Key performance index evaluation method for intelligent automobile in high and cold environment

Abstract

The invention provides a key performance index evaluation method for an intelligent automobile in a high and cold environment, and the method comprises the steps: determining a key performance index according to a key performance test item of the intelligent automobile in the high and cold environment, and constructing an evaluation index system according to the determined key performance index; determining the weight of each key performance index in the evaluation index system; various key performance expressions of the intelligent automobile in the high and cold environment are tested, and a test result is obtained; according to the test result, the evaluation index system and the weight of each key performance index, the key performance of the intelligent automobile in the high and cold environment is comprehensively evaluated, and according to the method, the key performance index evaluation system of the intelligent automobile in the high and cold environment is constructed from multiple dimensions of driving performance, safety performance, economic performance and the like. Developers can be helped to timely discover the performance defects of the intelligent automobile in the high and cold environment so as to optimize the intelligent automobile, and application of the intelligent automobile represented by a new energy automobile in the northern area can be promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile performance testing, and in particular to a method for evaluating key performance indicators of an intelligent automobile in a high-cold environment. Background Art

[0002] Smart cars are a comprehensive system that integrates environmental perception, planning and decision-making, and multi-level assisted driving. It uses computers, modern sensors, information fusion, communications, artificial intelligence, and automatic control technologies, and is a high-tech complex. The development of smart cars is the result of the deep integration of the automotive industry and information technology, and represents the direction of automotive technology progress. In order to ensure the rapid and stable development of smart cars, their performance must be repeatedly tested.

[0003] With the advancement of technology, the integration trend of new energy vehicles and smart cars is becoming more and more obvious. On the one hand, the power system of electric vehicles is more suitable for integrating advanced electronic control systems, which provides a good foundation for realizing intelligence. On the other hand, the development of intelligent technology has also brought better user experience and higher safety to electric vehicles. The sales volume of new energy vehicles has always been "high in the south and low in the north". This is because new energy vehicles have problems such as short driving range at low temperatures, long charging time, and high electricity consumption for air conditioning and heating. Therefore, the key performance testing and evaluation methods of new energy vehicles in high-cold environments are crucial to the development of new energy vehicles. At present, none of the projects with greater evaluation influence in the industry involve this aspect, and the industry urgently needs to fill the relevant gaps. Summary of the invention

[0004] The purpose of the present invention is to provide a key performance indicator evaluation method for smart cars in cold environments, to establish a key performance indicator evaluation system for smart cars in cold environments from multiple dimensions such as driving performance, safety performance and economic performance, and to conduct a scientific and reasonable evaluation of the key performance of smart cars in cold environments.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for evaluating key performance indicators of smart cars in a cold environment, the method comprising:

[0006] S101. Determine key performance indicators based on key performance test items of smart cars in cold weather environments, and construct an evaluation indicator system based on the determined key performance indicators;

[0007] S102. Determine the weight of each key performance indicator in the evaluation indicator system;

[0008] S103, testing various key performances of the smart car in a high-cold environment and obtaining test results;

[0009] S104. Based on the test results, the evaluation index system, and the weights of various key performance indicators, conduct a comprehensive evaluation of the key performance of the smart car in a high-cold environment.

[0010] Furthermore, the key performance indicators include driving performance indicators, economic performance indicators and safety performance indicators. The driving performance indicators include handling performance, acceleration performance, cold start and starting performance; the economic performance indicators include charging performance, energy consumption per unit distance, and cruising range; the safety performance indicators include defrost and defog performance, adaptive headlight performance, parking assist performance, lane keeping assist performance, and emergency braking performance.

[0011] Furthermore, constructing an evaluation index system based on the determined key performance indicators also includes selecting different types of membership functions to calculate the membership values ​​of the key performance indicators, and determining the grade standards of the key performance indicators based on the membership values, wherein the grade standards include multiple evaluation grades.

[0012] Furthermore, the weights of each key performance indicator in the evaluation index system are determined, including:

[0013] S201, constructing a network analysis structure, wherein the network analysis structure includes a control layer and a network layer, wherein the control layer includes a decision target and a decision criterion, and the network layer includes a plurality of element groups, each of which includes a plurality of elements, and there are influence relationships between different element groups;

[0014] S202, calculating a first weight of a key performance indicator according to a network analysis structure;

[0015] S203, calculating the second weight of the key performance indicator by a simple correlation function;

[0016] S204: Calculate a combined weight of the key performance indicator based on the first weight and the second weight, and use the combined weight as the final weight of the key performance indicator.

[0017] Furthermore, the first weight of the key performance indicator is calculated according to the network analysis structure, specifically including:

[0018] S301, determining the influence of two elements in the network layer on the secondary criterion element according to any decision criterion, thereby constructing a normalized eigenvector matrix;

[0019] S302, obtaining an unweighted supermatrix based on all normalized eigenvector matrices;

[0020] S303, calculating a weighted supermatrix based on the unweighted supermatrix;

[0021] S304: Calculate the weighted supermatrix to obtain a first weight.

[0022] Furthermore, the second weight of the key performance indicator is calculated by a simple correlation function, specifically including:

[0023] S401. Calculate the correlation between different key performance indicators and different evaluation levels;

[0024] S402, determining the evaluation level to which the key performance indicator belongs according to the correlation between the measured sample data and each evaluation level;

[0025] S403: assigning a second weight to the key performance indicator according to the evaluation level to which the key performance indicator falls.

[0026] Furthermore, the combined weight of the key performance indicator is calculated based on the first weight and the second weight, and the calculation formula is as follows:

[0027]

[0028] In the formula, cw i represents the combined weight of the i-th key performance indicator, f i represents the first weight of the i-th key performance indicator, s i represents the second weight of the i-th key performance indicator.

[0029] Furthermore, step S104 specifically includes:

[0030] S501, obtaining a fuzzy membership matrix of each key performance indicator according to the test results and the grade standard of each key performance indicator in the evaluation index system;

[0031] S502: Calculate the key performance evaluation of the smart car in a high-cold environment based on the fuzzy membership matrix obtained in the previous step, the comprehensive weights and memberships of each key performance indicator.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention provides a key performance indicator evaluation method for smart cars in high-cold environments. The key performance indicator evaluation system for smart cars in high-cold environments is constructed from multiple dimensions such as driving performance, safety performance, and economic performance, and the weights of each key performance indicator in the evaluation indicator system are determined. After testing each key performance of the smart car in the high-cold environment, a scientific and reasonable comprehensive evaluation of the key performance of the smart car in the high-cold environment is performed based on the test results, the evaluation indicator system, and the weights of each key performance indicator, thereby helping developers to promptly discover performance defects of smart cars in high-cold environments for optimization, and helping to promote the application of smart cars represented by new energy vehicles in northern regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 It is a schematic diagram of the overall process of a key performance indicator evaluation method for a smart car in a cold environment provided by an embodiment of the present invention.

[0036] Figure 2 It is a schematic diagram of the key performance indicator weight calculation process provided by an embodiment of the present invention.

[0037] Figure 3 It is a schematic diagram of a key performance indicator first weight calculation process provided by an embodiment of the present invention.

[0038] Figure 4 It is a schematic diagram of the second weight calculation process of the key performance indicator provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0040] Reference Figure 1 This embodiment provides a method for evaluating key performance indicators of a smart car in a cold environment, the method comprising the following steps:

[0041] S101. Determine key performance indicators based on key performance test items of smart cars in cold weather environments, and build an evaluation indicator system based on the determined key performance indicators.

[0042] In this embodiment, the key performance indicators include driving performance indicators, economic performance indicators and safety performance indicators. The driving performance indicators include handling performance, acceleration performance, cold start and starting performance; the economic performance indicators include charging performance, energy consumption per unit distance, and driving range; the safety performance indicators include defrost and defog performance, adaptive headlight performance, parking assist performance, lane keeping assist performance, and emergency braking performance.

[0043] Accordingly, the evaluation index system in this embodiment includes primary indicators and secondary indicators, wherein the primary indicators include driving performance indicators, economic performance indicators and safety performance indicators, and the secondary indicators include indicators under the primary indicators such as handling performance, acceleration performance, cold start and starting performance, charging performance, and defrosting and defogging performance.

[0044] S102. Determine the weight of each key performance indicator in the evaluation index system.

[0045] S103. Test various key performances of the smart car in a high-cold environment and obtain test results.

[0046] S104. Based on the test results, the evaluation index system, and the weights of various key performance indicators, conduct a comprehensive evaluation of the key performance of the smart car in a high-cold environment.

[0047] As a preferred implementation, constructing an evaluation index system based on the determined key performance indicators also includes selecting different types of membership functions to calculate the membership values ​​of the key performance indicators, and further determining the grade standards of the key performance indicators based on the membership values, wherein the grade standards include multiple evaluation grades. For example, for control performance, it may include multiple evaluation grades such as poor, general, good, and excellent.

[0048] As a preferred embodiment, refer to Figure 2 , determine the weights of each key performance indicator in the evaluation index system, including:

[0049] S201. Construct a network analysis structure, wherein the network analysis structure includes a control layer and a network layer. The control layer includes a decision target and a decision criterion. The network layer includes a plurality of element groups, each of which includes a plurality of elements. There are influence relationships between different element groups.

[0050] The control layer is relatively independent and is only affected by the next layer. Elements in the same element group in the network layer can affect each other, and elements in different element groups can also affect each other.

[0051] S202: Calculate the first weight of the key performance indicator according to the network analysis structure.

[0052] S203. Calculate the second weight of the key performance indicator by using a simple correlation function.

[0053] S204: Calculate a combined weight of the key performance indicator based on the first weight and the second weight, and use the combined weight as the final weight of the key performance indicator.

[0054] The weight is a quantitative distribution of the importance of various aspects of the smart car during the evaluation process. During the evaluation process, the role of each evaluation item is different, and the weight represents the impact of each key performance indicator on the smart car, that is, the relative importance of the key performance indicator in the overall evaluation. The determination of weights generally includes three methods: subjective determination, objective determination, and subjective and objective combination determination. This embodiment adopts a subjective and objective combination determination method to determine the weights of various key performance indicators of smart cars in high-cold environments, which can not only reflect the subjective and objective information of the key performance indicators at the same time, but also make the results more objective and credible.

[0055] Reference Figure 3 In this implementation, the first weight of the key performance indicator is calculated according to the network analysis structure, which specifically includes the following operations:

[0056] S301, determining the influence of two elements in the network layer on the secondary criterion element according to any decision criterion, thereby constructing a normalized eigenvector matrix. The secondary criterion element is an element other than the determined two elements.

[0057] Assume that the criteria layer has x1, x2, ..., x m There are multiple criteria, and the network layer has n element groups, represented by e i (i=1, 2, ..., n), the elements in each element group are represented by e ij (j=1, 2, ..., n), compare the influence degree of the elements with influence relationship, and use the x in the criterion layer i As the main criterion, e i An element e in ij As a secondary criterion, a 1-9 rating scale was used to evaluate e i Compare all elements in e with each other and get i The influence of any two elements on the secondary criteria under the control of the main criteria is constructed based on e ij is the normalized eigenvector matrix of the secondary criteria. For the element group e i If there are n elements, there are n judgment matrices and normalized eigenvectors. All eigenvectors are combined into a new matrix M ij , that is, the judgment matrix with the main criteria.

[0058] S302: Obtain an unweighted supermatrix based on all normalized eigenvector matrices.

[0059] In this step, all M ij (i=1, 2, ..., n; j=1, 2, ..., n) form an N×N block matrix M, and we can get the unweighted supermatrix under the corresponding criterion, M ijis a submatrix of M. The unweighted supermatrix M is a non-negative matrix. When there is no relationship between the elements, the normalized eigenvectors of M are obtained. ij is a 0 matrix.

[0060] S303: Calculate a weighted supermatrix based on the unweighted supermatrix.

[0061] In this step, the criterion layer x i As the main criteria, the network layer is based on e i (i = 1, 2, ..., n) is the secondary criterion, and the 1-9 scaling method is used to compare the influence of any two other element groups on the secondary criterion elements. Based on this, the weight matrix W is obtained. The weighted super matrix is ​​obtained by weighting the unweighted super matrix through the full matrix

[0062] S304: Calculate the weighted supermatrix to obtain a first weight.

[0063] In this step, the k-th step advantage between elements is obtained by exponentiating the weighted supermatrix k times, which is denoted as Sort the importance of all elements to the entire network, get the final set ranking by cumulative influence, and obtain the limit super matrix That is, the final weight ranking of all elements in the network, calculated as follows:

[0064]

[0065] If the limit obtained by solving the above equation converges and is unique, then the eigenvector of the limit supermatrix is ​​the first weight.

[0066] Reference Figure 4 In this implementation, the second weight of the key performance indicator is calculated by a simple correlation function, which specifically includes the following operations:

[0067] S401. Calculate the correlation between different key performance indicators and different evaluation levels.

[0068] S402: Determine the evaluation level to which the key performance indicator belongs based on the correlation between the measured sample data and each evaluation level.

[0069] S403: assigning a second weight to the key performance indicator according to the evaluation level to which the key performance indicator falls.

[0070] In extenics, a simple correlation function describes whether an element has a certain property, while a dynamic correlation function determines the weight of an indicator by mapping the indicator value into an interval. If the indicator value happens to fall within the interval, the indicator will be given a higher weight value because the evaluation level it represents is clear. The above method can effectively solve the problem of uncertain weights between different indicators, thereby improving the accuracy and credibility of the evaluation results. The higher the level that the indicator value falls into, the higher the evaluation level represented by the indicator, and the better the evaluation, the higher the weight should be given to the corresponding indicator.

[0071] Exemplarily, the calculation formula is as follows:

[0072]

[0073] In the above formula, r ij represents the correlation between the i-th key performance indicator and the j-th evaluation level, v i is the sample measured value of the i-th key performance indicator, v i ∈v p , v p is the value range of the key performance indicator, p represents the evaluation level, v ij is the value range of the i-th key performance indicator corresponding to the j-th evaluation level, and its value is v ij =(a ij , b ij ). ij is the lower limit of the value of the i-th key performance indicator, b ij is the upper limit of the value of the i-th key performance indicator. Therefore:

[0074] r ijmax (v i , v ij )=max{r ij (v i , v ij )}

[0075] If the key performance indicator falls into a higher evaluation level, a greater weight is given to the key performance indicator, which is expressed as:

[0076]

[0077] If the key performance indicator falls into a lower evaluation level, the key performance indicator is given a smaller weight, which is expressed as:

[0078]

[0079] In the above formula, s represents the evaluation grade grading standard, j maxRepresents the maximum value of the evaluation level. After normalization, the second weight s of the i-th key performance indicator is calculated j for:

[0080]

[0081] Finally, this embodiment combines the first weight and the second weight of the key performance indicator in the form of equal preference, and calculates the combined weight of the key performance indicator of the smart car in the high-cold environment through normalization processing. The calculation formula is as follows:

[0082]

[0083] In the formula, cw i represents the combined weight of the i-th key performance indicator, f i represents the first weight of the i-th key performance indicator, s i represents the second weight of the i-th key performance indicator.

[0084] As a preferred implementation, step S104 specifically includes the following operations:

[0085] S501. Obtain a fuzzy membership matrix of each key performance indicator according to the test results and the grade standard of each key performance indicator in the evaluation index system.

[0086] S502: Calculate the key performance evaluation of the smart car in a cold environment based on the fuzzy membership matrix obtained in the previous step, the comprehensive weights and memberships of each key performance indicator. The expression is as follows:

[0087] A=w * ·R ij

[0088] Among them, A represents the numerical set corresponding to each evaluation level, w * represents the set consisting of the combined weights of each key performance indicator, R ij Represents the fuzzy membership matrix. According to the above formula, the value of A can be calculated. According to the maximum membership principle, the value of A corresponds to the evaluation level, and the range of key performance of smart cars in high-cold environments can be known. By analyzing the elements in A, the situation of each evaluation item within its evaluation level can be known.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating key performance indicators of smart cars in cold environments, characterized in that: Said include: S101. Determine key performance indicators based on key performance test items of smart cars in cold weather environments, and construct an evaluation indicator system based on the determined key performance indicators; S102. Determine the weight of each key performance indicator in the evaluation indicator system; S103, testing various key performances of the smart car in a high-cold environment and obtaining test results; S104. Based on the test results, the evaluation index system, and the weights of various key performance indicators, conduct a comprehensive evaluation of the key performance of the smart car in a high-cold environment.

2. The key performance indicator evaluation method for intelligent vehicles in cold environments according to claim 1 is characterized in that: The key performance indicators include driving performance indicators, economic performance indicators and safety performance indicators. The driving performance indicators include handling performance, acceleration performance, cold start and starting performance; the economic performance indicators include charging performance, energy consumption per unit distance, and driving range; the safety performance indicators include defrost and defog performance, adaptive headlight performance, parking assist performance, lane keeping assist performance, and emergency braking performance.

3. The key performance indicator evaluation method for intelligent vehicles in cold environments according to claim 1 is characterized in that: Constructing an evaluation index system according to the determined key performance indicators also includes selecting different types of membership functions to calculate the membership values ​​of the key performance indicators, and determining the grade standards of the key performance indicators according to the membership values, wherein the grade standards include multiple evaluation grades.

4. The key performance indicator evaluation method for intelligent vehicles in cold environments according to claim 1 is characterized in that: Determine the weights of each key performance indicator in the evaluation index system, including: S201, constructing a network analysis structure, wherein the network analysis structure includes a control layer and a network layer, wherein the control layer includes a decision target and a decision criterion, and the network layer includes a plurality of element groups, each of which includes a plurality of elements, and there are influence relationships between different element groups; S202, calculating a first weight of a key performance indicator according to a network analysis structure; S203, calculating the second weight of the key performance indicator by a simple correlation function; S204: Calculate a combined weight of the key performance indicator based on the first weight and the second weight, and use the combined weight as the final weight of the key performance indicator.

5. The key performance indicator evaluation method for intelligent vehicles in cold environment according to claim 4 is characterized in that: The first weight of the key performance indicators is calculated based on the network analysis structure, including: S301, determining the influence of two elements in the network layer on the secondary criterion element according to any decision criterion, thereby constructing a normalized eigenvector matrix; S302, obtaining an unweighted supermatrix based on all normalized eigenvector matrices; S303, calculating a weighted supermatrix based on the unweighted supermatrix; S304: Calculate the weighted supermatrix to obtain a first weight.

6. The key performance indicator evaluation method for intelligent vehicles in cold environments according to claim 4 is characterized in that: The second weight of the key performance indicator is calculated through a simple correlation function, including: S401. Calculate the correlation between different key performance indicators and different evaluation levels; S402, determining the evaluation level to which the key performance indicator belongs according to the correlation between the measured sample data and each evaluation level; S403: assigning a second weight to the key performance indicator according to the evaluation level to which the key performance indicator falls.

7. The key performance indicator evaluation method for intelligent vehicles in cold environments according to claim 4 is characterized in that: The combined weight of the key performance indicators is calculated based on the first and second weights. The calculation formula is as follows: In the formula, cw i represents the combined weight of the i-th key performance indicator, f i represents the first weight of the i-th key performance indicator, s i represents the second weight of the i-th key performance indicator.

8. The key performance indicator evaluation method for intelligent vehicles in cold environments according to claim 1 is characterized in that: Step S104 specifically includes: S501, obtaining a fuzzy membership matrix of each key performance indicator according to the test results and the grade standard of each key performance indicator in the evaluation index system; S502: Calculate the key performance evaluation of the smart car in a high-cold environment based on the fuzzy membership matrix obtained in the previous step, the comprehensive weights and memberships of each key performance indicator.

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