CN114565283A - Method, system, equipment and medium for evaluating matching degree of performance requirements of electric vehicle - Google Patents

Method, system, equipment and medium for evaluating matching degree of performance requirements of electric vehicle Download PDF

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CN114565283A
CN114565283A CN202210196760.9A CN202210196760A CN114565283A CN 114565283 A CN114565283 A CN 114565283A CN 202210196760 A CN202210196760 A CN 202210196760A CN 114565283 A CN114565283 A CN 114565283A
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王震坡
刘鹏
贺劲松
雷宇涵
石友义
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Abstract

The invention provides a method, a system, equipment and a medium for evaluating the performance requirement matching degree of an electric vehicle, wherein the method comprises the following steps: extracting dynamic data and static data of the electric vehicle, acquiring effective data, and storing the effective data in a database; acquiring corresponding parameters based on analysis dimensions and vehicle type characteristics, and constructing a typical user vehicle use characteristic distribution function and a mainstream user vehicle use characteristic distribution function by combining corresponding calculation rules; respectively constructing a vehicle type user characteristic probability density function and a market subdivision user characteristic probability density function according to the two characteristic distribution functions, combining the construction of a matching degree function, and calculating the matching degree of the vehicle to be evaluated; and constructing a post-processing model according to the matching degree, acquiring the vehicle type performance matching degree, and judging the status of the vehicle type to be evaluated in the market segment and the quality of the vehicle type matching degree according to the vehicle type performance matching degree. The invention can accurately and reliably evaluate the performance and the requirement of the electric vehicle in a complete evaluation mode, and improves the evaluation efficiency.

Description

Method, system, equipment and medium for evaluating matching degree of performance requirements of electric vehicle
Technical Field
The invention relates to the technical field of electric vehicles, in particular to a method, a system, equipment and a medium for evaluating the performance requirement matching degree of an electric vehicle.
Background
With the increasing awareness of people on environmental protection, the problems of environmental pollution and resources are greatly concerned by people. The situation of energy conservation and emission reduction in the automobile industry is increasingly severe, and the development of energy-saving and environment-friendly automobiles becomes a necessary choice for the development of the automobile industry. Electric vehicles play a major role in new energy vehicles. An electric vehicle is an automobile driven by a motor using a vehicle-mounted power supply as power. Electric vehicles can be classified by power source into pure electric vehicles, hybrid electric vehicles, and fuel cell electric vehicles. The pure electric vehicle is a vehicle which takes a vehicle-mounted power supply as power and drives wheels to run by using a motor, and meets various requirements of road traffic and safety regulations. The automobile has a small influence on the environment, so that the prospect is widely seen, but the current technology is not mature.
After the existing electric automobile is sold, a complete evaluation method aiming at the aspects of endurance performance, charging speed, safety and the like of the electric automobile is not available, and the development and the improvement of the electric automobile are inconvenient to follow; or the performance and the requirement of the automobile are correspondingly evaluated in a user investigation mode, but the data volume obtained by the method is small, the investigation time period is long, the efficiency is low, the subjectivity is high, and the actual condition of the electric vehicle cannot be accurately reflected.
Therefore, a complete and reliable investigation and evaluation method for matching the performance of the electric vehicle with the requirement is needed.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, a system, a device and a medium for evaluating matching degree of performance requirements of an electric vehicle.
An electric vehicle performance requirement matching degree evaluation method comprises the following steps: extracting dynamic data and static data of the electric vehicle, acquiring effective data, and storing the effective data in a database; based on analysis dimensionality, obtaining a first parameter of a vehicle to be evaluated in the effective data, constructing a typical user vehicle using characteristic distribution function according to the first parameter and a first calculation rule, determining a corresponding market segment based on vehicle type characteristics of the vehicle to be evaluated, obtaining a second parameter in the market segment, and constructing a mainstream user vehicle using characteristic distribution function according to the second parameter and a second calculation rule; constructing a vehicle type user characteristic probability density function according to the typical user vehicle using characteristic distribution function, constructing a market segment user characteristic probability density function according to the mainstream user vehicle using characteristic distribution function, constructing a matching degree function according to the vehicle type user characteristic probability density function and the market segment user characteristic probability density function, and substituting a first parameter and a second parameter into the matching degree function to calculate the corresponding matching degree; and constructing a post-processing model according to the matching degree, calculating and obtaining the vehicle type performance matching degree of the vehicle type to be evaluated, and judging the status of the vehicle type to be evaluated in the market segment and the quality of the vehicle type matching degree according to the vehicle type performance matching degree.
In one embodiment, the extracting dynamic data and static data of the electric vehicle, obtaining valid data, and storing the valid data in a database specifically includes: extracting dynamic data and static data of the electric vehicle; performing data cleaning on the dynamic data and the static data; according to the driving state and the charging state, segmenting the cleaned data into a vehicle type driving segment and a vehicle charging segment, and keeping corresponding vehicle dynamic information; and classifying and calculating the segmented data according to the vehicle state and the parameters to obtain effective data.
In one embodiment, the extracting dynamic data and static data of the electric vehicle, obtaining valid data, and storing the valid data in a database further comprises: extracting a driving necessary characteristic and a charging necessary characteristic from the effective data according to the vehicle state, and storing the driving necessary characteristic and the charging necessary characteristic into a database; the necessary driving characteristics comprise driving mileage, the relation between the driving mileage and electric quantity, travel times, driving voltage, driving temperature, driving fault rate and driving speed; the charging necessary characteristics comprise charging current, charging voltage, probability that the charging temperature exceeds the upper limit cut-off temperature, charging power, charging time and charging fault rate.
In one embodiment, the obtaining a first parameter of the vehicle to be evaluated from the valid data based on the analysis dimension, and constructing a typical user vehicle utilization feature distribution function according to the first parameter and a first calculation rule specifically includes: performing clustering calculation on the necessary characteristics based on the analysis dimensionality to obtain first parameters, wherein the first parameters comprise a first trip parameter, a first charging parameter, a first energy consumption parameter, a first power parameter and first other parameters; obtaining a typical user of the vehicle type according to the first parameter and the first calculation rule, wherein the formula is as follows:
vehicle type user characteristic w1*A1+c1*A2+e1*A3+p1*A4+o1*A5；
Typical user U of vehicle type1Vehicle type user characteristic > q1；
Constructing a characteristic distribution function of a typical user vehicle using according to the first parameter and the vehicle type typical user as follows: g (w)1,c1,e1,p1,o1)；
Wherein, w1Is a first trip parameter, c1Is a first charging parameter, e1Is a first energy consumption parameter, p1Is a first power parameter, o1As a first further parameter, A1As a first trip weight, A2First charge weight, A3First energy consumption weight, A4First power weight, A5First other weight, q1Is a first preset threshold.
In one embodiment, the determining a corresponding market segment based on the vehicle type characteristics of the vehicle to be evaluated, obtaining a second parameter in the market segment, and constructing a mainstream user vehicle characteristic distribution function according to the second parameter and a second calculation rule specifically includes: determining a corresponding market segment according to the vehicle type characteristics of the vehicle to be evaluated, wherein the vehicle type characteristics comprise a price interval, a positive material, a range of driving mileage and a car grade; determining the vehicle type under the market segment according to the market segment corresponding to the vehicle to be evaluated, and extracting corresponding second parameters, wherein the second parameters comprise a second trip parameter, a second charging parameter, a second energy consumption parameter, a second power parameter and second other parameters; and determining the mainstream users of the market segmentation according to the second parameters and the second calculation rule, wherein the formula is as follows:
market segment user characteristics w2*B1+c2*B2+e2*B3+p2*B4+o2*B5；
Market segment mainstream user U2Market-segment user characteristics > q2；
According to the second parameter and market-segment mainstream users, constructing a characteristic distribution function of the mainstream users, namely F (w)2,c2,e2,p2,o2)；
Wherein, w2Is a second trip parameter, c2Is the second charging parameter, e2Is a second energy consumption parameter, p2Is a second power parameter, o2As a second other parameter, B1As a second trip weight, B2Second charge weight, B3Second energy consumption weight, B4Second power weight, B5Second other weight, q2Is a second preset threshold.
In one embodiment, the constructing a vehicle type user characteristic probability density function according to the typical user vehicle characteristic distribution function, constructing a market segment user characteristic probability density function according to the mainstream user vehicle characteristic distribution function, constructing a matching degree function according to the vehicle type 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 specifically includes: constructing a vehicle type user characteristic probability density function g (x) according to a typical user vehicle characteristic distribution function G (x), wherein the formula is as follows:
Figure BDA0003526169880000031
constructing a user characteristic probability density function f (x) of the market subdivision according to the mainstream user vehicle characteristic distribution function F (x), wherein the formula is as follows:
Figure BDA0003526169880000032
according to the vehicle type user characteristic probability density function and the market subdivision user characteristic probability density function, a matching degree function is constructed, and the formula is as follows:
h(x)＝min(f(x),g(x))；
and substituting the first parameter and the second parameter into the matching degree function to obtain a travel matching degree, a charging matching degree, an energy consumption matching degree, a power matching degree and other matching degrees.
In one embodiment, a post-processing model is constructed according to the matching degree, the vehicle type performance matching degree of the vehicle type to be evaluated is calculated and obtained, and the status and the vehicle type matching degree of the vehicle type to be evaluated in the market segment are judged according to the vehicle type performance matching degree, and the method specifically comprises the following steps: and constructing a post-processing model according to the matching degree as follows:
y＝∑(x*k(x)),x∈(P,W,C,E,O)；
wherein y is the matching degree of the vehicle type performance, 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 the other matching degrees; all matching degrees of the vehicle type to be evaluated are brought into the post-processing model, and vehicle type performance matching degrees are obtained; and judging the quality of the matching degree of the vehicle type and the status in the market segment according to the vehicle type performance matching degree.
An electric vehicle performance requirement matching degree evaluation system comprises: the effective data acquisition module is used for extracting dynamic data and static data of the electric vehicle, acquiring effective data and storing the effective data in a database; the characteristic distribution function building module is used for obtaining a first parameter of a vehicle to be evaluated in the effective data based on the analysis dimensionality, building a characteristic distribution function of the vehicle for typical users according to the first parameter and a first calculation rule, determining a corresponding market segment based on the vehicle type characteristics of the vehicle to be evaluated, obtaining a second parameter under the market segment, and building a characteristic distribution function of the vehicle for mainstream users according to the second parameter and the second calculation rule; the matching degree calculation module is used for constructing a vehicle type user characteristic probability density function according to the typical user vehicle using characteristic distribution function, constructing a market subdivision user characteristic probability density function according to the mainstream user vehicle using characteristic distribution function, constructing a matching degree function according to the vehicle type user characteristic probability density function and the market subdivision user characteristic probability density function, and substituting a first parameter and a second parameter into the matching degree function to calculate the corresponding matching degree; and the vehicle type matching degree evaluation module is used for constructing a post-processing model according to the matching degree, calculating and obtaining the vehicle type performance matching degree of the vehicle type to be evaluated, and judging the status of the vehicle type to be evaluated in the market segment and the quality of the vehicle type matching degree according to the vehicle type performance matching degree.
An apparatus comprising a memory, a processor and a computer program stored on the memory and operable on the processor, the processor implementing the steps of a method for matching a performance requirement of an electric vehicle as described in the various embodiments above when executing the program.
A medium having stored thereon a computer program which, when executed by a processor, implements the steps of a method for evaluating a matching degree of performance requirements of an electric vehicle as described in the above embodiments.
Compared with the prior art, the invention has the advantages and beneficial effects that: according to the method, the effective data are obtained by extracting the dynamic data and the static data of the electric vehicle and are stored in the database, so that various data of the electric vehicle are obtained, and complete evaluation on the matching degree of the electric vehicle is facilitated according to the various data; based on the analysis dimensionality, searching a first parameter of a vehicle to be evaluated in a database, constructing a typical user vehicle using characteristic distribution function according to the first parameter and a first calculation rule, determining a corresponding market segment based on the vehicle type characteristic of the vehicle to be evaluated, acquiring a second parameter under the market segment, and constructing a mainstream user vehicle using characteristic distribution function according to the second parameter and a second calculation rule, so that the matching degree of the electric vehicle can be analyzed according to a plurality of characteristics of the vehicle to be evaluated, and the data reliability is improved; the method comprises the steps of constructing a vehicle type user characteristic probability density function according to a typical user vehicle characteristic distribution function, constructing a market subdivision user characteristic probability density function according to a mainstream user vehicle characteristic distribution function, constructing a matching degree function according to the vehicle type characteristic probability density function and the market subdivision user characteristic probability density function, substituting a first parameter and a second parameter into the matching degree function to calculate corresponding matching degrees, and accordingly obtaining matching degrees of a vehicle type and the market subdivision respectively, facilitating matching degree evaluation of the vehicle type to be evaluated from the vehicle type and the market subdivision in which the vehicle type is located, and improving accuracy of an evaluation result; the method comprises the steps of constructing a post-processing model according to the matching degree, calculating and obtaining the vehicle type performance matching degree of the vehicle type to be evaluated, judging the status of the vehicle type to be evaluated in the market segment and the vehicle type matching degree according to the vehicle type performance matching degree, and accurately and reliably evaluating the performance and the requirement of the electric vehicle in a complete evaluation mode, so that the evaluation efficiency is improved.
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FIG. 1 is a schematic flow chart illustrating a method for matching performance requirements of an electric vehicle according to an embodiment;
FIG. 2 is a schematic diagram of an exemplary embodiment of a system for matching electric vehicle performance requirements;
fig. 3 is a schematic diagram of the internal structure of the apparatus in one embodiment.
Detailed Description
Before the description of the specific embodiments of the present invention, it should be noted that the electric vehicle described in the present application mainly refers to a pure electric vehicle.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings by way of specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In one embodiment, as shown in fig. 1, a method for evaluating matching degree of performance requirements of an electric vehicle is provided, which includes the following steps:
and S101, extracting dynamic data and static data of the electric vehicle, acquiring effective data, and storing the effective data in a database.
Specifically, on a platform related to electric vehicle information, dynamic data and static data of the electric vehicle are extracted, data which do not meet requirements, such as data with field loss or abnormal data range, are cleaned, the data which meet the requirements are segmented and clustered, effective data are obtained and stored in a database, and subsequent data calling is facilitated.
The dynamic data includes data parameters Of the vehicle running State and the charging State, such as time, total/cell voltage, current, vehicle speed, SOC (State Of Charge), mileage and the like;
the static data includes vehicle type information such as operating range, vehicle class, and battery information such as electric quantity, capacity, and number of cells.
And S102, acquiring a first parameter of the vehicle to be evaluated from the effective data based on the analysis dimensionality, constructing a characteristic distribution function of the vehicle for the typical user according to the first parameter and a first calculation rule, determining a corresponding market segment based on the vehicle type characteristic of the vehicle to be evaluated, acquiring a second parameter under the market segment, and constructing a characteristic distribution function of the vehicle for the mainstream user according to the second parameter and a second calculation rule.
Specifically, according to different dimensions of analysis, first parameters of the vehicle to be evaluated, such as a travel parameter, a charging parameter, an energy consumption parameter, a power parameter and the like, are searched in a database, and a characteristic distribution function of the typical user vehicle using is constructed according to the first parameters and a first calculation rule.
Dividing the subdivided markets of the vehicles according to different vehicle type characteristics, such as price intervals, anode materials, driving range ranges, car grades and the like, determining the corresponding subdivided markets according to the vehicle type characteristics of the vehicles to be evaluated, searching second parameters in the subdivided markets in a database, and constructing a characteristic distribution function of the vehicles for the mainstream users according to the second parameters and second calculation rules.
The user characteristics refer to travel parameters, charging parameters, energy consumption parameters, power parameters and other parameters of each vehicle type expressed under the use of a user.
And S103, constructing a vehicle type user characteristic probability density function according to the vehicle characteristic distribution function of the typical user, constructing a market segment user characteristic probability density function according to the vehicle characteristic distribution function of the mainstream user, constructing a matching degree function according to the vehicle type 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.
Specifically, a vehicle type user characteristic probability density function is constructed according to a typical user vehicle using characteristic distribution function; according to the feature distribution function of the main stream user vehicle utilization, a user feature probability density function of the market subdivision is built, according to the two probability density functions, a coincidence function of the two probability density functions is built, namely a matching degree function, a first parameter and a second parameter of a vehicle to be evaluated are brought into the matching degree function, and the corresponding matching degree is calculated and obtained.
The first parameter and the second parameter respectively comprise a power parameter, a trip parameter, a charging parameter and an energy consumption parameter, so that the corresponding power matching degree, trip matching degree, charging matching degree and energy consumption matching degree can be obtained according to the matching degree function, the performance and the demand of the electric vehicle are comprehensively evaluated, and the reliability of an evaluation result is improved.
And S104, constructing a post-processing model according to the matching degree, calculating and obtaining the vehicle type performance matching degree of the vehicle type to be evaluated, and judging the status of the vehicle type to be evaluated in the market segment and the quality of the vehicle type matching degree according to the vehicle type performance matching degree.
Specifically, a post-processing model is built according to the matching degree, the total performance matching degree of the vehicle type to be evaluated, namely the vehicle type matching degree, is calculated according to all the matching degrees of the vehicle type to be evaluated, and therefore the status of the vehicle type in the market segment and the good and bad conditions of the matching degree of the vehicle type are judged according to the vehicle type matching degree, the performance requirement matching condition of the vehicle to be evaluated is obtained, the reliability of an evaluation result is improved, and the electric vehicle can be improved subsequently.
In the embodiment, the dynamic data and the static data of the electric vehicle are extracted to obtain the effective data, and the effective data is stored in the database, so that various data of the electric vehicle are obtained, and complete evaluation on the matching degree of the electric vehicle is facilitated according to the various data; based on the analysis dimensionality, searching a first parameter of a vehicle to be evaluated in a database, constructing a typical user vehicle using characteristic distribution function according to the first parameter and a first calculation rule, determining a corresponding market segment based on the vehicle type characteristic of the vehicle to be evaluated, acquiring a second parameter under the market segment, and constructing a mainstream user vehicle using characteristic distribution function according to the second parameter and a second calculation rule, so that the matching degree of the electric vehicle can be analyzed according to a plurality of characteristics of the vehicle to be evaluated, and the data reliability is improved; the method comprises the steps of constructing a vehicle type user characteristic probability density function according to a typical user vehicle characteristic distribution function, constructing a market subdivision user characteristic probability density function according to a mainstream user vehicle characteristic distribution function, constructing a matching degree function according to the vehicle type characteristic probability density function and the market subdivision user characteristic probability density function, substituting a first parameter and a second parameter into the matching degree function to calculate corresponding matching degrees, and accordingly obtaining matching degrees of a vehicle type and the market subdivision respectively, facilitating matching degree evaluation of the vehicle type to be evaluated from the vehicle type and the market subdivision in which the vehicle type is located, and improving accuracy of an evaluation result; the method comprises the steps of constructing a post-processing model according to the matching degree, calculating and obtaining the vehicle type performance matching degree of the vehicle type to be evaluated, judging the status of the vehicle type to be evaluated in the market segment and the vehicle type matching degree according to the vehicle type performance matching degree, and accurately and reliably evaluating the performance and the requirement of the electric vehicle in a complete evaluation mode, so that the evaluation efficiency is improved.
Wherein, step S101 specifically includes: extracting dynamic data and static data of the electric vehicle; performing data cleaning on the dynamic data and the static data; according to the driving state and the charging state, segmenting the cleaned data into a vehicle type driving segment and a vehicle charging segment, and keeping corresponding vehicle dynamic information; and classifying and calculating the segmented data according to the vehicle state and the parameters to obtain effective data.
Specifically, dynamic data and static data of the electric vehicle are extracted from a data platform, and abnormal data, such as frames with missing important data or misinformation data with data exceeding a normal range, are processed in a data cleaning mode; then according to the classification of the vehicle running state or the charging state, segmenting the cleaned data to obtain a vehicle type running segment and a vehicle type charging segment, and reserving corresponding vehicle dynamic information to facilitate the respective acquisition of data information of the running segment and the charging segment; classifying and calculating the segmented segments according to the states and the parameter names to obtain effective data, such as clustering the speeds under the driving segments, identifying the driving segments, calculating a speed distribution interval, a highest speed, an early peak speed distribution and the like according to the required regulations.
Wherein, step S101 further includes: extracting the necessary driving characteristics and the necessary charging characteristics from the effective data according to the vehicle state, and storing the characteristics in a database; the necessary driving characteristics comprise driving mileage, the relation between the driving mileage and electric quantity, travel times, driving voltage, driving temperature, driving fault rate and driving speed; the charging necessary characteristics include charging current, charging voltage, probability that the charging temperature exceeds the upper limit cutoff temperature, charging power, charging time, and charging failure rate.
Specifically, the vehicle state includes a driving state and a charging state, and a driving necessary feature and a charging necessary feature are extracted from the effective data according to the vehicle state and stored in the database.
Wherein the driving mileage in the necessary driving characteristics is single driving mileage, daily average driving mileage utilization rate, monthly average driving mileage and the like; the relation between the driving mileage and the electric quantity is single driving SOC/electric quantity change, daily driving power consumption and the like; the trip times are daily average trip times, monthly average trip times and the like; the running voltage is the frequency exceeding or lower than the upper and lower limit threshold monomer voltage, and the like; the running temperature is the probability of exceeding the upper limit cut-off temperature; the running speed is the maximum vehicle speed, vehicle speed distribution and the like.
Wherein, the charging current in the charging necessary characteristics is the multiplying power relation between the maximum continuous charging current and the battery capacity, and the like; the probability that the charging voltage exceeds or is lower than the upper and lower limit threshold cell voltage, and the like; the probability that the charging temperature exceeds the upper limit cutoff temperature, and the like; the charging power is maximum charging power, average charging power, and the like.
It should be noted that the driving requirement characteristic and the charging requirement characteristic corresponding to the vehicle state include, but are not limited to, the above-described characteristics.
The construction of the typical user car-using feature distribution function specifically comprises the following steps: performing clustering calculation on the necessary characteristics based on the analysis dimensionality to obtain a first parameter, wherein the first parameter comprises a first trip parameter, a first charging parameter, a first energy consumption parameter and a first power parameter; obtaining a typical user of the vehicle type according to the first parameter and the first calculation rule, wherein the formula is as follows:
vehicle type user characteristic w1*A1+c1*A2+e1*A3+p1*A4+o1*A5；
Typical user U of vehicle type1Vehicle type user characteristic > q1；
Constructing a characteristic distribution function of a typical user vehicle using according to the first parameter and the vehicle type typical user as follows: g (w)1,c1,e1,p1,o1)；
Wherein, w1Is a first trip parameter, c1Is a first charging parameter, e1Is a first energy consumption parameter, p1Is a first power parameter, o1As a first further parameter, A1As a first trip weight, A2First charge weight, A3First energy consumption weight, A4First power weight, A5First other weight, q1Is a first preset threshold.
Specifically, the necessary features are clustered and calculated based on analysis dimensions, such as a trip dimension, a charging dimension, an energy consumption dimension, a power dimension and the like, a first parameter is obtained, a typical vehicle user is obtained according to the first parameter and a first calculation rule, statistical analysis is performed on distribution of characteristic values of the typical vehicle user, and if the characteristic value of the typical vehicle user is within a first preset threshold q1Within the range of (2), then defined as typical of the vehicle typeThe user, if the characteristic value of the vehicle type typical user is not in the first preset threshold q1Is not a typical user of the vehicle model. And constructing a characteristic distribution function of the vehicle for the typical user according to the first parameter and the typical user of the vehicle type, so as to be convenient for subsequently determining the probability density function of the vehicle type user.
The construction of the characteristic distribution function of the car for the mainstream user specifically comprises the following steps: determining a corresponding market segment according to the vehicle type characteristics of the vehicle to be evaluated, wherein the vehicle type characteristics comprise a price interval, a positive material, a range of driving mileage and a car class; determining the vehicle type under the market segment according to the market segment corresponding to the vehicle to be evaluated, and extracting corresponding second parameters, wherein the second parameters comprise a second trip parameter, a second charging parameter, a second energy consumption parameter and a second power parameter; and determining the mainstream users of the market segmentation according to the second parameter and the second calculation rule, wherein the formula is as follows:
market segment user characteristics w2*B1+c2*B2+e2*B3+p2*B4+o2*B5；
Market segment mainstream user U2Market segment user characteristics > q2；
According to the second parameter and market-segment mainstream users, constructing a characteristic distribution function of the mainstream users, namely F (w)2,c2,e2,p2,o2)；
Wherein, w2As a second trip parameter, c2Is the second charging parameter, e2Is a second energy consumption parameter, p2Is a second power parameter, o2As a second other parameter, B1As the second trip weight, B2Second charge weight, B3Second energy consumption weight, B4Second power weight, B5Second other weight, q2Is a second preset threshold.
Specifically, electric vehicles are classified into different market segments according to different vehicle type characteristics, such as price range, anode material, range of driving mileage, car class, and the like. For example, a class A car with 10-15 ten thousand positive electrode materials is divided into a market segment, and a car with 10-15 ten thousand positive electrode materials and three positive electrode materials with the endurance mileage of 300-. The market segmentation can be customized according to the difference of research objects. According to the vehicle type characteristics of the vehicle to be evaluated, a subdivided market corresponding to the vehicle to be evaluated is determined, the vehicle type corresponding to the subdivided market is determined, a second parameter corresponding to the vehicle type is searched in a database, subdivided market user characteristics are established according to the second parameter and a second calculation rule, mainstream users in the subdivided market are determined according to a second preset threshold, and a mainstream user characteristic distribution function for the mainstream users is established by combining the second parameter, so that the probability density function of the users in the subdivided market can be determined conveniently in the follow-up process.
Wherein, step S103 specifically includes: constructing a vehicle type user characteristic probability density function g (x) according to a typical user vehicle characteristic distribution function G (x), wherein the formula is as follows:
Figure BDA0003526169880000101
constructing a user characteristic probability density function f (x) of the market subdivision according to the vehicle characteristic distribution function F (x) of the mainstream user, wherein the formula is as follows:
Figure BDA0003526169880000102
according to the vehicle type user characteristic probability density function and the market subdivision user characteristic probability density function, a matching degree function is constructed, and the formula is as follows:
h(x)＝min(f(x),g(x))；
and substituting the first parameter and the second parameter into a matching degree function to obtain a travel matching degree, a charging matching degree, an energy consumption matching degree, a power matching degree and other matching degrees.
Specifically, since the first parameter and the second parameter both include the travel parameter, the charging parameter, the power parameter, the energy consumption parameter, and other parameters, the corresponding matching degrees can be obtained according to the parameters of the corresponding types, that is, the matching degrees obtained by calculating according to the matching degree function include the travel matching degree, the charging matching degree, the energy consumption matching degree, the power matching degree, and other matching degrees.
Wherein, step S104 specifically includes: the post-processing model is constructed according to the matching degree as follows:
y＝∑(x*k(x)),x∈(P,W,C,E,O)；
wherein y is the vehicle type performance matching degree, and k (x) is the corresponding weight function; all matching degrees of the vehicle type to be evaluated are brought into the post-processing model, and vehicle type performance matching degrees are obtained; and judging the quality of the matching degree of the vehicle type and the status in the market segment according to the performance matching degree of the vehicle type.
Specifically, taking the mileage matching degree as an example, the first preset threshold and the second preset threshold are both set to be 95 quantiles, and if the scores of the mileage matching degrees of the analyzed vehicle type 1 and the analyzed vehicle type 2 are 90 points and 80 points respectively, it is indicated that the mileage requirements of the main users of 90% and 80% can be compatible respectively in the endurance matching degree, and it can be found that the mileage matching degree of the vehicle type 1 is higher than that of the vehicle type 2, which means that the vehicle type 1 can meet the mileage requirements of more users in the segment market.
As shown in fig. 2, there is provided an electric vehicle performance requirement matching degree evaluation system 20, including: the system comprises an effective data acquisition module 21, a feature distribution function construction module 22, a matching degree calculation module 23 and a vehicle type matching degree evaluation module 24, wherein:
the effective data acquisition module 21 is used for extracting dynamic data and static data of the electric vehicle, acquiring effective data and storing the effective data in a database;
the characteristic distribution function building module 22 is used for obtaining a first parameter of the vehicle to be evaluated in the effective data based on the analysis dimensionality, building a characteristic distribution function of the vehicle for the typical user according to the first parameter and a first calculation rule, determining a corresponding market segment based on the vehicle type characteristic of the vehicle to be evaluated, obtaining a second parameter in the market segment, and building a characteristic distribution function of the vehicle for the mainstream user according to the second parameter and a second calculation rule;
the matching degree calculation module 23 is used for constructing a vehicle type user characteristic probability density function according to a typical user vehicle using characteristic distribution function, constructing a market subdivision user characteristic probability density function according to a mainstream user vehicle using characteristic distribution function, constructing a matching degree function according to the vehicle type user characteristic probability density function and the market subdivision user characteristic probability density function, and substituting a first parameter and a second parameter into the matching degree function to calculate the corresponding matching degree;
and the vehicle type matching degree evaluation module 24 is used for constructing a post-processing model according to the matching degree, calculating and obtaining the vehicle type performance matching degree of the vehicle type to be evaluated, and judging the status of the vehicle type to be evaluated in the market segment and the quality of the vehicle type matching degree according to the vehicle type performance matching degree.
In an embodiment, the valid data obtaining module 21 is specifically configured to: extracting dynamic data and static data of the electric vehicle; performing data cleaning on the dynamic data and the static data; according to the driving state and the charging state, segmenting the cleaned data into a vehicle type driving segment and a vehicle charging segment, and keeping corresponding vehicle dynamic information; and classifying and calculating the segmented data according to the vehicle state and the parameters to obtain effective data.
In one embodiment, the valid data acquisition module 21 is further configured to: and extracting the driving necessary characteristic and the charging necessary characteristic from the effective data according to the vehicle state, and storing the characteristics in a database.
In one embodiment, a device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 3. The device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the device is configured to provide computing and control capabilities. The memory of the device comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The database of the device is used for storing configuration templates and also can be used for storing target webpage data. The network interface of the device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize the method for evaluating the matching degree of the performance requirements of the electric vehicle.
Those skilled in the art will appreciate that the configuration shown in fig. 3 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation on the devices to which the present application may be applied, and that a particular device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a medium may be further provided, the medium stores a computer program, the computer program includes program instructions, when executed by a computer, the computer may be a part of the above-mentioned electric vehicle performance requirement matching degree evaluation system, the computer causes the computer to execute the method according to the foregoing embodiment.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented in a general purpose computing device, they may be centralized in a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented in program code executable by a computing device, such that they may be stored on a computer storage medium (ROM/RAM, magnetic disk, optical disk) for execution by a computing device, and in some cases, the steps shown or described may be performed in an order different from that described herein, or they may be separately fabricated as individual integrated circuit modules, or multiple ones of them may be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (10)

1. A method for evaluating the matching degree of the performance requirements of an electric vehicle is characterized by comprising the following steps:
extracting dynamic data and static data of the electric vehicle, acquiring effective data, and storing the effective data in a database;
based on analysis dimensionality, obtaining a first parameter of a vehicle to be evaluated in the effective data, constructing a typical user vehicle using characteristic distribution function according to the first parameter and a first calculation rule, determining a corresponding market segment based on vehicle type characteristics of the vehicle to be evaluated, obtaining a second parameter in the market segment, and constructing a mainstream user vehicle using characteristic distribution function according to the second parameter and a second calculation rule;
constructing a vehicle type user characteristic probability density function according to the typical user vehicle using characteristic distribution function, constructing a market segment user characteristic probability density function according to the mainstream user vehicle using characteristic distribution function, constructing a matching degree function according to the vehicle type user characteristic probability density function and the market segment user characteristic probability density function, and substituting a first parameter and a second parameter into the matching degree function to calculate the corresponding matching degree;
and constructing a post-processing model according to the matching degree, calculating and obtaining the vehicle type performance matching degree of the vehicle type to be evaluated, and judging the status of the vehicle type to be evaluated in the market segment and the quality of the vehicle type matching degree according to the vehicle type performance matching degree.
2. The method for evaluating the matching degree of the performance requirements of the electric vehicle according to claim 1, wherein the steps of extracting dynamic data and static data of the electric vehicle, acquiring effective data and storing the effective data in a database specifically comprise:
extracting dynamic data and static data of the electric vehicle;
performing data cleaning on the dynamic data and the static data;
dividing the cleaned data into a vehicle type driving segment and a vehicle charging segment according to the driving state and the charging state, and reserving corresponding vehicle dynamic information;
and classifying and calculating the segmented data according to the vehicle state and the parameters to obtain effective data.
3. The method for evaluating the matching degree of the performance requirements of the electric vehicle according to claim 1, wherein the steps of extracting dynamic data and static data of the electric vehicle, obtaining effective data and storing the effective data in a database further comprise:
extracting a driving necessary characteristic and a charging necessary characteristic from the effective data according to the vehicle state, and storing the driving necessary characteristic and the charging necessary characteristic into a database; the necessary driving characteristics comprise driving mileage, the relation between the driving mileage and electric quantity, travel times, driving voltage, driving temperature, driving fault rate and driving speed; the charging necessary characteristics comprise charging current, charging voltage, the probability that the charging temperature exceeds the upper limit cutoff temperature, charging power, charging time and charging fault rate.
4. The method for evaluating the matching degree of the performance demands of the electric vehicle according to claim 3, wherein the step of obtaining a first parameter of a vehicle to be evaluated from the effective data based on the analysis dimension and constructing a characteristic distribution function of a typical user vehicle according to the first parameter and a first calculation rule specifically comprises the steps of:
performing clustering calculation on the necessary characteristics based on the analysis dimensionality to obtain first parameters, wherein the first parameters comprise a first trip parameter, a first charging parameter, a first energy consumption parameter, a first power parameter and first other parameters;
obtaining a typical user of the vehicle type according to the first parameter and the first calculation rule, wherein the formula is as follows:
vehicle type user characteristic w1*A1+c1*A2+e1*A3+p1*A4+o1*A5；
Typical user U of vehicle type1Vehicle type user characteristic > q1；
Constructing a characteristic distribution function of a typical user vehicle using according to the first parameter and the vehicle type typical user as follows: g (w)1，c1，e1，p1，o1)；
Wherein, w1Is a first trip parameter, c1Is a first charging parameter, e1Is a first energy consumption parameter, p1Is a first power parameter, o1As a first further parameter, A1As a first trip weight, A2First charge weight, A3First energy consumption weight, A4First power weight, A5First other weight, q1Is a first preset threshold.
5. The method for evaluating the matching degree of the performance demands of the electric vehicle according to claim 4, wherein the step of determining the corresponding market segment based on the vehicle type characteristics of the vehicle to be evaluated, obtaining a second parameter under the market segment, and constructing a feature distribution function of the mainstream user vehicle according to the second parameter and a second calculation rule specifically comprises the steps of:
determining a corresponding market segment according to the vehicle type characteristics of the vehicle to be evaluated, wherein the vehicle type characteristics comprise a price interval, a positive material, a range of driving mileage and a car grade;
determining the vehicle type under the market segment according to the market segment corresponding to the vehicle to be evaluated, and extracting corresponding second parameters, wherein the second parameters comprise a second trip parameter, a second charging parameter, a second energy consumption parameter, a second power parameter and second other parameters;
and determining the mainstream users of the market segmentation according to the second parameters and the second calculation rule, wherein the formula is as follows:
market segment user characteristics w2*B1+c2*B2+e2*B3+p2*B4+o2*B5；
Market segment mainstream user U2Market segment user characteristics>q2；
According to the second parameters and market-segment mainstream users, a characteristic distribution function of the mainstream users for the car is constructed to be F (w)2，c2，e2，p2，o2)；
Wherein, w2Is a second trip parameter, c2Is the second charging parameter, e2Is a second energy consumption parameter, p2Is a second power parameter, o2As a second other parameter, B1As a second trip weight, B2Second charge weight, B3Second energy consumption weight, B4Second power weight, B5Second other weight, q2Is a second preset threshold.
6. The method for evaluating the matching degree of the performance requirements of the electric vehicle according to claim 5, wherein the steps of constructing a vehicle type user characteristic probability density function according to the typical user vehicle characteristic distribution function, constructing a market segment user characteristic probability density function according to the mainstream user vehicle characteristic distribution function, constructing a matching degree function according to the vehicle type user characteristic probability density function and the market segment user characteristic probability density function, and substituting a first parameter and a second parameter into the matching degree function to calculate the corresponding matching degree specifically comprise:
constructing a vehicle type user characteristic probability density function g (x) according to a typical user vehicle characteristic distribution function G (x), wherein the formula is as follows:
Figure FDA0003526169870000031
constructing a user characteristic probability density function f (x) of the market subdivision according to the mainstream user vehicle characteristic distribution function F (x), wherein the formula is as follows:
Figure FDA0003526169870000032
according to the vehicle type user characteristic probability density function and the market subdivision user characteristic probability density function, a matching degree function is constructed, and the formula is as follows:
h(x)＝min(f(x)，g(x))；
and substituting the first parameter and the second parameter into the matching degree function to obtain a travel matching degree, a charging matching degree, an energy consumption matching degree, a power matching degree and other matching degrees.
7. The method for evaluating the matching degree of the performance requirements of the electric vehicle according to claim 6, wherein a post-processing model is constructed according to the matching degree, the vehicle type performance matching degree of the vehicle type to be evaluated is calculated and obtained, and the status of the vehicle type to be evaluated in the market segment and the vehicle type matching degree are judged according to the vehicle type performance matching degree, and the method specifically comprises the following steps:
and constructing a post-processing model according to the matching degree as follows:
y＝∑(x*k(x))，x∈(P，W，C，E，O)；
wherein y is the vehicle type 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 type to be evaluated are brought into the post-processing model, and vehicle type performance matching degrees are obtained;
and judging the quality of the matching degree of the vehicle type and the status in the market segment according to the vehicle type performance matching degree.
8. An electric vehicle performance requirement matching degree evaluation system is characterized by comprising:
the effective data acquisition module is used for extracting dynamic data and static data of the electric vehicle, acquiring effective data and storing the effective data in a database;
the characteristic distribution function building module is used for obtaining a first parameter of a vehicle to be evaluated in the effective data based on the analysis dimensionality, building a characteristic distribution function of the vehicle for typical users according to the first parameter and a first calculation rule, determining a corresponding market segment based on the vehicle type characteristics of the vehicle to be evaluated, obtaining a second parameter under the market segment, and building a characteristic distribution function of the vehicle for mainstream users according to the second parameter and the second calculation rule;
the matching degree calculation module is used for constructing a vehicle type user characteristic probability density function according to the typical user vehicle using characteristic distribution function, constructing a market subdivision user characteristic probability density function according to the mainstream user vehicle using characteristic distribution function, constructing a matching degree function according to the vehicle type user characteristic probability density function and the market subdivision user characteristic probability density function, and substituting a first parameter and a second parameter into the matching degree function to calculate the corresponding matching degree;
and the vehicle type matching degree evaluation module is used for constructing a post-processing model according to the matching degree, calculating and obtaining the vehicle type performance matching degree of the vehicle type to be evaluated, and judging the status of the vehicle type to be evaluated in the market segment and the quality of the vehicle type matching degree according to the vehicle type performance matching degree.
9. An apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the computer program is executed by the processor.
10. A medium on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
CN202210196760.9A 2022-03-01 2022-03-01 Method, system, equipment and medium for evaluating matching degree of performance requirements of electric vehicle Pending CN114565283A (en)

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