Vehicle energy consumption data processing method and device

Abstract

This application provides a vehicle energy consumption data processing method and apparatus. By acquiring an energy consumption data query request, and based on the identification code of the target vehicle and the query time range specified in the request, the method retrieves the target energy consumption data and target attribute information of the target vehicle from stored daily operating index data and attribute information for each vehicle, and then displays the target energy consumption data and target attribute information. When a user's high energy consumption complaint is received, the target energy consumption data and target attribute information of the target vehicle can be quickly obtained simply by using the identification code and the required query time range. This information can be used for energy consumption analysis of the target vehicle, saving data processing time and improving work efficiency.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method and apparatus for processing vehicle energy consumption data. Background Art

[0002] With the advancement of the "carbon neutrality" policy, new energy vehicles have become a popular trend due to their advantages such as energy saving and environmental protection. Pure electric vehicles are a type of new energy vehicle, and their actual energy consumption during use is outside the range of energy consumption. In this case, it is necessary to conduct in-depth analysis through technical means to determine the cause of energy consumption in order to improve product performance.

[0003] Traditional energy consumption analysis methods involve technicians exporting raw energy consumption data of vehicles collected from a monitoring server. This raw energy consumption data includes, for example, the status of the drive motor, total mileage, vehicle drive mode signal value, air conditioning operating status signal value, and air conditioning set temperature at various collection points. Then, based on this raw energy consumption data, manual statistical calculations are performed using Excel formulas to obtain statistical results, and finally, energy consumption analysis is conducted based on the statistical results.

[0004] However, the data processing of the above energy consumption analysis methods is time-consuming, resulting in a waste of human resources. Summary of the Invention

[0005] This application provides a vehicle energy consumption data processing method and apparatus to solve the problem that the data processing process in the prior art is time-consuming and wastes human resources.

[0006] In a first aspect, this application provides a method for processing vehicle energy consumption data, the method comprising:

[0007] Obtain an energy consumption data query request; the energy consumption data query request includes the identification code of the target vehicle to be queried and the query time range;

[0008] According to the energy consumption data query request, the target energy consumption data and target attribute information of the target vehicle are obtained by querying the daily operation index data and attribute information of each vehicle stored respectively. The daily operation index data represents the daily energy consumption of the target vehicle within the query time range.

[0009] Display the target energy consumption data and the target attribute information.

[0010] Optionally, before obtaining the energy consumption data query request, the method further includes:

[0011] The raw energy consumption data collected from each vehicle is acquired at preset time intervals; wherein, the raw energy consumption data includes multiple data items, including at least one of the following: drive motor status, total mileage, vehicle drive mode signal value, air conditioning working status signal value, air conditioning set temperature, cumulative energy consumption of power battery discharge, high voltage bus voltage, high voltage DC current of motor controller, charging status, vehicle single charge amount, charger single charge amount, and circuit data of each core component;

[0012] Based on the original energy consumption data, the daily operating index data of each vehicle is calculated; the daily operating index data includes one or more of the following data: daily mileage, daily energy consumption, daily vehicle driving mode ratio, average vehicle speed, daily air conditioning set temperature distribution, daily energy consumption per 100 kilometers, energy recovery rate, charging efficiency, and average power consumption of core components.

[0013] Obtain the attribute information of each vehicle, which includes one or more of the following data: vehicle brand, vehicle model, license plate number, drive motor number, drive motor model, and vehicle region.

[0014] Store the daily operating index data and attribute information of each vehicle.

[0015] Optionally, after obtaining the energy consumption data and target attribute information of the target vehicle through the query, the method further includes:

[0016] Based on the daily operational indicator data, a trend chart of the changes in each operational indicator data within the query time range is obtained;

[0017] The trend charts of the changes in various operational indicators within the query time range are visualized using BI visualization tools.

[0018] Optionally, before calculating the daily operating index data of each vehicle based on the original energy consumption data, the method further includes:

[0019] The raw energy consumption data is cleaned to remove invalid data and data from when the vehicle is idling.

[0020] The step of calculating the daily operating index data for each vehicle based on the original energy consumption data includes:

[0021] Based on the raw energy consumption data after data cleaning, the daily operating index data of each vehicle are calculated.

[0022] Optionally, displaying the target energy consumption data and the target attribute information includes:

[0023] Based on the daily operating indicator data of the target vehicle within the query time range, statistical data of each operating indicator data of the target vehicle within the query time range are obtained.

[0024] The summary report is generated based on the target energy consumption data, the statistical data, and the target attribute information of the target vehicle.

[0025] Optionally, the target energy consumption data also includes daily operating index data of the target vehicle from the time it leaves the factory to the current time. Displaying the target energy consumption data and the target attribute information includes:

[0026] Based on the daily operating indicator data of the target vehicle within the query time range, statistical data of each operating indicator data of the target vehicle within the query time range are obtained.

[0027] Based on the daily operating index data of the target vehicle from the time it leaves the factory to the current time, historical statistical data of each operating index data of the target vehicle from the time it leaves the factory to the current time are obtained.

[0028] The system displays the target energy consumption data, the statistical data, the target vehicle's target attribute information, and the historical statistical data.

[0029] Secondly, this application provides a vehicle energy consumption data processing device, comprising:

[0030] The acquisition module is used to acquire energy consumption data query requests; the energy consumption data query requests include the identification code of the target vehicle to be queried and the query time range;

[0031] The query module is used to query the target energy consumption data and target attribute information of the target vehicle from the stored daily operation index data and attribute information of each vehicle according to the energy consumption data query request. The daily operation index data represents the daily energy consumption of the target vehicle within the query time range.

[0032] The display module is used to display the target energy consumption data and the target attribute information.

[0033] Thirdly, this application provides a vehicle energy consumption data processing device, including: a processor, and a memory communicatively connected to the processor;

[0034] The memory stores computer-executable instructions;

[0035] The processor executes computer execution instructions stored in the memory to implement the method as described in the first aspect.

[0036] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement a vehicle energy consumption data processing method as described in the first aspect.

[0037] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0038] This application provides a vehicle energy consumption data processing method and apparatus. By acquiring an energy consumption data query request, and based on the identification code and query time range of the target vehicle in the query request, the method retrieves the target energy consumption data and target attribute information of the target vehicle from stored daily operating index data and attribute information of various vehicles, and displays the target energy consumption data and target attribute information. When a user's high energy consumption complaint is received, the target energy consumption data and target attribute information of the target vehicle can be quickly obtained simply by using the identification code and the required query time range. This can be used for energy consumption analysis of the target vehicle, saving data processing time and improving work efficiency. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0040] Figure 1 This is a schematic diagram illustrating one application scenario to which this application applies;

[0041] Figure 2 This is a flowchart illustrating a vehicle energy consumption data processing method provided in Embodiment 1 of this application;

[0042] Figure 3 This is a graph showing the trend of daily average vehicle speed.

[0043] Figure 4 A graph showing the changing trend of the proportion of vehicle drive modes on a daily basis;

[0044] Figure 5 This is a flowchart illustrating a vehicle energy consumption data processing method provided in Embodiment 2 of this application;

[0045] Figure 6 This is a schematic diagram of the structure of a vehicle energy consumption data processing device provided in Embodiment 3 of this application;

[0046] Figure 7 This is a schematic diagram of the structure of a vehicle energy consumption data processing device provided in Embodiment 4 of this application.

[0047] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. Detailed Implementation

[0048] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0049] Traditional energy consumption analysis methods involve technicians exporting raw energy consumption data of vehicles collected from a monitoring server. This raw energy consumption data includes, for example, the status of the vehicle's drive motor, total mileage, vehicle drive mode signal value, air conditioning operating status signal value, and air conditioning set temperature at various collection points. Then, based on the raw energy consumption data, manual statistical calculations are performed using Excel formulas to obtain statistical results, and finally, energy consumption analysis is conducted based on the statistical results.

[0050] However, the data processing of the above-mentioned energy consumption analysis method is time-consuming, resulting in a waste of manpower. Moreover, the historical data of the monitoring server is uploaded in real time by the vehicle monitoring terminal. This data includes invalid data reported before and after vehicle startup. Statistical calculations based on this raw energy consumption data will result in inaccurate results, making energy consumption analysis more difficult.

[0051] Therefore, this application provides a vehicle energy consumption data processing method and apparatus. By obtaining the identification code of the target vehicle and the query time range, the target energy consumption data of the target vehicle within the query time range can be quickly obtained. The target energy consumption data is calculated based on the raw energy consumption data collected by the vehicle monitoring terminal at preset time intervals. Combined with the target attribute information of the vehicle, the energy consumption of the target vehicle can be analyzed. It is no longer necessary to manually obtain operating parameters and perform calculations, which saves data processing time and improves work efficiency.

[0052] refer to Figure 1 , Figure 1This diagram illustrates an application scenario applicable to this application. Vehicle 101, analysis server 102, and monitoring server 103 communicate via the internet. Vehicle 101 has a built-in Telematics Box (T-BOX). The T-BOX collects raw energy consumption data from vehicle 101 at preset time intervals and reports it to the new energy control platform 103 in real time. Analysis server 102 obtains the raw energy consumption data from monitoring server 103 and calculates the daily operating indicators of vehicle 101. These indicators may include one or more of the following: daily mileage, daily energy consumption, daily vehicle driving mode ratio, average vehicle speed, daily air conditioning set temperature distribution, daily energy consumption per 100 kilometers, energy recovery rate, charging efficiency, and average power consumption of core components. When analysis server 102 receives an energy consumption data query request, it can quickly retrieve the daily operating indicator data based on the request for energy consumption analysis, improving work efficiency.

[0053] It is understood that there can be multiple vehicles 101, analysis servers 102, and monitoring servers 103, which are not shown in the figure.

[0054] In addition, vehicle 101 can be a new energy vehicle such as a pure electric vehicle or a fuel cell electric vehicle, or a hybrid electric vehicle. This application does not limit the type of vehicle 101.

[0055] The technical solutions of this application and how they solve the aforementioned technical problems are described in detail below with specific embodiments. These specific embodiments may exist independently or in combination with each other. Similar or identical concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0056] refer to Figure 2 , Figure 2 This is a flowchart illustrating a vehicle energy consumption data processing method provided in Embodiment 1 of this application. This method can be executed by a vehicle energy consumption data processing device, which can be... Figure 1 The method, which uses a big data analytics server (hereinafter referred to as the server) in the context of big data analytics, includes the following steps.

[0057] S201, Obtain energy consumption data query request; The energy consumption data query request includes the target vehicle's identification code and the query time range.

[0058] When a user files a complaint about high energy consumption, the server can obtain an energy consumption data query request. The query request includes the target vehicle's vehicle identification number (VIN) and the query time range. This query request can be initiated by technicians after they enter the vehicle identification number and the query time range into the server's BI (Business Intelligence) visualization tool. This visualization tool allows technicians to perform the query through simple operations.

[0059] Before receiving an energy consumption data query request, the server can obtain raw energy consumption data for each vehicle from the monitoring server. Specifically, when the vehicle starts, the onboard T-BOX built into the vehicle sends a login request to the monitoring server. The monitoring server responds to the login request, causing the raw energy consumption data collected by the vehicle's onboard T-BOX at preset time intervals to be reported to the monitoring server for storage. For example, the preset time interval can be 1 second. The stored raw energy consumption data for each vehicle can be provided to the server for energy consumption analysis. This raw energy consumption data includes drive motor status, total mileage, vehicle drive mode signal value, air conditioning operating status signal value, air conditioning set temperature, cumulative energy consumption of power battery discharge, high-voltage bus voltage, high-voltage DC current of motor controller, vehicle single charge capacity, charger single charge capacity, and circuit data of various core components.

[0060] After obtaining the raw energy consumption data of each vehicle, the server can periodically calculate the daily operating index data for each vehicle based on the raw energy consumption data. The daily operating index data includes one or more of the following: daily mileage, daily energy consumption, daily vehicle driving mode ratio, average vehicle speed, daily air conditioning set temperature, daily energy consumption per 100 kilometers, energy recovery rate, charging efficiency, and average power consumption of core components. Different operating index data can be calculated based on different data in the raw energy consumption data. For the calculation of each data included in the daily operating index data, please refer to Example 2.

[0061] S202. Based on the energy consumption data query request, retrieve the target energy consumption data and target attribute information of the target vehicle from the stored daily operation index data and attribute information of each vehicle.

[0062] After receiving an energy consumption data query request, the server retrieves the target energy consumption data for the target vehicle from the stored daily operating index data of each vehicle. This target energy consumption data includes the daily operating index data of the target vehicle within the query time range. Furthermore, the server can retrieve target attribute information about the target vehicle from the attribute information of each vehicle stored on the monitoring server, based on the query request. This attribute information includes one or more of the following: vehicle brand, vehicle model, license plate number, drive motor serial number, drive motor model, and vehicle region. Alternatively, the server can retrieve and store the attribute information of each vehicle from the monitoring server before receiving the energy consumption data query request, then the server can retrieve the target attribute information of the target vehicle from the stored attribute information of each vehicle based on the query request.

[0063] After retrieving the target energy consumption data and target attribute information of the target vehicle, the server can obtain a trend chart of the changes in each operating indicator data within the query time range based on the daily operating indicator data. For example, Figure 3 As shown in the graph illustrating the trend of daily average vehicle speed, the horizontal axis X represents the date, and the vertical axis Y represents the average vehicle speed. The line graph in the graph is for illustrative purposes only. For example, ... Figure 4 As shown in the chart, the daily vehicle driving mode percentage trend is displayed, with the horizontal axis representing the date and the vertical axis representing the data percentage. The bar chart is for illustrative purposes only. This trend chart is then visualized using a BI visualization tool. This allows technicians to more intuitively view the changes in daily operational metrics within a given timeframe, facilitating energy consumption analysis.

[0064] S203. Display target energy consumption data and target attribute information.

[0065] After the server retrieves the target energy consumption data and target attribute information, it can display the daily operating indicator data and target attribute information from the target energy consumption data, providing them to technical analysts for further analysis.

[0066] Optionally, a summary report or table can be generated based on the target energy consumption data and target attribute information. This summary report or table includes the target vehicle's target attribute information and daily operating indicator data within the query time range. The daily operating indicator data in the summary report or table can also be presented in the form of a trend chart.

[0067] In one possible implementation, the server can also obtain statistical data of the target vehicle within the query time range based on the daily operating indicator data of the target vehicle within the query time range. That is, the daily operating indicator data can be accumulated and calculated to obtain the statistical data of each operating indicator data. Then the server can display the target energy consumption data, statistical data and target attribute information of the target vehicle. Similarly, the above data can also be displayed in the form of summary reports or tables.

[0068] In one possible implementation, the server can also collect daily operating indicator data for the target vehicle from the time it leaves the factory to the current time, where the current time can be the cutoff time for the query time range. The target energy consumption data can also include the daily operating indicator data for the target vehicle from the time it leaves the factory to the current time. The server can then compile historical statistical data for each operating indicator of the target vehicle from the time it leaves the factory to the current time based on this daily operating indicator data. The server can then display the target energy consumption data, statistical data, target vehicle attribute information, and historical statistical data. Alternatively, it can generate summary reports or tables from the above data for display.

[0069] In addition, the above statistical data may also include the maximum mileage of a single trip, vehicle ECU malfunction information, and the vehicle's last location. The maximum mileage of a single trip is the maximum value of the "high-precision mileage of this trip" in the raw energy consumption data collected by the vehicle within the query time range. The vehicle electronic control unit (ECU) malfunction information refers to the names and specific frequencies of the top three ECUs with the highest ECU malfunction frequency in the raw energy consumption data collected by the vehicle within the query time range. For example, the vehicle control unit (VCU) and frequency: 54 times, the power follow control module (PFCM) and frequency: 36 times, the battery management system (BMS) and frequency: 18 times, etc. The vehicle's last location is the GPS (Global Positioning System) information converted to reverse geocoding in the last reported raw energy consumption data before the deadline of the query time range.

[0070] The target energy consumption data and target attribute information can be used by technicians to conduct energy consumption analysis. Specifically, technicians can compare the target energy consumption data, statistical data and cumulative statistical data in the summary report with the corresponding preset thresholds. These preset thresholds are related to the target attribute information of the target vehicle, that is, different vehicles have different preset thresholds.

[0071] In this embodiment, the server receives an energy consumption data query request. Based on the identification code of the target vehicle and the query time range in the request, it retrieves the target energy consumption data and target attribute information of the target vehicle from the stored daily operating index data and attribute information of each vehicle, and displays the target energy consumption data and target attribute information. When a user's high energy consumption complaint is received, the target energy consumption data and target attribute information of the target vehicle can be quickly obtained simply by using the identification code and the required query time range. This information can be used for energy consumption analysis of the target vehicle, saving data processing time and improving work efficiency.

[0072] The calculation of the daily operating index data in Example 1 will be explained below through Example 2.

[0073] refer to Figure 5 , Figure 5 This is a flowchart illustrating a vehicle energy consumption data processing method provided in Embodiment 2 of this application. The method can be executed by a big data analysis server, hereinafter referred to as the server, and includes the following steps.

[0074] S501. Obtain the raw energy consumption data collected from each vehicle according to the preset time interval.

[0075] The server can obtain the raw energy consumption data collected by the vehicle-mounted T-BOX of each vehicle at preset time intervals. The raw energy consumption data includes multiple data items, including drive motor status signal value, total mileage, vehicle drive mode signal value, air conditioning working status signal value, air conditioning set temperature, cumulative energy consumption of power battery discharge, high voltage bus voltage, high voltage DC current of motor controller, vehicle single charge amount, charger single charge amount, and circuit data of various core components.

[0076] S502. Based on the original energy consumption data, the daily operation index data are calculated.

[0077] The server can periodically calculate daily operating indicators based on raw energy consumption data. For example, it can calculate the previous day's operating indicators at 00:00 every day. The daily operating indicators include one or more of the following: daily mileage, daily energy consumption, daily vehicle driving mode ratio, average vehicle speed, daily air conditioning set temperature distribution, daily energy consumption per 100 kilometers, energy recovery rate, charging efficiency, and average power consumption of core components.

[0078] The calculation of daily operational indicator data is explained below:

[0079] (1) Daily mileage

[0080] The server can calculate the daily operating mileage based on the total mileage in the daily raw energy consumption data. The daily operating mileage is the difference between the total mileage value in the last reported raw energy consumption data of the day and the total mileage value in the raw energy consumption data reported on the first day of the day.

[0081] (2) Daily energy consumption

[0082] The server can calculate the daily energy consumption based on the cumulative energy consumption of the power battery discharge in the daily raw energy consumption data. The daily energy consumption is the difference between the cumulative energy consumption of the power battery discharge in the last reported raw energy consumption data of the day and the cumulative energy consumption of the power battery discharge in the first reported raw energy consumption data of the day.

[0083] (3) Percentage of daily vehicle driving modes

[0084] The server can calculate the daily vehicle driving mode percentage based on the vehicle driving mode signal values ​​in the daily raw energy consumption data. The vehicle driving modes include normal mode, economy mode, hill climbing mode, and electric retarding mode. The signal values ​​for each mode are different. For example, the signal value for normal mode is 0X0, the signal value for economy mode is 0X1, the signal value for hill climbing mode is 0X2, and the signal value for electric retarding mode is 0X3. The percentage of each mode is the ratio of the number of raw energy consumption data entries corresponding to the signal values ​​of each mode to the total number of raw energy consumption data entries for that day.

[0085] (4) Average vehicle speed

[0086] Based on driving patterns, each time a vehicle is ignited, it will shut off once. The vehicle is in operation between the ignition and shutdown times; the time difference during this period is the vehicle's operating time for one cycle. Therefore, the server can calculate the vehicle's operating time for each cycle in chronological order and accumulate these times to calculate the vehicle's daily operating time. The server can determine whether ignition or shutdown has occurred based on the drive motor status in the daily raw energy consumption data. For example, if the drive motor status is OFF, ignition is confirmed, and the reporting time of the raw energy consumption data is the ignition time; if the drive motor status is ON, shutdown is confirmed, and the reporting time of the raw energy consumption data is the shutdown time.

[0087] To obtain an accurate average vehicle speed, the vehicle's idling time needs to be subtracted during calculation. Idling is a vehicle's operating state, referring to the engine running in neutral gear. It can be understood as the vehicle being stationary but not turned off. When the vehicle is in idling or the idling state changes, the raw energy consumption data reported by the vehicle includes the vehicle's idling state. The vehicle's idling time can be obtained by calculating the time difference between the first raw energy consumption data of the day when the vehicle is in idling and the first raw energy consumption data of the day when the idling state changes. The sum of multiple idling times per day can be used to obtain the daily idling time.

[0088] The server can calculate the vehicle's average daily speed based on the total mileage in the original energy consumption data, as well as the vehicle's daily idling time and daily running time. The vehicle's average daily speed is equal to the daily running mileage divided by the difference between the daily running time and the daily idling time. The calculation of daily running mileage is referenced in (1).

[0089] (5) Daily air conditioning set temperature distribution

[0090] The server can determine whether the air conditioner is on or off based on the air conditioner's operating status signal value in the daily raw energy consumption data. For example, if the air conditioner's operating status signal value is 0x7F60B = 0x0:OFF, then the air conditioner is off; if the air conditioner's operating status signal value is 0x7F60B = 0x0:ON, then the air conditioner is on. At this time, the server can determine the temperature range of the set temperature value based on the air conditioner's set temperature value in the raw energy consumption data. This temperature range is one of several preset temperature ranges.

[0091] The server determines the proportion of a given temperature range by comparing the number of raw energy consumption data entries within that temperature range with the total number of raw energy consumption data entries for that day. Based on the proportion of each temperature range, the daily distribution of air conditioner set temperatures can be determined.

[0092] (6) Daily energy consumption per 100 kilometers

[0093] The server can calculate the daily energy consumption per 100 kilometers based on the total mileage and cumulative energy consumption of the power battery discharge in the daily raw energy consumption data. Specifically, the daily energy consumption per 100 kilometers is equal to the daily energy consumption × 100 divided by the daily running mileage. The calculation of the daily running mileage is referenced in (1), and the calculation of the daily energy consumption is referenced in (2).

[0094] (7) Energy recovery rate

[0095] The server can calculate the daily energy recovery rate based on the high-voltage bus voltage and motor controller high-voltage DC current in the daily raw energy consumption data. The motor controller high-voltage DC current value can be positive or negative. A positive value indicates that the current value is the motor controller high-voltage DC current value during the vehicle's braking energy utilization phase, and the corresponding high-voltage bus voltage value in the raw energy consumption data corresponds to the vehicle's voltage value during this phase. A negative value indicates that the voltage value is the motor controller high-voltage DC current value during the vehicle's braking energy recovery phase, and the corresponding high-voltage bus voltage value in the raw energy consumption data corresponds to the vehicle's voltage value during this phase.

[0096] Daily energy recovery rate is equal to the ratio of daily driving energy Q1 to daily recovered energy Q2. Daily driving energy refers to the energy generated by the vehicle during the braking energy utilization phase each day, while daily recovered energy refers to the energy generated by the vehicle during the braking energy recovery phase each day.

[0097] Where, Q1=∑U′ i ×I′ i ×ΔT, where, U′ i I′ represents the high-voltage bus voltage value for each raw energy consumption data point during the vehicle's braking energy utilization phase. i The value of the high voltage DC current of the motor controller for each piece of raw energy consumption data during the braking energy utilization phase of the vehicle is ΔT, which is the raw energy consumption data reporting cycle. The raw energy consumption data reporting cycle is the preset time interval for the vehicle to collect data according to a preset time interval. For example, the preset time interval can be 1 second, then the raw energy consumption data reporting cycle is 1 second.

[0098] Q1=∑U″ i "×(-I") i )×ΔT, where U″ i I″ represents the high-voltage bus voltage value for each raw energy consumption data point during the vehicle's regenerative braking phase. i ΔT represents the high-voltage DC current value of the motor controller for each piece of raw energy consumption data during the vehicle's regenerative braking phase, and ΔT represents the raw energy consumption data reporting cycle.

[0099] Therefore, the daily energy recovery rate of a vehicle is equal to the ratio of the absolute value of Q1 to Q2.

[0100] (8) Charging efficiency

[0101] The server can determine each charging cycle of the vehicle each day based on the charging status in the daily raw energy consumption data. Each charging cycle is either an abnormal charging cycle or a normal charging cycle. The time from the first raw energy consumption data point with the charging status "parked charging" to the first raw energy consumption data point with the charging status "charging complete" constitutes one normal charging cycle. Alternatively, the time from the first raw energy consumption data point with the charging status "parked charging" to the first raw energy consumption data point with the charging status "not charging" constitutes one abnormal charging cycle.

[0102] During the charging process, the single-charge amount and the charger's single-charge amount in the original energy consumption data are continuously accumulated. Therefore, the single-charge efficiency of each charging cycle is the ratio of the single-charge amount to the charger's single-charge amount. The charging status in the original energy consumption data for both the single-charge amount and the charger's single-charge amount is either "charging complete" or "not charging." Thus, the vehicle's daily charging efficiency is equal to the ratio of the accumulated single-charge efficiency of each charging cycle to the number of charging cycles for that day.

[0103] (9) Average power consumption of core components

[0104] The server can calculate the average daily power consumption of the vehicle's core components based on the circuit data of each core component in the raw energy consumption data. These core components include the drive motor, DC-DC converter (DCDC), BDCDC (Brake DC-AC inverter), SDCDC (Power-steering DC-AC inverter), AC air conditioning unit, EDS, and TMS (Temperature management system).

[0105] Optionally, since the data collected from different vehicle types are different, when the circuit data of each core component collected from the vehicle is the output power, then the average power consumption of the core component is the sum of the output power of each core component.

[0106] Specifically, when the circuit data collected by the vehicle for each core component is current and voltage, then the average power consumption of each core component is equal to Q divided by the daily operating time, where Q is the daily energy of each core component, Q = ∑U i ×I i ×1.732×ΔT,U i For each piece of raw energy consumption data, I represents the voltage value of the core components. iThe current value of the core components in each piece of raw energy consumption data, ΔT is the same as above, which is the preset time interval. The calculation of the daily running time can be referred to (5). The average power consumption of each core component is obtained by the above calculation method. Then, the average power consumption of each core component is accumulated to obtain the average power consumption of the core component.

[0107] Optionally, before calculating the daily operating index data, the server can perform data cleaning on the raw energy consumption data to remove invalid data and data during vehicle idling. The invalid data can be invalid data reported before the vehicle starts. Removing invalid data and data during idling can improve the accuracy of the calculation results.

[0108] Data during vehicle idling can be determined as follows:

[0109] The server can determine a stroke based on the ignition time and shutdown time in chronological order, divide multiple raw energy consumption data into stroke units, and obtain raw energy consumption data corresponding to multiple stroke segments. The data during the idling state is the raw energy consumption data outside each stroke segment.

[0110] The raw energy consumption data corresponding to multiple trip segments are used to calculate the operating index data corresponding to the multiple trip segments at regular intervals. Then, the daily operating index data is obtained based on the operating index data corresponding to the multiple trip segments. This avoids using data from the vehicle in an idling state for calculation, which can easily lead to inaccurate calculation results. The above (1) to (9) are the calculation of daily operating index data based on the operating data of the undivided trip segments. The operating index data corresponding to each trip segment can be calculated by referring to the calculation method of (1) to (9). The operating index data corresponding to each trip segment is calculated, and then the operating index data of each multi-segment trip is accumulated to obtain the daily operating index data.

[0111] S503 stores daily operating indicator data for each vehicle.

[0112] After the server calculates the daily operating index data for each vehicle, it can store the daily operating index data for each vehicle so that the server can query it after receiving an energy consumption data query request.

[0113] In this embodiment, the server obtains the raw energy consumption data collected from each vehicle at preset time intervals, and then calculates the daily operating index data based on the raw energy consumption data. The server can also perform data cleaning on the raw energy consumption data before calculation to remove invalid data and data during vehicle idling. Then, based on the cleaned raw energy consumption data, the server calculates and stores the daily operating index data for each vehicle. By removing invalid data and data during vehicle idling, the accuracy of the calculation results is improved.

[0114] refer to Figure 6 , Figure 6 This is a schematic diagram of a vehicle energy consumption data processing device provided in Embodiment 3 of this application. Figure 6 As shown, the device 60 includes: an acquisition module 601, a query module 602, and a display module 603.

[0115] The acquisition module 601 is used to acquire energy consumption data query requests; the energy consumption data query requests include the identification code of the target vehicle and the query time range.

[0116] The query module 602 is used to query the target energy consumption data and target attribute information of the target vehicle from the stored daily operation index data and attribute information of each vehicle according to the energy consumption data query request; wherein, the daily operation index data represents the daily energy consumption of the target vehicle within the query time range.

[0117] Display module 603 is used to display target energy consumption data and target attribute information.

[0118] Optionally, before obtaining the energy consumption data query request, the following steps are also included:

[0119] The raw energy consumption data collected from each vehicle is acquired at preset time intervals. The raw energy consumption data includes multiple data items, including at least one of the following: drive motor status, total mileage, vehicle drive mode signal value, air conditioning working status signal value, air conditioning set temperature, cumulative energy consumption of power battery discharge, high voltage bus voltage, high voltage DC current of motor controller, charging status, single charge amount of vehicle, single charge amount of charger, and circuit data of each core component.

[0120] Based on the raw energy consumption data, the daily operating index data of each vehicle is calculated. The daily operating index data includes one or more of the following: daily mileage, daily energy consumption, daily vehicle driving mode ratio, average vehicle speed, daily air conditioning set temperature distribution, daily energy consumption per 100 kilometers, energy recovery rate, charging efficiency, and average power consumption of core components.

[0121] Obtain the attribute information of each vehicle; the attribute information includes one or more of the following data: vehicle brand, vehicle model, license plate number, drive motor number, drive motor model and vehicle region.

[0122] Store daily operating index data and attribute information for each vehicle.

[0123] Optionally, after retrieving the energy consumption data and target attribute information of the target vehicle, the query may also include:

[0124] Based on the daily operational indicator data, a trend chart of the changes in each operational indicator data within the query time range is obtained.

[0125] The trend charts of various operational indicators over the query time range are visualized using BI visualization tools.

[0126] Optionally, before calculating the daily operating index data for each vehicle based on the raw energy consumption data, the following steps are also included:

[0127] The raw energy consumption data is cleaned to remove invalid data and data from when the vehicle is idling.

[0128] Based on the raw energy consumption data, the daily operating indicators for each vehicle were calculated, including:

[0129] Based on the raw energy consumption data after data cleaning, the daily operating indicators of each vehicle are calculated.

[0130] Optionally, the display module 603 is specifically used for:

[0131] Based on the daily operating indicator data of the target vehicle within the query time range, statistical data of each operating indicator of the target vehicle within the query time range are obtained.

[0132] Displays target energy consumption data, statistical data, and target vehicle attribute information.

[0133] Optionally, the display module 603 is also used for:

[0134] Based on the daily operating indicator data of the target vehicle within the query time range, statistical data of each operating indicator of the target vehicle within the query time range are obtained.

[0135] Based on the daily operating data of the target vehicle from the time it leaves the factory to the current time, historical statistical data of each operating indicator of the target vehicle from the time it leaves the factory to the current time are obtained.

[0136] Displays target energy consumption data, statistical data, target vehicle attribute information, and historical statistical data.

[0137] The apparatus in this embodiment can be used to execute a vehicle energy consumption data processing method according to Embodiment 1 or Embodiment 2. The specific implementation and technical effects are similar, and will not be described again here.

[0138] refer to Figure 7 , Figure 7 This is a schematic diagram of the structure of a vehicle energy consumption data processing device provided in Embodiment 4 of this application. Figure 7As shown, the device 70 includes a processor 701, a memory 702, and a transceiver 703. The processor 701 executes computer execution instructions stored in the memory 702 and controls the receiving and sending actions of the transceiver 703, so that at least one processor executes the steps of a vehicle energy consumption data processing method in Embodiment 1 or Embodiment 2. The specific implementation and technical effects are similar and will not be described in detail here.

[0139] This application provides a computer-readable storage medium storing computer-executable instructions. When executed by a processor, these instructions are used to implement the steps of a vehicle energy consumption data processing method as described in Embodiment 1 or Embodiment 2 above. The specific implementation and technical effects are similar and will not be repeated here.

[0140] Embodiment 6 of the present invention provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps of a vehicle energy consumption data processing method as described in Embodiment 1 or Embodiment 2 above. The specific implementation method and technical effects are similar, and will not be repeated here.

[0141] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0142] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for processing vehicle energy consumption data, characterized in that, The method includes: Obtain an energy consumption data query request; the energy consumption data query request includes the identification code of the target vehicle to be queried and the query time range; According to the energy consumption data query request, the target energy consumption data and target attribute information of the target vehicle are obtained by querying the daily operation index data and attribute information of each vehicle stored respectively; wherein, the daily operation index data represents the daily energy consumption of the target vehicle within the query time range. Display the target energy consumption data and the target attribute information; Before the energy consumption data query request, the following is also included: The raw energy consumption data of each vehicle is acquired at preset time intervals. The raw energy consumption data is cleaned to remove invalid data and data from when the vehicle is idling. Based on the raw energy consumption data after data cleaning, the daily operating index data of each vehicle is calculated; the daily operating index data includes one or more of the following data: daily mileage, daily energy consumption, daily vehicle driving mode ratio, average vehicle speed, daily air conditioning set temperature distribution, daily energy consumption per 100 kilometers, energy recovery rate, charging efficiency, and average power consumption of core components. Obtain the attribute information of each vehicle; Store the daily operating index data and attribute information of each vehicle; The target energy consumption data also includes the daily operating index data of the target vehicle from the time it leaves the factory to the current time. Displaying the target energy consumption data and the target attribute information includes: Based on the daily operating indicator data of the target vehicle within the query time range, statistical data of each operating indicator data of the target vehicle within the query time range are obtained. Based on the daily operating index data of the target vehicle from the time it leaves the factory to the current time, historical statistical data of each operating index data of the target vehicle from the time it leaves the factory to the current time are obtained. The system displays the target energy consumption data, the statistical data, the attribute information of the target vehicle, and the historical statistical data.

2. The method according to claim 1, characterized in that, The raw energy consumption data includes multiple data items, including at least one of the following: drive motor status, total mileage, vehicle drive mode signal value, air conditioning working status signal value, air conditioning set temperature, cumulative energy consumption of power battery discharge, high voltage bus voltage, high voltage DC current of motor controller, charging status, vehicle single charge amount, charger single charge amount, and circuit data of each core component. The attribute information includes one or more of the following data: vehicle brand, vehicle model, license plate number, drive motor number, drive motor model, and vehicle region.

3. The method according to claim 1, characterized in that, After obtaining the energy consumption data and target attribute information of the target vehicle through the query, the process also includes: Based on the daily operational indicator data, a trend chart of the changes in each operational indicator data within the query time range is obtained; The trend charts of the changes in various operational indicators within the query time range are visualized using BI visualization tools.

4. The method according to any one of claims 1-3, characterized in that, The display of the target energy consumption data and the target attribute information includes: Based on the daily operating indicator data of the target vehicle within the query time range, statistical data of each operating indicator data of the target vehicle within the query time range are obtained. Displays the target energy consumption data, the statistical data, and the target attribute information of the target vehicle.

5. A vehicle energy consumption data processing device, characterized in that, include: The acquisition module is used to acquire energy consumption data query requests; the energy consumption data query requests include the identification code of the target vehicle to be queried and the query time range; The query module is used to query the target energy consumption data and target attribute information of the target vehicle from the stored daily operation index data and attribute information of each vehicle according to the energy consumption data query request. The daily operation index data represents the daily energy consumption of the target vehicle within the query time range. A display module is used to display the target energy consumption data and the target attribute information; Before acquiring the energy consumption data query request, the acquisition module is further configured to acquire raw energy consumption data collected from each vehicle at preset time intervals; perform data cleaning on the raw energy consumption data to remove invalid data and data from when the vehicle is idling; calculate the daily operating index data of each vehicle based on the cleaned raw energy consumption data; the daily operating index data includes one or more of the following: daily mileage, daily energy consumption, daily vehicle driving mode ratio, average vehicle speed, daily air conditioning set temperature distribution, daily energy consumption per 100 kilometers, energy recovery rate, charging efficiency, and average power consumption of core components; acquire the attribute information of each vehicle; and store the daily operating index data and attribute information of each vehicle. The target energy consumption data also includes the daily operating index data of the target vehicle from the time it leaves the factory to the current time; The display module is specifically used to: obtain statistical data of each operating indicator of the target vehicle within the query time range based on the daily operating indicator data of the target vehicle within the query time range; obtain historical statistical data of each operating indicator of the target vehicle from the time of manufacture to the current time based on the daily operating indicator data of the target vehicle from the time of manufacture to the current time; and display the target energy consumption data, the statistical data, the attribute information of the target vehicle, and the historical statistical data.

6. A vehicle energy consumption data processing device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes computer execution instructions stored in the memory to implement the method of any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the vehicle energy consumption data processing method according to any one of claims 1 to 4.

8. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 4.

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