Range remaining prediction method, apparatus, device, and readable storage medium

Abstract

This application discloses a method, apparatus, device, and readable storage medium for predicting remaining driving range. The method includes: acquiring real-time data of a target vehicle; the real-time data includes accessory power and the average vehicle speed corresponding to the planned route; determining the high-speed energy consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, a database constructed based on historical data of the target vehicle, and accessory power; the database includes the relationship between average vehicle speed, high-speed slices, and average MCU energy consumption, where high-speed slices are obtained by slicing target times in historical data where the vehicle speed is greater than a high-speed driving speed threshold; and determining the remaining high-speed driving range based on the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route. The technical solution disclosed in this application constructs a database based on the historical data of the target vehicle, predicts the vehicle's high-speed energy consumption and remaining driving range based on the database and real-time vehicle data, and improves the accuracy of predicting the remaining high-speed driving range of the target vehicle.

Description

Technical Field

[0001] This application relates to the field of new energy electric vehicle technology, and in particular to a method, apparatus, device and readable storage medium for predicting remaining range. Background Technology

[0002] With the development of new energy technologies, new energy electric vehicles are being used more and more widely. Currently, the standard driving range displayed by new energy electric vehicles differs significantly from the actual highway driving range, which may lead drivers to misjudge the remaining range and thus prevent them from reaching their destination.

[0003] In conclusion, how to predict the remaining range of a new energy electric vehicle at high speeds in order to reduce drivers' misjudgment of the remaining range is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, apparatus, device and readable storage medium for predicting the remaining range of a new energy electric vehicle at high speed, so as to reduce the driver's misjudgment of the remaining range.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] A method for predicting remaining range includes: acquiring real-time data of a target vehicle; the real-time data includes accessory power and average vehicle speed corresponding to the planned route; determining the high-speed energy consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, a database constructed based on historical data of the target vehicle, and the accessory power; the database includes the relationship between average vehicle speed, high-speed slice number, and MCU average energy consumption, wherein the high-speed slice is obtained by slicing the target time in the historical data where the vehicle speed is greater than the high-speed driving speed threshold; and determining the remaining high-speed driving range based on the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0007] Optionally, the real-time data also includes target parameters. The database includes the relationship between historical target parameters, average vehicle speed, number of high-speed slices, and average MCU power consumption. The target parameters include the number of occupants and / or the average gradient of the planned route. The historical target parameters include the historical number of occupants and / or the historical average gradient.

[0008] Determining the high-speed energy consumption corresponding to the planned route based on the average vehicle speed, the database, and the power of the attachments includes: determining the high-speed energy consumption corresponding to the planned route based on the average vehicle speed, the target parameters, the database, and the power of the attachments.

[0009] Optionally, the historical target parameters include historical occupant count and historical average gradient. Constructing a database based on the historical data of the target vehicle includes: acquiring historical data of the target vehicle; extracting the target time from the historical data; slicing the target time to obtain high-speed slices; extracting vehicle speed calculation data, MCU energy consumption calculation data, gradient calculation data, and historical occupant count corresponding to each high-speed slice from the historical data; determining the average vehicle speed corresponding to each high-speed slice based on the vehicle speed calculation data; determining the average MCU energy consumption corresponding to each high-speed slice based on the MCU energy consumption calculation data; determining the average gradient corresponding to each high-speed slice based on the gradient calculation data; and constructing the database based on the average vehicle speed, average MCU energy consumption, average gradient, and historical occupant count corresponding to each high-speed slice.

[0010] Optionally, the vehicle speed calculation data corresponding to each high-speed slice includes the initial stage mileage and the final stage mileage corresponding to each high-speed slice; the MCU energy consumption calculation data corresponding to each high-speed slice includes the MCU voltage and MCU current of each collection point in each high-speed slice, the initial stage mileage and the final stage mileage corresponding to each high-speed slice; and the slope data corresponding to each high-speed slice includes the slope of each collection point in each high-speed slice.

[0011] Determining the average vehicle speed corresponding to each high-speed slice based on the vehicle speed calculation data corresponding to each high-speed slice includes: utilizing... Calculate the average vehicle speed V corresponding to the i-th high-speed slice. i Among them, S i末 S represents the mileage of the final stage corresponding to the i-th high-speed slice. i初 Let T be the initial stage mileage corresponding to the i-th high-speed slice. h The duration of the high-speed slice is converted to hours.

[0012] The average power consumption of the MCU corresponding to each high-speed slice is determined based on the MCU power consumption calculation data corresponding to each high-speed slice, including: using... Calculate the average MCU power consumption E corresponding to the i-th high-speed slice. MCUi Among them, U n Let I be the MCU voltage at the nth acquisition point in the i-th high-speed slice. n Let t be the MCU current at the nth acquisition point in the i-th high-speed slice. n K1 is the time of the nth acquisition point in the i-th high-speed slice, K2 is the conversion factor between hours and the time unit of the acquisition point, and K2 is the conversion factor between kilowatts and MCU current * MCU voltage.

[0013] The average slope corresponding to each high-speed slice is determined based on the slope calculation data corresponding to each high-speed slice, including: using... Calculate the average slope α corresponding to the i-th high-speed slice. i ; where α n Let be the slope of the nth acquisition point in the i-th high-speed slice.

[0014] Optionally, the database is constructed based on the average vehicle speed, average MCU power consumption, average gradient, and historical occupant count corresponding to each high-speed slice, including: grouping according to the historical occupant count, average gradient, and average vehicle speed; wherein at least one of the historical occupant count, average gradient, and average vehicle speed is different between different data groups; determining the number of high-speed slices in each data group based on the historical occupant count, average gradient, and average vehicle speed in each data group; and determining the average MCU power consumption in each data group based on the number of high-speed slices in each data group and the average MCU power consumption corresponding to the corresponding high-speed slice.

[0015] Optionally, it further includes: when a new high-speed slice is added and the new high-speed slice corresponds to a target data group in the database, determining the average power consumption of the MCU in the target data group based on the current average power consumption of the MCU in the target data group, the current number of high-speed slices, the number of new high-speed slices, and the average power consumption of the MCU corresponding to the new high-speed slice, and determining the number of high-speed slices in the target data group based on the current number of high-speed slices and the number of new high-speed slices.

[0016] Optionally, after extracting the vehicle speed calculation data, MCU energy consumption calculation data and slope calculation data corresponding to each high-speed slice from the historical data, the method further includes cleaning the data extracted from the historical data.

[0017] Optionally, obtaining the average vehicle speed corresponding to the planned route includes: determining the average vehicle speed corresponding to the planned route based on the target parameters and the database.

[0018] Optionally, determining the average vehicle speed corresponding to the planned route based on the target parameters and the database includes: obtaining each average vehicle speed and each number of highway slices corresponding to the target parameters from the database; and determining the average vehicle speed corresponding to the planned route based on each average vehicle speed, each number of highway slices, and the duration of the highway slices in the database.

[0019] Optionally, determining the high-speed energy consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, the number of passengers, the average gradient of the planned route, the database, and the power of the accessories includes: obtaining the average MCU energy consumption corresponding to the average vehicle speed corresponding to the planned route from the database; determining the high-speed MCU average energy consumption corresponding to the planned route based on the corresponding MCU average energy consumption obtained from the database; and determining the high-speed energy consumption corresponding to the planned route based on the high-speed MCU average energy consumption corresponding to the planned route, the power of the accessories, and the average vehicle speed corresponding to the planned route.

[0020] Optionally, obtaining the accessory power of the target vehicle includes: obtaining the output voltage and output current of the battery in the target vehicle, and the input voltage and input current of the MCU; and determining the accessory power based on the output voltage and output current of the battery and the input voltage and input current of the MCU.

[0021] Optionally, when multiple planned routes are included, after determining the high-speed energy consumption corresponding to the planned routes, the method further includes: determining the optimal high-speed energy consumption from the high-speed energy consumption corresponding to each of the planned routes, and determining the planned route corresponding to the optimal high-speed energy consumption as the optimal route.

[0022] Determining the remaining high-speed driving range based on the remaining SOC of the target vehicle and the high-speed energy consumption of the planned route includes: determining the optimal high-speed driving range based on the remaining SOC of the target vehicle and the optimal high-speed energy consumption.

[0023] Optionally, it further includes: obtaining the navigation route mileage, determining whether the remaining optimal highway driving range is less than the navigation route mileage; if so, obtaining the location of charging piles on the optimal route, and sending the optimal highway energy consumption, the optimal route, and the location of the charging piles to the host of the target vehicle, which will then display and / or play the data via voice.

[0024] A remaining range prediction device includes: an acquisition module for acquiring real-time data of a target vehicle; the real-time data including accessory power and average vehicle speed corresponding to the planned route; a first determination module for determining the high-speed energy consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, a database constructed based on historical data of the target vehicle, and the accessory power; the database includes the relationship between average vehicle speed, high-speed slice number, and MCU average energy consumption, wherein the high-speed slice is obtained by slicing the target time in the historical data where the vehicle speed is greater than the high-speed driving speed threshold; and a second determination module for determining the remaining high-speed driving range based on the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0025] A remaining mileage prediction device includes: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the remaining mileage prediction method as described in any of the preceding claims.

[0026] A readable storage medium storing a computer program that, when executed by a processor, implements the steps of the residual process prediction method as described in any of the preceding claims.

[0027] This application provides a method, apparatus, device, and readable storage medium for predicting remaining range. The method includes: acquiring real-time data of a target vehicle; the real-time data includes accessory power and average vehicle speed corresponding to the planned route; determining the high-speed energy consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, a database constructed based on historical data of the target vehicle, and accessory power; the database includes the relationship between average vehicle speed, high-speed slice number, and MCU average energy consumption, wherein the high-speed slice is obtained by slicing the target time in historical data where the vehicle speed is greater than the high-speed driving speed threshold; and determining the remaining high-speed driving range based on the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0028] The technical solution disclosed in this application constructs a database containing the relationship between average vehicle speed, high-speed slice count, and average MCU power consumption based on the historical data of the target vehicle. This enables the analysis of the target vehicle's driver's high-speed driving habits by analyzing the historical data of the target vehicle, and the establishment of a corresponding database that can be used to predict high-speed power consumption. Then, based on the target vehicle's real-time data and the established database, the high-speed power consumption of the target vehicle can be predicted in advance, and the remaining high-speed driving range of the target vehicle can be predicted in advance. Furthermore, this application can improve the accuracy of the prediction of the remaining high-speed driving range of the target vehicle, thereby reducing the driver's misjudgment of the actual remaining driving range of the target vehicle, alleviating the driver's range anxiety, and improving the driver's driving experience.

[0029] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0030] Figure 1 A flowchart illustrating a remaining mileage prediction method provided in this application embodiment;

[0031] Figure 2 A schematic diagram illustrating the extraction of high-speed driving data and the resulting high-speed slices provided in an embodiment of this application;

[0032] Figure 3 A simplified working principle diagram of a remaining mileage prediction method provided in an embodiment of this application;

[0033] Figure 4 A detailed schematic diagram illustrating the working principle of a remaining mileage prediction method provided in this application embodiment;

[0034] Figure 5 This is a schematic diagram of the structure of a remaining mileage prediction device provided in an embodiment of this application;

[0035] Figure 6 This is a schematic diagram of the structure of a remaining mileage prediction device provided in an embodiment of this application. Detailed Implementation

[0036] With the development of new energy technologies, new energy electric vehicles are being used more and more widely. Currently, the standard driving range displayed by new energy electric vehicles differs significantly from the actual highway driving range, which may lead drivers to misjudge the remaining range and thus prevent them from reaching their destination.

[0037] To address this, this application provides a method, apparatus, device, and readable storage medium for predicting remaining mileage. It constructs a database based on historical data of a target vehicle, including the relationship between average vehicle speed, high-speed slice count, and average energy consumption of the MCU (Motor Control Unit). The high-speed slice count is obtained by slicing historical data at target times when the vehicle speed exceeds a high-speed driving speed threshold. By constructing the database based on the historical data of the target vehicle, a corresponding high-speed energy consumption model for the target vehicle driver is established by analyzing the driver's high-speed driving habits. Furthermore, real-time data of the target vehicle is acquired, including accessory power and the average vehicle speed corresponding to the planned route. Then, based on the average vehicle speed of the planned route, accessory power, and the database from the real-time data, the high-speed energy consumption corresponding to the planned route is predicted in advance. Finally, based on the predicted high-speed energy consumption corresponding to the planned route and the remaining SOC (State of Charge) of the target vehicle, the remaining high-speed driving range is predicted in advance. In other words, this application analyzes the historical data of the target vehicle to understand the driver's high-speed driving habits, and establishes a corresponding high-speed energy consumption model for the driver. Then, it collects real-time data of the target vehicle and predicts the high-speed energy consumption of the target vehicle in advance based on the real-time data and the established high-speed energy consumption model for the driver. Based on this, it predicts the remaining high-speed driving range in advance, thereby improving the accuracy of the prediction and reducing the driver's misjudgment of the remaining range, alleviating the driver's range anxiety, and improving the driver's driving experience.

[0038] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0039] See Figure 1 The diagram illustrates a flowchart of a remaining mileage prediction method provided in an embodiment of this application. This remaining mileage prediction method may include:

[0040] S11: Obtain real-time data of the target vehicle; real-time data includes the power of the accessories and the average speed corresponding to the planned route.

[0041] It should be noted that the execution entity of the remaining mileage prediction method provided in this application embodiment can be a cloud server or the target vehicle itself. To save the target vehicle's computing resources and improve the efficiency of obtaining remaining mileage, a cloud server can be specifically used as the execution entity of the remaining mileage prediction method provided in this application embodiment. This application embodiment uses a cloud server as the execution entity for illustration.

[0042] In this embodiment, sensors in the target vehicle can collect data from the vehicle in real time and feed the collected real-time data back to the cloud server, enabling the cloud server to obtain the real-time data of the target vehicle. The aforementioned real-time data includes, but is not limited to, accessory power, the planned route provided by the map (i.e., the planned driving route of the target vehicle), and the average speed of the planned route. Accessory power refers to the power of accessories in the target vehicle, such as DC-DC converters, PTC thermistors, and compressors. The average speed corresponding to the planned route refers to the average speed of the vehicle on the planned route, which can be provided by the map. Specifically, the map can determine the average speed corresponding to the planned route based on big data, or the map can provide the mileage and estimated time of the planned route to the vehicle, allowing the vehicle to calculate the average speed corresponding to the planned route (or the map can provide the mileage and estimated time of the planned route to the cloud server, allowing the cloud server to calculate the average speed corresponding to the planned route).

[0043] When the cloud server receives real-time data from the target vehicle, it can analyze the factors affecting energy consumption in the real-time data of the target vehicle, and input the parameters into the target vehicle driver's high-speed energy consumption model built based on the historical data of the target vehicle for calculation, predict high-speed energy consumption in advance, and predict the remaining high-speed driving range based on the predicted high-speed energy consumption.

[0044] S12: Determine the high-speed energy consumption of the planned route based on the average vehicle speed corresponding to the planned route, the database constructed based on the historical data of the target vehicle, and the power of the attachments. The database includes the relationship between the average vehicle speed, the number of high-speed slices, and the average energy consumption of the MCU. The high-speed slice is obtained by slicing the target time in the historical data where the vehicle speed is greater than the high-speed driving speed threshold.

[0045] It should be noted that the target vehicle can upload historical data to the cloud server (specifically, historical data can be uploaded to the cloud server before the remaining mileage is predicted). The historical data mentioned here can be historical real-time data, such as time, vehicle speed at the corresponding time, mileage displayed on the instrument panel, gradient, historical number of occupants, driving mode, energy recovery intensity, battery SOC, battery output voltage, battery output current, MCU voltage, MCU current, etc.

[0046] The cloud server can schedule historical vehicle data of the target vehicle driver driving at high speed from historical data, and slice this historical data (specifically, slice the target time when the vehicle speed is greater than the high speed threshold) to obtain high speed slices, and extract driving habits and the main factors affecting energy consumption in each high speed slice.

[0047] Specifically, the target time when the vehicle speed exceeds the high-speed driving speed threshold is obtained from historical data in the cloud server. This target time is then sliced ​​at preset intervals to obtain high-speed slices (i.e., the duration of each high-speed slice is a preset duration). The high-speed driving speed threshold can be set based on experience, for example, 80 km / h. The preset duration can be set based on the historical distribution of vehicle data at high speeds or by relevant personnel based on experience, for example, 60 seconds. See [link to details] for more information. Figure 2 It illustrates a schematic diagram of high-speed driving data extraction and high-speed slice division provided in an embodiment of this application, wherein, Figure 2 Taking a high-speed driving speed threshold of 80km / h and a preset duration of 60s as an example, the cloud server, after obtaining each high-speed slice, can extract the driving habits and key factors affecting energy consumption corresponding to each high-speed slice from historical data. Among them, the key factors affecting energy consumption are the key data that affect energy consumption, such as the mileage displayed on the instrument panel, time, MCU voltage, MCU current, etc.

[0048] After extracting data, the cloud server can construct a database (also called a database model or target vehicle driver high-speed energy consumption model) based on the extracted data, containing relationships between average vehicle speed, high-speed slice count, and MCU average energy consumption (the unit of MCU average energy consumption can be kWh / 100km; this application uses kWh / 100km as an example for illustration). The database can contain multiple data groups, each containing average vehicle speed, high-speed slice count, and MCU average energy consumption, with different average vehicle speeds in different data groups. Average vehicle speed refers to the average vehicle speed corresponding to the high-speed slice, which can be specifically determined using... Calculate the average vehicle speed V corresponding to the i-th high-speed slice. i Among them, V i The calculation result can be rounded to one decimal place (of course, this can be adjusted), V i The unit is km / h, S i初 S represents the initial stage mileage (in km) corresponding to the i-th high-speed slice. i末 T represents the final stage mileage (in km) corresponding to the i-th high-speed slice. h The duration is converted to hours (h) for each high-speed slice; the number of high-speed slices refers to the number of high-speed slices corresponding to the same average vehicle speed; the average MCU power consumption refers to the average power consumption of the MCU corresponding to the number of high-speed slices. (Specific details can be found using...) Calculate the average MCU power consumption E corresponding to the i-th high-speed slice. MCUi Among them, E MCUi The calculation result can be rounded to one decimal place (of course, this can be adjusted), E MCUi The unit is kWh / 100km, U n I represents the MCU voltage (in V) at the nth acquisition point in the i-th high-speed slice. n t represents the MCU current (in A) at the nth acquisition point in the i-th high-speed slice. n Let K1 be the time of the nth acquisition point in the i-th high-speed slice, and K1 be the conversion factor between hours and the time unit of the acquisition point. For example, see Table 1, which is a database built based on the historical data of the target vehicle:

[0049] Table 1. Database constructed based on historical data of the target vehicle.

[0050]

[0051]

[0052] Based on the above, the highway energy consumption (unit: kWh / 100km) corresponding to the planned route can be determined according to the average vehicle speed, accessory power, and a database constructed based on historical data of the target vehicle. Specifically, the average energy consumption of the highway MCU corresponding to the planned route can be determined according to the average vehicle speed and the database constructed based on historical data of the target vehicle, and the highway energy consumption E (kWh / 100km) corresponding to the planned route can be determined according to the average energy consumption of the highway MCU, accessory power, and the average vehicle speed corresponding to the planned route. Specifically, when determining the average energy consumption of the highway MCU corresponding to the planned route based on the average vehicle speed and the database constructed based on historical data of the target vehicle, if an average speed with the same value as the average vehicle speed corresponding to the planned route exists in the database, the average energy consumption of the MCU corresponding to that average vehicle speed in the database is determined as the average energy consumption of the highway MCU corresponding to the planned route; if no average speed with the same value as the average vehicle speed corresponding to the planned route exists in the database, the average speed adjacent to the average vehicle speed corresponding to the average vehicle speed corresponding to the planned route is determined, and the average energy consumption of the MCU corresponding to the adjacent average speed is interpolated to obtain the average energy consumption of the highway MCU corresponding to the planned route.

[0053] S13: Determine the remaining high-speed driving range based on the remaining SOC of the target vehicle and the high-speed energy consumption of the planned route.

[0054] Obtain the remaining SOC (State of Charge) of the target vehicle corresponding to the energy level ΔE. Based on the remaining SOC of the target vehicle corresponding to the energy level ΔE and the highway energy consumption E corresponding to the planned route, utilize... The remaining range for high-speed driving is calculated.

[0055] It should be noted that if the executing entity is a cloud server, after determining the highway energy consumption and remaining highway range for the planned route, the cloud server can also feed back these figures to the target vehicle. The target vehicle's infotainment system can then remind the driver of the predicted highway energy consumption and remaining highway range through voice prompts or screen displays. Additionally, the cloud server can obtain the locations of charging stations along the planned route and feed them back to the target vehicle along with the highway energy consumption and remaining highway range. The target vehicle can then remind the driver of this information through voice prompts and / or screen displays, allowing the driver to obtain the predicted highway energy consumption and remaining highway range information. This reduces misjudgments about the remaining highway range, enabling the driver to plan charging accordingly, reach their destination smoothly, reduce range anxiety, and improve the driving experience. When the executing entity is the target vehicle itself, after predicting the highway energy consumption, remaining highway range, and obtaining the locations of charging stations along the planned route, the target vehicle's main system can remind the driver of this information through voice prompts and / or screen displays.

[0056] The above method allows for the prediction of high-speed energy consumption and remaining high-speed driving range as soon as the driver and passengers board the vehicle. Furthermore, this process utilizes vehicle-to-everything (V2X) and cloud service technologies to analyze the driver's high-speed driving habits, establish a high-speed energy consumption model for the target vehicle, collect real-time data of the target vehicle, and predict the target vehicle's high-speed energy consumption and remaining high-speed driving range based on this data and the driver's high-speed energy consumption model. This not only enables advance prediction of high-speed energy consumption and remaining high-speed driving range but also allows for accurate and reliable prediction based on historical data, reducing the driver's misjudgment of the remaining range.

[0057] The technical solutions disclosed in this application construct a database containing the relationship between average vehicle speed, high-speed slice count, and average MCU power consumption based on the historical data of the target vehicle. This enables the analysis of the target vehicle's driver's high-speed driving habits by analyzing the historical data of the target vehicle, and the establishment of a corresponding database that can be used to predict high-speed power consumption. Then, based on the target vehicle's real-time data and the established database, the high-speed power consumption of the target vehicle can be predicted in advance, and the remaining high-speed driving range of the target vehicle can be predicted in advance. Furthermore, this application embodiment can improve the accuracy of predicting the remaining high-speed driving range of the target vehicle, thereby reducing the driver's misjudgment of the actual remaining driving range of the target vehicle, alleviating the driver's range anxiety, and improving the driver's driving experience.

[0058] The remaining mileage prediction method provided in this application embodiment may include target parameters in real-time data. The database may include the relationship between historical target parameters, average vehicle speed, number of high-speed slices and average power consumption of MCU. The target parameters may include the number of occupants and / or the average gradient of the planned route. The historical target parameters may include the historical number of occupants and / or the historical average gradient.

[0059] Determine the high-speed energy consumption of the planned route based on the average vehicle speed, database, and power of the attached components. This can include: determining the high-speed energy consumption of the planned route based on the average vehicle speed, target parameters, database, and power of the attached components.

[0060] In this embodiment, the real-time data of the target vehicle may further include target parameters, which may specifically include the number of occupants and / or the average gradient corresponding to the planned route. Furthermore, the database constructed based on the historical data of the target vehicle may specifically include the relationship between historical target parameters, average vehicle speed, number of high-speed slices, and average MCU energy consumption. That is, the key factors affecting energy consumption extracted by the cloud server from historical data may include, for example, historical occupant count, instrument mileage, gradient, MCU voltage, MCU current, etc. See Table 2 for details, which is a data table of extracted driving habits and key factors affecting energy consumption.

[0061] Table 2 shows the extracted data on driving habits and key factors affecting energy consumption.

[0062]

[0063]

[0064] After extracting the data, the cloud server can construct a database containing relationships between historical target parameters, average vehicle speed, high-speed slice count, and MCU average power consumption. This database can contain multiple data groups, each including historical target parameters, average vehicle speed, high-speed slice count, and MCU average power consumption. At least one of the historical target parameters or average vehicle speed must differ between different data groups. Historical occupant count refers to the number of passengers in the vehicle corresponding to each high-speed slice, and average gradient refers to the average gradient corresponding to each high-speed slice.

[0065] Based on the above, the high-speed energy consumption of the planned route can be determined according to the average vehicle speed, target parameters, accessory power, and a database constructed based on historical data of the target vehicle. Specifically, the average energy consumption of the high-speed MCU corresponding to the planned route can be determined according to the average vehicle speed, target parameters, and the database, and the high-speed energy consumption E corresponding to the planned route can be determined according to the average energy consumption of the high-speed MCU, accessory power, and average vehicle speed of the planned route.

[0066] In the above process, when the target parameter includes the number of occupants and the historical target parameter includes the historical number of occupants, the database includes the relationship between the historical number of occupants, average vehicle speed, number of high-speed slices, and average MCU power consumption. Accordingly, the high-speed power consumption corresponding to the planned route is determined based on the average vehicle speed, number of occupants, the database, and the power of the accessories. For example, see Table 3, which is a database built based on the historical data of the target vehicle:

[0067] Table 3. Database constructed based on historical data of the target vehicle.

[0068]

[0069] When the target parameters include the average gradient of the planned route, and the historical target parameters include the historical average gradient, the database includes the relationship between the historical average gradient, average vehicle speed, number of high-speed slices, and average MCU energy consumption. Accordingly, the high-speed energy consumption corresponding to the planned route is determined based on the average vehicle speed, number of occupants, database, and power of accessories. For example, see Table 4, which is a database built based on the historical data of the target vehicle:

[0070] Table 4. Database constructed based on historical data of the target vehicle.

[0071]

[0072]

[0073] When the target parameters include the number of occupants and the average gradient of the planned route, and the historical target parameters include the historical number of occupants and the historical average gradient, the database includes the relationship between the historical number of occupants, the historical average gradient, the average vehicle speed, the number of high-speed slices, and the average energy consumption of the MCU. Accordingly, the high-speed energy consumption corresponding to the planned route is determined based on the average vehicle speed, the average gradient of the planned route, the number of occupants, the database, and the power of accessories. For example, see Table 5, which is a database built based on the historical data of the target vehicle.

[0074] Table 5. Database constructed based on historical data of the target vehicle.

[0075]

[0076] Incorporating the number of passengers and / or the average gradient corresponding to the planned route into highway energy consumption prediction can further improve the accuracy of highway energy consumption prediction, thereby further improving the accuracy of remaining mileage prediction.

[0077] This application provides a method for predicting remaining mileage, wherein historical target parameters may include historical occupant count and historical average gradient, and a database is constructed based on historical data of the target vehicle, which may include:

[0078] Obtain historical data of the target vehicle and extract the target time from the historical data;

[0079] Slicing the target time into high-speed slices, and extracting vehicle speed calculation data, MCU energy consumption calculation data, gradient calculation data and historical passenger count corresponding to each high-speed slice from historical data;

[0080] The average vehicle speed corresponding to each high-speed slice is determined based on the vehicle speed calculation data corresponding to each high-speed slice. The average MCU power consumption corresponding to each high-speed slice is determined based on the MCU power consumption calculation data corresponding to each high-speed slice. The average slope corresponding to each high-speed slice is determined based on the slope calculation data corresponding to each high-speed slice.

[0081] A database is constructed based on the average vehicle speed, average MCU power consumption, average gradient, and historical occupant count corresponding to each high-speed slice. In this embodiment, the specific process of constructing the database based on the historical data of the target vehicle can be as follows:

[0082] S101: Obtain historical data of the target vehicle. Specifically, the target vehicle can upload real-time data to a cloud server for storage. The cloud server can then obtain the target vehicle's historical data, which may include time, vehicle speed at the corresponding time, odometer reading, gradient, historical occupant count, driving mode, energy recovery intensity, battery SOC, battery output voltage, battery output current, MCU voltage, and MCU current.

[0083] S102: Extract the target time when the vehicle speed is greater than the high-speed driving speed threshold from the historical data of the target vehicle.

[0084] S103: Slice the target time to obtain high-speed slices. Specifically, the target time can be sliced ​​according to a preset duration to obtain high-speed slices. The preset duration can be determined based on the length of the target time, or it can be set by relevant personnel based on experience, for example, 60 seconds. It should be noted that the duration of each high-speed slice can be kept consistent to ensure data consistency and facilitate subsequent calculations.

[0085] S104: Extract high-speed operating conditions from the target vehicle's historical data, specifically extracting vehicle speed calculation data (i.e., data used to calculate the average vehicle speed corresponding to each high-speed slice), MCU energy consumption calculation data (i.e., data used to calculate the average MCU energy consumption corresponding to each high-speed slice), and gradient calculation data (i.e., data used to calculate the average gradient corresponding to each high-speed slice). Additionally, extract the historical occupant count corresponding to each high-speed slice from the target vehicle's historical data.

[0086] S105: Calculate the average vehicle speed corresponding to each high-speed slice based on the vehicle speed calculation data corresponding to each high-speed slice, calculate the average MCU power consumption corresponding to each high-speed slice based on the MCU power consumption calculation data corresponding to each high-speed slice, and calculate the average slope corresponding to each high-speed slice based on the slope corresponding to each high-speed slice.

[0087] S106: Construct a database based on the average vehicle speed, average MCU power consumption, average gradient, and corresponding historical occupant count for each high-speed slice.

[0088] The above method enables the accurate and reliable construction of a database based on the historical data of the target vehicle, which includes the relationship between historical occupant count, average gradient, average vehicle speed, number of high-speed slices, and average MCU energy consumption. This facilitates the subsequent accurate prediction of the target vehicle's high-speed energy consumption and remaining high-speed driving range based on the database.

[0089] The remaining mileage prediction method provided in this application embodiment includes vehicle speed calculation data corresponding to each high-speed slice, which may include the initial stage mileage and the final stage mileage corresponding to each high-speed slice; MCU energy consumption calculation data corresponding to each high-speed slice, which may include the MCU voltage and MCU current of each collection point in each high-speed slice, the initial stage mileage and the final stage mileage corresponding to each high-speed slice; and slope data corresponding to each high-speed slice, which may include the slope of each collection point in each high-speed slice.

[0090] Determining the average vehicle speed for each high-speed segment based on the vehicle speed calculation data for each high-speed segment can include:

[0091] use Calculate the average vehicle speed V corresponding to the i-th high-speed slice. i Among them, S i末 S represents the mileage of the final stage corresponding to the i-th high-speed slice. i初 Let T be the initial stage mileage corresponding to the i-th high-speed slice. h The duration of the high-speed slice is converted to hours.

[0092] Determining the average MCU power consumption for each high-speed slice based on the MCU power consumption calculation data for each high-speed slice can include:

[0093] use Calculate the average MCU power consumption E corresponding to the i-th high-speed slice. MCUi Among them, U n Let I be the MCU voltage at the nth acquisition point in the i-th high-speed slice. n Let t be the MCU current at the nth acquisition point in the i-th high-speed slice. n K1 is the time of the nth acquisition point in the i-th high-speed slice, K2 is the conversion factor between hours and the time unit of the acquisition point, and K2 is the conversion factor between kilowatts and MCU current * MCU voltage.

[0094] Determining the average slope of each high-speed segment based on the slope calculation data can include:

[0095] use Calculate the average slope α corresponding to the i-th high-speed slice. i ; where α n Let be the slope of the nth acquisition point in the i-th high-speed slice.

[0096] In this embodiment, the vehicle speed calculation data corresponding to each high-speed slice may specifically include the initial stage mileage (specifically, the instrument mileage at the initial stage of the high-speed slice) and the final stage mileage (specifically, the instrument mileage at the final stage of the high-speed slice) corresponding to each high-speed slice. The MCU power consumption calculation data corresponding to each high-speed slice includes the MCU voltage and MCU current of each acquisition point in each high-speed slice (i.e., the time point of data acquisition, for example, data can be acquired once every 600ms, then every 600ms is a acquisition point, and a high-speed slice contains multiple acquisition points), the initial stage mileage and the final stage mileage corresponding to each high-speed slice, and the slope data corresponding to each high-speed slice includes the slope of each acquisition point in each high-speed slice.

[0097] Based on the above, the average vehicle speed corresponding to each high-speed slice can be determined by calculating the vehicle speed data corresponding to each high-speed slice. Specifically, this can be achieved by using... Calculate the average vehicle speed V corresponding to the i-th high-speed slice. i Among them, V i The calculation result can be rounded to one decimal place (of course, this can be adjusted), V i The unit is km / h, S i初 S represents the initial stage mileage (in km) corresponding to the i-th high-speed slice. i末 T represents the final stage mileage (in km) corresponding to the i-th high-speed slice. h The duration of a high-speed slice is converted to its hourly unit (h). For example, if the unit of the high-speed slice duration is seconds, then... Where T is the duration of high-speed slicing (in seconds).

[0098] The average power consumption of the MCU corresponding to each high-speed slice can be determined by using the MCU power consumption calculation data corresponding to each high-speed slice. Calculate the average MCU power consumption E corresponding to the i-th high-speed slice. MCUi Among them, E MCUi The calculation result can be rounded to one decimal place (of course, this can be adjusted), E MCUi The unit is kWh / 100km, U n I represents the MCU voltage (in V) at the nth acquisition point in the i-th high-speed slice. n t represents the MCU current (in A) at the nth acquisition point in the i-th high-speed slice. n Let K1 be the time of the nth acquisition point in the i-th high-speed slice. K1 is the conversion factor between hours and the time unit of the acquisition point. Taking the time unit of the acquisition point as s as an example, K1 = 3600. K2 is the conversion factor between kilowatts and the units of MCU current * MCU voltage. Taking the unit of MCU current as A and the unit of MCU voltage as V as an example, the unit of MCU current * MCU voltage is watts, and K2 = 1000.

[0099] The average slope of each high-speed segment can be determined by using the slope calculation data corresponding to each high-speed segment. Calculate the average slope α corresponding to the i-th high-speed slice. i ;α i The calculation result can be rounded to one decimal place (this can be adjusted), and its unit is %, α. n Let be the slope of the nth acquisition point in the i-th high-speed slice.

[0100] The above process can accurately calculate the average vehicle speed, average MCU power consumption, and average gradient corresponding to each high-speed slice, thereby improving the reliability and accuracy of database construction and facilitating the improvement of the accuracy of high-speed power consumption and high-speed remaining range prediction.

[0101] This application provides a remaining mileage prediction method that constructs a database based on the average vehicle speed, average MCU power consumption, average gradient, and historical occupant count corresponding to each highway slice. The method may include:

[0102] The data are grouped according to historical occupant count, average gradient, and average speed; at least one of these three parameters must differ between different data groups.

[0103] The number of high-speed slices in each data group is determined based on the historical number of occupants, average gradient, and average vehicle speed in each data group.

[0104] The average MCU power consumption of each data group is determined based on the number of high-speed slices in each data group and the average power consumption of the corresponding high-speed switching MCU.

[0105] In this embodiment of the application, the specific process of constructing a database based on the average vehicle speed, average MCU power consumption, average gradient, and corresponding historical occupant count for each high-speed slice can be as follows:

[0106] S201: Based on the historical number of occupants, average gradient, and average speed corresponding to each high-speed slice, multiple data groups are obtained; among them, at least one of the historical number of occupants, average gradient, and average speed is different between different data groups. For example: the historical number of occupants in data group 1 is 1, the average gradient is 0%, and the average speed is 80km / h; the historical number of occupants in data group 2 is 1, the average gradient is 1%, and the average speed is 80km / h, and so on.

[0107] In addition to grouping according to historical occupant counts, average gradient, and average speed corresponding to each high-speed slice, the data group to which each high-speed slice belongs can be determined based on the historical occupant counts, average gradient, and average speed corresponding to each high-speed slice. Alternatively, the high-speed slices corresponding to each data group can be determined based on the historical occupant counts, average gradient, and average speed within each data group. For example, if high-speed slice 1 has a historical occupant count of 1, an average gradient of 0%, and an average speed of 80 km / h; high-speed slice 2 has a historical occupant count of 1, an average gradient of 1%, and an average speed of 80 km / h; and high-speed slice 3 has a historical occupant count of 1, an average gradient of 1%, and an average speed of 80 km / h, then high-speed slice 1 belongs to data group 1, and high-speed slices 2 and 3 belong to data group 2.

[0108] S202: Based on the historical number of occupants, average gradient, and average vehicle speed in each data group, determine the number of high-speed slices in each data group.

[0109] Specifically, the number of high-speed slices in each data group that contain the corresponding historical number of occupants, average gradient, and average vehicle speed is counted, and the number of these high-speed slices is determined as the number of high-speed slices in the corresponding data group.

[0110] S203: Based on the number of high-speed slices in each data group and the average power consumption of the MCU corresponding to the high-speed slice in each data group, the average power consumption of the MCU in each data group is calculated using a simple arithmetic average method.

[0111] The above method can reliably and accurately build a database, so as to reliably and accurately predict the high-speed energy consumption and remaining high-speed driving range of the target vehicle.

[0112] The remaining mileage prediction method provided in this application embodiment may further include:

[0113] When a new high-speed slice is added and there is a corresponding target data group in the database, the average power consumption of the MCU in the target data group is determined based on the current average power consumption of the MCU in the target data group, the current number of high-speed slices, the number of newly added high-speed slices, and the average power consumption of the MCU corresponding to the newly added high-speed slice. The number of high-speed slices in the target data group is determined based on the current number of high-speed slices and the number of newly added high-speed slices.

[0114] In this embodiment of the application, the target vehicle can continuously generate historical data, and the generated historical data can be continuously uploaded to the cloud server so that the cloud server can process the newly added historical data in the above manner to obtain the newly added high-speed slice and the corresponding average vehicle speed, MCU average power consumption, average gradient and historical number of occupants. Then, the constructed database can be updated based on the corresponding average vehicle speed, MCU average power consumption, average gradient and historical number of occupants of the newly added high-speed slice.

[0115] Specifically, when a new high-speed slice corresponds to a target data group in the database, the following can be used: the current average power consumption of the MCU in the target data group, the current number of high-speed slices, the number of new high-speed slices, and the average power consumption of the MCU corresponding to the new high-speed slice. The average power consumption E of the MCU in the target data set was calculated. MCUi In which, E on the left side of the equals sign MCUi The average power consumption of the MCU in the target data set after adding the high-speed slice is E on the right side of the equals sign. MCUi N represents the current average power consumption of the MCU in the target data set. i X represents the current number of high-speed slices in the target data set, m represents the number of new high-speed slices to be added to the target data set, and X represents the number of high-speed slices to be added. j Let this be the average power consumption of the MCU corresponding to the j-th newly added high-speed slice in the target data group. Furthermore, the number of high-speed slices in the target data group is determined based on the current number of high-speed slices and the number of newly added high-speed slices.

[0116] For example, the historical occupant count is 1, the gradient is 0%, the vehicle speed is 80 km / h, the number of slices is N, and the average MCU power consumption is E. MCUN Now, one new high-speed slice is added. The historical occupant count corresponding to this new high-speed slice is 1, the gradient is 0%, the vehicle speed is 80km / h, and the MCU average energy consumption is X. Therefore, the high-speed slice count for the data group corresponding to the historical occupant count of 1, gradient of 0%, and vehicle speed of 80km / h in the database is updated to N+1. The MCU average energy consumption is adopted as follows: Calculated.

[0117] It should be noted that when a new high-speed slice does not have a corresponding target data group in the database, a new data group can be added in the database based on the historical number of occupants, average vehicle speed, average gradient, average MCU power consumption, and the number of new high-speed slices. The method of adding a new data group is the same as the method of creating a data group when building the database, and will not be repeated here.

[0118] The above method updates the data in the database when a new high-speed slice is added, thereby improving the reliability and accuracy of the database and making it easier to improve the accuracy of predicting the high-speed energy consumption and remaining high-speed driving range of the target vehicle.

[0119] The remaining mileage prediction method provided in this application embodiment, after extracting vehicle speed calculation data, MCU power consumption calculation data, and gradient calculation data corresponding to each high-speed slice from historical data, may further include:

[0120] The data extracted from historical data is cleaned.

[0121] In this embodiment, after extracting the vehicle speed calculation data, MCU power consumption calculation data, and gradient calculation data corresponding to each high-speed slice from the historical database, and before determining the average vehicle speed corresponding to each high-speed slice based on the vehicle speed calculation data, the average MCU power consumption corresponding to each high-speed slice based on the MCU power consumption calculation data, and the average gradient corresponding to each high-speed slice based on the gradient calculation data, the data extracted from the historical data can be cleaned. Specifically, incomplete or erroneous data can be processed (specifically, incomplete or erroneous data can be removed) to improve data quality, thereby improving the accuracy of the calculation of the average vehicle speed, average MCU power consumption, and average gradient corresponding to each high-speed slice, and thus improving the accuracy of database construction.

[0122] This application provides a method for predicting remaining mileage, which obtains the average vehicle speed corresponding to the planned route, and may include:

[0123] The average vehicle speed corresponding to the planned route is determined based on the target parameters and the database.

[0124] In this embodiment of the application, when the real-time data includes target parameters (number of occupants and / or average gradient corresponding to the planned route) and the database constructed based on the historical data of the target vehicle includes historical target parameters, the average speed corresponding to the planned route can be determined based on the obtained target parameters of the target vehicle and the database constructed based on the historical data of the target vehicle. This enables the determination of the average speed of the target vehicle on the planned route based on the driver's historical driving behavior habits, thereby improving the reliability and accuracy of determining the average speed corresponding to the planned route.

[0125] Of course, the map can provide the average speed corresponding to the planned route, or the map can provide the mileage and estimated time of the planned route to the vehicle, and the vehicle can calculate the average speed corresponding to the planned route (or the map can provide the mileage and estimated time of the planned route to the cloud server, and the cloud server can calculate the average speed corresponding to the planned route).

[0126] This application provides a method for predicting remaining mileage, which determines the average vehicle speed corresponding to a planned route based on target parameters and a database, and may include:

[0127] Retrieve the average vehicle speed and the number of high-speed slices corresponding to the target parameters from the database;

[0128] Based on the average vehicle speed, number of high-speed slices, and duration of high-speed slices corresponding to the target parameters in the database, the average vehicle speed corresponding to the planned route is determined.

[0129] In this embodiment, the average vehicle speed corresponding to the planned route can be determined by the following method: based on the number of occupants, the average gradient of the planned route, and a database constructed based on historical data of the target vehicle.

[0130] S301: Obtain the average vehicle speed and the number of high-speed slices corresponding to the target parameters in the real-time data of the target vehicle from the database built based on the historical data of the target vehicle.

[0131] Specifically, when the database contains multiple data groups, the data group corresponding to the target parameters in the real-time data of the target vehicle can be obtained from the database (the historical target parameters contained in these data groups correspond to the same target parameters in the real-time data of the target vehicle). Then, the average vehicle speed and the number of high-speed slices can be obtained from the corresponding data groups.

[0132] S302: Based on the average vehicle speed, number of high-speed slices, and duration of high-speed slices corresponding to the target parameters in the real-time data of the target vehicle in the database, the average speed corresponding to the planned route is calculated using the mean algorithm.

[0133] Specifically, it can be used Calculate the average vehicle speed V corresponding to the planned route. 平均(The unit can be km / h), where V1 is the first average vehicle speed corresponding to the target parameter in the real-time data of the target vehicle in the database (or the average vehicle speed in the first data group corresponding to the target parameter in the real-time data of the target vehicle in the database), N1 is the corresponding number of high-speed slices (or the number of high-speed slices in the first data group corresponding to the target parameter in the real-time data of the target vehicle in the database), T is the duration of the high-speed slice, and K3 is the conversion factor between the hour and the duration unit of the high-speed slice. The above formula takes the target parameter in the real-time data of the target vehicle corresponding to n average vehicle speeds and the number of high-speed slices in the database as an example. n Let N be the nth average vehicle speed corresponding to the target parameter in the real-time data of the target vehicle in the database (or the average vehicle speed in the nth data set corresponding to the target parameter in the real-time data of the target vehicle in the database). n This refers to the corresponding number of high-speed slices (or the number of high-speed slices in the nth data group corresponding to the target parameters in the real-time data of the target vehicle in the database).

[0134] Alternatively, based on the target parameters in the real-time data of the target vehicle, the minimum average speed and the maximum average speed in the database, the average speed can be calculated by increasing the minimum average speed by a preset speed step (e.g., 1 kph, which can be determined based on the average speed distribution in the database) to the maximum average speed, and then using an averaging algorithm to calculate the average speed corresponding to the planned route. For example, the minimum average speed in the database is 80 kph (represented as V in the following calculation formula). 80kp h), maximum average vehicle speed is 120 kph (expressed as V in the following calculation formula). 120kph Taking a high-speed slice duration of 60 seconds as an example, then we can utilize... Calculate the average vehicle speed V corresponding to the planned route. 平均 , where N 80kph This represents the number of highway slices in the database corresponding to a data set with an average vehicle speed of 80 kph. Specifically, it refers to the average vehicle speed V corresponding to the planned route calculated using the method described above. 平均 If the database does not contain an average vehicle speed corresponding to the target parameter, the number of high-speed slices can be recorded as 0 and included in the calculation of the above formula. For example, if the minimum average vehicle speed is 80 kph and the preset vehicle speed step size is 1 kph, then... If the database does not contain 81kph corresponding to the target parameter, then N 81kph That is, it equals 0.

[0135] Taking the target parameters including the number of passengers and the average speed corresponding to the planned route as an example, the average speed corresponding to the planned route is determined according to the target parameters and the database as follows: obtain the average speed and the number of high-speed segments corresponding to the number of passengers and the average gradient of the planned route from the database; determine the average speed corresponding to the planned route based on the average speed, the number of high-speed segments and the duration of the high-speed segments corresponding to the number of passengers and the average gradient of the planned route from the database.

[0136] The above method enables reliable and accurate prediction of the average speed of the planned route based on the target parameters in the real-time data of the target vehicle and the database (i.e., the historical data of the target vehicle), so as to improve the reliability and accuracy of predicting the target vehicle's high-speed energy consumption and remaining high-speed driving range.

[0137] This application provides a method for predicting remaining mileage, which determines the highway energy consumption of the planned route based on the average vehicle speed, database, and power of surrounding facilities. This method may include:

[0138] Retrieve the average MCU power consumption corresponding to the average vehicle speed of the planned route from the database;

[0139] Based on the corresponding average MCU power consumption obtained from the database, determine the average power consumption of the high-speed MCU corresponding to the planned route;

[0140] The high-speed energy consumption of the planned route is determined based on the average power consumption of the high-speed MCU, the power of the accessories, and the average vehicle speed corresponding to the planned route.

[0141] In this embodiment of the application, the high-speed energy consumption of the planned route can be determined based on the average speed, database, and power of the surrounding area.

[0142] S401: Retrieve the average MCU power consumption corresponding to the average vehicle speed of the planned route from the database.

[0143] It should be noted that if there is a data group in the database with the same average vehicle speed as the planned route (i.e., the average vehicle speed in the data group is the same as the average vehicle speed corresponding to the planned route), then the average power consumption of the MCU is obtained from that data group.

[0144] If no data set with the same average vehicle speed as the planned route exists in the database, then neighboring data sets can be obtained and identified as the data sets corresponding to the average vehicle speed of the planned route. Specifically, the left neighbor data set (where the average speed is lower than the average speed corresponding to the planned route) and the right neighbor data set (where the average speed is higher than the average speed corresponding to the planned route) are obtained, with left and right neighbors determined by data size. Then, the average MCU power consumption is obtained from the left and right neighbor data sets to serve as the average MCU power consumption corresponding to the average vehicle speed of the planned route in the database.

[0145] S402: Determine the average power consumption of the high-speed MCU corresponding to the planned route based on the corresponding average power consumption of the MCU obtained from the database.

[0146] Specifically, if there is a data group in the database that has the same average vehicle speed as the planned route, the average power consumption of the MCU obtained from that data group will be determined as the average power consumption of the high-speed MCU corresponding to the planned route.

[0147] If no data set with the same average vehicle speed as the planned route exists in the database, the average MCU power consumption obtained from the left and right neighboring data sets is interpolated (specifically, linear interpolation can be used) to determine the average power consumption of the high-speed MCU corresponding to the planned route.

[0148] S403: After determining the average power consumption of the high-speed MCU corresponding to the planned route, the power consumption of the high-speed MCU corresponding to the planned route, the power consumption of accessories, and the average vehicle speed corresponding to the planned route can be used to... The calculated high-speed energy consumption E (in kWh / 100km) for the planned route is obtained, where E MCU P represents the average power consumption (in kWh / 100km) of the high-speed MCU corresponding to the planned route. 附件功率 The power of the accessory (in kW), V 平均 The average vehicle speed (unit can be km / h) corresponding to the planned route.

[0149] When the real-time data also includes target parameters, and the database includes the relationship between historical target parameters, average vehicle speed, number of high-speed slices, and average MCU power consumption, and the target parameters include the number of occupants and the average gradient of the planned route, and the historical target parameters include the historical number of occupants and the historical average gradient, then the process of determining the high-speed power consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, target parameters, database, and accessory power can be specifically as follows:

[0150] S501: Retrieve from the database the average vehicle speed, number of passengers, and average MCU energy consumption corresponding to the average gradient of the planned route.

[0151] It should be noted that if there is a data set in the database that has the same average vehicle speed, number of occupants, and average gradient as the planned route (i.e., the average vehicle speed in the data set is the same as the average vehicle speed corresponding to the planned route, the historical number of occupants in the data set is the same as the number of occupants in the real-time data of the target vehicle, and the average gradient in the data set is the same as the average gradient of the planned route), then the average energy consumption of the MCU will be obtained from that data set.

[0152] It should be noted that, in the above process, if there is no data in the database that corresponds to the historical number of occupants, average speed, and average gradient as the number of occupants, average speed, and average gradient of the planned route, the rules for searching the database can be pre-set by relevant personnel, or can be set during the prediction process. For example, neighboring data groups can be obtained, and these neighboring data groups can be identified as those corresponding to the average vehicle speed, number of occupants, and average gradient of the planned route.

[0153] The specific rules for searching the database mentioned above can be as follows:

[0154] (1) Find the corresponding data sets in the database for the two items that correspond to the planned route: average vehicle speed, number of passengers, and average gradient of the planned route. The two items mentioned here can be historical number of passengers and average vehicle speed, historical number of passengers and average gradient, or average vehicle speed and average gradient.

[0155] (2) If there are two data sets in the database that correspond to the same two items in the average vehicle speed, number of passengers, and average gradient of the planned route, then obtain the left neighbor data set and the right neighbor data set from the corresponding data set. The left neighbor data set refers to the data set in which the different items (i.e., the average vehicle speed, historical number of passengers, and average gradient in the data set are different from the average vehicle speed, number of passengers, and average gradient of the planned route) are smaller than the corresponding items in the real-time data. The right neighbor data set refers to the data set in which the different items are larger than the corresponding items in the real-time data.

[0156] Specifically, the left and right neighbor data groups are obtained, with left and right neighbors determined by data size. For example, if a data group exists in the database that has the same number of occupants and average gradient as the planned route (the historical number of occupants in the data group is the same as the number of occupants in the real-time data of the target vehicle, and the average gradient in the data group is the same as the average gradient of the planned route, but the average speed in the data group is different from the average speed corresponding to the planned route), then the left neighbor data group (i.e., the historical number of occupants in the left neighbor data group is the same as the number of occupants in the real-time data of the target vehicle, the average gradient is the same as the average gradient of the planned route, but the average speed is to the left of the average speed corresponding to the planned route, or in other words, the average speed is smaller than the average speed corresponding to the planned route) and the right neighbor data group (i.e., the historical number of occupants in the right neighbor data group is the same as the number of occupants in the real-time data of the target vehicle, the average gradient is the same as the average gradient of the planned route, but the average speed is to the right of the average speed corresponding to the planned route, or in other words, the average speed is larger than the average speed corresponding to the planned route) can be obtained. Then, the average MCU power consumption is obtained from the left neighboring data group and the right neighboring data group, and used as the average MCU power consumption corresponding to the average vehicle speed, number of passengers and average gradient of the planned route in the database.

[0157] (3) If there is no data set in the database that corresponds to the same two items of average vehicle speed, number of passengers and average slope of the planned route, then obtain a data set in the database that corresponds to the same two items of average vehicle speed, number of passengers and average slope of the planned route.

[0158] (4) If there is a data group in the database that corresponds to the same item among the average vehicle speed, number of passengers and average gradient of the planned route, then obtain the left neighbor data group and the right neighbor data group from the corresponding data group.

[0159] (5) If there is no data group in the database that corresponds to the same item among the average vehicle speed, number of passengers and average slope of the planned route, then the historical number of passengers, average slope and average speed in each data group in the database can be used to form a first data vector, and the number of passengers, the average vehicle speed and the average slope of the planned route can be used to form a second data vector. The similarity between the first data vector and the second data vector corresponding to each data group is calculated. The data group corresponding to the first data vector with a similarity greater than the preset similarity (which can be set according to requirements, etc.) is determined as the data group corresponding to the average vehicle speed, number of passengers and the average slope of the planned route. The corresponding MCU average energy consumption is obtained from these data groups.

[0160] S502: Determine the average power consumption of the high-speed MCU corresponding to the planned route based on the corresponding average power consumption of the MCU obtained from the database.

[0161] Specifically, if there is a data set in the database that has the same average vehicle speed, number of passengers, and average gradient as the planned route, then the average power consumption of the MCU obtained from that data set will be determined as the average power consumption of the high-speed MCU corresponding to the planned route.

[0162] If no data group exists in the database that has the same average vehicle speed, number of passengers, and average gradient as the planned route, if step S501 obtains the corresponding average MCU energy consumption from the left and right neighboring data groups, then interpolation (specifically linear interpolation) can be used to determine the average high-speed MCU energy consumption corresponding to the planned route. If step 501 obtains the corresponding average MCU energy consumption from the data group corresponding to the first data vector whose similarity to the second data vector is greater than a preset similarity, then the average high-speed MCU energy consumption corresponding to the planned route can be calculated using an algorithmic averaging method. Alternatively, the weight of the average MCU energy consumption in the data group corresponding to each first data vector can be determined based on the similarity between the second data vector and each of the aforementioned first data vectors (i.e., the first data vectors whose similarity is greater than a preset similarity), and a weighted average algorithm can be used to calculate the average high-speed MCU energy consumption corresponding to the planned route.

[0163] S503: After determining the average power consumption of the high-speed MCU corresponding to the planned route, the system can utilize the high-speed MCU power consumption, accessory power, and average vehicle speed corresponding to the planned route to... The high-speed energy consumption E (in kWh / 100km) corresponding to the planned route was calculated.

[0164] It should be noted that if the target parameters include the number of passengers or the average gradient of the planned route, and the historical target parameters include the historical number of passengers or the historical average gradient, then the process of determining the high-speed energy consumption of the planned route based on the average vehicle speed, target parameters, database, and power of the accessories is similar to the above process, and will not be elaborated here.

[0165] The above method enables reliable and accurate prediction of high-speed energy consumption for the planned route based on real-time data of the target vehicle, including the number of occupants, the average gradient of the planned route, the power of accessories, and a database constructed based on historical data of the target vehicle, as well as the average vehicle speed corresponding to the planned route determined by the constructed database. This improves the reliability and accuracy of predicting the remaining high-speed driving range of the target vehicle.

[0166] This application provides a remaining mileage prediction method to obtain the accessory power of a target vehicle, which may include:

[0167] Obtain the output voltage and current of the battery in the target vehicle, and the input voltage and current of the MCU.

[0168] The accessory power is determined based on the battery's output voltage and current, and the MCU's input voltage and current.

[0169] In this embodiment of the application, the accessory power of the target vehicle can be obtained in the following manner:

[0170] S601: Obtain the output voltage and current of the battery in the target vehicle, and the input voltage and current of the MCU.

[0171] S602: Based on the battery's output voltage and current, and the MCU's input voltage and current, using P... 实时附件功率 =U 电池输出 *I 电池输出 -U MCU输入电流 *I MCU输入电流 The real-time accessory power P was calculated. 实时附件功率 Considering that the accessory power does not change significantly after the driver starts the vehicle, the real-time accessory power at each collection point can be obtained within a set time period (e.g., 1 minute or 5 minutes, the specific duration can be set based on experience, etc.). The real-time accessory power at each collection point within the preset time period is then averaged to obtain the accessory power P. 附件功率 .

[0172] In other words, the target vehicle can directly obtain the battery's output voltage and current, as well as the MCU's input voltage and current, and feed them back to the cloud server, allowing the cloud server to calculate the accessory power based on this data. Alternatively, the target vehicle can measure, obtain, or calculate the accessory power itself and feed it back to the cloud server.

[0173] The above methods can improve the accuracy of accessory power calculation, thereby improving the accuracy of predicting the target vehicle's high-speed energy consumption and remaining high-speed driving range.

[0174] See Figure 3 and Figure 4 ,in, Figure 3 This diagram illustrates a simplified working principle of a remaining mileage prediction method provided in an embodiment of this application. Figure 4 The diagram illustrates the detailed working principle of a remaining mileage prediction method provided in an embodiment of this application. It should be noted that... Figure 3 and Figure 4 The explanations all use a cloud server as the execution entity for the remaining process prediction method. Figure 4Taking three planned routes—Route A, Route B, and Route C—as an example, this application provides a remaining mileage prediction method. When multiple planned routes are included, after determining the highway energy consumption corresponding to each planned route, the method may further include:

[0175] The optimal high-speed energy consumption is determined from the high-speed energy consumption corresponding to each planned route, and the planned route corresponding to the optimal high-speed energy consumption is determined as the optimal route.

[0176] The remaining highway driving range is determined based on the remaining state of charge (SOC) of the target vehicle and the highway energy consumption corresponding to the planned route. This can include:

[0177] The optimal remaining high-speed driving range is determined based on the remaining SOC of the target vehicle and the optimal high-speed energy consumption.

[0178] In this embodiment, the real-time data of the target vehicle may include multiple planned routes (i.e., the map can plan multiple driving routes for the target vehicle) and the average gradient of each planned route. For each planned route, the average vehicle speed and the corresponding high-speed energy consumption can be calculated using the methods mentioned above. Then, the optimal high-speed energy consumption can be obtained from the high-speed energy consumption corresponding to each planned route. Specifically, the minimum high-speed energy consumption among the high-speed energy consumption corresponding to each planned route can be determined as the optimal high-speed energy consumption, and the planned route corresponding to the optimal high-speed energy consumption can be determined as the optimal route.

[0179] Based on the above, when determining the remaining high-speed driving range according to the remaining SOC of the target vehicle and the high-speed energy consumption of the planned route, the optimal high-speed driving range can be determined based on the remaining SOC of the target vehicle and the optimal high-speed energy consumption, so as to predict the optimal high-speed driving range in advance.

[0180] Of course, the remaining high-speed driving range for each planned route can also be calculated based on the remaining SOC of the target vehicle and the high-speed energy consumption of each planned route. The longest remaining high-speed driving range among the remaining high-speed driving ranges of each planned route can be determined as the optimal remaining high-speed driving range, and the planned route corresponding to the optimal remaining high-speed driving range can be determined as the optimal planned route.

[0181] After determining the high-speed energy consumption, optimal high-speed energy consumption, optimal route, and optimal remaining high-speed range for each planned route, this information, or a portion of it (such as optimal high-speed energy consumption, optimal route, and optimal remaining high-speed range), can be sent to the target vehicle. This allows the target vehicle's main unit to remind the cloud server of the predicted information through voice playback or screen display, enabling the driver to obtain relevant information and make reasonable travel arrangements.

[0182] The remaining mileage prediction method provided in this application embodiment may further include:

[0183] Obtain the mileage of the navigation route and determine whether the remaining range of the optimal high-speed driving is less than the mileage of the navigation route.

[0184] If so, the location of charging stations along the optimal route is obtained, and the optimal high-speed energy consumption, optimal route, and charging station location are sent to the host of the target vehicle, which will then display and / or play the information via voice.

[0185] In this embodiment of the application, when determining the optimal remaining high-speed driving range, the navigation route mileage can also be obtained (which can be uploaded to the cloud server from the target vehicle or the map cloud). Then, it is determined whether the optimal remaining high-speed driving range is less than the navigation route mileage.

[0186] If the remaining range of the optimal high-speed driving is less than the mileage of the navigation route, it means that the remaining battery power of the target vehicle cannot support the mileage of the navigation route. At this time, the location of charging stations on the optimal route can be obtained, and the optimal high-speed energy consumption, optimal route and charging station location can be sent to the host of the target vehicle. The host of the target vehicle can remind the driver of this information through voice playback and / or screen display, so that the driver can obtain this information in a timely manner.

[0187] If the remaining range of the optimal high-speed driving range is not less than the navigation route range, it means that the remaining battery power of the target vehicle can support the navigation route range. At this time, the cloud server can choose whether to send the optimal high-speed energy consumption, optimal route and charging station location to the target vehicle's host according to the settings (which can be set by relevant personnel based on experience, etc.).

[0188] The above process can transmit the optimal high-speed energy consumption, optimal route, and charging station location to the target vehicle's main unit, so that the driver can obtain this information in a timely manner, thereby making reasonable travel arrangements and whether to charge, and reducing range anxiety.

[0189] This application also provides a remaining mileage prediction device, see [link to relevant documentation]. Figure 5The diagram illustrates a structural schematic of a remaining range prediction device provided in an embodiment of this application. It may include: an acquisition module 51, used to acquire real-time data of the target vehicle; the real-time data may include accessory power and the average vehicle speed corresponding to the planned route; a first determination module 52, used to determine the high-speed energy consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, a database constructed based on historical data of the target vehicle, and accessory power; the database includes the relationship between average vehicle speed, high-speed slice number, and MCU average energy consumption, where high-speed slices are obtained by slicing target times in historical data where the vehicle speed is greater than a high-speed driving speed threshold; and a second determination module 53, used to determine the remaining high-speed driving range based on the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0190] The remaining mileage prediction device provided in this application embodiment may include target parameters in real-time data. The database may include the relationship between historical target parameters, average vehicle speed, number of high-speed slices and average power consumption of MCU. The target parameters may include the number of occupants and / or the average gradient of the planned route. The historical target parameters may include the historical number of occupants and / or the historical average gradient.

[0191] The first determining module 52 may include: a first determining submodule, used to determine the high-speed energy consumption corresponding to the planned route based on the average vehicle speed, target parameters, database and power of the surrounding area corresponding to the planned route.

[0192] This application provides a remaining mileage prediction device. Historical target parameters may include historical occupant count and historical average gradient. The remaining mileage prediction device may include a database construction module for building a database based on historical data of a target vehicle. The database construction module may include: a first acquisition submodule for acquiring historical data of the target vehicle and extracting the target time from the historical data; a slicing submodule for slicing the target time to obtain high-speed slices and extracting vehicle speed calculation data, MCU energy consumption calculation data, gradient calculation data, and historical occupant count corresponding to each high-speed slice from the historical data; a second determination submodule for determining the average vehicle speed corresponding to each high-speed slice based on the vehicle speed calculation data, determining the average MCU energy consumption corresponding to each high-speed slice based on the MCU energy consumption calculation data, and determining the average gradient corresponding to each high-speed slice based on the gradient calculation data; and a construction submodule for constructing a database based on the average vehicle speed, average MCU energy consumption, average gradient, and historical occupant count corresponding to each high-speed slice.

[0193] The remaining mileage prediction device provided in this application embodiment includes vehicle speed calculation data corresponding to each high-speed slice, which may include the initial stage mileage and the final stage mileage corresponding to each high-speed slice; MCU power consumption calculation data corresponding to each high-speed slice, which may include the MCU voltage and MCU current of each collection point in each high-speed slice, the initial stage mileage and the final stage mileage corresponding to each high-speed slice; and slope data corresponding to each high-speed slice, which may include the slope of each collection point in each high-speed slice.

[0194] The second determining submodule may include: a first calculation unit, used to utilize... Calculate the average vehicle speed V corresponding to the i-th high-speed slice. i Among them, S i末 S represents the mileage of the final stage corresponding to the i-th high-speed slice. i初 Let T be the initial stage mileage corresponding to the i-th high-speed slice. h The first unit converts the high-speed slice duration to hours; the second unit is used to utilize... Calculate the average MCU power consumption E corresponding to the i-th high-speed slice. MCUi Among them, U n Let I be the MCU voltage at the nth acquisition point in the i-th high-speed slice. n Let t be the MCU current at the nth acquisition point in the i-th high-speed slice. n Let K1 be the time of the nth acquisition point in the i-th high-speed slice, K2 be the conversion factor between hours and the time unit of the acquisition point, and K2 be the conversion factor between kilowatts and MCU current * MCU voltage; the third calculation unit is used to utilize... Calculate the average slope α corresponding to the i-th high-speed slice. i ; where α n Let be the slope of the nth acquisition point in the i-th high-speed slice.

[0195] This application provides a remaining mileage prediction device. The construction submodule may include: a grouping unit for grouping data based on historical occupant count, average gradient, and average vehicle speed; wherein at least one of historical occupant count, average gradient, and average vehicle speed differs between different data groups; a first determining unit for determining the number of high-speed slices in each data group based on the historical occupant count, average gradient, and average vehicle speed in each data group; and a second determining unit for determining the average MCU power consumption in each data group based on the number of high-speed slices in each data group and the average MCU power consumption corresponding to the high-speed switching.

[0196] The remaining mileage prediction device provided in this application embodiment may further include a construction submodule: a third determining unit, used to determine the average MCU power consumption in the target data group based on the current average MCU power consumption in the target data group, the current number of high-speed slices, the number of newly added high-speed slices, and the average MCU power consumption corresponding to the newly added high-speed slices when a new high-speed slice is added and the newly added high-speed slice corresponds to a target data group, and to determine the number of high-speed slices in the target data group based on the current number of high-speed slices and the number of newly added high-speed slices.

[0197] The remaining mileage prediction device provided in this application embodiment may further include a database construction module including a data cleaning submodule, which is used to clean the data extracted from the historical data after extracting the vehicle speed calculation data, MCU energy consumption calculation data and slope calculation data corresponding to each high-speed slice from the historical data.

[0198] The remaining mileage prediction device provided in this application embodiment may include a third determining submodule, which is used to determine the average vehicle speed corresponding to the planned route based on target parameters and a database.

[0199] The remaining mileage prediction device provided in this application embodiment includes a third determining submodule that may include: an acquisition unit, used to acquire from the database the average vehicle speeds and the number of high-speed slices corresponding to the target parameters; and a fourth determining unit, used to determine the average vehicle speed corresponding to the planned route based on the average vehicle speeds, the number of high-speed slices, and the duration of the high-speed slices corresponding to the target parameters in the database.

[0200] This application provides a remaining mileage prediction device. The first determining module 52 may include: a second obtaining submodule, used to obtain the average MCU power consumption corresponding to the average vehicle speed corresponding to the planned route from a database; a fourth determining submodule, used to determine the high-speed MCU average power consumption corresponding to the planned route based on the corresponding MCU average power consumption obtained from the database; and a fifth determining submodule, used to determine the high-speed power consumption corresponding to the planned route based on the high-speed MCU average power consumption, accessory power, and the average vehicle speed corresponding to the planned route.

[0201] The remaining mileage prediction device provided in this application embodiment may include an acquisition module 51 that includes: a third acquisition submodule for acquiring the output voltage and output current of the battery in the target vehicle, and the input voltage and input current of the MCU; and a sixth determination submodule for determining the accessory power based on the output voltage and output current of the battery and the input voltage and input current of the MCU.

[0202] The remaining mileage prediction device provided in this application embodiment may further include: a third determining module, which is used to determine the optimal high-speed energy consumption from the high-speed energy consumption corresponding to each planned route after determining the high-speed energy consumption corresponding to the planned route, and determine the planned route corresponding to the optimal high-speed energy consumption as the optimal route.

[0203] The second determining module 53 may include: a seventh determining submodule, used to determine the optimal remaining high-speed driving range based on the remaining SOC of the target vehicle and the optimal high-speed energy consumption.

[0204] The remaining mileage prediction device provided in this application embodiment may further include a second determining module 53, which may include: a fourth obtaining submodule, used to obtain the navigation route mileage and determine whether the optimal high-speed remaining mileage is less than the navigation route mileage; and a sending submodule, used to obtain the location of charging piles on the optimal route if the optimal high-speed remaining mileage is less than the navigation route mileage, and send the optimal high-speed energy consumption, the optimal route and the location of charging piles to the host of the target vehicle, which will then display and / or play the data via voice.

[0205] This application also provides a remaining mileage prediction device, see [link to relevant documentation]. Figure 6 It shows a schematic diagram of the structure of a remaining mileage prediction device provided in an embodiment of this application, which may include:

[0206] Memory 61 is used to store computer programs;

[0207] When processor 62 executes a computer program stored in memory 61, it can perform the following steps:

[0208] Acquire real-time data of the target vehicle; real-time data includes accessory power and average vehicle speed corresponding to the planned route; determine the high-speed energy consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, a database built based on the historical data of the target vehicle, and accessory power; the database includes the relationship between average vehicle speed, high-speed slice number, and MCU average energy consumption, and the high-speed slice is obtained by slicing the target time in the historical data where the vehicle speed is greater than the high-speed driving speed threshold; determine the remaining high-speed driving range based on the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0209] This application embodiment also provides a readable storage medium storing a computer program, which, when executed by a processor, can perform the following steps:

[0210] Acquire real-time data of the target vehicle; real-time data includes accessory power and average vehicle speed corresponding to the planned route; determine the high-speed energy consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, a database built based on the historical data of the target vehicle, and accessory power; the database includes the relationship between average vehicle speed, high-speed slice number, and MCU average energy consumption, and the high-speed slice is obtained by slicing the target time in the historical data where the vehicle speed is greater than the high-speed driving speed threshold; determine the remaining high-speed range based on the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0211] For a description of the relevant parts of the remaining mileage prediction device, equipment and readable storage medium provided in the embodiments of this application, please refer to the detailed description of the corresponding parts of the remaining mileage prediction method provided in the embodiments of this application, and will not be repeated here.

[0212] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0213] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0214] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0215] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0216] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0217] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A remaining distance prediction method characterized by, include: Acquire real-time data of the target vehicle; the real-time data includes accessory power and average vehicle speed corresponding to the planned route; Based on the average vehicle speed corresponding to the planned route, the database constructed based on the historical data of the target vehicle, and the power of the accessories, the high-speed energy consumption corresponding to the planned route is determined; the database includes the relationship between the average vehicle speed, the number of high-speed slices, and the average energy consumption of the MCU, and the high-speed slice is obtained by slicing the target time in the historical data where the vehicle speed is greater than the high-speed driving speed threshold. The remaining high-speed driving range is determined based on the remaining SOC of the target vehicle and the high-speed energy consumption of the planned route. The real-time data also includes target parameters. The database includes the relationship between historical target parameters, average vehicle speed, number of high-speed slices, and average MCU power consumption. The target parameters include the number of occupants and / or the average gradient of the planned route. The historical target parameters include the historical number of occupants and / or the historical average gradient. Based on the average vehicle speed corresponding to the planned route, the database, and the power of the accessories, the highway energy consumption corresponding to the planned route is determined, including: The high-speed energy consumption corresponding to the planned route is determined based on the average vehicle speed corresponding to the planned route, the target parameters, the database, and the power of the accessories. The historical target parameters include historical occupant count and historical average gradient. A database is constructed based on the historical data of the target vehicle, including: Obtain historical data of the target vehicle, and extract the target time from the historical data; The target time is sliced ​​to obtain a high-speed slice, and the vehicle speed calculation data, MCU energy consumption calculation data, gradient calculation data and historical passenger count corresponding to each high-speed slice are extracted from the historical data. The average vehicle speed corresponding to each high-speed slice is determined based on the vehicle speed calculation data corresponding to each high-speed slice, the average MCU power consumption corresponding to each high-speed slice is determined based on the MCU power consumption calculation data corresponding to each high-speed slice, and the average slope corresponding to each high-speed slice is determined based on the slope calculation data corresponding to each high-speed slice. The database is constructed based on the average vehicle speed, average MCU power consumption, average gradient, and historical number of occupants corresponding to each high-speed slice.

2. The remaining mileage prediction method according to claim 1, characterized in that, The vehicle speed calculation data corresponding to each high-speed slice includes the initial stage mileage and the final stage mileage corresponding to each high-speed slice; the MCU energy consumption calculation data corresponding to each high-speed slice includes the MCU voltage and MCU current of each collection point in each high-speed slice, the initial stage mileage and the final stage mileage corresponding to each high-speed slice; and the slope data corresponding to each high-speed slice includes the slope of each collection point in each high-speed slice. The average vehicle speed corresponding to each high-speed slice is determined based on the vehicle speed calculation data corresponding to each high-speed slice, including: use Calculate the average vehicle speed corresponding to the i-th high-speed slice. ;in, Let be the mileage of the final stage corresponding to the i-th high-speed slice. Let be the initial stage mileage corresponding to the i-th high-speed slice. The duration, expressed in hours, is obtained by converting the duration of a high-speed slice. The average power consumption of the MCU corresponding to each high-speed slice is determined based on the MCU power consumption calculation data corresponding to each high-speed slice, including: use Calculate the average power consumption of the MCU corresponding to the i-th high-speed slice. ;in, Let be the MCU voltage at the nth acquisition point in the i-th high-speed slice. Let be the MCU current at the nth sampling point in the i-th high-speed slice. Let be the time of the nth acquisition point in the i-th high-speed slice. This is the conversion factor between hours and the time unit of the data collection point. For kilowatts and MCU current Conversion factors between units of MCU voltage; The average slope corresponding to each high-speed slice is determined based on the slope calculation data corresponding to each high-speed slice, including: use Calculate the average slope corresponding to the i-th high-speed slice. ;in, Let be the slope of the nth acquisition point in the i-th high-speed slice.

3. The remaining mileage prediction method according to claim 1, characterized in that, The database is constructed based on the average vehicle speed, average MCU power consumption, average gradient, and historical occupant count corresponding to each high-speed slice, including: The data are grouped according to the historical number of occupants, the average gradient, and the average vehicle speed; wherein at least one of the historical number of occupants, the average gradient, and the average vehicle speed is different between different data groups. The number of high-speed slices in each data group is determined based on the historical number of occupants, average gradient, and average vehicle speed in each data group. The average power consumption of the MCU in each data group is determined based on the number of high-speed slices in each data group and the average power consumption of the MCU corresponding to the high-speed slice.

4. The remaining mileage prediction method according to claim 3, characterized in that, Also includes: When a new high-speed slice is added and there is a target data group corresponding to the new high-speed slice in the database, the average power consumption of the MCU in the target data group is determined based on the current average power consumption of the MCU in the target data group, the current number of high-speed slices, the number of new high-speed slices, and the average power consumption of the MCU corresponding to the new high-speed slice. The number of high-speed slices in the target data group is determined based on the current number of high-speed slices and the number of new high-speed slices.

5. The remaining mileage prediction method according to claim 1, characterized in that, After extracting the vehicle speed calculation data, MCU energy consumption calculation data, and gradient calculation data corresponding to each high-speed slice from the historical data, the method further includes: The data extracted from the historical data is cleaned.

6. The remaining mileage prediction method according to any one of claims 1 to 5, characterized in that, Obtaining the average vehicle speed corresponding to the planned route includes: The average vehicle speed corresponding to the planned route is determined based on the target parameters and the database.

7. The remaining mileage prediction method according to claim 6, characterized in that, Determining the average vehicle speed corresponding to the planned route based on the target parameters and the database includes: Obtain from the database the average vehicle speed and the number of high-speed slices corresponding to the target parameters; The average speed corresponding to the planned route is determined based on the average vehicle speed, the number of high-speed slices, and the duration of the high-speed slices in the database corresponding to the target parameters.

8. The remaining mileage prediction method according to any one of claims 1 to 5, characterized in that, Based on the average vehicle speed corresponding to the planned route, the database, and the power of the accessories, the highway energy consumption corresponding to the planned route is determined, including: Obtain the average MCU power consumption corresponding to the average vehicle speed of the planned route from the database; Based on the corresponding average MCU power consumption obtained from the database, determine the average power consumption of the high-speed MCU corresponding to the planned route; The high-speed energy consumption corresponding to the planned route is determined based on the average power consumption of the high-speed MCU corresponding to the planned route, the power of the accessory, and the average vehicle speed corresponding to the planned route.

9. The remaining mileage prediction method according to any one of claims 1 to 5, characterized in that, Obtain the accessory power of the target vehicle, including: Obtain the output voltage and current of the battery in the target vehicle, and the input voltage and current of the MCU. The power of the accessory is determined based on the output voltage and output current of the battery, and the input voltage and input current of the MCU.

10. The remaining mileage prediction method according to any one of claims 1 to 5, characterized in that, When multiple planned routes are included, after determining the high-speed energy consumption corresponding to the planned routes, the following is also included: Determine the optimal high-speed energy consumption from the high-speed energy consumption corresponding to each of the planned routes, and determine the planned route corresponding to the optimal high-speed energy consumption as the optimal route. The remaining high-speed driving range is determined based on the remaining state of charge (SOC) of the target vehicle and the high-speed energy consumption of the planned route, including: The optimal remaining high-speed driving range is determined based on the remaining SOC of the target vehicle and the optimal high-speed energy consumption.

11. The remaining mileage prediction method according to claim 10, characterized in that, Also includes: Obtain the navigation route mileage and determine whether the remaining optimal high-speed driving range is less than the navigation route mileage; If so, the location of the charging station on the optimal route is obtained, and the optimal high-speed energy consumption, the optimal route, and the location of the charging station are sent to the host of the target vehicle, which then displays and / or plays the information via voice.

12. A remaining mileage prediction device, characterized in that, A method for implementing the remaining mileage prediction method as described in any one of claims 1 to 11, comprising: The acquisition module is used to acquire real-time data of the target vehicle; the real-time data includes the power of the accessories and the average vehicle speed corresponding to the planned route. The first determining module is used to determine the high-speed energy consumption corresponding to the planned route based on the average vehicle speed corresponding to the planned route, a database constructed based on the historical data of the target vehicle, and the power of the attachments; the database includes the relationship between the average vehicle speed, the number of high-speed slices, and the average energy consumption of the MCU, and the high-speed slice is obtained by slicing the target time in the historical data where the vehicle speed is greater than the high-speed driving speed threshold. The second determining module is used to determine the remaining high-speed driving range based on the remaining SOC of the target vehicle and the high-speed energy consumption of the planned route.

13. A remaining mileage prediction device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the remaining mileage prediction method as described in any one of claims 1 to 11 when executing the computer program.

14. A readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the steps of the remaining mileage prediction method as described in any one of claims 1 to 11.

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