Vehicle mileage prediction method, vehicle mileage prediction device, vehicle and storage medium

Abstract

The present application provides a vehicle mileage prediction method, a vehicle mileage prediction device, a vehicle and a storage medium, and relates to the field of vehicle technology. The method includes: obtaining the current driving mode and historical power consumption data of the vehicle; based on the current driving mode, determining the first historical power consumption data corresponding to the current driving mode in the historical power consumption data; based on the first historical power consumption data, obtaining the predicted energy consumption of the vehicle in the current driving mode; based on the predicted energy consumption, predicting the vehicle's cruising range. Based on the above scheme, the accuracy of the vehicle's cruising range prediction can be improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle mileage prediction method, a vehicle mileage prediction device, a vehicle and a storage medium in the field of vehicle technology. Background Art

[0002] When using an electric vehicle, users will pay attention to whether the range of the electric vehicle can meet their travel needs, so as to avoid the travel being affected by the insufficient range of the electric vehicle. Therefore, the range of the electric vehicle is crucial to the user's travel.

[0003] In the related art, in order to predict the cruising range of an electric vehicle, the energy consumption of the electric vehicle can be predicted, so as to predict the cruising range of the electric vehicle through the energy consumption of the electric vehicle. However, when predicting the energy consumption of an electric vehicle, the historical average energy consumption of the electric vehicle is generally used to estimate the energy consumption, which cannot accurately reflect the current energy consumption of the electric vehicle, resulting in poor accuracy in the predicted current energy consumption of the electric vehicle, and further resulting in poor accuracy in the cruising range predicted by the current energy consumption of the electric vehicle, which affects the normal travel of users.

[0004] Therefore, how to improve the accuracy of vehicle range prediction is an urgent problem that needs to be solved. Summary of the invention

[0005] The present application provides a vehicle mileage prediction method, a vehicle mileage prediction device, a vehicle and a storage medium, and the method can improve the accuracy of the vehicle's cruising range prediction.

[0006] In a first aspect, the present application provides a vehicle mileage prediction method, the method comprising:

[0007] Obtain the current driving mode and historical power consumption data of the vehicle; based on the current driving mode, determine the first historical power consumption data corresponding to the current driving mode in the historical power consumption data; based on the first historical power consumption data, obtain the predicted energy consumption of the vehicle in the current driving mode; based on the predicted energy consumption, predict the vehicle's cruising range.

[0008] In an embodiment of the present application, when predicting the cruising range of a vehicle, the current driving mode of the vehicle can be obtained, and the historical power consumption data (i.e., the first historical power consumption data) corresponding to the current driving mode can be determined from the historical power consumption data of the vehicle obtained through the current driving mode of the vehicle. Afterwards, the energy consumption corresponding to the vehicle in the current driving mode is predicted by the determined first historical power consumption data, and finally the cruising range of the vehicle is predicted by the predicted vehicle energy consumption. Compared with the prior art, the energy consumption of the vehicle is estimated by using the historical average energy consumption of the vehicle, and then the cruising range of the vehicle is predicted by the estimated vehicle energy consumption, or the cruising range of the vehicle is predicted by using a fixed energy consumption, which leads to the problem of poor accuracy of the cruising range and mismatch with the current driving performance of the vehicle. The present application determines the first historical power consumption data corresponding to the current driving mode from the historical power consumption data of the vehicle obtained through the current driving mode of the vehicle, so that the vehicle energy consumption predicted by the first historical power consumption data matches the current driving performance of the vehicle, thereby improving the accuracy of the predicted vehicle energy consumption. Furthermore, on the basis of more accurate prediction of vehicle energy consumption, the vehicle range predicted by the predicted vehicle energy consumption can also be more accurate, thereby improving the accuracy of the prediction of vehicle range.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes:

[0010] Determine the energy consumption correction coefficient of the vehicle; the above-mentioned obtaining the predicted energy consumption of the vehicle in the current driving mode based on the first historical power consumption data includes: determining the historical driving energy consumption of the vehicle in the current driving mode based on the first historical power consumption data; correcting the historical driving energy consumption based on the energy consumption correction coefficient to obtain the first driving energy consumption; obtaining the predicted energy consumption based on the first driving energy consumption.

[0011] In the embodiment of the present application, when the historical driving energy consumption of the vehicle in the current driving mode is determined by the first historical power consumption data, the historical driving energy consumption of the vehicle in the current driving mode can be corrected by the energy consumption correction coefficient of the vehicle to obtain a more accurate corrected historical driving energy consumption (i.e., the first driving energy consumption), thereby making the vehicle energy consumption predicted based on the more accurate corrected historical driving energy consumption more accurate. Furthermore, on the basis of the more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0012] In combination with the first aspect and the foregoing implementations, in some implementations of the first aspect, the method further includes:

[0013] Obtain the historical mileage increment of the vehicle; determine the target weight based on the historical mileage increment; wherein the historical mileage increment is positively correlated with the target weight; the above-mentioned predicted energy consumption is obtained based on the first driving energy consumption, including: predicting the actual driving energy consumption of the vehicle in the current driving mode based on the historical mileage increment, the target weight and the first driving energy consumption; and obtaining the predicted energy consumption based on the actual driving energy consumption.

[0014] In the embodiment of the present application, by assigning corresponding weights to the historical mileage increments of the vehicle, the energy consumption corresponding to the vehicle in different driving stages can be better reflected, so that the actual driving energy consumption of the vehicle in the current driving mode predicted by the historical mileage increments of the vehicle, the weights corresponding to the historical mileage increments and the first driving energy consumption can be more accurate, thereby making the vehicle energy consumption predicted by the actual driving energy consumption more accurate. Furthermore, on the basis of more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0015] In combination with the first aspect and the foregoing implementations, in some implementations of the first aspect, the method further includes:

[0016] Obtain a historical average vehicle speed corresponding to the first historical power consumption data; based on the historical average vehicle speed, predict the non-driving energy consumption of the vehicle corresponding to the current driving mode; the above-mentioned based on the actual driving energy consumption, obtain the predicted energy consumption, including: adding the actual driving energy consumption to the non-driving energy consumption to obtain the first energy consumption; based on the first energy consumption, obtain the predicted energy consumption.

[0017] In the embodiment of the present application, based on the predicted actual driving energy consumption of the vehicle in the current driving mode, the non-driving energy consumption corresponding to the vehicle in the current driving mode is also introduced, taking into account the whole vehicle energy consumption (i.e., the first energy consumption) required by the vehicle in the current driving mode, so that the vehicle energy consumption predicted by the whole vehicle energy consumption is more accurate. Furthermore, based on the more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0018] In combination with the first aspect and the foregoing implementations, in some implementations of the first aspect, the method further includes:

[0019] Obtain an upper limit value and a lower limit value of energy consumption corresponding to a current driving mode; and obtain a second historical energy consumption corresponding to the vehicle in a first driving mode; wherein the energy consumption required for the vehicle in the first driving mode is greater than the energy consumption required in the current driving mode; based on the second historical energy consumption and a preset coefficient, determine a third historical energy consumption; the above-mentioned based on the first energy consumption, obtain predicted energy consumption, including: determining the maximum energy consumption among the third historical energy consumption, the lower limit value of energy consumption and the first energy consumption as the second energy consumption; determining the minimum energy consumption among the second energy consumption and the upper limit value of energy consumption as the predicted energy consumption.

[0020] In the embodiment of the present application, in order to avoid the predicted vehicle energy consumption being too high or too low, the predicted vehicle energy consumption can be limited by the upper and lower limits of the energy consumption corresponding to the current driving mode of the vehicle, and the energy consumption required by the vehicle in a non-current driving mode (i.e., the first driving mode), so that the predicted vehicle energy consumption is more in line with the current driving needs of the vehicle, and the accuracy of the predicted vehicle energy consumption is improved. Furthermore, on the basis of more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, thereby improving the accuracy of the vehicle's cruising range prediction.

[0021] In combination with the first aspect and the foregoing implementations, in some implementations of the first aspect, the method further includes:

[0022] Obtain the current state of the drive motor in the vehicle and the current vehicle speed; the above-mentioned determination of the energy consumption correction coefficient of the vehicle includes: if the current state indicates that the drive motor is driving, and the historical driving energy consumption is greater than or equal to the preset energy consumption, based on the current driving mode and the current vehicle speed, determining the first energy consumption correction coefficient; if the current state indicates that the drive motor is braking and recovering, and the historical driving energy consumption is less than the preset energy consumption, based on the current vehicle speed and the braking recovery power, determining the second energy consumption correction coefficient; wherein, the current vehicle speed is positively correlated with the first energy consumption correction coefficient; the current vehicle speed is negatively correlated with the second energy consumption correction coefficient; the braking recovery power is positively correlated with the second energy consumption correction coefficient.

[0023] In the embodiment of the present application, when the state of the driving motor in the vehicle indicates that the driving motor is driving and the historical driving energy consumption is greater than or equal to the preset energy consumption, the energy consumption correction coefficient can be determined by the current driving mode and current speed of the vehicle, so that the energy consumption correction coefficient is more in line with the current driving needs of the vehicle, thereby making the historical driving energy consumption corrected based on the energy consumption correction coefficient more accurate, and making the vehicle energy consumption predicted based on the more accurate corrected historical driving energy consumption more accurate. Furthermore, on the basis of more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0024] Alternatively, when the state of the driving motor in the vehicle indicates that the driving motor is braking and recovering, and the historical driving energy consumption is less than the preset energy consumption, the energy consumption correction coefficient can be determined by the current vehicle speed and the braking recovery power, so that the energy consumption correction coefficient is more in line with the current driving needs of the vehicle, thereby making the historical driving energy consumption corrected based on the energy consumption correction coefficient more accurate, and making the vehicle energy consumption predicted based on the more accurate corrected historical driving energy consumption more accurate. Furthermore, on the basis of more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0025] In combination with the first aspect and the above implementation manner, in some implementation manners of the first aspect, determining the first historical power consumption data corresponding to the current driving mode in the historical power consumption data based on the current driving mode includes:

[0026] Obtain the vehicle speed range and driving torque range corresponding to the current driving mode; determine the historical power consumption data corresponding to the historical vehicle speed being within the vehicle speed range and the historical driving of the vehicle being within the driving torque range as the first historical power consumption data.

[0027] In the embodiment of the present application, in the vehicle speed range and driving torque range corresponding to the current driving mode of the vehicle, the historical power consumption data corresponding to the historical speed of the vehicle being in the vehicle speed range and the historical driving of the vehicle being in the driving torque range is determined as the first historical power consumption data, so that the correspondence between the first historical power consumption data and the current driving mode is improved, and the determined first historical power consumption data is more accurate. Furthermore, on the basis that the first historical power consumption data is more accurate, the energy consumption of the vehicle predicted by the first historical power consumption data is also more accurate, so that on the basis that the predicted energy consumption of the vehicle is more accurate, the cruising range of the vehicle predicted by the predicted energy consumption of the vehicle can also be more accurate, thereby improving the accuracy of the prediction of the cruising range of the vehicle.

[0028] In a second aspect, the present application provides a vehicle mileage prediction device, the device comprising:

[0029] An acquisition module, used to obtain the current driving mode and historical power consumption data of the vehicle;

[0030] A determination module, configured to determine, based on the current driving mode, first historical power consumption data corresponding to the current driving mode in the historical power consumption data;

[0031] A processing module, configured to obtain a predicted energy consumption of the vehicle in a current driving mode based on the first historical power consumption data;

[0032] The prediction module is used to predict the vehicle's range based on the predicted energy consumption.

[0033] In a third aspect, the present application provides a vehicle, comprising a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the vehicle executes the method in the first aspect or any possible implementation of the first aspect.

[0034] In a fourth aspect, the present application provides a computer program product, which includes: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0035] In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of a scenario of vehicle energy consumption prediction in related technology.

[0037] Figure 2 It is a flow chart of a vehicle mileage prediction method provided in an embodiment of the present application.

[0038] Figure 3 It is a schematic diagram of data screening provided in an embodiment of the present application.

[0039] Figure 4 It is a structural schematic diagram of a vehicle mileage prediction device provided in an embodiment of the present application.

[0040] Figure 5 It is a schematic diagram of the structure of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The technical solution in the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0042] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0043] Figure 1 It is a schematic diagram of a scenario of vehicle energy consumption prediction in related technology.

[0044] For example, Figure 1 As shown, Figure 1 The vehicle 110 and the instrument panel 120 are included. The power source of the vehicle 110 includes a battery, and the instrument panel 120 is used to display the cruising range and energy consumption of the vehicle 110.

[0045] When the power source of the vehicle 110 includes a battery, the cruising range of the vehicle 110 is very important for the user's travel. In order to avoid the insufficient cruising range of the vehicle 110 affecting the user's travel, the cruising range of the vehicle 110 can be predicted and the predicted cruising range can be displayed on the instrument screen 120, so that the user can understand the cruising range of the vehicle 110 and arrange travel plans reasonably.

[0046] When predicting the cruising range of the vehicle 110 , the energy consumption of the vehicle 110 may be predicted first, and then the cruising range of the vehicle 110 may be predicted based on the predicted energy consumption of the vehicle 110 .

[0047] In the related art, the cruising range of vehicle 110 can be predicted by the fixed energy consumption of vehicle 110, or the energy consumption of vehicle 110 can be predicted by using the historical average energy consumption of vehicle 110, or the energy consumption required for a driving mode can be predicted separately. However, there may be problems such as fewer driving mode learning scenarios and insufficient samples. These methods cannot accurately predict the current energy consumption of vehicle 110, resulting in poor accuracy in the predicted current energy consumption of vehicle 110, and further resulting in poor accuracy in predicting the cruising range of vehicle 110 through the predicted energy consumption of vehicle 110, affecting the normal travel of users.

[0048] Therefore, in order to solve the problem of poor accuracy in predicting a vehicle's cruising range, the present application proposes a vehicle mileage prediction method, a vehicle mileage prediction device, a vehicle, and a storage medium.

[0049] Combine the following Figures 2 to 3 The vehicle mileage prediction method provided in the embodiment of the present application is described in detail.

[0050] Figure 2 is a flow chart of a vehicle mileage prediction method provided by an embodiment of the present application. The method can be Figure 1The vehicle 110 in the vehicle 110 is executed, or the vehicle control unit (VCU) in the vehicle 110 is executed.

[0051] For example, Figure 2 As shown, the method 200 includes S210-S240:

[0052] S210, obtaining the current driving mode and historical power consumption data of the vehicle.

[0053] For example, each time the vehicle is powered on, the driving mode used by the vehicle can be obtained, and the driving data of the vehicle in the driving mode can be collected, and the collected driving data can be stored in a corresponding storage unit on the vehicle, or the collected driving data can be uploaded to an associated server for cloud storage.

[0054] The driving data of the vehicle may include but is not limited to vehicle speed, mileage, power consumption data, gear position, driving torque, steering torque, braking force, air conditioning temperature and window opening. And, the associated server may represent a server that can mutually authenticate with the vehicle to store the driving data of the vehicle in the cloud.

[0055] For example, when the driving mode used by the vehicle is the Normal Economic Driving Mode (ECO), the vehicle speed collected under ECO is 100 km / h. There is a time correspondence between the vehicle's driving mode and the collected driving data of the vehicle in the driving mode, that is, the timestamp information between the vehicle's driving mode and the collected driving data of the vehicle in the driving mode is the same.

[0056] Exemplarily, when the vehicle is powered on, the driving mode currently used by the vehicle after this power-on (which may be referred to as the "current driving mode") can be obtained, and the driving data pre-stored by the vehicle before this power-on (which may be referred to as "historical driving data") can be obtained, for example, historical power consumption data.

[0057] The historical power consumption data may include but are not limited to historical driving power consumption data and historical auxiliary power consumption data. In addition, the auxiliary components in the vehicle may include but are not limited to voltage converters (DC-DC Converter, DCDC), air conditioning systems (Air Conditioning, A / C), positive temperature coefficient heaters (Positive Temperature Coefficient Heater, PTC) and other electrical components.

[0058] S220 , based on the current driving mode, determining first historical power consumption data corresponding to the current driving mode in the historical power consumption data.

[0059] The driving modes of the vehicle may include but are not limited to Power Saving Mode, ECO, Normal Mode, Sport Mode, Snow Mode, Off-Road Mode, Tow Mode and Track Mode. For ease of description, the Power Saving Mode is described as the current driving mode of the vehicle.

[0060] Optionally, in power saving mode, accessories in the vehicle are turned off to prevent them from consuming electricity; and the vehicle's braking recovery (also called "regeneration recovery") intensity can be automatically adjusted to "strong" to improve the vehicle's energy recovery capability and minimize the vehicle's electrical energy consumption.

[0061] Also, when the battery power of the vehicle is less than or equal to the preset power (for example, 20% or 30%), the power saving mode can be automatically or manually turned on by the user. In the power saving mode, the vehicle's overall power consumption can be reduced by limiting the vehicle's driving torque, vehicle speed, auxiliary power, adjusting the braking recovery intensity to "strong", turning off the intelligent driving assistance system, etc., so as to increase the vehicle's cruising range.

[0062] It should be understood that the preset power level is related to factors such as the vehicle model, vehicle load, and the environment in which the vehicle is located, and the embodiments of the present application are not limited to this.

[0063] Exemplarily, when the current driving mode of the vehicle (e.g., power saving mode) is obtained, the historical power consumption data corresponding to the current driving mode of the vehicle (which may be referred to as "first historical power consumption data") may be determined from the historical power consumption data obtained through the current driving mode, so as to learn and update the vehicle energy consumption of the current driving mode of the vehicle through the first historical power consumption data, thereby improving the accuracy of the vehicle's range prediction in the current driving mode. Furthermore, since the first historical power consumption data is collected during the historical driving process of the vehicle, it is not limited to the historical power consumption data corresponding to the same historical driving mode as the current driving mode of the vehicle, that is, the first historical power consumption data is collected during the daily driving of the vehicle, which can be more in line with the user's driving habits, and thus the vehicle energy consumption predicted based on the first historical power consumption data can also be more in line with the user's driving habits.

[0064] For example, when the current driving mode of the vehicle is the power saving mode, first historical power consumption data corresponding to the power saving mode may be determined from the historical power consumption data.

[0065] In one possible implementation, the above-mentioned first historical power consumption data corresponding to the current driving mode is determined in the historical power consumption data based on the current driving mode, including: obtaining the vehicle speed range and driving torque range corresponding to the current driving mode; and determining the historical power consumption data corresponding to the vehicle's historical speed being within the vehicle speed range and the vehicle's historical drive being within the driving torque range as the first historical power consumption data.

[0066] For example, when the current driving mode of the vehicle is obtained, the vehicle speed range and driving torque range after the current driving mode is limited can be determined. The driving torque range can be achieved by adding a driving torque limit parameter (which can be recorded as "Fac_TqDmdlim") to the vehicle's driving torque request (which can be recorded as "T_VehDmd"). In addition, the vehicle speed range and / or driving torque range corresponding to different current driving modes may be different or the same, which is not limited in the embodiment of the present application.

[0067] For example, when the current driving mode of the vehicle is the power saving mode, the corresponding vehicle speed range is (1km / h, 91km / h), and the driving torque range is (-300Nm, 300Nm).

[0068] For another example, when the current driving mode of the vehicle is track mode, the corresponding vehicle speed range is (1km / h, 60km / h), and the driving torque range is (-600Nm, 600Nm). The driving torque range is (-torque limit corresponding to the current driving mode, torque limit corresponding to the current driving mode). For example, (-300Nm, 300Nm) or (-600Nm, 600Nm).

[0069] Furthermore, a historical vehicle speed 1 and a historical driving torque 1 with the same timestamp information can be selected from the pre-stored historical driving data, and it can be determined whether the historical vehicle speed 1 is within the vehicle speed range (for example, (1 km / h, 91 km / h)), and whether the historical driving torque 1 is within the driving torque range (for example, (-300 Nm, 300 Nm)).

[0070] When the historical vehicle speed 1 is at (1km / h, 91km / h) and the historical driving torque 1 is at (-300Nm, 300Nm), it means that the historical power consumption data with the same timestamp as the historical vehicle speed 1 and / or the historical driving torque 1 meets the power consumption requirements of the current driving mode (e.g., power saving mode) of the vehicle, and the historical power consumption data 1 with the same timestamp as the historical vehicle speed 1 and / or the historical driving torque 1 can be determined as the first historical power consumption data corresponding to the current driving mode (e.g., power saving mode) of the vehicle. It should be understood that the historical vehicle speed 1 and the historical driving torque 1 may not be driving data in the power saving mode, but driving data corresponding to the vehicle in the trailer mode. However, since the historical vehicle speed 1 is at (1km / h, 91km / h) and the historical driving torque 1 is at (-300Nm, 300Nm), it can be explained that the power consumption requirements corresponding to the vehicle in the trailer mode at this time are similar to the power consumption requirements corresponding to the vehicle in the power saving mode, so the historical power consumption data corresponding to the vehicle in the trailer mode at this time can be determined as the historical power consumption data corresponding to the vehicle in the power saving mode.

[0071] Optionally, when the vehicle's historical driving mode (e.g., historical power saving mode) is the same as the current driving mode (e.g., current power saving mode), the historical power consumption data in the historical power saving mode can be directly determined as the first historical power consumption data corresponding to the vehicle in the current power saving mode.

[0072] Alternatively, when the historical vehicle speed 1 is not at (1km / h, 91km / h), and / or the historical driving torque 1 is not at (-300Nm, 300Nm), it means that the historical power consumption data with the same timestamp as the historical vehicle speed 1 and / or the historical driving torque 1 does not meet the power consumption requirements of the vehicle's current driving mode (e.g., power saving mode), and the historical power consumption data 1 with the same timestamp as the historical vehicle speed 1 and / or the historical driving torque 1 is not determined as the first historical power consumption data corresponding to the vehicle's current driving mode (e.g., power saving mode).

[0073] It should be noted that the vehicle speed range and the driving torque range are related to factors such as the vehicle model, road conditions and regulations, and the embodiments of the present application do not limit this.

[0074] Figure 3 It is a schematic diagram of data screening provided in an embodiment of the present application.

[0075] For example, Figure 3 As shown, Figure 3 The example shows the filtering of the power saving mode data when the current driving mode of the vehicle is the power saving mode. Figure 3The horizontal axis in represents the historical vehicle speed, and the vertical axis represents the historical driving torque. The data in area 310 is screened out by the vehicle speed limit corresponding to the historical vehicle speed and the power saving mode, and the driving torque limit corresponding to the historical driving torque and the power saving mode. Then, the historical power consumption data (i.e., the first historical power consumption data) with the same timestamp is determined by the timestamps of the historical vehicle speed and the historical driving torque in area 310.

[0076] In the embodiment of the present application, in the vehicle speed range and driving torque range corresponding to the current driving mode of the vehicle, the historical power consumption data corresponding to the historical speed of the vehicle being in the vehicle speed range and the historical driving of the vehicle being in the driving torque range is determined as the first historical power consumption data, so that the correspondence between the first historical power consumption data and the current driving mode is improved, and the determined first historical power consumption data is more accurate. Furthermore, on the basis that the first historical power consumption data is more accurate, the energy consumption of the vehicle predicted by the first historical power consumption data is also more accurate, so that on the basis that the predicted energy consumption of the vehicle is more accurate, the cruising range of the vehicle predicted by the predicted energy consumption of the vehicle can also be more accurate, thereby improving the accuracy of the prediction of the cruising range of the vehicle.

[0077] S230: Obtain predicted energy consumption of the vehicle in the current driving mode based on the first historical power consumption data.

[0078] Exemplarily, when the first historical power consumption data corresponding to the current driving mode of the vehicle (for example, the power saving mode) is determined, the energy consumption required for the vehicle to operate in the power saving mode after being powered on this time can be predicted using the first historical power consumption data to obtain the predicted energy consumption (which may be referred to as "predicted energy consumption") in kw.

[0079] In one possible implementation, an energy consumption correction coefficient of the vehicle is determined; the above-mentioned method of obtaining the predicted energy consumption of the vehicle in the current driving mode based on the first historical power consumption data includes: determining the historical driving energy consumption of the vehicle in the current driving mode based on the first historical power consumption data; correcting the historical driving energy consumption based on the energy consumption correction coefficient to obtain the first driving energy consumption; and obtaining the predicted energy consumption based on the first driving energy consumption.

[0080] For example, when the first historical power consumption data corresponding to the vehicle in the current driving mode (for example, the power saving mode) is determined, the historical driving energy consumption (which can be recorded as “E InsTrac ”, also known as “instantaneous driving energy consumption”), unit is kWh.

[0081] Optionally, the historical power consumption data (e.g., the historical discharge power of the battery in the vehicle) may be affected by the operation of accessories in addition to the vehicle driving. Therefore, in order to obtain the historical driving energy consumption of the vehicle in the power saving mode, the influence of the operation of accessories on the energy consumption needs to be removed.

[0082] Among them, E InsTrac =Historical discharge power - auxiliary power = historical current on the high-voltage side of the battery × historical voltage of the battery - auxiliary power.

[0083] Optionally, the auxiliary power may include but is not limited to DCDC power, A / C power and PTC power. Auxiliary power=DCDC power+A / C power+PTC power+…

[0084] Furthermore, based on E InsTrac The size of the E and the state of the drive motor in the vehicle can determine InsTrac Make corrections. InsTrac The size of E is determined by the state of the drive motor in the vehicle. InsTrac The correction factor of E InsTrac Corrected, the corrected E InsTrac (It can be called "first driving energy consumption", recorded as "E InsTracCorr ”), unit is kWh.

[0085] Optionally, the current state of the drive motor in the vehicle and the current vehicle speed are obtained; the above-mentioned determination of the energy consumption correction coefficient of the vehicle includes: if the current state indicates that the drive motor is driving, and the historical driving energy consumption is greater than or equal to the preset energy consumption, based on the current driving mode and the current vehicle speed, a first energy consumption correction coefficient is determined; if the current state indicates that the drive motor is braking and recovering, and the historical driving energy consumption is less than the preset energy consumption, based on the current vehicle speed and the braking recovery power, a second energy consumption correction coefficient is determined; wherein, the current vehicle speed is positively correlated with the first energy consumption correction coefficient; the current vehicle speed is negatively correlated with the second energy consumption correction coefficient; and the braking recovery power is positively correlated with the second energy consumption correction coefficient.

[0086] For example, when determining the energy consumption correction coefficient, the current state of the drive motor in the vehicle can be obtained first to determine whether the current state of the drive motor indicates that the drive motor is driving (which can be called "driving state") or whether the current state of the drive motor indicates that the drive motor is braking and recovering (which can be called "braking and recovering state"). And, determine E InsTrac Whether it is greater than or equal to the preset energy consumption (for example, 0).

[0087] In E InsTrac≥0, and the current state of the drive motor is the driving state, the drive energy consumption correction factor (which may be referred to as the "first energy consumption correction factor", denoted as "R TracDrvMod ”). By formula (1), E InsTracCorr To explain:

[0088]

[0089] Among them, R TracDrvMod It can be obtained by looking up the vehicle's current driving mode and current speed in a pre-calibrated mapping relationship (e.g., Map1). And, R TracDrvMod Positively correlated with the vehicle's current speed.

[0090] In E InsTrac <0, and the current state of the drive motor is the braking recovery state, the recovery energy consumption correction coefficient (which can be called the "second energy consumption correction coefficient", recorded as "R RecupCorr ”). By formula (2), E InsTracCorr To explain:

[0091]

[0092] Among them, R RecupCorr It can be obtained by looking up the vehicle's current braking recovery power and current vehicle speed in a pre-calibrated mapping relationship (e.g., Map2). And, R RecupCorr Negatively correlated with the current speed of the vehicle, R RecupCorr Positively correlated with braking recovery power.

[0093] Among them, the braking recovery power is positively correlated with the braking recovery level. RecupCorr Based on the positive correlation with the braking recovery power, R RecupCorr Positively correlated with the braking recovery level.

[0094] Optionally, one or more of the road type, road flatness, adhesion coefficient, weather conditions, vehicle load, etc. on which the vehicle is located may affect R TracDrvMod With R RecupCorr .

[0095] In the embodiment of the present application, when the state of the driving motor in the vehicle indicates that the driving motor is driving and the historical driving energy consumption is greater than or equal to the preset energy consumption, the energy consumption correction coefficient can be determined by the current driving mode and current speed of the vehicle, so that the energy consumption correction coefficient is more in line with the current driving needs of the vehicle, thereby making the historical driving energy consumption corrected based on the energy consumption correction coefficient more accurate, and making the vehicle energy consumption predicted based on the more accurate corrected historical driving energy consumption more accurate. Furthermore, on the basis of more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0096] Alternatively, when the state of the driving motor in the vehicle indicates that the driving motor is braking and recovering, and the historical driving energy consumption is less than the preset energy consumption, the energy consumption correction coefficient can be determined by the current vehicle speed and the braking recovery power, so that the energy consumption correction coefficient is more in line with the current driving needs of the vehicle, thereby making the historical driving energy consumption corrected based on the energy consumption correction coefficient more accurate, and making the vehicle energy consumption predicted based on the more accurate corrected historical driving energy consumption more accurate. Furthermore, on the basis of more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0097] Furthermore, after obtaining E InsTracCorr When E InsTracCorr The above predicted energy consumption is obtained.

[0098] In the embodiment of the present application, when the historical driving energy consumption of the vehicle in the current driving mode is determined by the first historical power consumption data, the historical driving energy consumption of the vehicle in the current driving mode can be corrected by the energy consumption correction coefficient of the vehicle to obtain a more accurate corrected historical driving energy consumption (i.e., the first driving energy consumption), thereby making the vehicle energy consumption predicted based on the more accurate corrected historical driving energy consumption more accurate. Furthermore, on the basis of the more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0099] In one possible implementation, the historical mileage increment of the vehicle is obtained; based on the historical mileage increment, a target weight is determined; wherein the historical mileage increment is positively correlated with the target weight; the above-mentioned predicted energy consumption is obtained based on the first driving energy consumption, including: based on the historical mileage increment, the target weight and the first driving energy consumption, predicting the actual driving energy consumption of the vehicle in the current driving mode; and obtaining the predicted energy consumption based on the actual driving energy consumption.

[0100] For example, the historical mileage increment with the same timestamp as the first historical power consumption data can be obtained, and then the corresponding target weight can be determined by the obtained historical mileage increment. InsTracCorr The actual driving energy consumption (which can be recorded as “E Trac ”) to make predictions.

[0101] Optionally, E can be calculated by Kalman filtering or piecewise weighting method Trac (Unit: kWh), and the segmented weight method is used as an example. Trac To explain:

[0102]

[0103] In formula (3), represents the mileage of energy consumption learning, in km; i represents the number of mileage segments in energy consumption learning, that is, Divide into i segments, for example, i=20; represents the weighting factor applicable to each incremental mileage segment (i.e. the target weight mentioned above), which should increase linearly from 0.2 (i=1) to 1 (i=20); Represents the mileage corresponding to each segment (i.e. the above historical mileage increment), that is .in, and i are calibration quantities, which are not limited in the present embodiment.

[0104] It should be noted that the calculation under different driving modes The methods may be the same or different, and the embodiments of the present application are not limited to this.

[0105] Furthermore, when calculating E Trac When E Trac Predict the energy consumption above.

[0106] In the embodiment of the present application, by assigning corresponding weights to the historical mileage increments of the vehicle, the energy consumption corresponding to the vehicle in different driving stages can be better reflected, so that the actual driving energy consumption of the vehicle in the current driving mode predicted by the historical mileage increments of the vehicle, the weights corresponding to the historical mileage increments and the first driving energy consumption can be more accurate, thereby making the vehicle energy consumption predicted by the actual driving energy consumption more accurate. Furthermore, on the basis of more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0107] Optionally, obtain the historical average vehicle speed corresponding to the first historical power consumption data; based on the historical average vehicle speed, predict the non-driving energy consumption of the vehicle corresponding to the current driving mode; the above-mentioned predicted energy consumption is obtained based on the actual driving energy consumption, including: adding the actual driving energy consumption to the non-driving energy consumption to obtain the first energy consumption; based on the first energy consumption, obtain the predicted energy consumption.

[0108] For example, when the first historical power consumption data corresponding to the current driving mode of the vehicle is obtained, the historical vehicle speed with the same timestamp as the first historical power consumption data can be obtained, and the historical vehicle speed is averaged to obtain the averaged historical vehicle speed (which can be called "historical average vehicle speed", denoted as "V Avg ”). Among them, V can be calculated by Kalman filtering or piecewise weighting method. Avg .

[0109] Assume that the auxiliary components that consume energy in the current driving mode of the vehicle are DCDC, A / C and PTC, and the corresponding auxiliary power (which can be recorded as "P Aux ”) is the sum of DCDC power, A / C power and PTC power. That is, P Aux =P DCDC +P ACSys +P PTC .

[0110] Optionally, since the vehicle can prohibit the opening of A / C power and PTC power in order to reduce energy consumption in the power saving mode, when the current driving mode of the vehicle is the power saving mode, P Aux =P DCDC .

[0111] Furthermore, the auxiliary power and V Avg After dimension conversion, we can get the auxiliary energy consumption (which can be called "non-driving energy consumption", recorded as "E Aux ”), unit is kWh / 100km. It is explained by formula (4):

[0112]

[0113] For example, when calculating E Aux With E Trac When, through E Aux With E Trac By adding them together, we can get the actual total energy consumption of the vehicle in the current driving mode (which can be called the "first energy consumption", recorded as "E PwrSave1 ”), that is, E PwrSave1 =E Aux +E Trac Then through E PwrSave1 Predict the energy consumption above.

[0114] In the embodiment of the present application, based on the predicted actual driving energy consumption of the vehicle in the current driving mode, the non-driving energy consumption corresponding to the vehicle in the current driving mode is also introduced, taking into account the whole vehicle energy consumption (i.e., the first energy consumption) required by the vehicle in the current driving mode, so that the vehicle energy consumption predicted by the whole vehicle energy consumption is more accurate. Furthermore, based on the more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, further improving the accuracy of the vehicle's cruising range prediction.

[0115] Optionally, an upper limit value and a lower limit value of energy consumption corresponding to the current driving mode are obtained; and a second historical energy consumption corresponding to the vehicle in the first driving mode is obtained; wherein the energy consumption required for the vehicle in the first driving mode is greater than the energy consumption required in the current driving mode; based on the second historical energy consumption and a preset coefficient, a third historical energy consumption is determined; the above-mentioned predicted energy consumption is obtained based on the first energy consumption, including: determining the maximum energy consumption among the third historical energy consumption, the lower limit value of energy consumption and the first energy consumption as the second energy consumption; and determining the minimum energy consumption among the second energy consumption and the upper limit value of energy consumption as the predicted energy consumption.

[0116] For example, if the vehicle is always driven vigorously during the historical driving process of the vehicle, that is, the historical vehicle speed 2 corresponding to the same timestamp is not at (1km / h, 91km / h), and / or the historical driving torque 2 is not at (-300Nm, 300Nm), it may result in less first historical energy consumption data that matches the current driving mode of the vehicle, making the predicted energy consumption corresponding to the current driving mode of the vehicle too small, affecting the accuracy of the predicted energy consumption. Therefore, in order to avoid the predicted energy consumption corresponding to the current driving mode of the vehicle being too large or too small, E PwrSave1 Make restrictions.

[0117] For example, in the case of E PwrSave1 When the vehicle is restricted, the energy consumption upper limit corresponding to the current driving mode of the vehicle can be obtained (which can be recorded as "E PwrSaveMax ”) and the lower limit of energy consumption (which can be recorded as “E PwrSaveMin ”); and the energy consumption required by the vehicle in a non-current driving mode (which may be referred to as the “first driving mode”) (which may be referred to as the “second historical energy consumption”, recorded as “E ECO ”). PwrSaveMax 、E PwrSaveMin 、E ECO The unit is kWh.

[0118] Among them, E PwrSaveMax >E PwrSaveMin And, the energy consumption required by the vehicle in a non-current driving mode is greater than the energy consumption required in the current driving mode. For example, the energy consumption required by the vehicle in ECO is greater than the energy consumption required by the vehicle in the power saving mode.

[0119] Furthermore, after obtaining E ECO When E ECO Adaptively reduce so that the reduced E ECO (It can be called the “third historical energy consumption”, denoted as “E ECO1 ”) to better understand the vehicle energy consumption corresponding to the current driving mode of the vehicle. For example, E ECO Multiplying by the set coefficient (0,1) (which can be called the "prediction coefficient"), we get E ECO1 That is, E ECO1 =E ECO The setting coefficient can be obtained through actual vehicle calibration, and the embodiment of the present application does not limit this.

[0120] Optionally, one can determine E ECO1 、E PwrSaveMin 、E PwrSave1 The maximum value among them is determined as the energy consumption to be determined (which can be called "second energy consumption"). Then determine the maximum value and E PwrSaveMax The minimum value is determined as the above predicted energy consumption.

[0121] For example, E ECO1 、E PwrSaveMin 、E PwrSave1 The maximum value in is E PwrSave1 , we can determine E PwrSave1 With E PwrSaveMax The minimum value in E PwrSave1 With E PwrSaveMax The minimum value in is E PwrSave1 When E PwrSave1 Determine the predicted energy consumption as above.

[0122] For example, E ECO1 E PwrSaveMin 、E PwrSave1 The maximum value in is E ECO1 , we can determine E ECO1 With E PwrSaveMax The minimum value in E ECO1 With E PwrSaveMax The minimum value in is E PwrSaveMax When E PwrSaveMax Determine the predicted energy consumption as above.

[0123] For example, the equation (5) can be used to describe the relationship between E PwrSave1 The limiting process:

[0124]

[0125] In formula (5), n can represent the limit ratio. And n can be expressed by EECO With E PwrSave1 The difference between them can be obtained by looking up the table, or it can be set to a fixed value, for example, 20%-40%.

[0126] In the embodiment of the present application, in order to avoid the predicted vehicle energy consumption being too high or too low, the predicted vehicle energy consumption can be limited by the upper and lower limits of the energy consumption corresponding to the current driving mode of the vehicle, and the energy consumption required by the vehicle in a non-current driving mode (i.e., the first driving mode), so that the predicted vehicle energy consumption is more in line with the current driving needs of the vehicle, and the accuracy of the predicted vehicle energy consumption is improved. Furthermore, on the basis of more accurate predicted vehicle energy consumption, the vehicle's cruising range predicted by the predicted vehicle energy consumption can also be more accurate, thereby improving the accuracy of the vehicle's cruising range prediction.

[0127] Optionally, when the navigation system of the vehicle is turned on, the navigation information of the vehicle can also be obtained, and the E PwrSave1 Make restrictions.

[0128] For example, the energy consumption 1 (which can be recorded as "E1") required for the vehicle to travel the road section can be predicted based on the distance and slope in the vehicle's navigation information, in kWh. When E1 is predicted, E1 and E PwrSaveMin 、E PwrSave1 The maximum value in E1, E PwrSaveMin 、E PwrSave1 The maximum value in is E PwrSave1 When E PwrSave1 With E PwrSaveMax The minimum value in E PwrSave1 With E PwrSaveMax The minimum value in is E PwrSave1 When E PwrSave1 Determine the predicted energy consumption as above.

[0129] Or, in E1, E PwrSaveMin 、E PwrSave1 When the maximum value in is E1, it can be determined that E1 and E PwrSaveMax The minimum value in E1 and E PwrSaveMax When the minimum value in is E1, E1 can be determined as the above predicted energy consumption.

[0130] For example, the equation (6) can be used to describe the relationship between E PwrSave1 The limiting process:

[0131]

[0132] Alternatively, first limit E1, multiply E1 by the set coefficient m (for example, 0.85), and obtain the limited E1 (which can be recorded as "E12 ”).

[0133] For example, in E 12 、E PwrSaveMin 、E PwrSave1 The maximum value in is E PwrSave1 When E PwrSave1 With E PwrSaveMax The minimum value in E PwrSave1 With E PwrSaveMax The minimum value in is E PwrSave1 When E PwrSave1 Determine the predicted energy consumption as above.

[0134] For example, in E 12 、E PwrSaveMin 、E PwrSave1 The maximum value in is E 12 When E 12 With E PwrSaveMax The minimum value in E 12 With E PwrSaveMax The minimum value in is E 12 When E 12 Determine the predicted energy consumption as above.

[0135] For example, the equation (7) can be used to describe the relationship between E PwrSave1 The limiting process:

[0136]

[0137] In formula (7), m can represent the limit ratio. Also, m can be expressed by E1 and E PwrSave1 The difference between them can be obtained by looking up the table, or it can be set to a fixed value, for example, 20%-40%.

[0138] S240, predicting a cruising range of the vehicle based on the predicted energy consumption.

[0139] For example, when the predicted energy consumption corresponding to the vehicle's current driving mode is determined, the vehicle's displayed mileage (which may be called "range") can be predicted based on the predicted energy consumption, and the predicted range can be displayed on the dashboard or display screen in the vehicle, so that users can understand the vehicle's range in a timely manner and plan travel plans reasonably.

[0140] Optionally, when predicting the range of the vehicle through the predicted energy consumption of the vehicle, the vehicle's range can also be predicted in combination with parameters such as the user's driving habits, road type, weather conditions and vehicle load, thereby improving the accuracy of the vehicle's range prediction.

[0141] In such Figure 2In the method 200 shown, when predicting the cruising range of the vehicle, the current driving mode of the vehicle can be obtained, and the historical power consumption data (i.e., the first historical power consumption data) corresponding to the current driving mode can be determined from the historical power consumption data of the vehicle obtained through the current driving mode of the vehicle. Afterwards, the energy consumption corresponding to the vehicle in the current driving mode is predicted by the determined first historical power consumption data, and finally the cruising range of the vehicle is predicted by the predicted vehicle energy consumption. Compared with the prior art, the energy consumption of the vehicle is estimated by using the historical average energy consumption of the vehicle, and then the cruising range of the vehicle is predicted by the estimated vehicle energy consumption, or the cruising range of the vehicle is predicted by using a fixed energy consumption, which leads to the problem of poor accuracy of the cruising range and mismatch with the current driving performance of the vehicle. The present application determines the first historical power consumption data corresponding to the current driving mode from the historical power consumption data of the vehicle obtained through the current driving mode of the vehicle, so that the vehicle energy consumption predicted by the first historical power consumption data matches the current driving performance of the vehicle, thereby improving the accuracy of the predicted vehicle energy consumption. Furthermore, on the basis of more accurate prediction of vehicle energy consumption, the vehicle range predicted by the predicted vehicle energy consumption can also be more accurate, thereby improving the accuracy of the prediction of vehicle range.

[0142] It should be understood that the above examples are intended to help those skilled in the art understand the embodiments of the present application, rather than to limit the embodiments of the present application to the specific numerical values ​​or specific scenarios illustrated. Those skilled in the art can obviously make various equivalent modifications or changes based on the above examples, and such modifications or changes also fall within the scope of the embodiments of the present application.

[0143] Combination of the above Figures 1 to 3 The vehicle mileage prediction method provided by the embodiment of the present application is described in detail; Figure 4 and Figure 5 The device embodiments of the present application are described in detail. It should be understood that the device in the embodiments of the present application can execute the various methods of the aforementioned embodiments of the present application, that is, the specific working processes of the following various products can refer to the corresponding processes in the aforementioned method embodiments.

[0144] Figure 4 It is a structural schematic diagram of a vehicle mileage prediction device provided in an embodiment of the present application.

[0145] For example, Figure 4 As shown, the device 400 includes:

[0146] An acquisition module 410 is used to acquire the current driving mode and historical power consumption data of the vehicle;

[0147] A determination module 420, configured to determine first historical power consumption data corresponding to the current driving mode in the historical power consumption data based on the current driving mode;

[0148] The processing module 430 is used to obtain the predicted energy consumption of the vehicle in the current driving mode based on the first historical power consumption data;

[0149] The prediction module 440 is used to predict the cruising range of the vehicle based on the predicted energy consumption.

[0150] In one possible implementation, the determination module 420 is also used to: determine the energy consumption correction coefficient of the vehicle; the processing module 430 is specifically used to: determine the historical driving energy consumption of the vehicle in the current driving mode based on the first historical power consumption data; correct the historical driving energy consumption based on the energy consumption correction coefficient to obtain the first driving energy consumption; and obtain the predicted energy consumption based on the first driving energy consumption.

[0151] In one possible implementation, the acquisition module 410 is also used to: obtain the historical mileage increment of the vehicle; determine the target weight based on the historical mileage increment; wherein the historical mileage increment is positively correlated with the target weight; the processing module 430 is specifically used to: predict the actual driving energy consumption of the vehicle in the current driving mode based on the historical mileage increment, the target weight and the first driving energy consumption; and obtain the predicted energy consumption based on the actual driving energy consumption.

[0152] In one possible implementation, the acquisition module 410 is also used to: obtain the historical average vehicle speed corresponding to the first historical power consumption data; based on the historical average vehicle speed, predict the non-driving energy consumption corresponding to the vehicle in the current driving mode; the processing module 430 is specifically used to: add the actual driving energy consumption to the non-driving energy consumption to obtain the first energy consumption; based on the first energy consumption, obtain the predicted energy consumption.

[0153] In one possible implementation, the acquisition module 410 is also used to: obtain an upper limit value and a lower limit value of energy consumption corresponding to the current driving mode; and obtain a second historical energy consumption corresponding to the vehicle in the first driving mode; wherein the energy consumption required for the vehicle in the first driving mode is greater than the energy consumption required in the current driving mode; based on the second historical energy consumption and a preset coefficient, determine a third historical energy consumption; the processing module 430 is specifically used to: determine the maximum energy consumption among the third historical energy consumption, the lower limit value of energy consumption and the first energy consumption as the second energy consumption; and determine the minimum energy consumption among the second energy consumption and the upper limit value of energy consumption as the predicted energy consumption.

[0154] In one possible implementation, the acquisition module 410 is also used to: acquire the current state of the drive motor in the vehicle and the current vehicle speed; the determination module 420 is also used to: if the current state indicates that the drive motor is driving, and the historical driving energy consumption is greater than or equal to the preset energy consumption, determine the first energy consumption correction coefficient based on the current driving mode and the current vehicle speed; if the current state indicates that the drive motor is braking and recovering, and the historical driving energy consumption is less than the preset energy consumption, determine the second energy consumption correction coefficient based on the current vehicle speed and the braking recovery power; wherein the current vehicle speed is positively correlated with the first energy consumption correction coefficient; the current vehicle speed is negatively correlated with the second energy consumption correction coefficient; and the braking recovery power is positively correlated with the second energy consumption correction coefficient.

[0155] In one possible implementation, the determination module 420 is specifically used to: obtain the vehicle speed range and drive torque range corresponding to the current driving mode; and determine the historical power consumption data corresponding to the vehicle's historical speed being within the vehicle speed range and the vehicle's historical drive being within the drive torque range as the first historical power consumption data.

[0156] It should be noted that the above device 400 is embodied in the form of a functional module. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0157] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit, and / or other suitable components that support the described functions.

[0158] Therefore, the modules of each example described in the embodiments of the present application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0159] Figure 5 It is a schematic diagram of the structure of a vehicle provided in an embodiment of the present application.

[0160] For example, Figure 5 As shown, the vehicle 500 includes: a memory 510 and a processor 520, wherein the memory 510 stores an executable program code 5101, and the processor 520 is used to call and execute the executable program code 5101 to perform a vehicle mileage prediction method.

[0161] The present application can divide the functional modules of the vehicle according to the above method example. For example, each functional module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0162] In the case of dividing each functional module according to each function, the vehicle may include: an acquisition module, a determination module, a processing module, a prediction module, etc. It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0163] The vehicle provided in the present application is used to execute the above-mentioned vehicle mileage prediction method, and thus can achieve the same effect as the above-mentioned implementation method.

[0164] In the case of an integrated unit, the vehicle may include a processing module and a storage module. The processing module may be used to control and manage the actions of the vehicle. The storage module may be used to support the vehicle in executing related program codes and data.

[0165] The processing module may be a processor or a controller, which may implement or execute various exemplary logic blocks, modules and circuits shown in combination with the contents disclosed in this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory.

[0166] The present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method of any of the above embodiments are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD (Digital Video Disc), a CD-ROM (Compact Disc Read-Only Memory), a microdrive, and a magneto-optical disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), an EPROM (Erasable Programmable Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), a DRAM (Dynamic Random Access Memory), a VRAM (Video Random Access Memory), a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0167] The present application also provides a computer program product. When the computer program product is run on a computer, the computer is enabled to execute the above-mentioned related steps to implement a vehicle mileage prediction method in the above-mentioned embodiment.

[0168] In addition, the vehicle provided in the embodiments of the present application may specifically be a chip, component or module, and the vehicle may include a connected processor and memory; wherein the memory is used to store instructions, and when the vehicle is running, the processor may call and execute instructions so that the chip executes a vehicle mileage prediction method in the above-mentioned embodiments.

[0169] Among them, the vehicle, computer-readable storage medium, computer program product or chip provided in this application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0170] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0171] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0172] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle mileage prediction method, characterized in that: The method comprises: Obtaining a current driving mode, historical power consumption data, and historical mileage increments of a vehicle; and determining an energy consumption correction factor for the vehicle; Based on the current driving mode, determining first historical power consumption data corresponding to the current driving mode in the historical power consumption data; and based on the historical mileage increment, determining a target weight; wherein the historical mileage increment is positively correlated with the target weight; determining, based on the first historical power consumption data, a historical driving energy consumption of the vehicle in the current driving mode; Correcting the historical driving energy consumption based on the energy consumption correction coefficient to obtain a first driving energy consumption; Predicting actual driving energy consumption of the vehicle in the current driving mode based on the historical mileage increment, the target weight and the first driving energy consumption; Based on the actual driving energy consumption, obtaining the predicted energy consumption of the vehicle in the current driving mode; Based on the predicted energy consumption, a cruising range of the vehicle is predicted.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining a historical average vehicle speed corresponding to the first historical power consumption data; Based on the historical average vehicle speed, predicting the non-driving energy consumption of the vehicle corresponding to the current driving mode; The obtaining, based on the actual driving energy consumption, the predicted energy consumption of the vehicle in the current driving mode includes: Adding the actual driving energy consumption to the non-driving energy consumption to obtain a first energy consumption; Based on the first energy consumption, the predicted energy consumption is obtained.

3. The method according to claim 2, characterized in that The method further comprises: Obtaining an upper limit value and a lower limit value of energy consumption corresponding to the current driving mode; and obtaining a second historical energy consumption corresponding to the vehicle in the first driving mode; wherein the energy consumption required by the vehicle in the first driving mode is greater than the energy consumption required in the current driving mode; Determining a third historical energy consumption based on the second historical energy consumption and a preset coefficient; The obtaining the predicted energy consumption based on the first energy consumption includes: Determine the maximum energy consumption among the third historical energy consumption, the energy consumption lower limit and the first energy consumption as the second energy consumption; The minimum energy consumption between the second energy consumption and the energy consumption upper limit is determined as the predicted energy consumption.

4. The method according to claim 2 or 3, characterized in that: The method further comprises: Acquiring the current state and current vehicle speed of the driving motor in the vehicle; Determining the energy consumption correction coefficient of the vehicle includes: If the current state indicates that the drive motor is driving, and the historical driving energy consumption is greater than or equal to the preset energy consumption, determining a first energy consumption correction coefficient based on the current driving mode and the current vehicle speed; If the current state indicates that the driving motor is performing braking recovery, and the historical driving energy consumption is less than the preset energy consumption, determining a second energy consumption correction coefficient based on the current vehicle speed and the braking recovery power; Among them, the current vehicle speed is positively correlated with the first energy consumption correction coefficient; the current vehicle speed is negatively correlated with the second energy consumption correction coefficient; and the braking recovery power is positively correlated with the second energy consumption correction coefficient.

5. The method according to any one of claims 1 to 3, characterized in that The determining, based on the current driving mode, first historical power consumption data corresponding to the current driving mode in the historical power consumption data includes: Obtaining a vehicle speed range and a driving torque range corresponding to the current driving mode; The historical vehicle speed of the vehicle is within the vehicle speed range, and the historical driving of the vehicle is within the driving torque range corresponding to the historical power consumption data, and is determined as the first historical power consumption data.

6. A vehicle mileage prediction device, characterized in that: The device comprises: An acquisition module, used to acquire the current driving mode, historical power consumption data and historical mileage increment of the vehicle; and determine the energy consumption correction coefficient of the vehicle; a determination module, configured to determine, based on the current driving mode, first historical power consumption data corresponding to the current driving mode in the historical power consumption data; and determine a target weight based on the historical mileage increment; wherein the historical mileage increment is positively correlated with the target weight; a processing module, configured to determine, based on the first historical power consumption data, a historical driving energy consumption of the vehicle in the current driving mode; correct the historical driving energy consumption based on the energy consumption correction coefficient to obtain a first driving energy consumption; predict the actual driving energy consumption of the vehicle in the current driving mode based on the historical mileage increment, the target weight and the first driving energy consumption; and obtain a predicted energy consumption of the vehicle in the current driving mode based on the actual driving energy consumption; A prediction module is used to predict the cruising range of the vehicle based on the predicted energy consumption.

7. A vehicle, characterized in that: The vehicle comprises: A memory for storing executable program codes; A processor, configured to call and run the executable program code from the memory, so that the vehicle executes the method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 5 is implemented.

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