CN114954022A - Electric automobile cloud cooperative control device and method - Google Patents
Electric automobile cloud cooperative control device and method Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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Abstract
The application relates to a vehicle cloud cooperative control device and method for electric vehicles, which comprises an electric vehicle management system, a cloud platform and a charger system, wherein charging historical data and energy consumption historical data of N electric vehicles in the same batch in one operation period are obtained through the cloud platform; calculating a first estimated charging time T according to the charging historical data and the energy consumption historical data a The second estimated charging time T b And simulating driving range S 1 (ii) a Calculating a first average accuracy rate a, a second average accuracy rate b and a third average accuracy rate c; and obtaining the estimated charging time T and the estimated driving range S through weighting calculation. The method is based on the cloud management platform and the big data, and the data is collected according to the actual operation condition of the vehicleThe vehicle cloud collaborative intelligent management is realized by combining the online calculation and the offline calculation, the vehicle charging remaining time and the remaining mileage are accurately estimated, the worry of the driver mileage is eliminated, and the safe and reliable operation of the vehicle is guaranteed.
Description
Technical Field
The application relates to the field of electric vehicle control management, in particular to a vehicle cloud cooperative control device and method for an electric vehicle.
Background
At present, the worry of a driver about the mileage of an electric vehicle is still a difficult problem which troubles the development of the industry, because of the complexity of the working condition and the operation environment of the electric vehicle, the remaining mileage of the vehicle is estimated according to the whole vehicle state information at the current moment, the error is large, the jump of the driving mileage is frequently displayed, unexpected vehicle breakdown accidents happen suddenly, the anxiety and the dissatisfaction of users are caused, and the popularization and the application of new energy vehicles are also seriously influenced. In addition, when the electric vehicle is charged, because the charging time is influenced by various factors, the estimation difficulty of the charging remaining time in the current state is high, and when a user charges the electric vehicle, the waiting expectation of the customer is influenced because the charging remaining time is inaccurate, the vehicle operation route cannot be planned in advance, the vehicle operation mode is interfered, and the benefit maximization cannot be realized.
Disclosure of Invention
The invention aims to provide a vehicle cloud cooperative control device and method for an electric vehicle, which are based on a cloud management platform and big data, adopt a mode of combining online calculation and offline calculation according to the actual operation condition of the vehicle, realize vehicle cloud cooperative intelligent management, accurately estimate the residual charging time and the residual mileage of the vehicle, eliminate the worry about the mileage of a driver and ensure the safe and reliable operation of the vehicle.
The technical scheme adopted by the application is as follows: a vehicle cloud cooperative control method for an electric vehicle comprises the following steps:
s101: acquiring charging historical data and energy consumption historical data of N electric vehicles in the same batch in an operation period, wherein the charging historical data comprises charging time of the N electric vehicles under different battery electric quantity values, charging current values and battery temperature values, and the energy consumption historical data comprises driving ranges of the N electric vehicles under different environmental temperatures, battery electric quantity values, battery temperature values, air conditioner starting conditions and road conditions;
s102: calculating a first estimated charging time T according to the charging time under different battery electric quantity values and charging current values for the charging history data obtained in the step S101 a Calculating a second estimated charging time T according to the charging times of different battery electric quantity values and battery temperature values b ；
Inputting the environmental temperature, the battery electric quantity value, the battery temperature value, the air conditioner starting condition and the road condition of each energy consumption historical data into the trained BP neural network according to the energy consumption historical data obtained in the step S101 to obtain the simulated driving range S corresponding to each energy consumption historical data 1 ；
S103: for the N electric vehicles in the step S101, each charging history data of each electric vehicle and the corresponding first estimated time T a And a second estimated time T b Comparing, and respectively calculating a first estimated charging time accuracy rate and a second estimated charging time accuracy rate of each electric automobile; calculating an average value of the first estimated charging time accuracy rates of the N electric automobiles to obtain a first average accuracy rate a; calculating an average value of second estimated charging time accuracy rates of the N electric automobiles to obtain a second average accuracy rate b;
comparing each energy consumption historical data of each electric automobile with the corresponding simulated driving range, respectively calculating the estimated driving range accuracy of each electric automobile, and calculating the average value of the estimated driving range accuracy of the N electric automobiles to obtain a third average accuracy c;
s104: according to the first average accuracy rate a and the second average accuracy rate b, the first estimated charging time T is calculated a The second estimated charging time T b Performing weighted calculation to obtain estimated charging time T; simulating the driving range S according to the third average accuracy rate c 1 And performing weighted calculation to obtain the estimated driving range S.
Further, the specific method for calculating the first estimated charging time and the second estimated charging time in step S102 is as follows:
the battery electric quantity value range is [0, 100 ]]Segmenting at fixed intervals, and setting the range of charging current value [0, I max ]At a fixed intervalIs subjected to segmentation, wherein I max Is the maximum charging current; averaging the data of the charging historical data in different battery electric quantity value sections and charging current value sections to serve as a first estimated charging time T under the current battery electric quantity value section and the charging current value section a ；
The battery electric quantity value range is [0, 100 ]]Segmented at regular intervals, the battery temperature value [ T min ，T max ]Is segmented at regular intervals, where T min Is the lowest temperature T of the normal work of the power battery max The maximum temperature of the power battery in normal operation is obtained; averaging the charging historical data in different battery electric quantity value sections and battery temperature value sections to serve as a second estimated charging time T under the current battery electric quantity value section and the charging current value section b 。
Further, the battery electric quantity value range is segmented at intervals of 5%, the charging current value range is segmented at intervals of 5A, and the battery temperature value range is segmented at intervals of 5 ℃.
Further, the charging history data is discarded without being used for calculating the first estimated charging time T according to the rule that the charging time is longer when the charging history data meets the rule that the charging current value is smaller and the charging current value is smaller a 。
Further, the charging history data is discarded according to the rule that the lower the battery electric quantity value is met, the lower the battery temperature value is at the proper charging temperature, or the higher the battery temperature value is at the proper charging temperature and the longer the charging time is, and is not used for calculating the second estimated charging time T b 。
Further, the specific method for calculating the first estimated charging time accuracy and the second estimated charging time accuracy of each electric vehicle in step S103 is as follows:
in one operation period, X charging historical data T are provided for a single-quantity electric automobile r Calculating the first estimated time T corresponding to each charging history data a And a second estimated time T b And calculates the individual charging history of the individual cars according to the following formulaAccuracy of data:
a 1 ＝1-|T a -T r |/T r
b 1 ＝1-|T b -T r |/T r
wherein, a 1 First estimated charging time accuracy of individual charging history data for individual vehicles, b 1 A second estimated charging time accuracy for a single charging history data of a single vehicle;
and respectively averaging the first estimated charging time accuracy rate and the second estimated charging time accuracy rate of X charging historical data of a single electric vehicle to obtain the first estimated charging time accuracy rate and the second estimated charging time accuracy rate of each electric vehicle.
Further, the specific method for calculating the estimated driving range accuracy of each electric vehicle in step S103 is as follows:
within one operation period, Y energy consumption historical data S are available for a single quantity of electric vehicles r Calculating the corresponding simulated driving range S of each energy consumption historical data 1 And calculating the accuracy of the energy consumption historical data of the single-quantity vehicle according to the following formula:
c 1 ＝1-|S 1 -S r |/S r
wherein, c 1 The accuracy of the single energy consumption historical data of the single quantity vehicle;
and respectively averaging the accuracy rates of the Y charging historical data of the single quantity of electric vehicles to serve as the estimated driving range accuracy rate of each electric vehicle.
Further, the specific method for calculating the estimated charging time T in step S104 is as follows:
T＝a/(a+b)*T 1 +b/(a+b)*T 2
further, the specific method for calculating the estimated driving range S in step S104 is as follows:
S＝S 1 /c
the other technical scheme adopted by the invention is as follows: a vehicle cloud cooperative control device for an electric vehicle comprises an electric vehicle management system, a cloud platform and a charger system; the electric automobile management system comprises a battery management system BMS, a whole vehicle control system VCU, a vehicle monitoring system and an instrument display device, wherein the battery management system BMS is in data connection with the instrument display device, the vehicle monitoring system and a charger system through CAN communication, the whole vehicle control system VCU is in data connection with the instrument display device and the vehicle monitoring system through CAN communication, and the vehicle monitoring system and a cloud end platform are in data connection through wireless transmission.
The invention has the beneficial effects that:
(1) the method comprises the steps of calculating the remaining charging time and the remaining mileage of the electric vehicle in an off-line manner by calling historical data of vehicles in the same batch on a cloud platform, calculating the remaining charging time and the remaining mileage on line and updating the remaining charging time and the remaining mileage in real time according to the running state parameters of the electric vehicle, accurately estimating the remaining charging time and the remaining mileage, eliminating the worry of the mileage of a driver and ensuring safe and reliable operation of the vehicle;
(2) the estimated charging time and the estimated driving range are calculated and corrected by screening the historical data according with the rule, calculating the first estimated charging time and the second estimated charging time in a segmented mode, calculating the average accuracy of data and weighting calculation, so that the data error is effectively reduced, the calculation precision is improved, the vehicle charging remaining time and the vehicle charging remaining range are accurately estimated, and the vehicle cloud collaborative intelligent management is realized.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic structural diagram of an apparatus according to an embodiment of the present invention;
FIG. 2 is a step diagram of an embodiment of the present invention;
FIG. 3 is a flowchart illustrating calculation of an estimated charging time T according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating calculation of an estimated driving range S according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a BP network model used in the embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and thus the present invention is not limited to the specific embodiments disclosed below.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The use of "first," "second," and similar terms in the description and claims of this patent application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. Also, the use of the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
As shown in fig. 1, an electric vehicle cloud cooperative control device is adopted in the embodiment of the present invention, and includes an electric vehicle management system, a cloud platform, and a charger system; the electric automobile management system comprises a battery management system BMS, a whole vehicle control system VCU, a vehicle monitoring system and an instrument display device, wherein the battery management system BMS is in data connection with the instrument display device, the vehicle monitoring system and a charger system through CAN communication, the whole vehicle control system VCU is in data connection with the instrument display device and the vehicle monitoring system through CAN communication, and the vehicle monitoring system and a cloud end platform are in data connection through wireless transmission.
The battery management system BMS is used for realizing information monitoring, safety evaluation and charging remaining time on-line estimation of a battery system, communicating with a vehicle monitoring system and a charger system and realizing data transmission; the vehicle monitoring system is used for collecting all information of the whole vehicle and realizing wireless data transmission with the cloud platform; the instrument display device is used for displaying the estimated residual charging time and the estimated driving range; the cloud platform is used for achieving big data storage and offline calculation functions in the life cycle of the electric vehicle.
As shown in fig. 2, based on the control device shown in fig. 1, the embodiment of the present invention adopts a vehicle cloud cooperative control method for an electric vehicle, which includes the following steps:
s101: acquiring charging historical data and energy consumption historical data of N electric vehicles in the same batch in one operation cycle from a cloud platform, wherein the charging historical data comprises charging time of the N electric vehicles at different battery electric quantity values, charging current values and battery temperature values, and the energy consumption historical data comprises driving range of the N electric vehicles at different environmental temperatures, battery electric quantity values, battery temperature values, air conditioner starting conditions and road conditions; in the embodiment of the invention, the charging historical data and the energy consumption historical data are derived from historical data of 5 electric vehicles in the same batch within 100 days.
S102: calculating a first estimated charging time T according to the charging time under different battery electric quantity values and charging current values for the charging history data obtained in the step S101 a Calculating a second estimated charging time T according to the charging times of different battery electric quantity values and battery temperature values b The specific method comprises the following steps:
the battery electric quantity value range is [0, 100 ]]Segmenting at fixed intervals, and setting the range of charging current value [0, I max ]Is segmented at regular intervals, wherein I max Is the maximum charging current; averaging the data of the charging historical data in different battery electric quantity value sections and charging current value sections to serve as a first estimated charging time T under the current battery electric quantity value section and the charging current value section a 。
The battery electric quantity value range is [0, 100 ]]Segmented at regular intervals, the battery temperature value [ T min ，T max ]Is segmented at fixed intervals, whichMiddle T min Is the lowest temperature T of the normal work of the power battery max The maximum temperature of the power battery in normal operation is obtained; averaging the data of the charging historical data in different battery electric quantity value sections and battery temperature value sections to serve as a second estimated charging time T under the current battery electric quantity value section and the charging current value section b 。
In the charging process of the vehicle, the battery electric quantity value (namely the SOC), the charging current value and the battery temperature value are main factors influencing the charging time, and the influence degree of the battery electric quantity value is larger, so that the charging time is subjected to sectional analysis by taking the battery electric quantity value as a primary factor and combining the charging current value and the battery temperature value respectively, and the estimation precision is improved. Generally, the smaller the segmentation range, the higher the estimation accuracy, but the larger the calculation amount of data, so in the embodiment of the present invention, the estimation accuracy and the calculation efficiency are considered in combination, the battery electric quantity value range is segmented at 5% intervals, the charging current value range is segmented at 5A intervals, and the battery temperature value is segmented at 5 ℃ intervals. And the charging historical data meets the rule that the lower the battery electric quantity value is, the smaller the charging current value is, and the longer the charging time is, the data which does not meet the rule in the charging historical data is abandoned and is not used for calculating the first estimated charging time T a . The charging historical data also meet the rule that the lower the battery electric quantity value is, the lower the battery temperature value is at the proper charging temperature, or the higher the battery temperature value is at the proper charging temperature, the longer the charging time is, the data which do not meet the rule in the charging historical data are abandoned and are not used for calculating the second estimated charging time T b . Through screening and segmenting the historical data, the data error is effectively reduced, and the calculation precision is improved. Calculating according to the steps to obtain the first estimated charging time T corresponding to different battery electric quantity value sections and charging current value sections shown in the table 1 a And second estimated charging time T corresponding to different battery electric quantity value sections and battery temperature value sections shown in Table 2 b . The charging current value ranges and battery temperature value ranges in tables 1 and 2, as well as the battery charging temperature, are calibrated according to the specific battery type used.
TABLE 1 first estimated charging time T corresponding to different battery electric quantity value sections and charging current value sections a
TABLE 2 second estimated charging time T corresponding to different battery electric quantity value sections and battery temperature value sections b
Inputting the environmental temperature, the battery electric quantity value, the battery temperature value, the air conditioner starting condition and the road condition of each energy consumption historical data into the trained BP neural network according to the energy consumption historical data obtained in the step S101 to obtain the simulated driving range S corresponding to each energy consumption historical data 1 (ii) a Fig. 5 shows a BP network model diagram adopted in the embodiment of the present invention.
S103: for the N electric vehicles in the step S101, each charging history data of each electric vehicle and the corresponding first estimated time T a And a second estimated time T b Comparing, and respectively calculating a first estimated charging time accuracy rate and a second estimated charging time accuracy rate of each electric automobile; the specific method for calculating the first estimated charging time accuracy and the second estimated charging time accuracy of each electric automobile comprises the following steps:
in one operation period, X charging historical data T are provided for a single-quantity electric automobile r Calculating the first estimated time T corresponding to each charging history data a And a second estimated time T b And calculating the accuracy of the single charging historical data of the single measuring vehicle according to the following formula:
a 1 ＝1-|T a -T r |/T r
b 1 ＝1-|T b -T r |/T r
wherein, a 1 First predictive charging of individual charging history data for individual vehiclesTime accuracy, b 1 A second estimated charge time accuracy for a single charge history for a single vehicle.
And respectively averaging the first estimated charging time accuracy rate and the second estimated charging time accuracy rate of X charging historical data of a single electric vehicle to obtain the first estimated charging time accuracy rate and the second estimated charging time accuracy rate of each electric vehicle. Calculating an average value of the first estimated charging time accuracy rates of the N electric automobiles to obtain a first average accuracy rate a; and calculating the average value of the second estimated charging time accuracy rates of the N electric automobiles to obtain a second average accuracy rate b.
Comparing each energy consumption historical data of each electric automobile with the corresponding simulated driving range, and respectively calculating the estimated driving range accuracy of each electric automobile, wherein the specific method for calculating the estimated driving range accuracy of each electric automobile comprises the following steps:
within one operation period, Y energy consumption historical data S are available for a single quantity of electric vehicles r Calculating the corresponding simulated driving range S of each energy consumption historical data 1 And calculating the accuracy of the energy consumption historical data of the single-quantity vehicle according to the following formula:
c 1 ＝1-|S 1 -S r |/S r
wherein, c 1 The accuracy of the single energy consumption historical data of the single vehicle.
And respectively averaging the accuracy rates of the Y charging historical data of the single quantity of electric vehicles to serve as the estimated driving range accuracy rate of each electric vehicle. And calculating the average value of the estimated driving range accuracy rates of the N electric automobiles to obtain a third average accuracy rate c.
In the embodiment of the present invention, the first average accuracy a is calculated to be 90%, the second average accuracy b is calculated to be 80%, and the third average accuracy c is calculated to be 95%.
S104: according to the first average accuracy rate a and the second average accuracy rate b, the first estimated charging time T is calculated a The second estimated charging time T b Performing weighted calculation to obtain estimated charging time T; simulating the driving range according to the third average accuracy rate cS 1 Performing weighted calculation to obtain estimated driving range S; the calculation formula of the estimated charging time T and the estimated driving range S is as follows:
T＝a/(a+b)*T 1 +b/(a+b)*T 2
S＝S 1 /c
through the steps, the off-line estimation of the remaining charging time and the remaining mileage of the electric vehicle is completed, in the running process of the electric vehicle, battery parameters such as a battery electric quantity value, a charging current value and a battery temperature value can be obtained through a battery management system BMS on the electric vehicle, and vehicle running environment parameters such as an environment temperature, an air conditioner starting condition and a road condition are obtained through a vehicle monitoring system. As shown in FIG. 3 and FIG. 4, when the vehicle is in a charging state, a first estimated charging time T is obtained according to the current battery parameters a The second estimated charging time T b The first estimated charging time T is measured through the first average accuracy rate a and the second average accuracy rate b a And a second estimated charging time T b The estimated charging time T can be calculated on line and updated in real time by correcting; when the vehicle is in a running state, the corresponding simulated driving range S can be obtained according to the current battery parameters and the vehicle running environment parameters 1 The simulated driving range S is subjected to the third average accuracy rate c 1 And the estimated driving range S can be calculated on line and updated in real time by correcting. The embodiment of the invention can accurately predict the residual charging time and the residual mileage, eliminate the worry about the mileage of a driver and ensure the safe and reliable operation of the vehicle.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The cloud cooperative control method for the electric vehicle is characterized by comprising the following steps:
s101: acquiring charging historical data and energy consumption historical data of N electric vehicles in the same batch in an operation period, wherein the charging historical data comprises charging time of the N electric vehicles under different battery electric quantity values, charging current values and battery temperature values, and the energy consumption historical data comprises driving ranges of the N electric vehicles under different environmental temperatures, battery electric quantity values, battery temperature values, air conditioner starting conditions and road conditions;
s102: calculating a first estimated charging time T according to the charging time under different battery electric quantity values and charging current values for the charging history data obtained in the step S101 a Calculating a second estimated charging time T according to the charging times of different battery electric quantity values and battery temperature values b ；
Inputting the environmental temperature, the battery electric quantity value, the battery temperature value, the air conditioner starting condition and the road condition of each energy consumption historical data into the trained BP neural network according to the energy consumption historical data obtained in the step S101 to obtain the simulated driving range S corresponding to each energy consumption historical data 1 ；
S103: for the N electric vehicles in the step S101, each charging history data of each electric vehicle and the corresponding first estimated time T a And a second estimated time T b Comparing, and respectively calculating a first estimated charging time accuracy rate and a second estimated charging time accuracy rate of each electric vehicle; calculating an average value of the first estimated charging time accuracy rates of the N electric automobiles to obtain a first average accuracy rate a; calculating an average value of second estimated charging time accuracy rates of the N electric automobiles to obtain a second average accuracy rate b;
comparing each energy consumption historical data of each electric automobile with the corresponding simulated driving range, respectively calculating the estimated driving range accuracy of each electric automobile, and calculating the average value of the estimated driving range accuracy of the N electric automobiles to obtain a third average accuracy c;
s104: according to the first average accuracy rate a and the second average accuracy rate b, the first estimated charging time T is calculated a The second estimated charging time T b Performing weighted calculation to obtain estimated charging time T; simulating continuous driving according to the third average accuracy rate cMileage S 1 And performing weighted calculation to obtain the estimated driving range S.
2. The cloud cooperative control method for the electric vehicle as claimed in claim 1, wherein the specific method for calculating the first estimated charging time and the second estimated charging time in step S102 is as follows:
the battery electric quantity value range is [0, 100 ]]Segmenting at fixed intervals, and setting the range of charging current value [0, I max ]Is segmented at regular intervals, wherein I max Is the maximum charging current; averaging the data of the charging historical data in different battery electric quantity value sections and charging current value sections to serve as a first estimated charging time T under the current battery electric quantity value section and the charging current value section a ；
The battery electric quantity value range is [0, 100 ]]Segmented at regular intervals, the battery temperature value [ T min ，T max ]Is segmented at regular intervals, where T min Is the lowest temperature T of the normal work of the power battery max The maximum temperature of the power battery in normal operation is obtained; averaging the data of the charging historical data in different battery electric quantity value sections and battery temperature value sections to serve as a second estimated charging time T under the current battery electric quantity value section and the charging current value section b 。
3. The electric vehicle cloud cooperative control method according to claim 2, wherein the battery electric quantity value range is segmented at 5% intervals, the charging current value range is segmented at 5A intervals, and the battery temperature value range is segmented at 5 ℃.
4. The cloud cooperative control method for electric vehicles according to claim 2, wherein the charging history data satisfies a rule that the lower the battery electric quantity value is, the smaller the charging current value is, the longer the charging time is, and data which does not satisfy the rule in the charging history data is discarded and is not used for calculating the first estimated charging time T a 。
5. The cloud cooperative control method for electric vehicles according to claim 2, wherein the charging history data is discarded as the charging history data satisfies the rule that the lower the battery electric quantity value is, the lower the battery temperature value is at the proper charging temperature, or the higher the battery temperature value is at the proper charging temperature, the longer the charging time is, and the data which does not satisfy the rule in the charging history data is not used for calculating the second estimated charging time T b 。
6. The cloud cooperative control method for the electric vehicles according to claim 1, wherein the specific method for calculating the first estimated charging time accuracy rate and the second estimated charging time accuracy rate of each electric vehicle in step S103 is as follows:
in one operation period, X charging historical data T are provided for a single-quantity electric automobile r Calculating the first estimated time T corresponding to each charging history data a And a second estimated time T b And calculating the accuracy of the single charging historical data of the single measuring vehicle according to the following formula:
a 1 ＝1-|T a -T r |/T r
b 1 ＝1-|T b -T r |/T r
wherein, a 1 First estimated charging time accuracy of individual charging history data for individual vehicles, b 1 A second estimated charging time accuracy for a single charging history data of a single vehicle;
and respectively averaging the first estimated charging time accuracy rate and the second estimated charging time accuracy rate of X charging historical data of a single electric vehicle to obtain the first estimated charging time accuracy rate and the second estimated charging time accuracy rate of each electric vehicle.
7. The vehicle cloud cooperative control method of the electric vehicle as claimed in claim 1, wherein the specific method for calculating the estimated driving range accuracy of each electric vehicle in the step S103 is as follows:
in the period of one operation, the operation time,for a single quantity of electric vehicles, Y energy consumption historical data S r Calculating the corresponding simulated driving range S of each energy consumption historical data 1 And calculating the accuracy of the energy consumption historical data of the single-quantity vehicle according to the following formula:
c 1 ＝1-|S 1 -S r |/S r
wherein, c 1 The accuracy of the single energy consumption historical data of the single quantity vehicle;
and respectively averaging the accuracy rates of Y charging historical data of a single quantity of electric vehicles to serve as the estimated driving range accuracy rate of each electric vehicle.
8. The cloud cooperative control method for the electric vehicle as claimed in claim 1, wherein the specific method for calculating the estimated charging time T in the step S104 is as follows:
T＝a/(a+b)*T 1 +b/(a+b)*T 2 。
9. the cloud cooperative control method for the electric vehicle as claimed in claim 1, wherein the specific method for calculating the estimated driving range S in the step S104 is as follows:
S＝S 1 /c 。
10. a vehicle cloud cooperative control device of an electric vehicle is characterized by comprising an electric vehicle management system, a cloud platform and a charger system; the electric automobile management system comprises a battery management system BMS, a whole vehicle control system VCU, a vehicle monitoring system and an instrument display device, wherein the battery management system BMS is in data connection with the instrument display device, the vehicle monitoring system and a charger system through CAN communication, the whole vehicle control system VCU is in data connection with the instrument display device and the vehicle monitoring system through CAN communication, and the vehicle monitoring system and a cloud end platform are in data connection through wireless transmission.
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