number of decision making units, s - is a redundant variable of m input indicators. In the present invention, the average temperature of the occupant's face and the average temperature of the air outlet are set as input indicators. s + is a redundant variable of the s-item output index. In the present invention, the average wind speed at the air outlet and the air tightness of the whole vehicle are defined as output indicators. x j is the input index value of the decision-making unit, x0 is the initial value of the input index, y j is the output index value of the decision-making unit, y0 is the initial value of the output index, λ j is the weight coefficient; (2) Determination and statistics of DEA effectiveness of air conditioning system performance of test vehicle Solve the DEA-BCC model planning equation and get the decision unit j i The optimal solutions are θ i ,s i - ,s i + , If θ i =1, then decision unit j i It is effective for DEA; If θ i <1, then decision unit j i Invalid for DEA; DEA's effective decision-making units indicate that the air-conditioning system has good performance, while DEA's invalid decision-making units indicate that the air-conditioning system has redundant performance.

4. The automobile air conditioning system performance evaluation method according to claim 1 or 3, characterized in that: For the test sample vehicle with redundant air-conditioning system performance, further analyze the redundancy of each input index and output index of the air-conditioning system performance, and calculate the redundancy ratio: (1) Calculate the redundancy ratio of the test vehicle input index according to the following formula: I mi =s - mi / x mi Among them, s - mi is the redundant variable of the mth input index of the i-th test vehicle, x mi is the value of the mth input index of the i-th test vehicle, I mi That is, the redundancy ratio of the mth input index of the i-th test vehicle; (2) Calculate the redundant ratio of the test vehicle output index according to the following formula: I si =s + si / y si Among them, s + si is the redundant variable of the sth output index of the i-th test vehicle, y si is the value of the sth output index of the i-th test vehicle, I si That is, the redundancy ratio of the sth output index of the i-th test vehicle; The redundant proportions of input indicators and output indicators of the DEA invalid test sample vehicles are sorted and counted respectively, and targeted improvement suggestions are given.

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