Remaining mileage prediction method, device and equipment and readable storage medium

Abstract

The invention discloses a remaining mileage prediction method, device and equipment and a readable storage medium. The method comprises the following steps: acquiring real-time data of a target vehicle; the real-time data comprises accessory power and average vehicle speed corresponding to the planned route; determining high-speed energy consumption corresponding to the planned route according to 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 comprises the relationship among the average vehicle speed, the number of high-speed slices and the average energy consumption of the MCU, and the high-speed slices are obtained by slicing target time when the vehicle speed is greater than a high-speed driving speed threshold value in historical data; and determining the high-speed endurance remaining mileage according to the electric quantity corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route. According to the technical scheme, the database is constructed based on the historical data of the target vehicle, the high-speed energy consumption and the remaining mileage of the vehicle are predicted based on the database and the real-time data of the vehicle, and the accuracy of predicting the high-speed endurance remaining mileage of the target vehicle is improved.

Description

Technical Field

[0001] The present application relates to the technical field of new energy electric vehicles, and in particular to a remaining mileage prediction method, device, equipment and readable storage medium. Background Art

[0002] With the development of new energy technology, new energy electric vehicles have been used more and more widely. At present, the standard range displayed by new energy electric vehicles is very different from the actual high-speed range, which may cause the driver to misjudge the remaining range and thus fail to reach the destination.

[0003] In summary, how to predict the remaining high-speed cruising range of new energy electric vehicles in order to reduce the driver's misjudgment of the remaining mileage is a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0004] In view of this, the purpose of this application is to provide a remaining mileage prediction method, device, equipment and readable storage medium for predicting the remaining mileage of high-speed cruising of new energy electric vehicles, so as to reduce the driver's misjudgment of the remaining mileage.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] A remaining mileage prediction method comprises: acquiring real-time data of a target vehicle; the real-time data comprises accessory power and an average vehicle speed corresponding to a planned route; determining a high-speed energy consumption corresponding to the planned route according to 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 comprises a relationship between the average vehicle speed, the number of high-speed slices, and the average energy consumption of an MCU, wherein a high-speed slice is obtained by slicing a target time in the historical data when the vehicle speed is greater than a high-speed driving speed threshold; and determining a high-speed cruising remaining mileage according to the electric quantity corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0007] Optionally, the real-time data further includes target parameters, the database includes historical target parameters, average vehicle speed, the number of high-speed slices and the relationship between the average energy consumption of the MCU, the target parameters include the number of passengers and / or the average slope of the planned route, and the historical target parameters include the historical number of passengers and / or the historical average slope;

[0008] Determine the high-speed energy consumption corresponding to the planned route according to the average vehicle speed corresponding to the planned route, the database and the accessory power, including: determine the high-speed energy consumption corresponding to the planned route according to the average vehicle speed corresponding to the planned route, the target parameter, the database and the accessory power.

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

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

[0011] Determining the average vehicle speed corresponding to each high-speed slice according to the vehicle speed calculation data corresponding to each high-speed slice includes: using Calculate the average vehicle speed V corresponding to the i-th high-speed slice i ; Among them, S i末 is the end stage mileage corresponding to the ith high-speed slice, S i初 is the initial stage mileage corresponding to the ith high-speed slice, T h The duration in hours converted from the duration of high-speed slicing;

[0012] Determining the average energy consumption of the MCU corresponding to each high-speed slice according to the MCU energy consumption calculation data corresponding to each high-speed slice includes: using Calculate the average MCU energy consumption E corresponding to the i-th high-speed slice MCUi Among them, U n is the MCU voltage at the nth acquisition point in the ith high-speed slice, I n is the MCU current at the nth acquisition point in the ith high-speed slice, t n is the time of the nth acquisition point in the ith high-speed slice, K1 is the conversion factor between hours and the time unit of the acquisition point, and K2 is the conversion factor between kilowatts and the units of MCU current*MCU voltage;

[0013] Determining the average slope corresponding to each high-speed slice according to the slope calculation data corresponding to each high-speed slice includes: using Calculate the average slope α corresponding to the i-th high-speed slice i ; Among them, α n is the slope of the nth acquisition point in the ith high-speed slice.

[0014] Optionally, the database is constructed according to the average vehicle speed, average MCU energy consumption, average slope and historical number of passengers corresponding to each of the high-speed slices, including: grouping according to the historical number of passengers, the average slope and the average vehicle speed; wherein at least one of the historical number of passengers, the average slope and the average vehicle speed is different between different data groups; determining the number of high-speed slices in each of the data groups according to the historical number of passengers, the average slope and the average vehicle speed in each of the data groups; determining the average MCU energy consumption in each of the data groups according to the number of high-speed slices in each of the data groups and the average MCU energy consumption corresponding to the corresponding high-speed slices.

[0015] Optionally, it also includes: when a new high-speed slice is added and the new high-speed slice corresponds to a target data group in the database, the average MCU energy consumption in the target data group is determined according to the current average MCU energy consumption in the target data group, the current number of high-speed slices, the number of new high-speed slices and the average MCU energy consumption corresponding to the new high-speed slice, and the number of high-speed slices in the target data group is determined according to the current number of high-speed slices in the target data group 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, it also 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 according to the target parameter and the database.

[0018] Optionally, determining the average vehicle speed corresponding to the planned route according to the target parameters and the database includes: obtaining the average vehicle speeds and the number of high-speed slices corresponding to the target parameters from the database; determining the average vehicle speed corresponding to the planned route according to 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.

[0019] Optionally, the high-speed energy consumption corresponding to the planned route is determined according to the average vehicle speed corresponding to the planned route, the number of passengers, the average slope of the planned route, the database and the accessory power, including: obtaining from the database an average MCU energy consumption corresponding to the average vehicle speed corresponding to the planned route; determining the high-speed MCU average energy consumption corresponding to the planned route according to the corresponding average MCU energy consumption obtained from the database; determining the high-speed energy consumption corresponding to the planned route according to the high-speed MCU average energy consumption corresponding to the planned route, the accessory power 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 a battery in the target vehicle, and the MCU input voltage and input current; and determining the accessory power according to the output voltage and output current of the battery, and the MCU input voltage and input current.

[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 consumptions corresponding to the planned routes, and determining the planned route corresponding to the optimal high-speed energy consumption as the optimal route;

[0022] The remaining high-speed cruising range is determined according to the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route, including: determining the optimal high-speed cruising range according to the power corresponding to the remaining SOC of the target vehicle and the optimal high-speed energy consumption.

[0023] Optionally, it also includes: obtaining the navigation route mileage, determining whether the optimal high-speed cruising remaining mileage is less than the navigation route mileage; if so, obtaining the charging pile position on the optimal route, sending the optimal high-speed energy consumption, the optimal route and the charging pile position to the host of the target vehicle, which is displayed and / or voice played by the host of the target vehicle.

[0024] A remaining mileage prediction device comprises: an acquisition module, used for acquiring real-time data of a target vehicle; the real-time data comprises accessory power and an average vehicle speed corresponding to a planned route; a first determination module, used for determining the high-speed energy consumption corresponding to the planned route according to 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 comprises the relationship between the average vehicle speed, the number of high-speed slices and the average energy consumption of an MCU, and the high-speed slice is obtained by slicing a target time in the historical data when the vehicle speed is greater than a high-speed driving speed threshold; a second determination module, used for determining the remaining high-speed cruising range according to the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0025] A remaining mileage prediction device comprises: a memory for storing a computer program; and a processor for implementing the steps of any one of the remaining process prediction methods described above when executing the computer program.

[0026] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the remaining process prediction method as described in any one of the above items are implemented.

[0027] The present application provides a remaining mileage prediction method, device, equipment and readable storage medium, wherein the method comprises: obtaining real-time data of a target vehicle; the real-time data comprises accessory power and average vehicle speed corresponding to a planned route; determining high-speed energy consumption corresponding to the planned route according to 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 comprises the relationship between the average vehicle speed, the number of high-speed slices and the average energy consumption of the MCU, wherein the high-speed slice is obtained by slicing the target time in the historical data when the vehicle speed is greater than a high-speed driving speed threshold; determining the remaining high-speed cruising range according to the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0028] The above-mentioned technical scheme disclosed in the present application constructs a database including the relationship between the average vehicle speed, the number of high-speed slices and the average energy consumption of the MCU based on the historical data of the target vehicle, so as to analyze the high-speed driving habits of the target vehicle driver by analyzing the historical data of the target vehicle, and establish a corresponding database that can be used to predict the high-speed energy consumption. Then, the high-speed energy consumption of the target vehicle is predicted in advance based on the real-time data of the target vehicle and the established database, and the remaining high-speed cruising range of the target vehicle is predicted in advance accordingly. In addition, the accuracy of the prediction of the remaining high-speed cruising range of the target vehicle can also be improved through the present application, so as to reduce the driver's misjudgment of the actual remaining cruising range of the target vehicle, alleviate the driver's mileage anxiety, and improve the driver's driving experience.

[0029] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flowchart of a remaining mileage prediction method provided in an embodiment of the present application;

[0031] Figure 2 A schematic diagram of extracting high-speed driving data and dividing high-speed slices provided in an embodiment of the present application;

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

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

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

[0035] Figure 6 A schematic diagram of the structure of a remaining mileage prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] With the development of new energy technology, new energy electric vehicles have been used more and more widely. At present, the standard range displayed by new energy electric vehicles is very different from the actual high-speed range, which may cause the driver to misjudge the remaining range and thus fail to reach the destination.

[0037] To this end, the present application provides a remaining mileage prediction method, device, equipment and readable storage medium, which constructs a database based on the historical data of the target vehicle, and the constructed database includes the relationship between the average vehicle speed, the number of high-speed slices and the average energy consumption of the MCU (Motor Control Unit, motor controller), wherein the number of high-speed slices is obtained by slicing the target time when the vehicle speed is greater than the high-speed driving speed threshold in the historical data, and the corresponding target vehicle driver high-speed energy consumption model is established by constructing a database based on the historical data of the target vehicle to analyze the high-speed driving habits of the target vehicle driver. On this basis, the real-time data of the target vehicle is obtained, wherein the real-time data includes the accessory power and the average vehicle speed corresponding to the planned route, and then, based on the average vehicle speed of the planned route in the real-time data, the accessory power and the database, the high-speed energy consumption corresponding to the planned route is predicted in advance, and the high-speed remaining mileage is predicted in advance based on the predicted high-speed energy consumption corresponding to the planned route and the remaining SOC (State of Charge) of the target vehicle. That is, the present application analyzes the high-speed driving habits of the target vehicle driver by analyzing the historical data of the target vehicle, and establishes a corresponding high-speed energy consumption model of the target vehicle driver based on this, and then 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 of the target vehicle driver, and predicts the remaining mileage of high-speed cruising in advance based on this, so as to realize the prediction of the remaining mileage of high-speed cruising, and improve the accuracy of the prediction of the remaining mileage of high-speed cruising, so as to reduce the driver's misjudgment of the remaining mileage, alleviate the driver's mileage anxiety, and improve the driver's driving experience.

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

[0039] See also Figure 1 , which shows a flowchart of a remaining mileage prediction method provided by an embodiment of the present application. The remaining mileage prediction method provided by an embodiment of the present application may include:

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

[0041] It should be noted that the execution subject of the remaining mileage prediction method provided in the embodiment of the present application can be a cloud server or the target vehicle itself. In order to save the computing resources of the target vehicle and improve the efficiency of obtaining the remaining mileage, the cloud server can be used as the execution subject of the remaining mileage prediction method provided in the embodiment of the present application. Among them, the embodiment of the present application is explained by taking the execution subject as a cloud server as an example.

[0042] In an embodiment of the present application, the sensor in the target vehicle can collect data of the target vehicle in real time, and feed the collected real-time data back to the cloud server so that the cloud server can obtain the real-time data of the target vehicle. Wherein, the aforementioned real-time data includes but is not limited to accessory power, the planned route given by the map (i.e., the driving route planned for the target vehicle) and the average speed of the planned route. Accessory power refers to the power of the accessories in the target vehicle, and the accessories can be, for example, DCDC (direct current-to-direct current converter), PTC (positive temperature coefficient thermistor) and compressor, etc., that is, accessory power refers to the power of DCDC, PTC and compressor, etc. The average speed corresponding to the planned route refers to the average speed corresponding to the vehicle on the planned route, which can be specifically given by the map, wherein the map can specifically 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, and the vehicle calculates the average speed corresponding to the planned route (of course, it can also be that the map provides the mileage and estimated time of the planned route to the cloud server, and the cloud server calculates the average speed corresponding to the planned route).

[0043] When receiving real-time data feedback from the target vehicle, the cloud server 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 constructed based on the historical data of the target vehicle for calculation, predict the high-speed energy consumption in advance, and predict the remaining mileage of high-speed cruising based on the predicted high-speed energy consumption in advance.

[0044] S12: Determine the high-speed energy consumption corresponding to the planned route according to 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 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 when 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, the historical data can be uploaded to the cloud server before predicting the remaining mileage), where the historical data mentioned here can specifically be historical real-time data, for example, may include time, vehicle speed at the corresponding time, mileage displayed on the instrument, slope, historical number of passengers, driving mode, energy recovery intensity, battery SOC, battery output voltage, battery output current, MCU voltage, MCU current, etc.

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

[0047] Specifically, the target time when the vehicle speed is greater than the high-speed driving speed threshold is obtained from the historical data in the cloud server, and the target time is sliced ​​every preset time to obtain a high-speed slice (that is, the duration of the high-speed slice is the preset time). The high-speed driving speed threshold can be set based on experience, for example, it can be 80km / h, and the preset time can be set based on the distribution of historical vehicle data of high-speed driving or by relevant personnel based on experience, for example, it can be 60s. For details, please refer to Figure 2 , which shows a schematic diagram of extracting high-speed driving data and dividing high-speed slices provided in an embodiment of the present application, wherein: Figure 2 Take the high-speed driving speed threshold of 80km / h and the preset duration of 60s as an example. After obtaining each high-speed slice, the cloud server can extract the driving habits and key factors affecting energy consumption corresponding to each high-speed slice from the historical data, where the key factors affecting energy consumption are the key data affecting energy consumption, such as the mileage displayed on the instrument, time, MCU voltage, MCU current, etc.

[0048] After extracting the data, the cloud server can construct a database (also called a database model or a high-speed energy consumption model of the target vehicle driver) containing the relationship between the average vehicle speed, the number of high-speed slices, and the average energy consumption of the MCU (the unit of the average energy consumption of the MCU can be kWh / 100km, and the unit of energy consumption is kWh / 100km as an example in this application) based on the extracted data. The database can contain multiple data groups, each of which can contain the average vehicle speed, the number of high-speed slices, and the average energy consumption of the MCU, and the average vehicle speed in different data groups is different. The average vehicle speed refers to the average vehicle speed corresponding to the high-speed slice, which can be specifically used Calculate the average vehicle speed V corresponding to the i-th high-speed slice i ; Among them, V i The calculation result can be accurate to one decimal place (of course, this can also be adjusted), V i The unit is km / h, S i初 is the initial stage mileage corresponding to the ith high-speed slice (in km), S i末 is the end stage mileage corresponding to the ith high-speed slice (in km), T h The duration in hours (i.e., the unit is h) converted from the duration of the 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 energy consumption refers to the average MCU energy consumption corresponding to the corresponding number of high-speed slices. Calculate the average MCU energy consumption E corresponding to the i-th high-speed slice MCUi ; Among them, E MCUi The calculation result can be accurate to one decimal place (of course, this can also be adjusted). MCUi The unit is kWh / 100km, U n is the MCU voltage at the nth acquisition point in the ith high-speed slice (in V), I n is the MCU current of the nth acquisition point in the ith high-speed slice (in A), t n is the time of the nth acquisition point in the ith high-speed slice, and K1 is the conversion coefficient between hours and the time unit of the acquisition point. For example, see Table 1, which is a database constructed based on the historical data of the target vehicle:

[0049] Table 1 Database built based on historical data of target vehicles

[0050]

[0051]

[0052] On the basis of the above, the high-speed energy consumption corresponding to the planned route can be determined according to the average vehicle speed corresponding to the planned route, the accessory power, and the database constructed based on the historical data of the target vehicle (the unit can be kWh / 100km). Specifically, the high-speed MCU average energy consumption corresponding to the planned route can be determined according to the average vehicle speed corresponding to the planned route and the database constructed based on the historical data of the target vehicle, and the high-speed energy consumption E corresponding to the planned route can be determined according to the high-speed MCU average energy consumption corresponding to the planned route, the accessory power, and the average vehicle speed corresponding to the planned route (which can be kWh / 100km). Among them, when the high-speed MCU average energy consumption corresponding to the planned route is determined according to the average vehicle speed corresponding to the planned route and the database constructed based on the historical data of the target vehicle, if there is an average vehicle speed in the database that is the same as the average vehicle speed corresponding to the planned route, the MCU average energy consumption corresponding to the average vehicle speed in the database is determined as the high-speed MCU average energy consumption corresponding to the planned route; if there is no average vehicle speed in the database that is the same as the average vehicle speed corresponding to the planned route, the average vehicle speed adjacent to the average vehicle speed corresponding to the planned route is determined from the database, and the MCU average energy consumption corresponding to the adjacent average vehicle speed is interpolated to obtain the high-speed MCU average energy consumption corresponding to the planned route.

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

[0054] Obtain the power △E corresponding to the remaining SOC of the target vehicle, and use the power △E corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption E corresponding to the planned route to Calculate the remaining high-speed cruising range.

[0055] It should be noted that if the execution subject is a cloud server, after determining the high-speed energy consumption and high-speed cruising remaining mileage corresponding to the planned route, the cloud server can also feed back the high-speed energy consumption and high-speed cruising remaining mileage corresponding to the determined planned route to the target vehicle, and the target vehicle vehicle system can remind the driver of the predicted high-speed energy consumption and high-speed cruising remaining mileage through voice playback or screen display. In addition, the cloud server can also obtain the location of the charging pile on the planned route, and can feed back the charging pile location together with the high-speed energy consumption and high-speed cruising remaining mileage to the target vehicle, so that the target vehicle can remind the driver of the relevant information obtained through voice playback and / or screen display, so that the driver can obtain the predicted high-speed energy consumption, high-speed cruising remaining mileage and other information to reduce the misjudgment of the high-speed cruising remaining mileage, so that the driver can reasonably arrange whether to charge, etc., and arrive at the destination smoothly, reduce mileage anxiety, and improve the driving experience. When the execution subject is the target vehicle itself, after the target vehicle predicts the high-speed energy consumption, high-speed cruising remaining mileage and obtains the location of the charging pile on the planned route, the host system of the target vehicle can remind the driver of the relevant information obtained through voice playback and / or screen display.

[0056] The above method can be used to predict high-speed energy consumption and high-speed cruising remaining mileage in advance after the driver and passengers get on the vehicle. In addition, the above process analyzes the high-speed driving habits of the target vehicle driver through the Internet of Vehicles and cloud service technology, establishes a high-speed energy consumption model for the target vehicle driver, collects real-time data of the target vehicle, predicts the high-speed energy consumption of the target vehicle in advance based on the real-time data of the target vehicle and the high-speed energy consumption model of the target vehicle driver, and predicts the high-speed cruising remaining mileage of the target vehicle based on the high-speed energy consumption of the target vehicle, so as to not only predict the high-speed energy consumption and high-speed cruising remaining mileage of the target vehicle in advance, but also accurately and reliably predict the high-speed energy consumption and high-speed cruising remaining mileage of the target vehicle based on historical data, so as to reduce the driver's misjudgment of the remaining mileage.

[0057] The above-mentioned technical scheme disclosed in the embodiment of the present application constructs a database including the relationship between the average vehicle speed, the number of high-speed slices and the average energy consumption of the MCU based on the historical data of the target vehicle, so as to analyze the high-speed driving habits of the driver of the target vehicle by analyzing the historical data of the target vehicle, and establish a corresponding database that can be used to predict the high-speed energy consumption. Then, the high-speed energy consumption of the target vehicle is predicted in advance based on the real-time data of the target vehicle and the established database, and the remaining high-speed cruising range of the target vehicle is predicted in advance accordingly. The accuracy of the prediction of the remaining high-speed cruising range of the target vehicle can also be improved through the embodiment of the present application, so as to reduce the driver's misjudgment of the actual remaining cruising range of the target vehicle, alleviate the driver's mileage anxiety, and improve the driver's driving experience.

[0058] In a remaining mileage prediction method provided by an embodiment of the present application, the real-time data may further include a target parameter, the database may include a historical target parameter, an average vehicle speed, a high-speed slice number, and a relationship between an average MCU energy consumption, the target parameter may include the number of passengers and / or an average slope of a planned route, and the historical target parameter may include the historical number of passengers and / or a historical average slope;

[0059] Determining the high-speed energy consumption corresponding to the planned route according to the average vehicle speed, database and accessory power corresponding to the planned route may include: determining the high-speed energy consumption corresponding to the planned route according to the average vehicle speed, target parameters, database and accessory power corresponding to the planned route.

[0060] In an embodiment of the present application, the real-time data of the target vehicle obtained may also include target parameters, which may specifically include the number of passengers and / or the average slope corresponding to the planned route. Moreover, 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, such as the historical number of passengers, instrument mileage, slope, historical number of passengers, MCU voltage, MCU current, etc. For details, please refer to Table 2, which is a table of extracted driving habits and key factors affecting energy consumption:

[0061] Table 2 Extracted driving habits and key factors affecting energy consumption

[0062]

[0063]

[0064] After extracting the data, the cloud server can build a database containing the relationship between historical target parameters, average vehicle speed, number of high-speed slices, and average MCU energy consumption based on the extracted data. The database may contain multiple data groups, each of which may contain historical target parameters, average vehicle speed, number of high-speed slices, and average MCU energy consumption, and at least one of the historical target parameters and average vehicle speeds in different data groups is different. The historical number of passengers refers to the number of people on the vehicle corresponding to the high-speed slice, and the average slope refers to the average slope corresponding to the high-speed slice.

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

[0066] In the above process, when the target parameter includes the number of passengers and the historical target parameter includes the historical number of passengers, the database includes the relationship between the historical number of passengers, 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 according to the average vehicle speed, the number of passengers, the database and the accessory power corresponding to the planned route. For example, see Table 3, which is a database constructed based on the historical data of the target vehicle:

[0067] Table 3 Database constructed based on historical data of target vehicles

[0068]

[0069] When the target parameter includes the average slope of the planned route and the historical target parameter includes the historical average slope, the database includes the relationship between the historical average slope, 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 according to the average vehicle speed, the number of passengers, the database and the accessory power corresponding to the planned route. For example, see Table 4, which is a database constructed based on the historical data of the target vehicle:

[0070] Table 4 Database constructed based on historical data of target vehicles

[0071]

[0072]

[0073] When the target parameters include the number of passengers and the average slope of the planned route, and the historical target parameters include the historical number of passengers and the historical average slope, the database includes the relationship between the historical number of passengers, the historical average slope, 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 according to the average vehicle speed corresponding to the planned route, the average slope of the planned route, the number of passengers, the database, and the accessory power. For example, see Table 5, which is a database constructed based on the historical data of the target vehicle:

[0074] Table 5 Database constructed based on historical data of target vehicles

[0075]

[0076] The accuracy of the high-speed energy consumption prediction can be further improved by incorporating the number of passengers and / or the average slope corresponding to the planned route into the high-speed energy consumption prediction, thereby further improving the accuracy of the remaining mileage prediction.

[0077] The present application provides a method for predicting remaining mileage, wherein the historical target parameters may include the historical number of passengers and the historical average slope, and the database is constructed based on the 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] Slice the target time to obtain high-speed slices, and extract the vehicle speed calculation data, MCU energy consumption calculation data, slope calculation data and historical passenger number corresponding to each high-speed slice from the historical data;

[0080] Determine the average vehicle speed corresponding to each high-speed slice according to the vehicle speed calculation data corresponding to each high-speed slice, determine the average MCU energy consumption corresponding to each high-speed slice according to the MCU energy consumption calculation data corresponding to each high-speed slice, and determine the average slope corresponding to each high-speed slice according to the slope calculation data corresponding to each high-speed slice;

[0081] The database is constructed based on the average vehicle speed, MCU average energy consumption, average slope and historical number of passengers corresponding to each high-speed slice. In the embodiment of the present application, 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 the cloud server for storage, and the cloud server can obtain historical data of the target vehicle accordingly. The historical data mentioned here may include time, vehicle speed at the corresponding time, instrument mileage, slope, historical number of passengers, driving mode, energy recovery intensity, battery SOC, battery output voltage, battery output current, MCU voltage, MCU current, etc.

[0083] S102: Extracting a target time when the vehicle speed is greater than a high-speed driving speed threshold from 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 according to the length of the target time, or 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: extracting high-speed operating conditions from the historical data of the target vehicle, specifically extracting the vehicle speed calculation data corresponding to each high-speed slice (i.e., data for calculating the average vehicle speed corresponding to the high-speed slice), MCU energy consumption calculation data (i.e., data for calculating the average MCU energy consumption corresponding to the high-speed slice), and slope calculation data (i.e., data for calculating the average slope corresponding to the high-speed slice). In addition, the historical number of passengers corresponding to each high-speed slice is also extracted from the historical data of the target vehicle.

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

[0087] S106: Construct a database according to the average vehicle speed, average MCU energy consumption, average slope and corresponding historical number of passengers corresponding to each high-speed slice.

[0088] Through the above method, it is possible to accurately and reliably construct a database containing the relationship between the historical number of passengers, average slope, average vehicle speed, number of high-speed slices and average energy consumption of the MCU based on the historical data of the target vehicle, so as to facilitate the subsequent accurate prediction of the high-speed energy consumption and remaining mileage of the high-speed cruising range of the target vehicle based on the database.

[0089] A remaining mileage prediction method provided by an embodiment of the present application, wherein the vehicle speed calculation data corresponding to each high-speed slice may include the initial stage mileage and the end stage mileage corresponding to each high-speed slice, the MCU energy consumption calculation data corresponding to each high-speed slice may include the MCU voltage and MCU current of each acquisition point in each high-speed slice, the initial stage mileage and the end stage mileage corresponding to each high-speed slice, and the slope data corresponding to each high-speed slice may include the slope of each acquisition point in each high-speed slice;

[0090] Determining the average vehicle speed corresponding to each high-speed slice according to the vehicle speed calculation data corresponding to each high-speed slice may include:

[0091] use Calculate the average vehicle speed V corresponding to the i-th high-speed slice i ; Among them, S i末 is the end stage mileage corresponding to the ith high-speed slice, S i初 is the initial stage mileage corresponding to the ith high-speed slice, T h The duration in hours converted from the duration of high-speed slicing;

[0092] Determining the average MCU energy consumption corresponding to each high-speed slice according to the MCU energy consumption calculation data corresponding to each high-speed slice may include:

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

[0094] Determining the average slope corresponding to each high-speed slice according to the slope calculation data corresponding to each high-speed slice may include:

[0095] use Calculate the average slope α corresponding to the i-th high-speed slice i ; Among them, α n is the slope of the nth acquisition point in the ith high-speed slice.

[0096] In an embodiment of the present application, the vehicle speed calculation data corresponding to each high-speed slice may specifically include the initial stage mileage (specifically the instrument mileage of the initial stage of the high-speed slice) and the ending stage mileage (specifically the instrument mileage of the ending stage of the high-speed slice) corresponding to each high-speed slice, and the MCU energy consumption calculation data corresponding to each high-speed slice include the MCU voltage and MCU current of each collection point in each high-speed slice (that is, the time point of data collection, for example, data can be collected once every 600 ms, then every 600 ms is a collection point, and a high-speed slice contains multiple collection points), the initial stage mileage and the ending stage mileage corresponding to each high-speed slice, and the slope data corresponding to each high-speed slice include the slope of each collection point in each high-speed slice.

[0097] On the basis of the above, the average vehicle speed corresponding to each high-speed slice can be determined according to the vehicle speed calculation data corresponding to each high-speed slice: Calculate the average vehicle speed V corresponding to the i-th high-speed slice i ; Among them, V i The calculation result can be accurate to one decimal place (of course, this can also be adjusted), V i The unit is km / h, S i初 is the initial stage mileage corresponding to the ith high-speed slice (in km), S i末 is the end stage mileage corresponding to the ith high-speed slice (in km), T h The duration in hours (i.e., the unit is h) obtained by converting the duration of the high-speed slice. For example, when the unit of the duration of the high-speed slice is s, then Wherein, T is the duration of high-speed slicing (in seconds).

[0098] According to the MCU energy consumption calculation data corresponding to each high-speed slice, the average MCU energy consumption corresponding to each high-speed slice can be determined by: Calculate the average MCU energy consumption E corresponding to the i-th high-speed slice MCUi ; Among them, E MCUi The calculation result can be accurate to one decimal place (of course, this can also be adjusted). MCUi The unit is kWh / 100km, U n is the MCU voltage at the nth acquisition point in the ith high-speed slice (in V), I n is the MCU current of the nth acquisition point in the ith high-speed slice (in A), t n is the time of the nth acquisition point in the ith high-speed slice, K1 is the conversion coefficient 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 coefficient between kilowatts and the units of MCU current * MCU voltage, taking the MCU current unit as A and the MCU voltage unit as V as an example, the unit of MCU current * MCU voltage is w, K2=1000.

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

[0100] Through the above process, the average vehicle speed, MCU average energy consumption and average slope corresponding to each high-speed slice can be accurately calculated to improve the reliability and accuracy of database construction, thereby facilitating the improvement of the accuracy of high-speed energy consumption and high-speed remaining mileage prediction.

[0101] A remaining mileage prediction method provided in an embodiment of the present application builds a database according to the average vehicle speed, MCU average energy consumption, average slope and historical number of passengers corresponding to each high-speed slice, which may include:

[0102] Grouping is performed according to the number of historical passengers, average slope, and average vehicle speed; wherein at least one of the number of historical passengers, average slope, and average vehicle speed is different between different data groups;

[0103] Determine the number of high-speed slices in each data group based on the historical number of passengers, average slope and average vehicle speed in each data group;

[0104] According to the number of high-speed slices in each data group and the average MCU energy consumption corresponding to the corresponding high-speed switching, the average MCU energy consumption in each data group is determined.

[0105] In the embodiment of the present application, the specific process of constructing a database according to the average vehicle speed, the average MCU energy consumption, the average slope and the corresponding historical number of passengers corresponding to each high-speed slice can be as follows:

[0106] S201: Grouping is performed according to the historical number of passengers, average slope and average vehicle speed corresponding to each highway slice to obtain multiple data groups; wherein at least one of the historical number of passengers, average slope and average vehicle speed is different between different data groups, for example: the historical number of passengers in data group 1 is 1, the average slope is 0%, and the average vehicle speed is 80km / h; the historical number of passengers in data group 2 is 1, the average slope is 1%, and the average vehicle speed is 80km / h….

[0107] Among them, after grouping according to the historical number of passengers, the average slope and the average speed corresponding to each high-speed slice, the data group to which each high-speed slice belongs can also be determined according to the historical number of passengers, the average slope and the average speed corresponding to each high-speed slice, or the high-speed slice corresponding to each data group can be determined according to the historical number of passengers, the average slope and the average speed in each data group. For example, the historical number of passengers corresponding to high-speed slice 1 is 1, the average slope is 0%, and the average speed is 80km / h, the historical number of passengers corresponding to high-speed slice 2 is 1, the average slope is 1%, and the average speed is 80km / h, and the historical number of passengers corresponding to high-speed slice 3 is 1, the average slope is 1%, and the average speed is 80km / h. It can be determined that high-speed slice 1 belongs to data group 1, and high-speed slices 2 and high-speed slices 3 belong to data group 2.

[0108] S202: According to the historical number of passengers, average slope and average vehicle speed in each data group, the number of high-speed slices in each data group is statistically determined.

[0109] Specifically, the number of high-speed slices containing the corresponding historical number of passengers, average slope and average vehicle speed in each data group is counted, and the number of high-speed slices is determined as the number of high-speed slices in the corresponding data group.

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

[0111] The above method can reliably and accurately construct a database to facilitate the reliable and accurate prediction of the high-speed energy consumption and high-speed remaining mileage of the target vehicle.

[0112] A remaining mileage prediction method provided in an embodiment of the present application may further include:

[0113] When a new high-speed slice is added and the new high-speed slice corresponds to a target data group in the database, the average energy consumption of the MCU in the target data group is determined according to the current average energy 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 energy consumption of the MCU corresponding to the new high-speed slice, and the number of high-speed slices in the target data group is determined according to the current number of high-speed slices in the target data group and the number of new high-speed slices.

[0114] In an embodiment of the present 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 processes the newly added historical data in the above-mentioned manner to obtain newly added high-speed slices and the average vehicle speed, MCU average energy consumption, average slope and historical number of passengers corresponding to the newly added high-speed slices. Afterwards, the constructed database can be updated based on the average vehicle speed, MCU average energy consumption, average slope and historical number of passengers corresponding to the newly added high-speed slices.

[0115] Specifically, when the newly added high-speed slice corresponds to a target data group in the database, the average MCU energy consumption, the current number of high-speed slices, the number of newly added high-speed slices and the average MCU energy consumption corresponding to the newly added high-speed slices can be used. Calculate the average energy consumption E of the MCU in the target data group MCUi , where E on the left side of the equal sign MCUi The E on the right side of the equal sign is the average energy consumption of the MCU in the target data group calculated after adding high-speed slicing. MCUi is the current average energy consumption of the MCU in the target data group, N i is the current number of high-speed slices in the target data group, m is the number of newly added high-speed slices in the target data group, X j is the average energy consumption of the MCU corresponding to the jth high-speed slice newly added in the target data group. In addition, the number of high-speed slices in the target data group is determined according to the current number of high-speed slices in the target data group and the number of newly added high-speed slices.

[0116] For example, the number of historical passengers is 1, the slope is 0%, the speed is 80 km / h, the number of slices is N, and the average energy consumption of MCU is E MCUN , now add 1 high-speed slice number, the new high-speed slice number corresponds to the historical number of passengers 1, slope 0%, speed 80km / h, MCU average energy consumption is X, then the high-speed slice number of the data group corresponding to the historical number of passengers 1, slope 0% and speed 80km / h in the database is updated to N+1, and the MCU average energy consumption is: Calculated.

[0117] It should be noted that when a newly added high-speed slice does not correspond to a target data group in the database, a new data group can be added to the database based on the historical number of passengers, average vehicle speed, average slope, average MCU power consumption and the number of newly added high-speed slices corresponding to the newly added high-speed slices. The method of adding a new data group is the same as the method of creating a data group when constructing the above-mentioned database, which will not be repeated here.

[0118] The above method is used to update the data in the database when a new high-speed slice is added to improve the reliability and accuracy of the database, thereby facilitating improving the accuracy of the high-speed energy consumption and high-speed remaining mileage prediction of the target vehicle.

[0119] A remaining mileage prediction method provided in an embodiment of the present application may further include, after extracting vehicle speed calculation data, MCU energy consumption calculation data and slope calculation data corresponding to each high-speed slice from historical data:

[0120] Clean the data extracted from historical data.

[0121] In an embodiment of the present application, 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 database, before determining the average vehicle speed corresponding to each high-speed slice according to the vehicle speed calculation data corresponding to each high-speed slice, determining the MCU average energy consumption corresponding to each high-speed slice according to the MCU energy consumption calculation data corresponding to each high-speed slice, and determining the average slope corresponding to each high-speed slice according to the slope calculation data corresponding to each high-speed slice, the data extracted from the historical data can also be cleaned, and specifically, incomplete or erroneous data can be processed (specifically, incomplete and erroneous data can be eliminated) to improve data quality, thereby improving the accuracy of the average vehicle speed, MCU average energy consumption and average slope calculation corresponding to each high-speed slice, thereby improving the accuracy of database construction.

[0122] A remaining mileage prediction method provided in an embodiment of the present application, for obtaining an average vehicle speed corresponding to a planned route, may include:

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

[0124] In an embodiment of the present application, when the real-time data includes target parameters (the number of occupants and / or the average slope corresponding to the planned route) and the database constructed based on the historical data of the target vehicle includes historical target parameters, the average vehicle speed corresponding to the planned route can be determined based on the acquired target parameters of the target vehicle and the database constructed based on the historical data of the target vehicle, so as to determine the average vehicle speed corresponding to the planned route based on the driver's historical driving behavior habits, thereby improving the reliability and accuracy of determining the average vehicle speed corresponding to the planned route.

[0125] Of course, the map can also give the average vehicle 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 vehicle 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 vehicle speed corresponding to the planned route).

[0126] A remaining mileage prediction method provided in an embodiment of the present application, which determines the average vehicle speed corresponding to the planned route according to the target parameters and the database, may include:

[0127] Obtaining average vehicle speeds and numbers of high-speed slices corresponding to target parameters from a database;

[0128] The average vehicle speed corresponding to the planned route is determined according to the average vehicle speeds, the number of highway slices and the duration of the highway slices corresponding to the target parameters in the database.

[0129] In the embodiment of the present application, the following method can be specifically used to determine the average vehicle speed corresponding to the planned route based on the number of passengers, the average slope of the planned route, and a database constructed based on historical data of the target vehicle:

[0130] S301: Obtaining average vehicle speeds and high-speed slice numbers corresponding to target parameters in real-time data of the target vehicle from a database constructed based on historical data of the target vehicle.

[0131] Specifically, when the database contains multiple data groups, data groups 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), and then the average vehicle speed and the number of high-speed slices can be obtained from each corresponding data group.

[0132] S302: Calculate the average speed corresponding to the planned route using a mean algorithm according to 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 real-time data of the target vehicle in the database.

[0133] Specifically, you can use Calculate the average vehicle speed V corresponding to the planned route 平均(the unit can be km / h), wherein 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, K3 is the conversion coefficient between the unit of hour and the duration of the high-speed slice, and 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, V n is 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 group corresponding to the target parameter in the real-time data of the target vehicle in the database), N n is the corresponding high-speed slice number (or the high-speed slice number in the nth data group corresponding to the target parameter in the real-time data of the target vehicle in the database).

[0134] Alternatively, the target parameters in the real-time data of the target vehicle, the minimum average speed and the maximum average speed in the database can be used to increase the speed from the minimum average speed to the maximum average speed according to a preset speed step (e.g., 1 kph, which can be determined based on the average speed distribution in the database), and the average speed corresponding to the planned route can be calculated using the mean algorithm. For example, the minimum average speed in the database is 80 kph (expressed as V in the following calculation formula). 80kp h), the maximum average vehicle speed is 120 kph (expressed as V in the following calculation formula 120kph ), take the high-speed slicing duration as 60 seconds as an example, you can use Calculate the average vehicle speed V corresponding to the planned route 平均 , where N 80kph Indicates the number of high-speed slices corresponding to the average vehicle speed of 80 kph in the data group corresponding to the target parameter in the database. 平均 If there is no average speed corresponding to the target parameter in the database, the number of high-speed slices can be recorded as 0 and participate in the calculation of the above formula. For example, if the minimum average speed is 80 kph and the preset speed step is 1 kph, then If there is no 81 kph corresponding to the target parameter in the database, then N 81kph That is equal to 0.

[0135] Taking the example that the target parameters include the number of passengers and the average vehicle speed corresponding to the planned route, the average vehicle speed corresponding to the planned route is determined according to the target parameters and the database: the average vehicle speeds and the number of highway slices corresponding to the number of passengers and the average slope of the planned route are obtained from the database; the average vehicle speed corresponding to the planned route is determined according to the average vehicle speeds, the number of highway slices and the duration of the highway slices corresponding to the number of passengers and the average slope of the planned route in the database.

[0136] The above method can be used to reliably and accurately predict 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 the prediction of the high-speed energy consumption and the remaining mileage of the high-speed cruising range of the target vehicle.

[0137] A remaining mileage prediction method provided in an embodiment of the present application determines the high-speed energy consumption corresponding to the planned route according to the average vehicle speed, database and accessory power corresponding to the planned route, which may include:

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

[0139] According to the corresponding MCU average energy consumption obtained from the database, determine the high-speed MCU average energy consumption corresponding to the planned route;

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

[0141] In the embodiment of the present application, the following method can be used to determine the high-speed energy consumption corresponding to the planned route according to the average speed, database and accessory power corresponding to the planned route:

[0142] S401: Obtain the average energy consumption of the MCU corresponding to the average vehicle speed corresponding to 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 that corresponding to the planned route (ie, the average vehicle speed in the data group is the same as the average vehicle speed corresponding to the planned route), the average energy consumption of the MCU is obtained from the data group.

[0144] If there is no data group with the same average speed corresponding to the planned route in the database, the data group adjacent to the data can be obtained, and the adjacent data group is determined as the data group corresponding to the average speed corresponding to the planned route. Specifically, the left adjacent data group (the average speed in the left adjacent data group is smaller than the average speed corresponding to the planned route) and the right adjacent data group (the average speed in the right adjacent data group is larger than the average speed corresponding to the planned route) are obtained, and the left adjacent and right adjacent are determined by the data size. Then, the MCU average energy consumption is obtained from the left adjacent data group and the right adjacent data group as the MCU average energy consumption corresponding to the average speed corresponding to the planned route in the database.

[0145] S402: Determine the high-speed MCU average energy consumption corresponding to the planned route according to the corresponding MCU average energy consumption obtained from the database.

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

[0147] If there is no data group in the database with the same average vehicle speed as that corresponding to the planned route, the corresponding MCU average energy consumption obtained from the left adjacent data group and the right adjacent data group is interpolated (specifically, linear interpolation processing can be used) to determine the high-speed MCU average energy consumption corresponding to the planned route.

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

[0149] Among them, when the real-time data also includes target parameters, the database includes historical target parameters, average vehicle speed, the relationship between the number of high-speed slices and the average energy consumption of the MCU, the target parameters include the number of passengers and the average slope of the planned route, and the historical target parameters include the historical number of passengers and the historical average slope, then according to the average vehicle speed, target parameters, database and accessory power corresponding to the planned route, the process of determining the high-speed energy consumption corresponding to the planned route can be specifically as follows:

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

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

[0152] It should be noted that, in the above process, when there is no data in the database that has the same historical number of passengers, average speed and average slope as the number of passengers, average speed corresponding to the planned route and average slope of the planned route, the rules for searching the database can be set in advance by relevant personnel, or can be set during the prediction process. For example, a data group adjacent to the data can be obtained, and the adjacent data group can be determined as a data group corresponding to the average speed, number of passengers and average slope of the planned route corresponding to the planned route.

[0153] The above-mentioned rule for searching the database may specifically be:

[0154] (1) searching from a database for two items corresponding to the same data group, namely, the average vehicle speed, the number of passengers, and the average slope of the planned route; the two items mentioned here may be the historical number of passengers and the average vehicle speed, the historical number of passengers and the average slope, or the average vehicle speed and the average slope;

[0155] (2) If there are two data groups corresponding to the same two items of the average vehicle speed, the number of passengers, and the average slope of the planned route in the database, then the left adjacent data group and the right adjacent data group are obtained from the corresponding data group. The left adjacent data group refers to different items of data in the data group (i.e., the average vehicle speed, the number of passengers in the history, and the average slope in the data group are different from the average vehicle speed, the number of passengers, and the average slope in the planned route) that are smaller than the corresponding items of data in the real-time data, and the right adjacent data group refers to different items of data in the data group that are larger than the corresponding items of data in the real-time data.

[0156] Specifically, a left adjacent data group and a right adjacent data group are obtained, and the left adjacent data group and the right adjacent data group are determined by the data size. Taking the data group having the same number of passengers and the average slope of the planned route as the database (the historical number of passengers in the data group is the same as the number of passengers in the real-time data of the target vehicle, the average slope in the data group is the same as the average slope of the planned route, but the average speed in the data group is different from the average speed corresponding to the planned route) as an example, the left adjacent data group (that is, the historical number of passengers in the left adjacent data group is the same as the number of passengers in the real-time data of the target vehicle, the average slope is the same as the average slope of the planned route, but the average speed is located to the left of the average speed corresponding to the planned route, or the average speed is smaller than the average speed corresponding to the planned route) and the right adjacent data group (that is, the historical number of passengers in the right adjacent data group is the same as the number of passengers in the real-time data of the target vehicle, the average slope is the same as the average slope of the planned route, but the average speed is located to the right of the average speed corresponding to the planned route, or the average speed is greater than the average speed corresponding to the planned route) can be obtained. Then, the average energy consumption of the MCU is obtained from the left adjacent data group and the right adjacent data group to serve as the average energy consumption of the MCU corresponding to the average vehicle speed, the number of passengers and the average slope of the planned route in the database.

[0157] (3) if the database does not contain the same data set of two items of the average vehicle speed, the number of passengers, and the average slope of the planned route, then obtain the same data set of two items of the average vehicle speed, the number of passengers, and the average slope of the planned route from the database;

[0158] (4) If there exists in the database an item corresponding to the average vehicle speed, the number of passengers, and the average slope of the planned route that corresponds to the same data group, then the left adjacent data group and the right adjacent data group are obtained from the corresponding data group;

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

[0160] S502: Determine the high-speed MCU average energy consumption corresponding to the planned route according to the corresponding MCU average energy consumption obtained from the database.

[0161] Specifically, if there is a data group in the database with the same average vehicle speed, number of passengers and average slope of the planned route, the MCU average energy consumption obtained from the data group will be determined as the high-speed MCU average energy consumption corresponding to the planned route.

[0162] If there is no data group in the database with the same average vehicle speed, number of passengers and average slope of the planned route as those of the planned route, if step S501 is the corresponding MCU average energy consumption obtained from the left adjacent data group and the right adjacent data group, an interpolation processing method (specifically, a linear interpolation processing method) can be used to determine the high-speed MCU average energy consumption corresponding to the planned route; if step 501 is the corresponding MCU average energy consumption obtained from the data group corresponding to the first data vector whose similarity to the second data vector is greater than the preset similarity, the algorithm averaging method can be used to calculate the high-speed MCU average energy consumption corresponding to the planned route, or the weight of the MCU average energy consumption in the data group corresponding to each first data vector mentioned above (i.e., the first data vector whose similarity is greater than the preset similarity) can be determined, and the weighted average algorithm can be used to calculate the high-speed MCU average energy consumption corresponding to the planned route.

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

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

[0165] The above method can realize reliable and accurate prediction of the high-speed energy consumption corresponding to the planned route based on the number of passengers in the real-time data of the target vehicle, the average slope of the planned route, the accessory power, and the database constructed based on the historical data of the target vehicle, and the average vehicle speed corresponding to the planned route determined based on the constructed database, so as to improve the reliability and accuracy of the prediction of the remaining mileage of the high-speed cruising range of the target vehicle.

[0166] A remaining mileage prediction method provided by an embodiment of the present application, for obtaining the accessory power of a target vehicle, may include:

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

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

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

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

[0171] S602: Based on the output voltage and output current of the battery, the input voltage and input current of the MCU, use 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 Calculate the real-time accessory power P 实时附件功率 Considering that the accessory power does not change much after the driver starts the vehicle, the real-time accessory power at each collection point within a set time length (for example, 1 minute or 5 minutes, and the specific time length can be set according to experience, etc.) can be obtained, and the real-time accessory power at each collection point within the preset time length is averaged to obtain the accessory power P 附件功率 .

[0172] That is, the target vehicle can directly obtain the output voltage and output current of the battery, the input voltage and input current of the MCU and feed them back to the cloud server, so that the cloud server can calculate the accessory power based on these data. Of course, the target vehicle can also measure or calculate the accessory power by itself and feed it back to the cloud server.

[0173] The above method can improve the accuracy of accessory power calculation, so as to improve the accuracy of high-speed energy consumption and high-speed remaining mileage prediction of the target vehicle.

[0174] See also Figure 3 and Figure 4 ,in, Figure 3 A simplified working principle diagram of a remaining mileage prediction method provided by an embodiment of the present application is shown. Figure 4 The detailed working principle diagram of a remaining mileage prediction method provided by an embodiment of the present application is shown. It should be noted that: Figure 3 and Figure 4 The remaining process prediction method is described by taking the cloud server as the execution subject as an example. Figure 4The following is an example of three planned routes, namely, route A, route B, and route C. When a plurality of planned routes are included, a remaining mileage prediction method provided in an embodiment of the present application may further include, after determining the high-speed energy consumption corresponding to the planned routes:

[0175] Determine the optimal high-speed energy consumption from the high-speed energy consumptions corresponding to the various planned routes, and determine the planned route corresponding to the optimal high-speed energy consumption as the optimal route;

[0176] Determining the remaining high-speed cruising range based on the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route may include:

[0177] The optimal high-speed cruising range remaining mileage is determined based on the power corresponding to the remaining SOC of the target vehicle and the optimal high-speed energy consumption.

[0178] In an embodiment of the present application, the real-time data of the target vehicle may include multiple planned routes (that is, the map may plan multiple driving routes for the target vehicle) and the average slope of each planned route. For each planned route, the above-mentioned method can be used to calculate the average vehicle speed corresponding to each planned route and the high-speed energy consumption corresponding to each planned route. 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] On the basis of the above, when determining the remaining process of high-speed cruising according to the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route, the optimal high-speed cruising remaining mileage can be determined based on the power corresponding to the remaining SOC of the target vehicle and the optimal high-speed energy consumption, so as to realize the advance prediction of the optimal high-speed cruising remaining mileage.

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

[0181] Among them, after determining the high-speed energy consumption, optimal high-speed energy consumption, optimal route, and optimal high-speed remaining mileage corresponding to each planned route, this information or part of this information (such as optimal high-speed energy consumption, optimal route, and optimal high-speed remaining mileage) can also be sent to the target vehicle, so that the host system of the target vehicle can remind the cloud server of the information predicted by voice playback or screen display, so that the driver can obtain relevant information and arrange travel reasonably.

[0182] A remaining mileage prediction method provided in an embodiment of the present application may further include:

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

[0184] If so, the location of the charging pile on the optimal route is obtained, and the optimal high-speed energy consumption, the optimal route and the location of the charging pile are sent to the host of the target vehicle, which is displayed and / or voice-played by the host of the target vehicle.

[0185] In an embodiment of the present application, when determining the remaining mileage of the optimal high-speed cruising range, the navigation route mileage can also be obtained (specifically, it can be uploaded to the cloud server by the target vehicle or the cloud of the map), and then it is determined whether the remaining mileage of the optimal high-speed cruising range is less than the navigation route mileage.

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

[0187] If the optimal high-speed remaining mileage is not less than the navigation route mileage, it means that the remaining power of the target vehicle can support the navigation route mileage. 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 host of the target vehicle according to the settings (which can be set by relevant personnel based on experience, etc.).

[0188] Through the above process, the optimal high-speed energy consumption, optimal route and charging station location can be sent to the host of the target vehicle, so that the driver can obtain this information in time, thereby reasonably arranging travel and whether to charge, etc., reducing mileage anxiety.

[0189] The present application also provides a remaining mileage prediction device. Figure 5, which shows a schematic structural diagram of a remaining mileage prediction device provided by an embodiment of the present application, which may include: an acquisition module 51, used to acquire real-time data of a target vehicle; the real-time data may include accessory power and an average vehicle speed corresponding to a planned route; a first determination module 52, used to determine the high-speed energy consumption corresponding to the planned route according to 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 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 when the vehicle speed is greater than the high-speed driving speed threshold; a second determination module 53, used to determine the remaining high-speed cruising range according to the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0190] In a remaining mileage prediction device provided by an embodiment of the present application, the real-time data may further include a target parameter, the database may include a historical target parameter, an average vehicle speed, a high-speed slice number, and a relationship between an average MCU energy consumption, the target parameter may include the number of passengers and / or an average slope of a planned route, and the historical target parameter may include the historical number of passengers and / or the historical average slope;

[0191] The first determination module 52 may include: a first determination submodule, for determining the high-speed energy consumption corresponding to the planned route according to the average vehicle speed, target parameters, database and accessory power corresponding to the planned route.

[0192] An embodiment of the present application provides a remaining mileage prediction device, in which historical target parameters may include a historical number of passengers and a historical average slope. The remaining mileage prediction device may include a database construction module for constructing a database based on historical data of a target vehicle, and the database construction module may include: a first acquisition submodule, for acquiring historical data of the target vehicle and extracting a 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, slope calculation data and historical number of passengers corresponding to each high-speed slice from the historical data; a second determination submodule, for determining an average vehicle speed corresponding to each high-speed slice according to the vehicle speed calculation data corresponding to each high-speed slice, determining an MCU average energy consumption corresponding to each high-speed slice according to the MCU energy consumption calculation data corresponding to each high-speed slice, and determining an average slope corresponding to each high-speed slice according to the slope calculation data corresponding to each high-speed slice; and a construction submodule, for constructing a database according to the average vehicle speed, MCU average energy consumption, average slope and historical number of passengers corresponding to each high-speed slice.

[0193] The embodiment of the present application provides a remaining mileage prediction device, wherein the vehicle speed calculation data corresponding to each high-speed slice may include the initial stage mileage and the end stage mileage corresponding to each high-speed slice, the MCU energy consumption calculation data corresponding to each high-speed slice may include the MCU voltage and MCU current of each acquisition point in each high-speed slice, the initial stage mileage and the end stage mileage corresponding to each high-speed slice, and the slope data corresponding to each high-speed slice may include the slope of each acquisition point in each high-speed slice;

[0194] The second determining submodule may include: a first calculating unit, configured to use Calculate the average vehicle speed V corresponding to the i-th high-speed slice i ; Among them, S i末 is the end stage mileage corresponding to the ith high-speed slice, S i初 is the initial stage mileage corresponding to the ith high-speed slice, T h The duration in hours is obtained by converting the duration of the high-speed slice; the second calculation unit is used to use Calculate the average MCU energy consumption E corresponding to the i-th high-speed slice MCUi Among them, U n is the MCU voltage at the nth acquisition point in the ith high-speed slice, I n is the MCU current at the nth acquisition point in the ith high-speed slice, t n is the time of the nth acquisition point in the i-th high-speed slice, K1 is the conversion coefficient between the hour and the time unit of the acquisition point, K2 is the conversion coefficient between the kilowatt and the unit of MCU current*MCU voltage; the third calculation unit is used to use Calculate the average slope α corresponding to the i-th high-speed slice i ; Among them, α n is the slope of the nth acquisition point in the ith high-speed slice.

[0195] A remaining mileage prediction device provided by an embodiment of the present application may include a construction submodule: a grouping unit, which is used to group according to the historical number of passengers, the average slope, and the average vehicle speed; wherein at least one of the historical number of passengers, the average slope, and the average vehicle speed is different between different data groups; a first determination unit, which is used to determine the number of high-speed slices in each data group according to the historical number of passengers, the average slope, and the average vehicle speed in each data group; and a second determination unit, which is used to determine the average energy consumption of the MCU in each data group according to the number of high-speed slices in each data group and the average energy consumption of the MCU corresponding to the corresponding high-speed switching.

[0196] A remaining mileage prediction device provided by an embodiment of the present application may further include a construction submodule: a third determination unit, which is used to determine the average MCU energy consumption in the target data group according to the current MCU average energy 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 energy consumption corresponding to the newly added high-speed slices when a new high-speed slice is added and the new high-speed slice corresponds to a target data group in the database, and determine the number of high-speed slices in the target data group according to the current number of high-speed slices in the target data group and the number of newly added high-speed slices.

[0197] A remaining mileage prediction device provided by an embodiment of the present application, the database construction module may also include: a data cleaning sub-module, 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] An embodiment of the present application provides a remaining mileage prediction device, in which the acquisition module 51 may include: a third determination submodule, configured to determine an average vehicle speed corresponding to a planned route according to target parameters and a database.

[0199] A remaining mileage prediction device provided by an embodiment of the present application, the third determination submodule may include: an acquisition unit, used to obtain the average vehicle speeds and the number of highway slices corresponding to the target parameters from a database; a fourth determination unit, used to determine the average vehicle speed corresponding to the planned route based on the average vehicle speeds, the number of highway slices and the duration of the highway slices corresponding to the target parameters in the database.

[0200] A remaining mileage prediction device provided by an embodiment of the present application, the first determination module 52 may include: a second acquisition submodule, used to obtain the MCU average energy consumption corresponding to the average vehicle speed corresponding to the planned route from the database; a fourth determination submodule, used to determine the high-speed MCU average energy consumption corresponding to the planned route based on the corresponding MCU average energy consumption obtained from the database; a fifth determination submodule, used to determine 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 accessory power and the average vehicle speed corresponding to the planned route.

[0201] An embodiment of the present application provides a remaining mileage prediction device, and the acquisition module 51 may include: a third acquisition submodule, used to obtain the output voltage and output current of the battery in the target vehicle, and the MCU input voltage and input current; a sixth determination submodule, used to determine the accessory power based on the output voltage and output current of the battery, and the MCU input voltage and input current.

[0202] A remaining mileage prediction device provided by an embodiment of the present application, when including multiple planned routes, may further include: a third determination module, configured to determine the optimal high-speed energy consumption from the high-speed energy consumptions corresponding to the planned routes after determining the high-speed energy consumptions corresponding to the planned routes, and determine the planned route corresponding to the optimal high-speed energy consumption as the optimal route;

[0203] The second determination module 53 may include: a seventh determination submodule, configured to determine the optimal high-speed cruising range remaining mileage according to the power corresponding to the remaining SOC of the target vehicle and the optimal high-speed energy consumption.

[0204] In a remaining mileage prediction device provided in an embodiment of the present application, the second determination module 53 may also include: a fourth acquisition submodule, used to obtain the navigation route mileage, and determine whether the optimal high-speed cruising remaining mileage is less than the navigation route mileage; a sending submodule, used to obtain the charging pile position on the optimal route if the optimal high-speed cruising remaining mileage is less than the navigation route mileage, and send the optimal high-speed energy consumption, optimal route and charging pile position to the host of the target vehicle, which will be displayed and / or voice played by the host of the target vehicle.

[0205] The present application also provides a remaining mileage prediction device. Figure 6 , which shows a schematic diagram of the structure of a remaining mileage prediction device provided in an embodiment of the present application, which may include:

[0206] A memory 61, used for storing computer programs;

[0207] The processor 62, when used to execute the computer program stored in the memory 61, can implement the following steps:

[0208] Acquire real-time data of the target vehicle; the 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 according to 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 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 when the vehicle speed is greater than the high-speed driving speed threshold; determine the remaining mileage of the high-speed cruising range according to the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0209] The present application also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the following steps can be implemented:

[0210] Acquire real-time data of the target vehicle; the 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 historical data of the target vehicle, and accessory power; 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 when the vehicle speed is greater than the high-speed driving speed threshold; determine the remaining mileage of high-speed cruising based on the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

[0211] For the description of the relevant parts of a remaining mileage prediction device, equipment and readable storage medium provided in an embodiment of the present application, please refer to the detailed description of the corresponding parts of a remaining mileage prediction method provided in an embodiment of the present application, which 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 specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0213] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0214] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0215] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0216] In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0217] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for predicting remaining mileage, characterized in that: 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; Determine the high-speed energy consumption corresponding to the planned route according to 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 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 when the vehicle speed is greater than the high-speed driving speed threshold; The remaining mileage of high-speed cruising is determined based on the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

2. The remaining mileage prediction method according to claim 1, characterized in that: The real-time data also includes target parameters, the database includes historical target parameters, average vehicle speed, the number of high-speed slices and the relationship between the average energy consumption of the MCU, the target parameters include the number of passengers and / or the average slope of the planned route, and the historical target parameters include the historical number of passengers and / or the historical average slope; Determining the high-speed energy consumption corresponding to the planned route according to the average vehicle speed corresponding to the planned route, the database and the accessory power, including: The high-speed energy consumption corresponding to the planned route is determined according to the average vehicle speed corresponding to the planned route, the target parameter, the database and the accessory power.

3. The remaining mileage prediction method according to claim 2, characterized in that: The historical target parameters include the historical number of passengers and the historical average slope. A database is constructed based on the historical data of the target vehicle, including: Acquire historical data of the target vehicle, and extract the target time from the historical data; Slicing the target time to obtain high-speed slices, and extracting vehicle speed calculation data, MCU energy consumption calculation data, slope calculation data and historical passenger number corresponding to each high-speed slice from the historical data; Determine the average vehicle speed corresponding to each high-speed slice according to the vehicle speed calculation data corresponding to each high-speed slice, determine the average MCU energy consumption corresponding to each high-speed slice according to the MCU energy consumption calculation data corresponding to each high-speed slice, and determine the average slope corresponding to each high-speed slice according to the slope calculation data corresponding to each high-speed slice; The database is constructed according to the average vehicle speed, average MCU energy consumption, average slope and historical number of passengers corresponding to each high-speed slice.

4. The remaining mileage prediction method according to claim 3, characterized in that: The vehicle speed calculation data corresponding to each of the high-speed slices include the initial stage mileage and the end stage mileage corresponding to each of the high-speed slices, the MCU energy consumption calculation data corresponding to each of the high-speed slices include the MCU voltage and MCU current of each acquisition point in each of the high-speed slices, the initial stage mileage and the end stage mileage corresponding to each of the high-speed slices, and the slope data corresponding to each of the high-speed slices include the slope of each acquisition point in each of the high-speed slices; Determining the average vehicle speed corresponding to each of the high-speed slices according to the vehicle speed calculation data corresponding to each of the high-speed slices includes: use Calculate the average vehicle speed V corresponding to the i-th high-speed slice i ; Among them, S i末 is the end stage mileage corresponding to the ith high-speed slice, S i初 is the initial stage mileage corresponding to the ith high-speed slice, T h The duration in hours converted from the duration of high-speed slicing; Determining the average energy consumption of the MCU corresponding to each of the high-speed slices according to the MCU energy consumption calculation data corresponding to each of the high-speed slices includes: use Calculate the average MCU energy consumption E corresponding to the i-th high-speed slice MCUi Among them, U n is the MCU voltage at the nth acquisition point in the ith high-speed slice, I n is the MCU current at the nth acquisition point in the ith high-speed slice, t n is the time of the nth acquisition point in the ith high-speed slice, K1 is the conversion factor between hours and the time unit of the acquisition point, and K2 is the conversion factor between kilowatts and the units of MCU current*MCU voltage; Determining the average slope corresponding to each of the high-speed slices according to the slope calculation data corresponding to each of the high-speed slices includes: use Calculate the average slope α corresponding to the i-th high-speed slice i ; Among them, α n is the slope of the nth acquisition point in the ith high-speed slice.

5. The remaining mileage prediction method according to claim 3, characterized in that: The database is constructed according to the average vehicle speed, the average MCU energy consumption, the average slope and the number of historical passengers corresponding to each high-speed slice, including: Grouping is performed according to the historical number of passengers, the average slope, and the average vehicle speed; wherein at least one of the historical number of passengers, the average slope, and the average vehicle speed is different between different data groups; Determining the number of high-speed slices in each of the data groups according to the historical number of passengers, average slope and average vehicle speed in each of the data groups; The average energy consumption of the MCU in each of the data groups is determined according to the number of high-speed slices in each of the data groups and the average energy consumption of the MCU corresponding to the corresponding high-speed slices.

6. The remaining mileage prediction method according to claim 5, characterized in that: Also includes: When a new high-speed slice is added and the new high-speed slice corresponds to a target data group in the database, the average MCU energy consumption in the target data group is determined according to the current average MCU energy consumption in the target data group, the current number of high-speed slices, the number of new high-speed slices and the average MCU energy consumption corresponding to the new high-speed slice, and the number of high-speed slices in the target data group is determined according to the current number of high-speed slices in the target data group and the number of new high-speed slices.

7. The remaining mileage prediction method according to claim 3, characterized in that: 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, it also includes: The data extracted from the historical data is cleaned.

8. The remaining mileage prediction method according to any one of claims 2 to 7, 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 according to the target parameter and the database.

9. The remaining mileage prediction method according to claim 8, characterized in that: Determining an average vehicle speed corresponding to the planned route according to the target parameter and the database includes: Acquire each average vehicle speed and each high-speed slice number corresponding to the target parameter from the database; The average vehicle speed corresponding to the planned route is determined according to 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.

10. The remaining mileage prediction method according to any one of claims 1 to 7, characterized in that: Determining the high-speed energy consumption corresponding to the planned route according to the average vehicle speed corresponding to the planned route, the database and the accessory power, including: Obtaining from the database an average energy consumption of the MCU corresponding to an average vehicle speed corresponding to the planned route; Determine the high-speed MCU average energy consumption corresponding to the planned route according to the corresponding MCU average energy consumption obtained from the database; The high-speed energy consumption corresponding to the planned route is determined according to the high-speed MCU average energy consumption corresponding to the planned route, the accessory power and the average vehicle speed corresponding to the planned route.

11. The remaining mileage prediction method according to any one of claims 1 to 7, characterized in that: Get the target vehicle's accessory power, including: Obtaining the output voltage and output current of the battery in the target vehicle, and the input voltage and input current of the MCU; The accessory power is determined according to the output voltage and output current of the battery, and the input voltage and input current of the MCU.

12. The remaining mileage prediction method according to any one of claims 1 to 7, characterized in that: When multiple planned routes are included, after determining the high-speed energy consumption corresponding to the planned routes, the method further includes: Determine the optimal high-speed energy consumption from the high-speed energy consumptions corresponding to the planned routes, and determine the planned route corresponding to the optimal high-speed energy consumption as the optimal route; Determining the remaining high-speed cruising range according to the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route includes: The optimal high-speed cruising range remaining mileage is determined based on the power corresponding to the remaining SOC of the target vehicle and the optimal high-speed energy consumption.

13. The remaining mileage prediction method according to claim 12, characterized in that: Also includes: Obtaining the mileage of the navigation route, and determining whether the optimal high-speed cruising range remaining mileage is less than the mileage of the navigation route; If so, the location of the charging pile on the optimal route is obtained, and the optimal high-speed energy consumption, the optimal route and the location of the charging pile are sent to the host of the target vehicle, which is displayed and / or voice-played by the host of the target vehicle.

14. A remaining mileage prediction device, characterized in that: include: An acquisition module, used to acquire real-time data of the target vehicle; the real-time data includes accessory power and average vehicle speed corresponding to the planned route; A first determination module is used to determine the high-speed energy consumption corresponding to the planned route according to the average vehicle speed corresponding to the planned route, a database constructed based on the historical data of the target vehicle, and the accessory power; 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 when the vehicle speed in the historical data is greater than the high-speed driving speed threshold; The second determination module is used to determine the remaining high-speed cruising range according to the power corresponding to the remaining SOC of the target vehicle and the high-speed energy consumption corresponding to the planned route.

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

16. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the remaining process prediction method according to any one of claims 1 to 13 are implemented.

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