A method, system, device and medium for evaluating the matching degree of electric vehicle performance requirements

Abstract

The present invention provides a method, system, device and medium for evaluating the matching degree of performance requirements of electric vehicles, wherein the method includes: extracting dynamic data and static data of electric vehicles, obtaining valid data, and storing it in a database; based on analysis dimensions and vehicle model characteristics, obtaining corresponding parameters, and constructing a typical user vehicle usage characteristic distribution function and a mainstream user vehicle usage characteristic distribution function in combination with corresponding calculation rules; constructing a vehicle model user characteristic probability density function and a segmented market user characteristic probability density function based on the above two characteristic distribution functions, combining them to construct a matching degree function, and calculating the matching degree of the vehicle to be evaluated; constructing a post-processing model based on the matching degree, obtaining the vehicle model performance matching degree, and judging the status of the vehicle model to be evaluated in the segmented market and the quality of the vehicle model matching degree based on the vehicle model performance matching degree. The present invention can accurately and reliably evaluate the performance and demand of electric vehicles through a complete evaluation method, and improves the evaluation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a method, system, device and medium for evaluating the matching degree of performance requirements of electric vehicles. Background Art

[0002] As people become more aware of environmental protection, environmental pollution and resource issues are receiving significant attention. The automotive industry faces an increasingly challenging energy conservation and emission reduction situation, making the development of energy-efficient and environmentally friendly vehicles an inevitable choice for its development. Electric vehicles, in particular, play a crucial role among new energy vehicles. Electric vehicles are powered by an onboard electrical system and driven by an electric motor. Electric vehicles can be categorized by their power source: pure electric vehicles, hybrid electric vehicles, and fuel cell electric vehicles. Pure electric vehicles are powered by an onboard electrical system, with the motor driving the wheels, and complying with all road traffic and safety regulations. Their potential is widely anticipated due to their lower environmental impact compared to traditional vehicles, but the current technology is still immature.

[0003] However, after existing electric vehicles are sold, there is no complete evaluation method for their range, charging speed, and safety, which makes it difficult to facilitate the subsequent development and improvement of electric vehicles. Alternatively, user surveys are used to evaluate the performance and needs of the vehicles. However, this method only obtains a small amount of data, takes a long time to conduct surveys, is inefficient, and is highly subjective, which cannot accurately reflect the actual situation of electric vehicles.

[0004] Therefore, there is an urgent need for a complete and reliable research and evaluation method that can match the performance of electric vehicles with demand. Summary of the Invention

[0005] Based on this, it is necessary to provide an electric vehicle performance requirement matching evaluation method, system, equipment and medium to address the above technical issues.

[0006] A method for evaluating the matching degree of performance requirements of electric vehicles comprises the following steps: extracting dynamic data and static data of the electric vehicle to obtain valid data and storing the data in a database; obtaining a first parameter of the vehicle to be evaluated from the valid data based on an analysis dimension, constructing a typical user vehicle usage characteristic distribution function according to the first parameter and a first calculation rule, determining a corresponding market segment based on the vehicle model characteristics of the vehicle to be evaluated, obtaining a second parameter in the market segment, and constructing a mainstream user vehicle usage characteristic distribution function according to the second parameter and a second calculation rule; constructing a vehicle model user characteristic probability density function according to the typical user vehicle usage characteristic distribution function, and constructing a market segment user characteristic probability density function according to the mainstream user vehicle usage characteristic distribution function; constructing a matching degree function according to the vehicle model user characteristic probability density function and the market segment user characteristic probability density function, and substituting the first parameter and the second parameter into the matching degree function to calculate the corresponding matching degree; constructing a post-processing model according to the matching degree, calculating and obtaining the vehicle model performance matching degree of the vehicle model to be evaluated, and judging the status of the vehicle model to be evaluated in the market segment and the quality of its vehicle model matching degree according to the vehicle model performance matching degree.

[0007] In one embodiment, the extraction of dynamic data and static data of electric vehicles, obtaining valid data, and storing them in a database specifically includes: extracting dynamic data and static data of electric vehicles; performing data cleaning on the dynamic data and static data; dividing the cleaned data into vehicle driving segments and vehicle charging segments according to driving status and charging status, and retaining corresponding vehicle dynamic information; classifying and calculating the segmented data according to vehicle status and parameters to obtain valid data.

[0008] In one embodiment, the extracting of dynamic data and static data of the electric vehicle, obtaining valid data, and storing the data in a database further includes: extracting necessary driving characteristics and necessary charging characteristics from the valid data according to the vehicle status, and storing the data in a database; the necessary driving characteristics include driving mileage, the relationship between driving mileage and power, the number of trips, driving voltage, driving temperature, driving failure rate, and driving speed; the necessary charging characteristics include charging current, charging voltage, the probability of the charging temperature exceeding the upper limit cutoff temperature, charging power, charging time, and charging failure rate.

[0009] In one embodiment, based on the analysis dimension, a first parameter of the vehicle to be evaluated is obtained from the valid data, and a typical user vehicle usage characteristic distribution function is constructed according to the first parameter and the first calculation rule. Specifically, the following steps are performed: based on the analysis dimension, the necessary characteristics are clustered to obtain the first parameter, which includes a first travel parameter, a first charging parameter, a first energy consumption parameter, a first power parameter, and a first other parameter; based on the first parameter and the first calculation rule, a typical user of the vehicle type is obtained, and the formula is:

[0010] Vehicle model user characteristics = w1*A1+c1*A2+e1*A3+p1*A4+o1*A5;

[0011] Typical user of vehicle model U1 = vehicle model user characteristics > q1;

[0012] According to the first parameter and the typical users of vehicle models, the typical user vehicle usage characteristic distribution function is constructed as: G(w1,c1,e1,p1,o1);

[0013] Among them, w1 is the first travel parameter, c1 is the first charging parameter, e1 is the first energy consumption parameter, p1 is the first power parameter, o1 is the first other parameter, A1 is the first travel weight, A2 is the first charging weight, A3 is the first energy consumption weight, A4 is the first power weight, A5 is the first other weight, and q1 is the first preset threshold.

[0014] In one embodiment, the method of determining the corresponding market segment based on the model characteristics of the vehicle to be evaluated, obtaining the second parameter under the market segment, and constructing the mainstream user vehicle usage characteristic distribution function according to the second parameter and the second calculation rule specifically includes: determining the corresponding market segment according to the model characteristics of the vehicle to be evaluated, wherein the model characteristics include price range, positive electrode material, cruising range and car grade; determining the model under the market segment according to the market segment corresponding to the vehicle to be evaluated, and extracting the corresponding second parameter, wherein the second parameter includes a second travel parameter, a second charging parameter, a second energy consumption parameter, a second power parameter and a second other parameter; determining the mainstream users in the market segment according to the second parameter and the second calculation rule, wherein the formula is:

[0015] User characteristics of the market segment = w2*B1+c2*B2+e2*B3+p2*B4+o2*B5;

[0016] Mainstream users in the market segment U2 = user characteristics in the market segment > q2;

[0017] Based on the second parameter and the mainstream users in the segmented market, a distribution function of the mainstream users’ car usage characteristics is constructed as F(w2,c2,e2,p2,o2);

[0018] Among them, w2 is the second travel parameter, c2 is the second charging parameter, e2 is the second energy consumption parameter, p2 is the second power parameter, o2 is the second other parameter, B1 is the second travel weight, B2 is the second charging weight, B3 is the second energy consumption weight, B4 is the second power weight, B5 is the second other weight, and q2 is the second preset threshold.

[0019] In one embodiment, the method constructs a vehicle type user characteristic probability density function based on the typical user vehicle usage characteristic distribution function, and constructs a segmented market user characteristic probability density function based on the mainstream user vehicle usage characteristic distribution function. A matching function is constructed based on the vehicle type user characteristic probability density function and the segmented market user characteristic probability density function, and the first parameter and the second parameter are brought into the matching function to calculate the corresponding matching degree, specifically including: constructing a vehicle type user characteristic probability density function g(x) based on the typical user vehicle usage characteristic distribution function G(x), and the formula is:

[0020]

[0021] Based on the distribution function F(x) of mainstream users’ car usage characteristics, the probability density function f(x) of user characteristics in the segmented market is constructed. The formula is:

[0022]

[0023] According to the probability density function of vehicle model user characteristics and the probability density function of market segment user characteristics, a matching function is constructed. The formula is:

[0024] h(x)=min(f(x),g(x));

[0025] The first parameter and the second parameter are substituted into the matching function to obtain travel matching, charging matching, energy consumption matching, power matching and other matching.

[0026] In one embodiment, a post-processing model is constructed based on the matching degree, and the vehicle performance matching degree of the vehicle model to be evaluated is calculated and obtained. Based on the vehicle performance matching degree, the position of the vehicle model to be evaluated in the market segment and the quality of vehicle model matching are judged. Specifically, the post-processing model is constructed based on the matching degree as follows:

[0027] y=∑(x*k(x)),x∈(P,W,C,E,O);

[0028] Where y is the vehicle model performance matching degree, k(x) is the corresponding weight function, P is the power matching degree, W is the mileage matching degree, C is the charging matching degree, E is the energy consumption matching degree, and O is other matching degrees. All matching degrees of the vehicle model to be evaluated are brought into the post-processing model to obtain the vehicle model performance matching degree. Based on the vehicle model performance matching degree, the matching quality of the vehicle model and its position in the market segment are judged.

[0029] A system for evaluating the performance requirements matching degree of an electric vehicle includes: an effective data acquisition module for extracting dynamic and static data of the electric vehicle, acquiring effective data, and storing the acquired data in a database; a feature distribution function construction module for acquiring, based on an analysis dimension, a first parameter of a vehicle to be evaluated from the effective data, constructing a typical user vehicle usage feature distribution function according to the first parameter and a first calculation rule, determining a corresponding market segment based on the vehicle model characteristics of the vehicle to be evaluated, acquiring a second parameter in the market segment, and constructing a mainstream user vehicle usage feature distribution function according to the second parameter and a second calculation rule; a matching degree calculation module for constructing a vehicle model user feature probability density function based on the typical user vehicle usage feature distribution function, constructing a market segment user feature probability density function based on the mainstream user vehicle usage feature distribution function, constructing a matching degree function based on the vehicle model user feature probability density function and the market segment user feature probability density function, and substituting the first parameter and the second parameter into the matching degree function to calculate the corresponding matching degree; and a vehicle model matching degree evaluation module for constructing a post-processing model based on the matching degree, calculating and acquiring the vehicle model performance matching degree of the vehicle to be evaluated, and judging the status of the vehicle to be evaluated in the market segment and the quality of its vehicle model matching degree based on the vehicle model performance matching degree.

[0030] A device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of a method for evaluating the matching degree of performance requirements of an electric vehicle described in each of the above embodiments are implemented.

[0031] A medium stores a computer program, which, when executed by a processor, implements the steps of a method for evaluating the matching degree of electric vehicle performance requirements described in each of the above embodiments.

[0032] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: the present invention obtains effective data by extracting dynamic data and static data of electric vehicles and stores them in a database, thereby obtaining a variety of data of electric vehicles, which is convenient for performing a complete evaluation of the matching degree of electric vehicles based on a variety of data; based on the analysis dimension, the first parameter of the vehicle to be evaluated is searched in the database, a typical user vehicle usage characteristic distribution function is constructed according to the first parameter and the first calculation rule, and the corresponding market segment is determined based on the vehicle model characteristics of the vehicle to be evaluated, the second parameter under the market segment is obtained, and a mainstream user vehicle usage characteristic distribution function is constructed according to the second parameter and the second calculation rule, so that the matching degree of the electric vehicle can be analyzed according to multiple characteristics of the vehicle to be evaluated, thereby improving data reliability; the vehicle model characteristics distribution function is constructed according to the typical user vehicle usage characteristic distribution function A probability density function of user characteristics is constructed, and a probability density function of user characteristics in the market segment is constructed based on the distribution function of the car usage characteristics of mainstream users. A matching function is constructed based on the probability density function of vehicle model characteristics and the probability density function of user characteristics in the market segment. The first parameter and the second parameter are brought into the matching function to calculate the corresponding matching degree, thereby obtaining the matching degree of the vehicle model and the market segment respectively, which is convenient for performing matching evaluation on the vehicle model to be evaluated from the perspective of the vehicle model itself and the market segment in which it is located, thereby improving the accuracy of the evaluation results. A post-processing model is constructed based on the matching degree to calculate and obtain the vehicle performance matching degree of the vehicle model to be evaluated. Based on the vehicle performance matching degree, the status of the vehicle model to be evaluated in the market segment and the quality of the vehicle matching degree are judged. Through a complete evaluation method, the performance and demand of electric vehicles are accurately and reliably evaluated, thereby improving the evaluation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 1 is a flow chart of a method for evaluating the matching degree of performance requirements of an electric vehicle in one embodiment;

[0034] Figure 2 Schematic diagram of the structure of a system for evaluating the performance requirements matching degree of an electric vehicle in one embodiment;

[0035] Figure 3 Schematic diagram of the internal structure of a device in one embodiment. DETAILED DESCRIPTION

[0036] Before describing the specific embodiments of the present invention, it should be noted that the electric vehicle described in this application mainly refers to a pure electric vehicle.

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0038] In one embodiment, Figure 1As shown, a method for evaluating the matching degree of electric vehicle performance requirements is provided, comprising the following steps:

[0039] Step S101: extract dynamic data and static data of the electric vehicle, obtain valid data, and store them in a database.

[0040] Specifically, on the relevant platform for electric vehicle information, the dynamic and static data of electric vehicles are extracted, and the data that does not meet the requirements is cleaned, such as data with missing fields or abnormal data ranges. The data that meets the requirements is segmented and clustered, and valid data is obtained and stored in the database to facilitate subsequent data calls.

[0041] Among them, dynamic data includes data parameters under vehicle driving status and charging status, such as time, total / single cell voltage, current, vehicle speed, SOC (State of Charge) and mileage;

[0042] Static data includes vehicle model information and battery information. Vehicle model information includes operating range and vehicle level, and battery information includes power level, capacity and number of cells.

[0043] In step S102, based on the analysis dimension, a first parameter of the vehicle to be evaluated is obtained from the valid data, a typical user vehicle usage characteristic distribution function is constructed according to the first parameter and the first calculation rule, and a corresponding market segment is determined based on the vehicle model characteristics of the vehicle to be evaluated, a second parameter under the market segment is obtained, and a mainstream user vehicle usage characteristic distribution function is constructed according to the second parameter and the second calculation rule.

[0044] Specifically, according to different dimensions of analysis, the first parameters of the vehicle to be evaluated, such as travel parameters, charging parameters, energy consumption parameters, and power parameters, are searched in the database, and a typical user vehicle usage characteristic distribution function is constructed based on the first parameters and the first calculation rules.

[0045] According to different vehicle model characteristics, such as price range, positive electrode material, cruising range, sedan grade, etc., the vehicle market segments are divided, and the corresponding market segments are determined according to the model characteristics of the vehicle to be evaluated. The second parameter under the market segment is searched in the database, and the mainstream user vehicle usage characteristic distribution function is constructed based on the second parameter and the second calculation rule.

[0046] Among them, user characteristics refer to the travel parameters, charging parameters, energy consumption parameters, power parameters and other parameters of each model when used by users.

[0047] Step S103: construct a vehicle type user characteristic probability density function based on the typical user vehicle usage characteristic distribution function, and construct a segmented market user characteristic probability density function based on the mainstream user vehicle usage characteristic distribution function. Based on the vehicle type user characteristic probability density function and the segmented market user characteristic probability density function, construct a matching degree function, and substitute the first parameter and the second parameter into the matching degree function to calculate the corresponding matching degree.

[0048] Specifically, based on the distribution function of typical user vehicle usage characteristics, a probability density function of vehicle model user characteristics is constructed; based on the distribution function of mainstream user vehicle usage characteristics, a probability density function of market segment user characteristics is constructed. Based on the above two probability density functions, an overlap function of the two probability density functions is constructed, which is the matching function. The first parameter and the second parameter of the vehicle to be evaluated are substituted into the matching function to calculate and obtain the corresponding matching degree.

[0049] Among them, since the first parameter and the second parameter both include power parameters, travel parameters, charging parameters and energy consumption parameters, the corresponding power matching degree, travel matching degree, charging matching degree and energy consumption matching degree can be obtained according to the matching degree function, thereby conducting a more comprehensive evaluation of the performance and needs of the electric vehicle and improving the reliability of the evaluation results.

[0050] Step S104 , constructing a post-processing model based on the matching degree, calculating and obtaining the vehicle performance matching degree of the vehicle model to be evaluated, and judging the position of the vehicle model to be evaluated in the market segment and the quality of the vehicle model matching degree based on the vehicle performance matching degree.

[0051] Specifically, a post-processing model is constructed based on the matching degree, and the total performance matching degree of the vehicle to be evaluated, that is, the vehicle model matching degree, is calculated based on all the matching degrees of the vehicle model to be evaluated. Based on the vehicle model matching degree, the position of the vehicle model in the market segment and the quality of the vehicle model matching degree are judged, thereby obtaining the performance requirement matching of the vehicle to be evaluated, improving the reliability of the evaluation results, and facilitating subsequent improvements to electric vehicles.

[0052] In this embodiment, by extracting dynamic data and static data of the electric vehicle, obtaining valid data and storing it in a database, a variety of data of the electric vehicle is obtained, which facilitates a complete evaluation of the matching degree of the electric vehicle based on the various data; based on the analysis dimension, the first parameter of the vehicle to be evaluated is searched in the database, and a typical user vehicle usage characteristic distribution function is constructed based on the first parameter and the first calculation rule, and the corresponding market segment is determined based on the vehicle model characteristics of the vehicle to be evaluated, and the second parameter under the market segment is obtained. The mainstream user vehicle usage characteristic distribution function is constructed based on the second parameter and the second calculation rule, so that the matching degree of the electric vehicle can be analyzed according to multiple characteristics of the vehicle to be evaluated, thereby improving data reliability; the vehicle model user characteristic probability density function is constructed based on the typical user vehicle usage characteristic distribution function , and construct a probability density function of user characteristics in the market segment based on the distribution function of the mainstream user's car usage characteristics, and construct a matching function based on the vehicle model characteristic probability density function and the probability density function of user characteristics in the market segment, and bring the first parameter and the second parameter into the matching function to calculate the corresponding matching degree, so as to obtain the matching degree of the vehicle model and the market segment respectively, which is convenient for the matching degree evaluation of the vehicle model to be evaluated from the vehicle model itself and the market segment it is in, thereby improving the accuracy of the evaluation results; construct a post-processing model based on the matching degree, calculate and obtain the vehicle performance matching degree of the vehicle model to be evaluated, and judge the status of the vehicle model to be evaluated in the market segment and the quality of the vehicle model matching based on the vehicle performance matching degree, and accurately and reliably evaluate the performance and demand of electric vehicles through a complete evaluation method, thereby improving the evaluation efficiency.

[0053] Among them, step S101 specifically includes: extracting dynamic data and static data of electric vehicles; performing data cleaning on the dynamic data and static data; dividing the cleaned data into vehicle driving segments and vehicle charging segments according to the driving status and charging status, and retaining the corresponding vehicle dynamic information; classifying and calculating the segmented data according to the vehicle status and parameters to obtain valid data.

[0054] Specifically, the dynamic and static data of electric vehicles are extracted on the data platform, and data cleaning is used to process abnormal data, such as frames with missing important data or false alarm data with data exceeding the normal range; then the cleaned data is segmented according to the classification of the vehicle's driving status or charging status to obtain vehicle driving segments and vehicle charging segments, and the corresponding vehicle dynamic information is retained to facilitate the acquisition of data information of driving segments and charging segments respectively; the segmented segments are classified and calculated according to the status and parameter name to obtain valid data, such as clustering the vehicle speed under the driving segment, identifying the driving segment, and calculating the speed distribution range, maximum speed, morning peak speed distribution, etc. according to the required regulations.

[0055] Among them, step S101 also includes: according to the vehicle status, extracting necessary driving characteristics and necessary charging characteristics from the valid data, and storing them in the database; the necessary driving characteristics include mileage, the relationship between mileage and power, number of trips, driving voltage, driving temperature, driving failure rate and driving speed; the necessary charging characteristics include charging current, charging voltage, the probability of the charging temperature exceeding the upper limit cutoff temperature, charging power, charging time and charging failure rate.

[0056] Specifically, the vehicle status includes a driving status and a charging status. The necessary driving features and the necessary charging features are extracted from the valid data according to the vehicle status and stored in the database.

[0057] Among them, the mileage in the necessary driving characteristics includes single driving mileage, average daily mileage, average daily mileage utilization rate, average monthly mileage, etc.; the relationship between mileage and power includes single driving SOC / power change, daily driving power consumption, etc.; the number of trips includes the average daily number of trips, the average monthly number of trips, etc.; the driving voltage includes the frequency of exceeding or falling below the upper and lower limit threshold single cell voltage, etc.; the driving temperature includes the probability of exceeding the upper limit cutoff temperature; the driving speed includes the maximum vehicle speed, vehicle speed distribution, etc.

[0058] Among them, the charging current in the necessary charging characteristics is the relationship between the maximum continuous charging current and the battery capacity; the charging voltage is the probability of exceeding or falling below the upper and lower threshold single cell voltages; the charging temperature is the probability of exceeding the upper limit cutoff temperature; the charging power is the maximum charging power, average charging power, etc.

[0059] It should be noted that the necessary driving characteristics and charging characteristics corresponding to the vehicle status include but are not limited to the above-mentioned characteristics.

[0060] The construction of the typical user vehicle usage characteristic distribution function specifically includes: based on the analysis dimension, clustering the necessary characteristics to obtain the first parameter, which includes the first travel parameter, the first charging parameter, the first energy consumption parameter, and the first power parameter; according to the first parameter and the first calculation rule, the typical user of the vehicle model is obtained. The formula is:

[0061] Vehicle model user characteristics = w1*A1+c1*A2+e1*A3+p1*A4+o1*A5;

[0062] Typical user of vehicle model U1 = vehicle model user characteristics > q1;

[0063] According to the first parameter and the typical users of vehicle models, the typical user vehicle usage characteristic distribution function is constructed as: G(w1,c1,e1,p1,o1);

[0064] Among them, w1 is the first travel parameter, c1 is the first charging parameter, e1 is the first energy consumption parameter, p1 is the first power parameter, o1 is the first other parameter, A1 is the first travel weight, A2 is the first charging weight, A3 is the first energy consumption weight, A4 is the first power weight, A5 is the first other weight, and q1 is the first preset threshold.

[0065] Specifically, based on analysis dimensions such as travel, charging, energy consumption, and power, necessary features are clustered and calculated to obtain a first parameter. Based on the first parameter and a first calculation rule, typical users of a vehicle model are identified. The distribution of characteristic values ​​of these users is statistically analyzed. If the characteristic value of a typical user falls within a first preset threshold value q1, the user is defined as a typical user of the vehicle model. If the characteristic value of a typical user does not fall within the first preset threshold value q1, the user is not a typical user of the vehicle model. A distribution function for typical user vehicle usage characteristics is constructed based on the first parameter and the typical users of the vehicle model, facilitating the subsequent determination of the probability density function of the vehicle model users.

[0066] The construction of the distribution function of vehicle usage characteristics of mainstream users specifically includes: determining the corresponding market segment based on the model characteristics of the vehicle to be evaluated, which include price range, positive electrode material, cruising range and car grade; determining the model in the market segment based on the market segment corresponding to the vehicle to be evaluated, and extracting the corresponding second parameters, which include the second travel parameter, the second charging parameter, the second energy consumption parameter and the second power parameter; determining the mainstream users in the market segment based on the second parameters and the second calculation rules, and the formula is:

[0067] User characteristics of the market segment = w2*B1+c2*B2+e2*B3+p2*B4+o2*B5;

[0068] Mainstream users in the market segment U2 = user characteristics in the market segment > q2;

[0069] Based on the second parameter and the mainstream users in the segmented market, a distribution function of the mainstream users’ car usage characteristics is constructed as F(w2,c2,e2,p2,o2);

[0070] Among them, w2 is the second travel parameter, c2 is the second charging parameter, e2 is the second energy consumption parameter, p2 is the second power parameter, o2 is the second other parameter, B1 is the second travel weight, B2 is the second charging weight, B3 is the second energy consumption weight, B4 is the second power weight, B5 is the second other weight, and q2 is the second preset threshold.

[0071] Specifically, electric vehicles are divided into different market segments based on different vehicle model characteristics, such as price range, positive electrode material, range of driving mileage, and car grade. For example, a Class A car with a price range of RMB 100,000 to RMB 150,000 and a ternary positive electrode material can be divided into one market segment, and a car with a range of 300-450km and a price range of RMB 100,000 to RMB 150,000 and a ternary positive electrode material can be divided into another market segment. The division of market segments can be customized according to the differences in the research objects. According to the vehicle model characteristics of the vehicle to be evaluated, the market segment corresponding to the vehicle to be evaluated is determined, the vehicle model corresponding to the market segment is determined, and the second parameter corresponding to the vehicle model is searched in the database. The user characteristics of the market segment are constructed based on the second parameter and the second calculation rule, and the mainstream users of the market segment are determined based on the second preset threshold. In combination with the second parameter, a distribution function of the vehicle usage characteristics of the mainstream users is constructed to facilitate the subsequent determination of the probability density function of the users of the market segment.

[0072] Step S103 specifically includes: constructing a vehicle type user characteristic probability density function g(x) based on a typical user vehicle characteristic distribution function G(x), the formula is:

[0073]

[0074] Based on the distribution function F(x) of mainstream users’ car usage characteristics, the probability density function f(x) of user characteristics in the segmented market is constructed. The formula is:

[0075]

[0076] According to the probability density function of vehicle model user characteristics and the probability density function of market segment user characteristics, a matching function is constructed. The formula is:

[0077] h(x)=min(f(x),g(x));

[0078] Substitute the first parameter and the second parameter into the matching function to obtain travel matching, charging matching, energy consumption matching, power matching and other matching.

[0079] Specifically, since both the first parameter and the second parameter include travel parameters, charging parameters, power parameters, energy consumption parameters and other parameters, the corresponding matching degree can be obtained according to the corresponding type of parameters, that is, the matching degree obtained according to the matching degree function includes travel matching degree, charging matching degree, energy consumption matching degree, power matching degree and other matching degrees.

[0080] Step S104 specifically includes: constructing a post-processing model according to the matching degree:

[0081] y=∑(x*k(x)),x∈(P,W,C,E,O);

[0082] Where y is the vehicle model performance matching degree, and k(x) is the corresponding weight function; all matching degrees of the vehicle model to be evaluated are brought into the post-processing model to obtain the vehicle model performance matching degree; based on the vehicle model performance matching degree, the matching quality of the vehicle model and its position in the market segment are judged.

[0083] Specifically, taking mileage matching as an example, the first preset threshold and the second preset threshold are both set to the 95th percentile. If the mileage matching scores of analyzed model 1 and analyzed model 2 are 90 points and 80 points respectively, it means that the endurance matching can be compatible with 90% and 80% of mainstream users' mileage requirements respectively, and it can be found that model 1 has a higher mileage matching than model 2, which means that model 1 can meet the mileage requirements of more users in this market segment.

[0084] like Figure 2 As shown, a system 20 for evaluating the performance requirements matching of electric vehicles is provided, comprising: an effective data acquisition module 21, a characteristic distribution function construction module 22, a matching degree calculation module 23 and a vehicle type matching degree evaluation module 24, wherein:

[0085] The effective data acquisition module 21 is used to extract the dynamic data and static data of the electric vehicle, obtain the effective data, and store it in the database;

[0086] A feature distribution function construction module 22 is configured to obtain a first parameter of the vehicle to be evaluated from the valid data based on the analysis dimension, construct a typical user vehicle usage feature distribution function based on the first parameter and a first calculation rule, determine a corresponding market segment based on the vehicle model characteristics of the vehicle to be evaluated, obtain a second parameter for the market segment, and construct a mainstream user vehicle usage feature distribution function based on the second parameter and the second calculation rule;

[0087] A matching degree calculation module 23 is configured to construct a vehicle type user characteristic probability density function based on a typical user vehicle usage characteristic distribution function, and to construct a segmented market user characteristic probability density function based on a mainstream user vehicle usage characteristic distribution function. Based on the vehicle type user characteristic probability density function and the segmented market user characteristic probability density function, a matching degree function is constructed, and the first and second parameters are substituted into the matching degree function to calculate the corresponding matching degree.

[0088] The vehicle model matching evaluation module 24 is used to construct a post-processing model based on the matching degree, calculate and obtain the vehicle performance matching degree of the vehicle model to be evaluated, and judge the position of the vehicle model to be evaluated in the market segment and the quality of the vehicle model matching degree based on the vehicle performance matching degree.

[0089] In one embodiment, the effective data acquisition module 21 is specifically used to: extract dynamic data and static data of electric vehicles; perform data cleaning on the dynamic data and static data; divide the cleaned data into vehicle driving segments and vehicle charging segments according to the driving status and charging status, and retain the corresponding vehicle dynamic information; classify and calculate the segmented data according to the vehicle status and parameters to obtain effective data.

[0090] In one embodiment, the effective data acquisition module 21 is further configured to extract necessary driving features and necessary charging features from the effective data according to the vehicle status, and store the extracted features in a database.

[0091] In one embodiment, a device is provided. The device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the device is used to store configuration templates and can also be used to store target web page data. The network interface of the device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for evaluating the matching degree of performance requirements of electric vehicles is implemented.

[0092] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the device to which the solution of the present application is applied. The specific device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0093] In one embodiment, a medium may also be provided, wherein the medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the method as described in the aforementioned embodiment. The computer may be part of the above-mentioned electric vehicle performance requirement matching evaluation system.

[0094] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0095] Obviously, those skilled in the art should understand that the modules or steps of the present invention described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices. Alternatively, they can be implemented using program codes executable by the computing device, so that they can be stored in a computer storage medium (ROM / RAM, magnetic disk, optical disk) and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than herein, or they can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module for implementation. Therefore, the present invention is not limited to any specific combination of hardware and software.

[0096] The above content is a further detailed description of the present invention in conjunction with specific embodiments, and the specific implementation of the present invention cannot be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the matching degree of electric vehicle performance requirements, characterized in that: The following steps are involved: Extract dynamic and static data of electric vehicles, obtain valid data, and store them in the database; Based on the analysis dimension, cluster calculation is performed on the valid data to obtain first parameters, where the first parameters include a first travel parameter, a first charging parameter, a first energy consumption parameter, a first power parameter, and a first other parameter; According to the first parameter and the first calculation rule, the typical users of the vehicle model are obtained using the following formula: Vehicle Type User Characteristics = * + * + * + * + * ; Typical users of vehicle models User characteristics for vehicle models> users; According to the first parameter and the typical users of vehicle models, the typical user vehicle usage characteristic distribution function is constructed as follows: G ( , , , , ); in, is the first travel parameter, is the first charging parameter, is the first energy consumption parameter, is the first dynamic parameter, is the first other parameter, is the first travel weight, First charging weight, The first energy consumption weight, First power weight, First other weights, is a first preset threshold; Determine the corresponding market segment based on the characteristics of the vehicle to be evaluated, including price range, cathode material, driving range, and sedan class; Determine the vehicle model in the market segment according to the market segment corresponding to the vehicle to be evaluated, and extract the corresponding second parameters, where the second parameters include a second travel parameter, a second charging parameter, a second energy consumption parameter, a second power parameter, and a second other parameter; The mainstream users in the market segment are determined based on the second parameter and the second calculation rule. The formula is: User characteristics of market segments = * + * + * + * + * ; Mainstream users in the market segment For market segments where user characteristics are greater than users; According to the second parameter and the mainstream users in the segmented market, the distribution function of the mainstream users’ car usage characteristics is constructed as follows: ; in, is the second travel parameter, is the second charging parameter, is the second energy consumption parameter, is the second dynamic parameter, is the second other parameter, is the second travel weight, Second charging weight, The second energy consumption weight, Second power weight, Second other weights, is a second preset threshold; Based on the typical user vehicle usage characteristic distribution function, a vehicle model user characteristic probability density function is constructed. Based on the mainstream user vehicle usage characteristic distribution function, a segmented market user characteristic probability density function is constructed. Based on the vehicle model user characteristic probability density function and the segmented market user characteristic probability density function, a matching function is constructed. The first and second parameters are substituted into the matching function to calculate the corresponding matching degree. A post-processing model is constructed based on the matching degree to calculate and obtain the model performance matching degree of the model to be evaluated. Based on the model performance matching degree, the position of the model to be evaluated in the market segment and the quality of the model matching degree are judged.

2. The electric vehicle performance requirement matching evaluation method according to claim 1, characterized in that: The extracting of dynamic data and static data of the electric vehicle, obtaining valid data, and storing the data in a database specifically includes: Extract dynamic and static data of electric vehicles; Performing data cleaning on the dynamic data and static data; According to the driving status and charging status, the cleaned data is divided into vehicle driving segments and vehicle charging segments, and the corresponding vehicle dynamic information is retained; The segmented data is classified and calculated according to the vehicle status and parameters to obtain valid data.

3. The electric vehicle performance requirement matching evaluation method according to claim 1, characterized in that: The method of extracting dynamic data and static data of the electric vehicle, obtaining valid data, and storing the data in a database further includes: According to the vehicle status, necessary driving characteristics and necessary charging characteristics are extracted from the valid data and stored in a database; the necessary driving characteristics include driving mileage, the relationship between driving mileage and power, number of trips, driving voltage, driving temperature, driving failure rate and driving speed; the necessary charging characteristics include charging current, charging voltage, the probability of charging temperature exceeding the upper limit cutoff temperature, charging power, charging time and charging failure rate.

4. The method for evaluating the matching degree of electric vehicle performance requirements according to claim 1, characterized in that: The method constructs a vehicle type user characteristic probability density function based on a typical user vehicle usage characteristic distribution function, and constructs a segmented market user characteristic probability density function based on a mainstream user vehicle usage characteristic distribution function. The method constructs a matching function based on the vehicle type user characteristic probability density function and the segmented market user characteristic probability density function, and substitutes the first parameter and the second parameter into the matching function to calculate the corresponding matching degree. Specifically, the method includes: According to the distribution function of typical user car usage characteristics Constructing the probability density function of vehicle model user characteristics , the formula is: ； According to the distribution function of mainstream users' car usage characteristics Constructing the probability density function of user characteristics in the segmented market , the formula is: ； According to the probability density function of vehicle model user characteristics and the probability density function of market segment user characteristics, a matching function is constructed. The formula is: ； The first parameter and the second parameter are substituted into the matching function to obtain travel matching, charging matching, energy consumption matching, power matching and other matching.

5. The method for evaluating the matching degree of electric vehicle performance requirements according to claim 4, characterized in that: The post-processing model is constructed based on the matching degree to calculate and obtain the vehicle performance matching degree of the vehicle model to be evaluated. Based on the vehicle performance matching degree, the position of the vehicle model to be evaluated in the market segment and the quality of vehicle matching degree are judged, specifically including: The post-processing model constructed according to the matching degree is: ； in, For vehicle performance matching, is the corresponding weight function, For power matching, For mileage matching, Charging compatibility, Energy consumption matching, For other matching degrees; Bringing all matching degrees of the vehicle model to be evaluated into the post-processing model to obtain the vehicle model performance matching degree; Based on the performance matching degree of the model, judge the matching quality of the model and its position in the market segment.

6. An electric vehicle performance requirement matching evaluation system, characterized in that: include: The effective data acquisition module is used to extract the dynamic data and static data of the electric vehicle, obtain the effective data, and store it in the database; a characteristic distribution function construction module, configured to perform cluster calculation on valid data based on an analysis dimension to obtain first parameters, where the first parameters include a first travel parameter, a first charging parameter, a first energy consumption parameter, a first power parameter, and a first other parameter; According to the first parameter and the first calculation rule, the typical users of the vehicle model are obtained using the following formula: Vehicle Type User Characteristics = * + * + * + * + * ; Typical users of vehicle models User characteristics for vehicle models> users; According to the first parameter and the typical users of vehicle models, the typical user vehicle usage characteristic distribution function is constructed as follows: G ( , , , , ); in, is the first travel parameter, is the first charging parameter, is the first energy consumption parameter, is the first dynamic parameter, is the first other parameter, is the first travel weight, First charging weight, The first energy consumption weight, First power weight, First other weights, is a first preset threshold; Determine the corresponding market segment based on the characteristics of the vehicle to be evaluated, including price range, cathode material, driving range, and sedan class; Determine the vehicle model in the market segment according to the market segment corresponding to the vehicle to be evaluated, and extract the corresponding second parameters, where the second parameters include a second travel parameter, a second charging parameter, a second energy consumption parameter, a second power parameter, and a second other parameter; The mainstream users in the market segment are determined based on the second parameter and the second calculation rule. The formula is: User characteristics of market segments = * + * + * + * + * ; Mainstream users in the market segment For market segments where user characteristics are greater than users; According to the second parameter and the mainstream users in the segmented market, the distribution function of the mainstream users’ car usage characteristics is constructed as follows: ; in, is the second travel parameter, is the second charging parameter, is the second energy consumption parameter, is the second dynamic parameter, is the second other parameter, is the second travel weight, Second charging weight, The second energy consumption weight, Second power weight, Second other weights, is a second preset threshold; a matching degree calculation module, configured to construct a vehicle type user characteristic probability density function based on a typical user vehicle usage characteristic distribution function, and to construct a segmented market user characteristic probability density function based on a mainstream user vehicle usage characteristic distribution function; and to construct a matching degree function based on the vehicle type user characteristic probability density function and the segmented market user characteristic probability density function, and to substitute the first parameter and the second parameter into the matching degree function to calculate the corresponding matching degree; The vehicle model matching evaluation module is used to build a post-processing model based on the matching degree, calculate the vehicle performance matching degree of the vehicle model to be evaluated, and judge the position of the vehicle model to be evaluated in the market segment and the quality of its vehicle matching degree based on the vehicle performance matching degree.

7. An electric vehicle performance requirement matching evaluation device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer program storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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