Pure electric vehicle power battery energy consumption management method, system, equipment and medium

Abstract

The invention belongs to the technical field of new energy automobile power battery management, and particularly relates to a pure electric automobile power battery energy consumption management method, system and device and a medium, and the method comprises the steps: collecting vehicle travel data, determining driving condition characteristic parameters and energy consumption, and forming a sample data set; based on the data set, the energy consumption level and the driving intensity information of the historical journey are calculated, the energy consumption level is divided by adopting a quartile method, and the energy consumption difference and the driving intensity difference are calculated. And after the data is updated in real time, the energy consumption and driving intensity information is pushed to the driver through a display or voice medium. The system comprises a data acquisition module, a data processing module, an energy consumption analysis module and a data updating and pushing module. By means of the method, a driver can visually know energy consumption and driving intensity, energy-saving driving is effectively guided, energy consumption is reduced, the energy utilization efficiency is improved, dependence on external real-time information is not needed, historical driving situations and habits are combined, and the habit of energy-saving driving of the driver is cultivated.

Description

Technical Field

[0001] The present invention belongs to the technical field of power battery management for new energy vehicles, and particularly relates to a method, a system, a device and a medium for managing the energy consumption of a power battery of a pure electric vehicle. Background Art

[0002] In recent years, with the development of technologies such as big data and intelligent networking of electric vehicles, researchers have gradually started the research on the intelligent management of vehicles. And the energy consumption management of electric vehicles is one of the key directions, aiming to achieve the goal of energy-saving driving. Among them, there is a method of estimating the actual available power of a vehicle by identifying road conditions information such as road length, slope, curvature and adhesion, and at the same time, combining the driving behavior of the driver to generate a driving style corresponding to the best energy consumption and recommending it to the driver to achieve the purpose of reducing the vehicle energy consumption; there is also a method based on the vehicle networking environment to obtain the driving conditions and energy consumption data of other vehicles of the same model, grouping the vehicles based on the data, and referring to the vehicle information of the lowest energy consumption group, recommending and guiding the current vehicle to change driving state control signals such as vehicle speed, air conditioner switch and power recovery to reduce the energy consumption.

[0003] However, in the above methods, they all need to rely on the acquisition of external real-time information, including identifying road conditions information and receiving the driving data of other vehicles, etc. The usage cost and usage scenarios have relatively high limitations. In addition, when recommending driving state control signals to the driver, the information such as the energy consumption change amount generated by changing the driving state is not quantitatively displayed to the driver, which cannot intuitively and efficiently guide the driver to drive in an energy-saving manner, and the recommended driving state control information belongs to short-term guiding information, without considering the driving situation and driving habits of the driver in the long historical period, and cannot cultivate the driver's habit of energy-saving driving. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, a system, a device and a medium for managing the energy consumption of a power battery of a pure electric vehicle, so as to solve the technical problems that the existing electric vehicle energy consumption management method has a high dependence on external real-time information, limited usage, and does not fully combine the driver's historical driving situation and habits to efficiently guide energy-saving driving.

[0005] The present invention realizes the above purpose through the following technical solutions:

[0006] In the first aspect, the present invention proposes a method for managing the energy consumption of a power battery of a pure electric vehicle, and the method includes the following steps:

[0007] S1. Collect the trip data of each vehicle trip and store it;

[0008] S2. Based on the trip data, determine the driving condition characteristic parameters and energy consumption of each vehicle trip data, and form a vehicle trip sample data set;

[0009] S3. Calculate the energy consumption level and driving intensity information for all historical trips of the vehicle based on the vehicle trip sample data set. The energy consumption level includes determining the energy consumption grade for each historical trip through the quartile method, as well as the energy consumption difference and driving intensity difference for each grade.

[0010] S4. Calculate the energy consumption level and driving intensity information for all historical trips of the vehicle based on the vehicle trip sample data set. The energy consumption level includes determining the energy consumption grade for each historical trip through the quartile method, as well as the energy consumption difference and driving intensity difference for each grade.

[0011] Furthermore, the trip data includes the working voltage, current, temperature, and rated capacity of the vehicle's power battery, as well as the vehicle's driving speed, driving mileage, and power consumption of electrical accessories.

[0012] Furthermore, the driving condition characteristic parameters include driving mileage, maximum vehicle speed, average vehicle speed, vehicle speed standard deviation, average acceleration, average deceleration, acceleration and deceleration standard deviation, maximum discharge rate, average discharge rate, maximum charge rate, average charge rate, charge and discharge rate standard deviation, average battery temperature, maximum battery temperature, acceleration time ratio, constant speed time ratio, and parking time ratio.

[0013] Furthermore, step S3 includes:

[0014] S31. Screen out the trip sample data with a single driving mileage greater than the set value in the vehicle trip sample data set;

[0015] S32. Based on the trip sample data, use the quartile method to determine the energy consumption grade for each historical trip, and calculate the grade energy consumption and grade energy consumption difference for each grade;

[0016] S33. Based on the trip sample data, perform a correlation analysis between the driving condition characteristic parameters and energy consumption using the Pearson correlation coefficient to determine the main driving condition characteristic parameters affecting energy consumption;

[0017] S34. Based on the results of steps S32 and S33, determine the driving intensity grade for each historical trip, and calculate the grade driving intensity and grade driving intensity difference for each grade.

[0018] Furthermore, the method for determining the energy consumption grade is as follows: Sort the energy consumption data of all historical trips in ascending order. According to the quartile method, determine that the energy consumption grade corresponding to the energy consumption data in the top 25% is grade 1, the energy consumption grade corresponding to the energy consumption data between 25% and 50% is grade 2, the energy consumption grade corresponding to the energy consumption data between 50% and 75% is grade 3, and the energy consumption grade corresponding to the energy consumption data in the last 25% is grade 4;

[0019] The method for calculating the energy consumption level is as follows: obtaining the average value of the energy consumption data belonging to the same energy consumption level;

[0020] The method for calculating the difference in energy consumption levels is as follows:

[0021]

[0022] In the formula, δ i_i-1 is the difference in energy consumption levels between energy consumption level i and level i - 1; E i is the energy consumption level of level i.

[0023] Furthermore, calculating the driving intensity level and the difference in driving intensity levels for each level includes:

[0024] The method for determining the driving intensity level is as follows: defining the driving intensity level to describe the intensity of the driving style, and classifying the driving intensity according to the energy consumption level, divided into level A, level B, level C, and level D, that is, the driving intensity level corresponding to energy consumption level 1 is level A, the driving intensity level corresponding to energy consumption level 2 is level B, the driving intensity level corresponding to energy consumption level 3 is level C, and the driving intensity level corresponding to energy consumption level 4 is level D;

[0025] The method for calculating the driving intensity level is as follows:

[0026]

[0027] In the formula, α j is the driving intensity level corresponding to the i-th energy consumption level; w j is the correlation coefficient between the j-th main working condition characteristic parameter affecting energy consumption and energy consumption; is the average value of the j-th characteristic parameter of all trip samples belonging to the i-th energy consumption level; n is the total number of main working condition characteristic parameters affecting energy consumption determined in step S33;

[0028] The method for calculating the difference in driving intensity levels is as follows:

[0029]

[0030] In the formula, φ i_i-1 is the difference in driving intensity levels between the driving intensity levels corresponding to energy consumption level i and level i - 1; α i is the driving intensity level corresponding to energy consumption level i.

[0031] Furthermore, in step S4, updating the energy consumption level and the driving intensity information and pushing them includes:

[0032] Push driving intensity, vehicle energy consumption level, and vehicle speed information to the driver through a vehicle display medium or a voice medium, including the driving intensity, driving intensity level, energy consumption, energy consumption level, difference in driving intensity level, and difference in energy consumption level relative to the adjacent previous time period within a set time period.

[0033] In a second aspect, the present invention provides a power battery energy consumption management system for a pure electric vehicle, which is used to execute the battery energy consumption management method described in any one of the above. The system includes:

[0034] A data acquisition module, which is used to collect the trip data of each vehicle trip and store it.

[0035] A data processing module, which is used to determine the driving condition characteristic parameters and energy consumption of each vehicle trip data based on the trip data, and form a vehicle trip sample data set.

[0036] An energy consumption analysis module, which is used to calculate the energy consumption level and driving intensity information of all historical vehicle trips based on the vehicle trip sample data set. The energy consumption level includes determining the energy consumption level of each historical trip through the quartile method, as well as the energy consumption difference and driving intensity difference of each level.

[0037] A data update and push module, which is used to update the energy consumption level and the driving intensity information and push them when real-time trip data is collected.

[0038] In a third aspect, the present invention provides an electronic device, including:

[0039] A processor; a memory for storing instructions executable by the processor;

[0040] Wherein, the processor is configured to execute the instructions to implement the battery energy consumption management method described in any one of the above.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by the processor of an electronic device, the electronic device can execute the battery energy consumption management method described in any one of the above.

[0042] The beneficial effects of the present invention are as follows:

[0043] 1. By comprehensively collecting the trip data of each vehicle trip and conducting in-depth analysis, the present invention can accurately calculate the energy consumption level and driving intensity information of the vehicle. By fully considering the long-term driving conditions and habits of the driver, it provides long-term quantitative feedback guidance information such as driving intensity and energy consumption level, which is conducive to cultivating the driver's awareness of energy-saving driving and achieving the goal of reducing vehicle energy consumption.

[0044] 2. The present invention describes the intensity of driving style by defining the driving intensity level, and classifies the driving intensity according to the energy consumption level. The present invention realizes the quantitative evaluation of driving behavior. At the same time, by calculating the level driving intensity and the difference in level driving intensity of each energy consumption level, the present invention further reveals the internal relationship between driving behavior and energy consumption. This innovative energy consumption management method and system not only helps drivers form the awareness of energy-saving driving, but also can gradually guide drivers to adjust their driving habits through long-term energy consumption data and driving behavior tracking analysis, so as to achieve the purpose of energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic flow chart of a method for managing the energy consumption of a power battery of a pure electric vehicle provided in Embodiment 1 of the present application;

[0046] Figure 2 It is a system block diagram of a system for managing the energy consumption of a power battery of a pure electric vehicle provided in Embodiment 2 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The present application will be further described in detail below with reference to the accompanying drawings. It is necessary to point out here that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0048] Embodiment 1

[0049] As Figure 1 shown, this embodiment proposes a method for managing the energy consumption of a power battery of a pure electric vehicle, and the method includes the following steps:

[0050] S1. Collect the trip data of each vehicle trip and store it; specifically, obtain the data in real time through the sensors and control systems built in the vehicle; the trip data includes the working voltage, current, temperature, rated capacity of the vehicle's power battery, as well as the driving speed, driving mileage and power consumption of electrical accessories of the vehicle.

[0051] S2. Based on the trip data, determine the driving condition characteristic parameters and energy consumption of each vehicle trip data, and form a vehicle trip sample data set; the driving condition characteristic parameters include driving mileage, maximum speed, average speed, speed standard deviation, average acceleration, average deceleration, acceleration and deceleration standard deviation, maximum discharge rate, average discharge rate, maximum charge rate, average charge rate, charge and discharge rate standard deviation, average battery temperature, maximum battery temperature, acceleration time ratio, constant speed time ratio and parking time ratio.

[0052] Among them, the energy consumption refers to the electricity consumption per 100 kilometers of driving (unit: kWh / 100km), and the calculation method is as follows:

[0053]

[0054] Wherein, E d is the electricity consumption per 100 kilometers for a single trip; U is the working voltage of the power battery; I is the working current of the power battery; T is the duration of a single trip; S is the mileage of a single trip; E c is the power consumption of electrical accessories such as air conditioners during a single trip.

[0055] The charge rate and discharge rate respectively refer to the ratios of the battery discharge current and the regenerative charge current to the battery rated capacity.

[0056] S3. Based on the vehicle trip sample data set, calculate the energy consumption level and driving intensity information of all historical trips of the vehicle. The energy consumption level includes determining the energy consumption grade of each historical trip through the quartile method, as well as the energy consumption difference and driving intensity difference of each grade;

[0057] Further, step S3 includes:

[0058] S31. Screen out the trip sample data with a single driving mileage greater than the set value in the vehicle trip sample data set;

[0059] S32. Based on the trip sample data, use the quartile method to determine the energy consumption grade of each historical trip, and calculate the grade energy consumption and grade energy consumption difference of each grade;

[0060] S33. Based on the trip sample data, use the Pearson correlation coefficient to conduct a correlation analysis between the driving condition characteristic parameters and the energy consumption, and determine the main condition characteristic parameters affecting the energy consumption;

[0061] S34. Based on the results of steps S32 and S33, determine the driving intensity grade of each historical trip, and calculate the grade driving intensity and grade driving intensity difference of each grade.

[0062] S4. After collecting real-time trip data, update the energy consumption level and driving intensity information and push them.

[0063] Further, the method for determining the energy consumption grade is: sort the energy consumption data of all historical trips from low to high. According to the quartile method, determine that the energy consumption grade corresponding to the top 25% of the ranked energy consumption data is level 1, the energy consumption grade corresponding to the energy consumption data ranked between 25% and 50% is level 2, the energy consumption grade corresponding to the energy consumption data ranked between 50% and 75% is level 3, and the energy consumption grade corresponding to the bottom 25% of the ranked energy consumption data is level 4;

[0064] The method for calculating the grade energy consumption is: obtain the average value of the energy consumption data belonging to the same energy consumption grade;

[0065] The calculation method of the energy consumption difference between levels is as follows:

[0066]

[0067] In the formula, δ i_i-1 is the energy consumption difference between energy consumption level i and level i - 1; E i is the energy consumption of level i.

[0068] Furthermore, calculate the driving intensity of each level and the difference in driving intensity between levels, including:

[0069] The method for determining the driving intensity level is: Define the driving intensity level to describe the intensity of the driving style. Classify the driving intensity according to the energy consumption level, which is divided into level A, level B, level C, and level D. That is, the driving intensity level corresponding to energy consumption level 1 is level A, the driving intensity level corresponding to energy consumption level 2 is level B, the driving intensity level corresponding to energy consumption level 3 is level C, and the driving intensity level corresponding to energy consumption level 4 is level D;

[0070] The calculation method of the driving intensity of each level is:

[0071]

[0072] In the formula, α j is the driving intensity of the i-th energy consumption level; w j is the correlation coefficient between the j-th main working condition characteristic parameter affecting energy consumption and energy consumption; is the average value of the j-th characteristic parameter of all trip samples belonging to the i-th energy consumption level; n is the total number of main working condition characteristic parameters affecting energy consumption determined in step S33;

[0073] The calculation method of the difference in driving intensity between levels is:

[0074]

[0075] In the formula, φ i_i-1 is the difference in driving intensity between the driving intensity corresponding to energy consumption level i and level i - 1; α i is the driving intensity corresponding to energy consumption level i.

[0076] Furthermore, in step S4, update the energy consumption level and driving intensity information and push it, including:

[0077] Push the driving intensity, vehicle energy consumption level, and vehicle speed information to the driver through the vehicle display medium or voice medium, including the driving intensity, driving intensity level, energy consumption, energy consumption level, difference in driving intensity between levels, and difference in energy consumption between levels within a set time period compared to the adjacent previous time period.

[0078] Among them, the driving intensity and energy consumption within a period are respectively the average values of the driving intensity and energy consumption of all trips within the period; the energy consumption level within the period is the ceiling value of the average value of the energy consumption levels of all trips within the period; the driving intensity level within the period is the driving intensity level corresponding to the energy consumption level within the period; the differences in driving intensity levels and energy consumption levels relative to the previous adjacent time period are obtained by querying based on the driving intensity levels and energy consumption levels of the current period and the previous adjacent period, in combination with the differences in driving intensity levels and energy consumption levels of each level after the latest update.

[0079] In some alternative embodiments, the above vehicle display medium or voice medium can be an in-vehicle display screen, an in-vehicle voice prompt system, a mobile terminal device, including a smart phone, a tablet computer, etc., which performs data interaction with the vehicle through wireless communication and displays information such as driving intensity, energy consumption level, vehicle speed, etc. in the application program of the mobile terminal device; an in-vehicle infotainment system, a vehicle instrument panel, etc.

[0080] In order to more clearly illustrate the above-mentioned Embodiment 1 and its advantages, the following will further explain the method provided by the present invention in combination with specific examples and relevant partial figures.

[0081] Combined with the historical trip data of a certain vehicle within a certain time range, the driving intensity and energy consumption level of the vehicle are calculated as shown in the following table:

[0082]

[0083] In the above results, the description of the difference in energy consumption levels is as follows: The overall energy consumption level of level 2 is 12.22% higher than that of level 1, the overall energy consumption level of level 3 is 8.13% higher than that of level 2, and the overall energy consumption level of level 4 is 11.37% higher than that of level 3.

[0084] The description of the difference in driving intensity levels is as follows: The overall driving intensity of level B is +18.74% higher than that of level A, the overall driving intensity of level C is +10.07% higher than that of level B, and the overall driving intensity of level D is +16.04% higher than that of level C. Therefore, if the energy consumption level is reduced from level 4 to level 3, the driving intensity needs to be reduced from level D to level C, that is, the driving intensity needs to be reduced by 16.04%, and the corresponding energy consumption level is reduced by 11.37%.

[0085] Among them, in step S3, a correlation analysis is performed between the driving condition characteristic parameters obtained in the previous steps and the energy consumption data, and the three characteristic parameters with the highest Pearson correlation coefficient in the analysis results are taken to characterize the calculation of the driving intensity, including the standard deviation of the charge-discharge rate, the average discharge rate, and the average charge rate, and the Pearson correlation coefficients are 0.87, 0.73, and 0.7 respectively.

[0086] In addition, it can be seen that as the driving intensity increases in turn, the corresponding energy consumption level also increases in turn. Among them, the numerical values of the operating condition characteristic parameters such as the standard deviation of the charge and discharge rate, the average discharge rate, and the average charge rate used to characterize the driving intensity also increase step by step, which conforms to the theory that the larger the numerical value of the parameter, the higher the working intensity of the power battery in essence, and the higher the energy consumption of the power battery.

[0087] The method proposed in this embodiment fully considers the long-term driving conditions and habits of the driver, provides long-term quantitative feedback guidance information such as driving intensity and energy consumption level, which is conducive to cultivating the driver's awareness of energy-saving driving and achieving the goal of reducing vehicle energy consumption.

[0088] Embodiment 2

[0089] As Figure 2 shown, based on the same inventive concept, this embodiment proposes a power battery energy consumption management system for a pure electric vehicle, which is used to execute the battery energy consumption management method as in Embodiment 1. The system includes:

[0090] A data acquisition module, which is used to collect the trip data of each vehicle trip and store it; specifically, it obtains data in real time through the sensors and control systems built in the vehicle;

[0091] A data processing module, which is used to determine the driving condition characteristic parameters and energy consumption of each vehicle trip data based on the trip data, and form a vehicle trip sample data set; the energy consumption is usually measured by the electricity consumption per 100 kilometers (unit: kWh / 100km);

[0092] An energy consumption analysis module, which is used to calculate the energy consumption level and driving intensity information of all historical trips of the vehicle based on the vehicle trip sample data set. The energy consumption level includes determining the energy consumption grade of each historical trip through the quartile method, as well as the energy consumption difference and driving intensity difference of each grade;

[0093] Quartile method: Sort all the historical trip energy consumption data in ascending order, divide the energy consumption data into four grades according to the quartile method, and calculate the average energy consumption and energy consumption difference of each grade.

[0094] Driving intensity calculation: Describe the intensity of the driving style by defining the driving intensity grade, and divide the driving intensity according to the energy consumption grade. At the same time, calculate the grade driving intensity and grade driving intensity difference of each energy consumption grade to reveal the internal relationship between driving behavior and energy consumption;

[0095] A data update and push module, which is used to update the energy consumption level and driving intensity information and push it when real-time trip data is collected.

[0096] It should be noted here that each module in the above energy consumption management system corresponds to steps S1 to S4 in the energy consumption management method in the above-mentioned Embodiment 1. The examples and application scenarios implemented by multiple modules and the corresponding steps are the same, but are not limited to the content disclosed in the above-mentioned Embodiment 1.

[0097] In another embodiment provided by the present invention, an electronic device is further provided, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to execute the instructions to implement the battery energy consumption management method as described above.

[0098] In another embodiment provided by the present invention, a computer-readable storage medium is further provided. When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device can execute the battery energy consumption management method as described above.

[0099] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0100] In addition, in each embodiment of the present application, the various functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0101] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for managing energy consumption of a power battery of a pure electric vehicle, characterized in that: The method comprises the following steps: S1. Collect and store the travel data of each vehicle trip; S2. Based on the travel data, determine the driving condition characteristic parameters and energy consumption of each vehicle travel data to form a vehicle travel sample data set; S3. Based on the vehicle trip sample data set, calculate the energy consumption level and driving intensity information of all historical trips of the vehicle, wherein the energy consumption level includes determining the energy consumption level of each historical trip by the quartile method, and the energy consumption difference and driving intensity difference of each level; S4. After the real-time travel data is collected, the energy consumption level and the driving intensity information are updated and pushed.

2. A method for managing energy consumption of a power battery of a pure electric vehicle according to claim 1, characterized in that: The trip data includes the operating voltage, current, temperature, rated capacity of the vehicle's power battery, as well as the vehicle's driving speed, mileage, and power consumption of electric accessories.

3. A method for managing energy consumption of a power battery of a pure electric vehicle according to claim 1, characterized in that: The driving condition characteristic parameters include mileage, maximum vehicle speed, average vehicle speed, vehicle speed standard deviation, average acceleration, average deceleration, acceleration and deceleration standard deviation, maximum discharge rate, average discharge rate, maximum charge rate, average charge rate, charge and discharge rate standard deviation, battery average temperature, battery maximum temperature, acceleration time ratio, uniform speed time ratio and parking time ratio.

4. A method for managing energy consumption of a power battery of a pure electric vehicle according to claim 1, characterized in that: Step S3 includes: S31. Filter out the travel sample data whose single driving mileage is greater than the set value in the vehicle travel sample data set; S32. Based on the trip sample data, the energy consumption level of each historical trip is determined by using the quartile method, and the energy consumption level and energy consumption difference of each level are calculated; S33. Based on the trip sample data, the Pearson correlation coefficient is used to perform a correlation analysis between the driving condition characteristic parameters and energy consumption to determine the main condition characteristic parameters that affect energy consumption; S34. Based on the results of steps S32 and S33, determine the driving intensity level of each historical trip, and calculate the driving intensity level and driving intensity difference of each level.

5. A method for managing energy consumption of a power battery of a pure electric vehicle according to claim 4, characterized in that: The energy consumption level determination method is as follows: the energy consumption data of all historical trips are sorted from low to high, and according to the quartile method, the energy consumption level corresponding to the energy consumption data ranked in the top 25% is determined to be level 1, the energy consumption data ranked between 25% and 50% is determined to be level 2, the energy consumption data ranked between 50% and 75% is determined to be level 3, and the energy consumption data ranked in the bottom 25% is determined to be level 4; The energy consumption calculation method of the level is as follows: the energy consumption data belonging to the same energy consumption level are averaged to obtain the average value; The calculation method of the energy consumption difference of the level is: In the formula, δ i_i-1 is the energy consumption difference between energy consumption level i and level i-1; E i is the energy consumption of level i.

6. A method for managing energy consumption of a power battery of a pure electric vehicle according to claim 4, characterized in that: The calculating of the level driving intensity and the level driving intensity difference of each level includes: The driving intensity level determination method is as follows: defining a driving intensity level to describe the intensity of the driving style, and dividing the driving intensity into levels A, B, C, and D according to the energy consumption level, that is, the driving intensity level corresponding to level 1 energy consumption is level A, the driving intensity level corresponding to level 2 energy consumption is level B, the driving intensity level corresponding to level 3 energy consumption is level C, and the driving intensity level corresponding to level 4 energy consumption is level D; The calculation method of the level driving intensity is: In the formula, α j is the driving intensity corresponding to the i-th energy consumption level; w j is the correlation coefficient between the jth main operating condition characteristic parameter affecting energy consumption and energy consumption; β ij is the average value of the jth characteristic parameter of all trip samples belonging to the i-th energy consumption level; n is the total number of main operating condition characteristic parameters affecting energy consumption determined in step S33; The calculation method of the level driving intensity difference is: In the formula, φ i_i-1 is the driving intensity difference between the driving intensity corresponding to energy consumption level i and level i-1; α i is the driving intensity level corresponding to energy consumption level i.

7. A method for managing energy consumption of a power battery of a pure electric vehicle according to claim 1, characterized in that: In step S4, the updating of the energy consumption level and the driving intensity information and the pushing of the information include: The driving intensity, vehicle energy consumption level and vehicle speed information are pushed to the driver through the vehicle display media or voice media, including the driving intensity, driving intensity level, energy consumption, energy consumption level, driving intensity difference and energy consumption difference relative to the previous adjacent time period within a set time period.

8. A pure electric vehicle power battery energy consumption management system, used to execute the battery energy consumption management method according to any one of claims 1 to 7, characterized in that: The system comprises: A data collection module is used to collect and store the travel data of each vehicle trip; A data processing module, for determining driving condition characteristic parameters and energy consumption of each vehicle trip data based on the trip data, and forming a vehicle trip sample data set; An energy consumption analysis module, for calculating the energy consumption level and driving intensity information of all historical trips of the vehicle based on the vehicle trip sample data set, wherein the energy consumption level includes determining the energy consumption level of each historical trip by a quartile method, and the energy consumption difference and driving intensity difference of each level; The data updating and pushing module is used to update the energy consumption level and the driving intensity information and push them after collecting the real-time travel data.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the battery energy consumption management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the battery energy consumption management method as claimed in any one of claims 1 to 7.

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