Hybrid vehicle user energy consumption evaluation system and method based on cloud big data

Abstract

The invention discloses a hybrid power vehicle user energy consumption evaluation system based on cloud big data. The system comprises a data acquisition module for acquiring travel energy consumption data of a hybrid power vehicle user; the user energy consumption matrix construction module classifies the travel energy consumption data to obtain an energy consumption value of the hybrid power vehicle under each working condition, and establishes a user energy consumption matrix; the energy consumption attenuation coefficient matrix construction module calculates the energy consumption attenuation coefficient under each working condition according to the energy consumption value through the hybrid electric vehicle energy consumption authentication value, and establishes an energy consumption attenuation coefficient matrix; the weight coefficient matrix construction module performs quantity statistics on different working conditions according to set time points on the cloud big data to obtain a weight coefficient matrix of user energy consumption; and the energy consumption calculation module multiplies the hybrid power vehicle energy consumption authentication value by the hybrid power vehicle user energy consumption comprehensive weight coefficient to obtain a hybrid power vehicle user energy consumption value. According to the method, actual energy consumption evaluation can be carried out on the energy consumption of the hybrid power vehicle user in different urban user scenes, and the accuracy of a calculation result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving technologies for hybrid vehicles, and particularly to a hybrid vehicle user energy consumption evaluation system and method based on cloud big data. Background Art

[0002] Users' complaints about vehicle energy consumption come from two aspects. On the one hand, it comes from the vehicle's own energy consumption technology, and on the other hand, it comes from the difference between the actual energy consumption and the announced certification value. The existing technology predicts the user's travel energy consumption based on fragments of the vehicle's historical driving data. It can neither obtain a comprehensive travel scenario, nor reflect the impact brought by the driver's operating habits, nor can it be associated with the energy consumption announcement value, and thus cannot fully reflect the user's complaints about energy consumption. The existing technology predicts the energy consumption of future driving conditions based on fragments of the vehicle's historical driving data, rather than an energy consumption evaluation based on the user's full scenario. Summary of the Invention

[0003] The purpose of the present invention is to provide a hybrid vehicle user energy consumption evaluation system and method based on cloud big data, which improves the scientificity of the evaluation method and the accuracy of the calculation results.

[0004] To achieve this purpose, the hybrid vehicle user energy consumption evaluation system designed by the present invention based on cloud big data includes:

[0005] The data acquisition module is used to obtain the travel energy consumption data of hybrid vehicle users through the cloud big data platform;

[0006] The user energy consumption matrix construction module is used to classify the user travel energy consumption data according to multiple set dimensions, obtain the energy consumption values of the hybrid vehicle under each working condition, and establish a user energy consumption matrix based on the energy consumption values of the hybrid vehicle under each working condition;

[0007] The energy consumption decay coefficient matrix construction module is used to calculate the energy consumption decay coefficient under each working condition through the hybrid vehicle energy consumption certification value for the energy consumption values in the user energy consumption matrix, and establish an energy consumption decay coefficient matrix;

[0008] The weight coefficient matrix construction module is used to count the number of different working conditions according to the set time points for the cloud vehicle working condition big data, obtain a working condition quantity matrix, use the total count of all working condition quantities in the working condition quantity matrix as the denominator, and the statistical quantity of each working condition as the numerator to calculate the weight coefficient of each working condition, and obtain a weight coefficient matrix for user energy consumption;

[0009] The energy consumption calculation module is used to multiply the energy consumption decay coefficient corresponding to each working condition in the energy consumption decay coefficient matrix by the weight coefficient of the user's energy consumption corresponding to the same working condition in the weight coefficient matrix of the user's energy consumption, and then sum them up to obtain the comprehensive weight coefficient of the user's energy consumption of the hybrid vehicle. Then, multiply the energy consumption certification value of the hybrid vehicle by the comprehensive weight coefficient of the user's energy consumption of the hybrid vehicle to obtain the user's energy consumption value of the hybrid vehicle.

[0010] Preferably, the specific content of obtaining the travel energy consumption data of the hybrid vehicle user includes:

[0011] Divide the data collected through the cloud big data platform according to the set time interval A, calculate the average vehicle speed, driving mileage, power change amount, fuel injection amount data within this time interval, and record the driving mode, driving pattern, and environmental temperature of the vehicle driving as the travel energy consumption data of the hybrid vehicle user.

[0012] Preferably, the method of classifying the user travel energy consumption data according to multiple dimensions is to perform corresponding working condition classification according to the vehicle speed interval, environmental temperature interval, driving mode, and driving pattern. Among them, the driving mode includes pure electric mode and hybrid mode, and the driving pattern includes comfort mode and sport mode.

[0013] Preferably, the calculation formula for the energy consumption value of the hybrid vehicle under each working condition is:

[0014]

[0015] Where: K fuel is the fuel-electric conversion coefficient of the hybrid vehicle; FC is the energy consumption value in a certain time interval; ΔF fuel is the fuel consumption in a certain time interval; ΔEC is the power consumption in a certain time interval; Δs is the driving mileage in a certain time interval.

[0016] Preferably, the specific process of establishing the user energy consumption matrix includes:

[0017] Classify and divide the obtained user travel energy consumption data. The vehicle speed starts from 0, and every time the vehicle speed increases by B km / h, it is used as a new division interval; the temperature starts from C °C, and every time the temperature rises by D °C, it is used as a new division interval; to obtain the hybrid vehicle user energy consumption matrix FC1 based on the vehicle speed interval and environmental temperature interval:

[0018]

[0019] Among them, FC ij represents the user energy consumption value corresponding to the i-th row and the j-th column;

[0020] Then, further classify the above hybrid vehicle user energy consumption matrix FC1 according to the comfort mode and sport mode, pure electric mode and hybrid mode:

[0021] Establish the user energy consumption matrix FC under the comfort mode and the pure - electric mode CE :

[0022]

[0023] Among them, FC CE-11 ~FC CE-ij respectively represent the user energy consumption values in each vehicle speed range and environmental temperature range under the comfort mode and the pure - electric mode;

[0024] Establish the user energy consumption matrix FC under the comfort mode and the hybrid mode CH :

[0025]

[0026] Among them, FC CH-11 ~FC CH-ij respectively represent the user energy consumption values in each vehicle speed range and environmental temperature range under the comfort mode and the hybrid mode;

[0027] Establish the user energy consumption matrix FC under the sport mode and the pure - electric mode SE :

[0028]

[0029] Among them, FC SE-11 ~FC SE-ij respectively represent the user energy consumption values in each vehicle speed range and environmental temperature range under the sport mode and the pure - electric mode;

[0030] Establish the user energy consumption matrix FC under the sport mode and the hybrid mode SH :

[0031]

[0032] Among them, FC SH-11 ~FC SH-ij respectively represent the user energy consumption values in each vehicle speed range and environmental temperature range under the sport mode and the hybrid mode;

[0033] Obtain the user energy consumption matrix FC2 based on the vehicle speed range, environmental temperature range, and different driving modes and driving patterns:

[0034]

[0035] Preferably, the specific process of establishing the energy consumption attenuation coefficient matrix is as follows:

[0036] Calculate the energy consumption attenuation coefficient according to the energy consumption certification value of the hybrid vehicle:

[0037]

[0038] Among them: μ is the energy consumption decay coefficient of the hybrid vehicle under the specified working conditions; FC ij is the energy consumption value in the user energy consumption matrix; FC cs认证 is the certified fuel consumption value of the hybrid vehicle in the charge-sustaining mode;

[0039] Through the energy consumption decay coefficient calculation formula, replace the energy consumption value in the user energy consumption matrix with the corresponding energy consumption decay coefficient under each working condition;

[0040] Obtain the energy consumption decay coefficient matrix μ1 of the hybrid vehicle based on the vehicle speed range and the ambient temperature range:

[0041]

[0042] Among them, μ ij represents the energy consumption decay coefficient corresponding to the i-th row and the j-th column;

[0043] Then, further classify the above energy consumption decay coefficient matrix μ1 according to the comfort mode and the sport mode, the pure electric mode and the hybrid mode:

[0044] Establish the energy consumption decay coefficient matrix μ CE :

[0045]

[0046] Among them, μ CE-11 ~μ CE-ij respectively represent the energy consumption decay coefficients of each vehicle speed range and ambient temperature range in the comfort mode and the pure electric mode;

[0047] Establish the energy consumption decay coefficient matrix μ CH :

[0048]

[0049] Among them, μ CH-11 ~μ CH-ij respectively represent the energy consumption decay coefficients of each vehicle speed range and ambient temperature range in the comfort mode and the hybrid mode;

[0050] Establish the energy consumption decay coefficient matrix μ SE :

[0051]

[0052] Among them, μ SE-11 ~μ SE-ij respectively represent the energy consumption decay coefficients of each vehicle speed range and ambient temperature range in the sport mode and the pure electric mode;

[0053] Establish the energy consumption attenuation coefficient matrix μ under the sport mode and the hybrid mode SH :

[0054]

[0055] Among them, μ SH-11 ~μ SH-ij respectively represent the energy consumption attenuation coefficients in each vehicle speed range and environmental temperature range under the sport mode and the hybrid mode;

[0056] Obtain the energy consumption attenuation coefficient matrix μ2 based on the vehicle speed range, environmental temperature range, and different driving modes and driving patterns:

[0057]

[0058] Preferably, the specific process of establishing the weight coefficient matrix is as follows:

[0059] Take the data collected through the cloud big data platform with a set time period A as a data point. According to the different working conditions corresponding to each data point, count the number of data points under each working condition to obtain the working condition number matrix within the set time. Use the total number of the working condition number matrix as the denominator and the statistical number of each working condition point as the numerator;

[0060] Obtain the weight coefficient matrix W1 of the hybrid vehicle based on the vehicle speed range and environmental temperature range:

[0061]

[0062] Among them, W ij represents the weight coefficient corresponding to the i-th row and j-th column;

[0063] Then, further classify the above-mentioned weight coefficient matrix W1 of the hybrid vehicle according to the comfort mode and sport mode, pure electric mode and hybrid mode:

[0064] Establish the weight coefficient matrix W CE :

[0065]

[0066] Among them, W CE-11 ~W CE-ij respectively represent the weight coefficients in each vehicle speed range and environmental temperature range under the comfort mode and pure electric mode;

[0067] Establish the weight coefficient matrix W CH :

[0068]

[0069] Among them, W CH-11 ~W CH-ij respectively represent the weight coefficients of each vehicle speed range and ambient temperature range in the comfort mode and the hybrid mode;

[0070] Establish a weight coefficient matrix W SE :

[0071]

[0072] Among them, W SE-11 ~W SE-ij respectively represent the weight coefficients of each vehicle speed range and ambient temperature range in the sport mode and the pure - electric mode;

[0073] Establish a weight coefficient matrix W SH :

[0074]

[0075] Among them, W SH-11 ~W SH-ij respectively represent the weight coefficients of each vehicle speed range and ambient temperature range in the sport mode and the hybrid mode;

[0076] Obtain a weight coefficient matrix W2 based on the vehicle speed range, ambient temperature range, and different driving modes and driving patterns:

[0077]

[0078] Preferably, the specific process for calculating the energy consumption value of the hybrid vehicle user is as follows:

[0079] Multiply the energy consumption attenuation coefficients in the energy consumption attenuation coefficient matrix by the weight coefficients in the weight coefficient matrix corresponding to all the divided operating points and then sum them up to obtain the comprehensive weight coefficient μ of the hybrid vehicle user's energy consumption 综合 :

[0080]

[0081] Among them: μ CE-ij is the energy consumption attenuation coefficient under the pure - electric condition of the comfort mode of the hybrid vehicle; μ CH-ij is the energy consumption attenuation coefficient under the hybrid condition of the comfort mode of the hybrid vehicle; μ SE-ij is the energy consumption attenuation coefficient under the pure - electric condition of the sport mode of the hybrid vehicle; μ SH-ij is the energy consumption attenuation coefficient under the hybrid condition of the sport mode of the hybrid vehicle; W CE-ij is the weight corresponding to the energy consumption attenuation coefficient under the pure - electric condition of the comfort mode of the hybrid vehicle; W CH-ij is the weight corresponding to the energy consumption attenuation coefficient under the hybrid condition of the comfort mode of the hybrid vehicle;SE-ij is the weight corresponding to the energy consumption decay coefficient under the pure electric condition of the hybrid vehicle's motion mode; W SH-ij is the weight corresponding to the energy consumption decay coefficient under the hybrid condition of the hybrid vehicle's motion mode;

[0082] Multiply the energy consumption certification value of the hybrid vehicle by the comprehensive weight coefficient of the hybrid vehicle user's energy consumption to obtain the hybrid vehicle user's energy consumption value:

[0083] FC 用户 = FC cs认证 × μ 综合 .

[0084] A method for evaluating the energy consumption of hybrid vehicle users based on cloud big data, which includes the following steps:

[0085] Obtain the travel energy consumption data of hybrid vehicle users through the cloud big data platform;

[0086] Classify the user travel energy consumption data according to multiple set dimensions to obtain the energy consumption value of the hybrid vehicle under each working condition, and establish a user energy consumption matrix based on the energy consumption value of the hybrid vehicle under each working condition;

[0087] Calculate the energy consumption decay coefficient under each working condition through the energy consumption certification value of the hybrid vehicle in the user energy consumption matrix, and establish an energy consumption decay coefficient matrix;

[0088] Count the number of different working conditions of the cloud vehicle condition big data according to the set time point to obtain a working condition number matrix. Use the total count of all working condition numbers in the working condition number matrix as the denominator, and the count number of each working condition as the numerator to calculate the weight coefficient of each working condition, and obtain the weight coefficient matrix of the user energy consumption;

[0089] Multiply the energy consumption decay coefficient corresponding to each working condition in the energy consumption decay coefficient matrix by the weight coefficient of the user energy consumption corresponding to the same working condition in the weight coefficient matrix of the user energy consumption, and then sum them up to obtain the comprehensive weight coefficient of the hybrid vehicle user's energy consumption. Then multiply the energy consumption certification value of the hybrid vehicle by the comprehensive weight coefficient of the hybrid vehicle user's energy consumption to obtain the hybrid vehicle user's energy consumption value.

[0090] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the above method.

[0091] The beneficial effects of the present invention:

[0092] The present invention can be directly applied to the actual energy consumption evaluation in different urban user scenarios, and at the same time takes into account the market problem of complaints caused by the difference between the user's energy consumption and the fuel consumption certification value; it can obtain the actual energy consumption levels of hybrid vehicles in all scenarios in different cities or regions; because the cloud big data takes into account the actual driving behavior habits of users and these influencing factors such as different road conditions, the energy consumption evaluation of the present invention has a certain real-time nature, improving the scientific nature of the energy consumption evaluation method and the accuracy of the calculation results; at the same time, the present invention can inversely guide the rationality of the fuel consumption certification value of the hybrid vehicle's charge-sustaining mode, and can also accurately guide the market complaint points of the energy consumption of hybrid vehicle users through different scenario divisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 is a schematic structural diagram of the present invention;

[0094] Figure 2 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0095] The following further describes the present invention in detail with reference to the drawings and specific embodiments:

[0096] Embodiment 1

[0097] A hybrid vehicle user energy consumption evaluation system based on cloud big data, as Figure 1 shown, it includes:

[0098] The data acquisition module is used to obtain the travel energy consumption data of hybrid vehicle users through the cloud big data platform (the cloud big data platform is a professional data platform established by the state; upload the vehicle's background data to the cloud and record the vehicle's driving state, including vehicle speed, accelerator pedal opening, SOC level, instantaneous power consumption, energy consumption), and this design can improve the efficiency and accuracy of data acquisition;

[0099] The user energy consumption matrix construction module is used to classify the user travel energy consumption data according to multiple set dimensions to obtain the energy consumption values of the hybrid vehicle under each working condition, and establish a user energy consumption matrix based on the energy consumption values of the hybrid vehicle under each working condition. This design can intuitively display the energy consumption values of the hybrid vehicle under different working conditions;

[0100] The energy consumption decay coefficient matrix construction module is used to calculate the energy consumption decay coefficient under each working condition through the hybrid vehicle energy consumption certification value for the energy consumption values in the user energy consumption matrix, and establish an energy consumption decay coefficient matrix. This design calculates the energy consumption decay coefficient through the hybrid vehicle energy consumption certification value, and can more accurately evaluate the difference between the actual energy consumption performance of the vehicle and the certification value;

[0101] The weight coefficient matrix construction module is used to count the number of different working conditions according to the set time point for the big data of vehicle working conditions in the cloud (classified by different vehicle speed intervals, different environmental temperature intervals, as well as different driving modes and driving patterns), and obtain a working condition quantity matrix (the working condition quantity matrix takes the statistical quantity of each working condition as an element in the working condition quantity matrix, which can intuitively represent the quantity of different working conditions). Taking the total statistical quantity of all working condition quantities in the working condition quantity matrix as the denominator and the statistical quantity of each working condition as the numerator, calculate the weight coefficient of each working condition, and obtain the weight coefficient matrix of user energy consumption. This design determines the weight coefficient by counting the occurrence frequency of different working conditions through the quantity matrix, making the energy consumption evaluation more objective and fair;

[0102] The energy consumption calculation module is used to multiply the energy consumption decay coefficient corresponding to each working condition in the energy consumption decay coefficient matrix by the weight coefficient of user energy consumption corresponding to the same working condition in the weight coefficient matrix of user energy consumption, and then sum them up to obtain the comprehensive weight coefficient of hybrid vehicle user energy consumption. Then multiply the hybrid vehicle energy consumption certification value by the comprehensive weight coefficient of hybrid vehicle user energy consumption to obtain the hybrid vehicle user energy consumption value. This design can obtain a more comprehensive and accurate hybrid vehicle user energy consumption value, and the obtained output result can be used to guide users to improve driving habits, optimize vehicle configurations, and provide product improvement suggestions for manufacturers.

[0103] In the above technical solution, the specific content of obtaining the travel energy consumption data of hybrid vehicle users includes:

[0104] Divide the data collected through the cloud big data platform according to the set time interval A (A is 10s), calculate the average vehicle speed, driving mileage, power change amount, fuel injection amount data within this time interval (these data such as average vehicle speed, driving mileage, power change amount, fuel injection amount data are used in the calculation of converting power consumption to fuel consumption), and record the driving pattern, driving mode, and environmental temperature of the vehicle during driving as the travel energy consumption data of hybrid vehicle users; The above design obtains the travel energy consumption data of hybrid vehicle users for use in the calculation of converting power consumption to fuel consumption.

[0105] In the above technical solution, the method of classifying the user travel energy consumption data according to multiple dimensions is to perform corresponding working condition classification according to vehicle speed interval, environmental temperature interval, driving pattern, and driving mode (one working condition is a working condition point, and each vehicle speed interval, environmental temperature interval, driving pattern, and driving mode corresponds to one working condition). Among them, the driving pattern includes pure electric mode and hybrid mode, and the driving mode includes comfort mode and sport mode; The above design classifies the user travel energy consumption data according to multiple dimensions, which can improve the accuracy of data analysis.

[0106] In the above technical solution, the calculation formula for the energy consumption value of the hybrid vehicle under each working condition is:

[0107]

[0108] Where: K fuel is the fuel - electricity conversion coefficient of the hybrid vehicle, used to convert the electricity consumption per unit interval into fuel consumption, with the measurement unit of L / 100km; FC is the energy consumption value in a certain time interval, that is, the energy consumption value of the hybrid vehicle under each working condition, with the measurement unit of L / 100km; ΔF fuel is the fuel consumption in a certain time interval, with the measurement unit of mL; ΔEC is the electricity consumption in a certain time interval, with the measurement unit of Wh; Δs is the driving mileage in a certain time interval, with the measurement unit of km; Through the introduction of the fuel - electricity conversion coefficient of the hybrid vehicle, the above design enables the electricity consumption to be converted into fuel consumption, thus realizing the unified measurement of the energy consumption of the hybrid vehicle, and can calculate the energy consumption value of the hybrid vehicle under each working condition more accurately, improving the accuracy of energy consumption calculation.

[0109] In the above technical solution, the specific process of establishing the user energy consumption matrix includes:

[0110] Classify and divide the obtained user travel energy consumption data. The vehicle speed starts from 0, and every time the vehicle speed increases by B (B = 10) km / h, it is used as a new division interval; The temperature starts from C (C = - 15) °C, and every time the temperature rises by D (D = 5) °C, it is used as a new division interval; Obtain the hybrid vehicle user energy consumption matrix FC1 based on the vehicle speed interval and the environmental temperature interval:

[0111]

[0112] Among them, FC ij represents the user energy consumption value corresponding to the i - th row and j - th column;

[0113] Then, further classify the above - mentioned hybrid vehicle user energy consumption matrix FC1 according to the comfort mode and the sport mode, the pure - electric mode and the hybrid mode:

[0114] Establish the user energy consumption matrix FC CE :

[0115]

[0116] Among them, FC CE-11 ~FC CE-ij respectively represent the user energy consumption values in each vehicle speed interval and environmental temperature interval under the comfort mode and the pure - electric mode;

[0117] Establish the user energy consumption matrix FC CH :

[0118]

[0119] Among them, FC CH-11 ~FC CH-ij respectively represent the user energy consumption values in each vehicle speed range and ambient temperature range under the comfort mode and the hybrid mode;

[0120] Establish the user energy consumption matrix FC SE :

[0121]

[0122] Among them, FC SE-11 ~FC SE-ij respectively represent the user energy consumption values in each vehicle speed range and ambient temperature range under the sport mode and the pure - electric mode;

[0123] Establish the user energy consumption matrix FC SH :

[0124]

[0125] Among them, FC SH-11 ~FC SH-ij respectively represent the user energy consumption values in each vehicle speed range and ambient temperature range under the sport mode and the hybrid mode;

[0126] Obtain the user energy consumption matrix FC2 based on the vehicle speed range, ambient temperature range, and different driving modes and driving patterns:

[0127]

[0128] That is, obtain the user energy consumption as shown in the following table based on the vehicle speed range, ambient temperature range, and different driving modes and driving patterns:

[0129]

[0130] Among them, FC CE-ij represents the user energy consumption value when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C under the comfort mode and the pure - electric mode;

[0131] FC CH-ij represents the user energy consumption value when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C under the comfort mode and the hybrid mode;

[0132] FC SE-ij represents the user energy consumption value when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C under the sport mode and the pure - electric mode;

[0133] FC SH-ijIt represents the user energy consumption value when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C in the sport mode and the hybrid mode;

[0134] The above design can visually display the energy consumption values of hybrid vehicles under different working conditions, and prepare data for calculating the energy consumption attenuation coefficient.

[0135] In the above technical solution, the specific process of establishing the energy consumption attenuation coefficient matrix is as follows:

[0136] Calculate the energy consumption attenuation coefficient according to the energy consumption certification value of the hybrid vehicle:

[0137]

[0138] Where: μ is the energy consumption attenuation coefficient of the hybrid vehicle under the specified working condition; FC ij is the energy consumption value in the user energy consumption matrix (unit: L / 100 km); FC cs认证 is the certified fuel consumption value of the hybrid vehicle in the charge-sustaining mode (unit: L / 100 km);

[0139] Through the energy consumption attenuation coefficient calculation formula, replace the energy consumption values in the user energy consumption matrix with the corresponding energy consumption attenuation coefficients for each working condition;

[0140] Obtain the energy consumption attenuation coefficient matrix μ1 of the hybrid vehicle based on the vehicle speed range and the ambient temperature range:

[0141]

[0142] Where, μ ij represents the energy consumption attenuation coefficient corresponding to the i-th row and the j-th column;

[0143] Then, further classify the above energy consumption attenuation coefficient matrix μ1 according to the comfort mode and the sport mode, the pure electric mode and the hybrid mode:

[0144] Establish the energy consumption attenuation coefficient matrix μ CE :

[0145]

[0146] Where, μ CE-11 ~μ CE-ij respectively represent the energy consumption attenuation coefficients of each vehicle speed range and ambient temperature range in the comfort mode and the pure electric mode;

[0147] Establish the energy consumption attenuation coefficient matrix μ CH :

[0148]

[0149] Among them, μ CH-11 ~μ CH-ij respectively represent the energy consumption attenuation coefficients in each vehicle speed range and ambient temperature range under the comfort mode and the hybrid mode;

[0150] Establish the energy consumption attenuation coefficient matrix μ SE :

[0151]

[0152] Among them, μ SE-11 ~μ SE-ij respectively represent the energy consumption attenuation coefficients in each vehicle speed range and ambient temperature range under the sport mode and the pure electric mode;

[0153] Establish the energy consumption attenuation coefficient matrix μ SH :

[0154]

[0155] Among them, μ SH-11 ~μ SH-ij respectively represent the energy consumption attenuation coefficients in each vehicle speed range and ambient temperature range under the sport mode and the hybrid mode;

[0156] Obtain the energy consumption attenuation coefficient matrix μ2 based on the vehicle speed range, the ambient temperature range, and different driving modes and driving patterns:

[0157]

[0158] That is, obtain the energy consumption attenuation coefficients shown in the following table based on the vehicle speed range, the ambient temperature range, and different driving modes and driving patterns:

[0159]

[0160] Among them, μ CE-ij represents the energy consumption attenuation coefficient when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C under the comfort mode and the pure electric mode;

[0161] μ CH-ij represents the energy consumption attenuation coefficient when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C under the comfort mode and the hybrid mode;

[0162] μ SE-ij represents the energy consumption attenuation coefficient when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C under the sport mode and the pure electric mode;

[0163] μ SH-ijIt represents the energy consumption attenuation coefficient when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C in the sports mode and the hybrid mode;

[0164] The above design can calculate the energy consumption attenuation coefficient through the certified value of the energy consumption of the hybrid vehicle, and can more accurately evaluate the difference between the actual energy consumption performance of the vehicle and the certified value.

[0165] In the above technical solution, the specific process of establishing the weight coefficient matrix is as follows:

[0166] Taking the data collected through the cloud big data platform with a set time period A (A is 10 s) as a data point, according to the different working conditions corresponding to each data point, counting the number of data points under each working condition, obtaining the working condition number matrix within the set time, using the total number of the working condition number matrix as the denominator and the statistical number of each working condition point as the numerator;

[0167] Obtaining the weight coefficient matrix W1 of the hybrid vehicle based on the vehicle speed range and the ambient temperature range:

[0168]

[0169] Among them, W ij represents the weight coefficient corresponding to the i-th row and the j-th column;

[0170] Then, further classify the above weight coefficient matrix W1 of the hybrid vehicle according to the comfort mode and the sports mode, and the pure electric mode and the hybrid mode:

[0171] Establishing the weight coefficient matrix W CE :

[0172]

[0173] Among them, W CE-11 ~W CE-ij respectively represent the weight coefficients of each vehicle speed range and ambient temperature range in the comfort mode and the pure electric mode;

[0174] Establishing the weight coefficient matrix W CH :

[0175]

[0176] Among them, W CH-11 ~W CH-ij respectively represent the weight coefficients of each vehicle speed range and ambient temperature range in the comfort mode and the hybrid mode;

[0177] Establishing the weight coefficient matrix W SE :

[0178]

[0179] Among them, W SE-11 ~W SE-ij respectively represent the weight coefficients for each vehicle speed range and ambient temperature range under the sports mode and the pure electric mode;

[0180] Establish the weight coefficient matrix W under the sports mode and the hybrid mode SH :

[0181]

[0182] Among them, W SH-11 ~W SH-ij respectively represent the weight coefficients for each vehicle speed range and ambient temperature range under the sports mode and the hybrid mode;

[0183] Obtain the weight coefficient matrix W2 based on the vehicle speed range, the ambient temperature range, and different driving modes and driving patterns:

[0184]

[0185] That is, obtain the weight coefficients shown in the following table based on the vehicle speed range, the ambient temperature range, and different driving modes and driving patterns:

[0186]

[0187]

[0188] Among them, W CE-ij represents the weight coefficient when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C in the comfort mode and the pure electric mode;

[0189] W CH-ij represents the weight coefficient when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C in the comfort mode and the hybrid mode;

[0190] W SE-ij represents the weight coefficient when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C in the sports mode and the pure electric mode;

[0191] W SH-ij represents the weight coefficient when the vehicle speed range is 110 - 120 km / h and the ambient temperature range is 40 - 45 °C in the sports mode and the hybrid mode;

[0192] The above design statistically analyzes the occurrence frequencies under different working conditions and determines the weight coefficients, making the energy consumption evaluation more objective and fair.

[0193] In the above technical solution, the specific process of calculating the energy consumption value of the hybrid vehicle user is as follows:

[0194] Multiply the energy consumption attenuation coefficient in the energy consumption attenuation coefficient matrix by the weight coefficient in the weight coefficient matrix corresponding to all the divided working condition points and then sum them up to obtain the comprehensive weight coefficient μ of the energy consumption of the hybrid vehicle user 综合 :

[0195]

[0196] Where: μ CE-ij is the energy consumption attenuation coefficient in the pure electric working condition of the comfortable mode of the hybrid vehicle; μ CH-ij is the energy consumption attenuation coefficient in the hybrid working condition of the comfortable mode of the hybrid vehicle; μ SE-ij is the energy consumption attenuation coefficient in the pure electric working condition of the sport mode of the hybrid vehicle; μ SH-ij is the energy consumption attenuation coefficient in the hybrid working condition of the sport mode of the hybrid vehicle; W CE-ij is the weight corresponding to the energy consumption attenuation coefficient in the pure electric working condition of the comfortable mode of the hybrid vehicle; W CH-ij is the weight corresponding to the energy consumption attenuation coefficient in the hybrid working condition of the comfortable mode of the hybrid vehicle; W SE-ij is the weight corresponding to the energy consumption attenuation coefficient in the pure electric working condition of the sport mode of the hybrid vehicle; W SH-ij is the weight corresponding to the energy consumption attenuation coefficient in the hybrid working condition of the sport mode of the hybrid vehicle;

[0197] Multiply the energy consumption certification value of the hybrid vehicle by the comprehensive weight coefficient of the energy consumption of the hybrid vehicle user to obtain the energy consumption value of the hybrid vehicle user:

[0198] FC 用户 = FC cs认证 × μ 综合 ;

[0199] Through the above design, the comprehensive weight coefficient of the energy consumption of the hybrid vehicle user is obtained through the energy consumption attenuation coefficient and the corresponding weight coefficient, so as to obtain a more comprehensive and accurate energy consumption value of the hybrid vehicle user. The obtained output result can be used to guide users to improve driving habits, optimize vehicle configurations, and provide product improvement suggestions for manufacturers.

[0200] Embodiment 2

[0201] A method for evaluating the energy consumption of hybrid vehicle users based on cloud big data, such as Figure 2As shown, obtain the travel energy consumption data of hybrid vehicle users; classify the user travel energy consumption data to obtain the energy consumption values of hybrid vehicles under each working condition, and establish a user energy consumption matrix; calculate the energy consumption attenuation coefficient under each working condition by using the energy consumption value through the hybrid vehicle energy consumption certification value, and establish an energy consumption attenuation coefficient matrix; count the number of different working conditions of the cloud big data according to the set time point to obtain a working condition number matrix, use the total number of statistical quantities in the working condition number matrix as the denominator and the statistical quantity of each working condition as the numerator to obtain the weight coefficient matrix of user energy consumption; multiply the hybrid vehicle energy consumption certification value by the comprehensive weight coefficient of hybrid vehicle user energy consumption to obtain the hybrid vehicle user energy consumption value.

[0202] The specific method for evaluating the energy consumption of hybrid vehicle users includes the following steps:

[0203] Through the cloud big data platform, obtain the travel energy consumption data of hybrid vehicle users;

[0204] Classify the user travel energy consumption data according to multiple set dimensions to obtain the energy consumption values of hybrid vehicles under each working condition, and establish a user energy consumption matrix based on the energy consumption values of hybrid vehicles under each working condition;

[0205] Calculate the energy consumption attenuation coefficient under each working condition by using the energy consumption value in the user energy consumption matrix through the hybrid vehicle energy consumption certification value, and establish an energy consumption attenuation coefficient matrix;

[0206] Count the number of different working conditions of the cloud vehicle working condition big data according to the set time point to obtain a working condition number matrix, use the total number of all working condition quantities in the working condition number matrix as the denominator and the statistical quantity of each working condition as the numerator to calculate the weight coefficient of each working condition, and obtain the weight coefficient matrix of user energy consumption;

[0207] Multiply and accumulate the energy consumption attenuation coefficient corresponding to each working condition in the energy consumption attenuation coefficient matrix by the weight coefficient of user energy consumption corresponding to the same working condition in the weight coefficient matrix of user energy consumption to obtain the comprehensive weight coefficient of hybrid vehicle user energy consumption, and then multiply the hybrid vehicle energy consumption certification value by the comprehensive weight coefficient of hybrid vehicle user energy consumption to obtain the hybrid vehicle user energy consumption value.

[0208] Embodiment 3

[0209] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method described in Embodiment 2.

[0210] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0211] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a system for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of multiple flows and / or blocks.

[0212] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction system that realizes the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of multiple flows and / or blocks.

[0213] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of multiple flows and / or blocks.

[0214] The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

Claims

1. A hybrid vehicle user energy consumption evaluation system based on cloud big data, characterized in that it include: The data collection module is used to obtain the travel energy consumption data of hybrid vehicle users through the cloud big data platform; The user energy consumption matrix building module is used to classify the user travel energy consumption data according to multiple set dimensions, obtain the energy consumption value of the hybrid vehicle under each working condition, and establish the user energy consumption matrix according to the energy consumption value of the hybrid vehicle under each working condition; The energy consumption attenuation coefficient matrix building module is used to calculate the energy consumption attenuation coefficient under each working condition by using the energy consumption value in the user energy consumption matrix through the hybrid vehicle energy consumption certification value, and establish the energy consumption attenuation coefficient matrix; The weight coefficient matrix construction module is used to count the number of different working conditions according to the set time point of the cloud vehicle working condition big data to obtain the working condition quantity matrix, and the total number of all working conditions in the working condition quantity matrix is ​​used as the denominator, and the statistical number of each working condition is used as the numerator to calculate the weight coefficient of each working condition, and obtain the weight coefficient matrix of user energy consumption; The energy consumption calculation module is used to multiply the energy consumption attenuation coefficient corresponding to each working condition in the energy consumption attenuation coefficient matrix by the weight coefficient of the user energy consumption under the corresponding working condition in the user energy consumption weight coefficient matrix, and then add them together to obtain the comprehensive weight coefficient of the hybrid vehicle user energy consumption, and then multiply the hybrid vehicle energy consumption certification value by the hybrid vehicle user energy consumption comprehensive weight coefficient to obtain the hybrid vehicle user energy consumption value.

2. The hybrid vehicle user energy consumption evaluation system based on cloud big data according to claim 1 is characterized by: The specific contents of obtaining the travel energy consumption data of hybrid vehicle users include: The data collected through the cloud big data platform is divided according to the set time interval A, and the average vehicle speed, mileage, battery change, and fuel injection data within the time interval are calculated. The vehicle's driving mode, driving mode, and ambient temperature are also recorded as travel energy consumption data for hybrid vehicle users.

3. The hybrid vehicle user energy consumption evaluation system based on cloud big data according to claim 1 is characterized by: The method of classifying the user travel energy consumption data according to multiple dimensions is to classify the corresponding working conditions according to vehicle speed range, ambient temperature range, driving mode and driving mode, where the driving mode includes pure electric mode and hybrid mode, and the driving mode includes comfort mode and sports mode.

4. The hybrid vehicle user energy consumption evaluation system based on cloud big data according to claim 1 is characterized by: The calculation formula of the energy consumption value of the hybrid vehicle under each working condition is: Where: K fuel is the oil-to-electric conversion coefficient of the hybrid vehicle; FC is the energy consumption value in a certain time interval; ΔF fuel is the fuel consumption in a certain time interval; ΔEC is the electricity consumption in a certain time interval; Δs is the mileage in a certain time interval.

5. The hybrid vehicle user energy consumption evaluation system based on cloud big data according to claim 1 is characterized by: The specific process of establishing the user energy consumption matrix includes: The obtained user travel energy consumption data is classified and divided. The vehicle speed is taken as 0 as the starting point, and each increase in vehicle speed is a new division interval. The temperature is taken as C℃ as the starting point, and each increase in temperature is D℃ as a new division interval. The hybrid vehicle user energy consumption matrix FC1 based on the vehicle speed interval and the ambient temperature interval is obtained: Among them, FC ij Indicates the user energy consumption value corresponding to the i-th row and j-th column; Then, the above hybrid vehicle user energy consumption matrix FC1 is further classified according to comfort mode and sports mode, pure electric mode and hybrid mode: Establish the user energy consumption matrix FC in comfort mode and pure electric mode CE : Among them, FC CE-11 ~FC CE-ij Respectively represent the user energy consumption values ​​in each vehicle speed range and ambient temperature range in comfort mode and pure electric mode; Establish the user energy consumption matrix FC in comfort mode and hybrid mode CH : Among them, FC CH-11 ~FC CH-ij Respectively represent the user energy consumption values ​​in each vehicle speed range and ambient temperature range in comfort mode and hybrid mode; Establish the user energy consumption matrix FC in sports mode and pure electric mode SE : Among them, FC SE-11 ~FC SE-ij Respectively represent the user energy consumption values ​​in each vehicle speed range and ambient temperature range in sports mode and pure electric mode; Establish the user energy consumption matrix FC in sports mode and hybrid mode SH : Among them, FC SH-11 ~FC SH-ij Respectively represent the user energy consumption values ​​in each vehicle speed range and ambient temperature range in sports mode and hybrid mode; The user energy consumption matrix FC2 based on vehicle speed range, ambient temperature range, and different driving modes and travel modes is obtained:

6. The hybrid vehicle user energy consumption evaluation system based on cloud big data according to claim 1 is characterized by: The specific process of establishing the energy consumption attenuation coefficient matrix is ​​as follows: According to the hybrid vehicle energy consumption certification value, calculate the energy consumption attenuation coefficient: Where: μ is the energy consumption attenuation coefficient of the hybrid vehicle under specified working conditions; FC ij is the energy consumption value in the user energy consumption matrix; FC cs认证 Certified value for fuel consumption in power-sustaining mode for hybrid vehicles; Through the energy consumption attenuation coefficient calculation formula, the energy consumption value in the user energy consumption matrix is ​​replaced with the corresponding energy consumption attenuation coefficient under each working condition; The hybrid vehicle energy consumption attenuation coefficient matrix μ1 based on the vehicle speed range and ambient temperature range is obtained: Among them, μ ij represents the energy consumption attenuation coefficient corresponding to the i-th row and j-th column; Then, the energy consumption attenuation coefficient matrix μ1 is further classified according to comfort mode, sports mode, pure electric mode and hybrid mode: Establish the energy consumption attenuation coefficient matrix μ in comfort mode and pure electric mode CE : Among them, μ CE-11 ~μ CE-ij Respectively represent the energy consumption attenuation coefficients in each vehicle speed range and ambient temperature range in comfort mode and pure electric mode; Establish the energy consumption attenuation coefficient matrix μ in comfort mode and hybrid mode CH : Among them, μ CH-11 ~μ CH-ij Respectively represent the energy consumption attenuation coefficients in each vehicle speed range and ambient temperature range in comfort mode and hybrid mode; Establish the energy consumption attenuation coefficient matrix μ in sports mode and pure electric mode SE : Among them, μ SE-11 ~μ SE-ij Respectively represent the energy consumption attenuation coefficients in various vehicle speed ranges and ambient temperature ranges in sports mode and pure electric mode; Establish the energy consumption attenuation coefficient matrix μ in sports mode and hybrid mode SH : Among them, μ SH-11 ~μ SH-ij Respectively represent the energy consumption attenuation coefficients in each vehicle speed range and ambient temperature range in sports mode and hybrid mode; The energy consumption attenuation coefficient matrix μ2 based on vehicle speed range, ambient temperature range, and different driving modes and travel modes is obtained:

7. The hybrid vehicle user energy consumption evaluation system based on cloud big data according to claim 1 is characterized by: The specific process of establishing the weight coefficient matrix is ​​as follows: The data collected through the cloud big data platform is set to a time period A as a data point. According to the different working conditions corresponding to each data point, the number of data points under each working condition is counted to obtain the working condition quantity matrix within the set time, with the total number of the working condition quantity matrix as the denominator and the statistical number of each working condition point as the numerator; The hybrid vehicle weight coefficient matrix W1 based on the vehicle speed range and ambient temperature range is obtained: Among them, W ij Represents the weight coefficient corresponding to the i-th row and j-th column; Then, the hybrid vehicle weight coefficient matrix W1 is further classified according to comfort mode and sports mode, pure electric mode and hybrid mode: Establish the weight coefficient matrix W in comfort mode and pure electric mode CE : Among them, W CE-11 ~W CE-ij Respectively represent the weight coefficients of each vehicle speed range and ambient temperature range in comfort mode and pure electric mode; Establish the weight coefficient matrix W in comfort mode and hybrid mode CH : Among them, W CH-11 ~W CH-ij Respectively represent the weight coefficients of each vehicle speed range and ambient temperature range in comfort mode and hybrid mode; Establish the weight coefficient matrix W in sports mode and pure electric mode SE : Among them, W SE-11 ~W SE-ij Respectively represent the weight coefficients of each vehicle speed range and ambient temperature range in sports mode and pure electric mode; Establish the weight coefficient matrix W in sports mode and hybrid mode SH : Among them, W SH-11 ~W SH-ij Respectively represent the weight coefficients of each vehicle speed range and ambient temperature range in the sports mode and hybrid mode; The weight coefficient matrix W2 based on the vehicle speed range, ambient temperature range, and different driving modes and travel modes is obtained:

8. The hybrid vehicle user energy consumption evaluation system based on cloud big data according to claim 6 and claim 7 is characterized in that: The specific process of calculating the hybrid vehicle user energy consumption value is as follows: The energy consumption attenuation coefficient in the energy consumption attenuation coefficient matrix is ​​multiplied by the weight coefficient in the weight coefficient matrix according to all the divided working conditions, and then added up to obtain the comprehensive weight coefficient μ of the hybrid vehicle user energy consumption. 综合 : Where: μ CE-ij is the energy consumption attenuation coefficient of the hybrid vehicle in the pure electric condition in the comfort mode; μ CH-ij is the energy consumption attenuation coefficient of the hybrid vehicle in the comfort mode of hybrid operation; μ SE-ij is the energy consumption attenuation coefficient of the hybrid vehicle in pure electric mode; μ SH-ij W is the energy consumption attenuation coefficient of the hybrid vehicle in sports mode and hybrid working condition; CE-ij W is the weight corresponding to the energy consumption attenuation coefficient of the hybrid vehicle in the pure electric condition in the comfort mode; CH-ij W is the weight corresponding to the energy consumption attenuation coefficient of the hybrid vehicle in the comfort mode of hybrid operation; SE-ij W is the weight corresponding to the energy consumption attenuation coefficient of the hybrid vehicle in pure electric mode; SH-ij The corresponding weight of the energy consumption attenuation coefficient under the hybrid working condition of the hybrid vehicle sports mode; Multiply the hybrid vehicle energy consumption certification value by the hybrid vehicle user energy consumption comprehensive weight coefficient to obtain the hybrid vehicle user energy consumption value: FC 用户 =FC cs认证 ×μ 综合 。 9. A hybrid vehicle user energy consumption evaluation method based on cloud big data, characterized in that: It follows the steps: Obtain travel energy consumption data of hybrid vehicle users through the cloud big data platform; The user travel energy consumption data is classified according to multiple set dimensions to obtain the energy consumption value of the hybrid vehicle under each working condition, and a user energy consumption matrix is ​​established according to the energy consumption value of the hybrid vehicle under each working condition; The energy consumption values ​​in the user energy consumption matrix are calculated by using the hybrid vehicle energy consumption certification value to calculate the energy consumption attenuation coefficient under each working condition, and an energy consumption attenuation coefficient matrix is ​​established; The cloud-based vehicle operating condition big data is used to count the number of different operating conditions at set time points to obtain an operating condition quantity matrix. The total number of all operating conditions in the operating condition quantity matrix is ​​used as the denominator, and the statistical number of each operating condition is used as the numerator to calculate the weight coefficient of each operating condition to obtain the weight coefficient matrix of user energy consumption; The energy consumption attenuation coefficient corresponding to each working condition in the energy consumption attenuation coefficient matrix is ​​multiplied by the weight coefficient of the user energy consumption under the corresponding working condition in the user energy consumption weight coefficient matrix, and then the comprehensive weight coefficient of the hybrid vehicle user energy consumption is obtained. Then, the hybrid vehicle energy consumption certification value is multiplied by the comprehensive weight coefficient of the hybrid vehicle user energy consumption to obtain the hybrid vehicle user energy consumption value.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in claim 9 are implemented.

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