Driving range determination method, computing device and readable storage medium

Abstract

The invention discloses a driving range determination method, computing equipment and a computer readable storage medium, and the driving range determination method comprises the following steps: obtaining target driving condition information of a future driving road section, the target driving condition information comprising a road condition type and / or at least one driving condition feature under the road condition type; determining target unit mileage energy consumption matched with the target driving condition information of the future driving road section; and the driving range is obtained according to the target unit mileage energy consumption and the vehicle energy remaining amount. According to the technical scheme, when the target unit mileage energy consumption used for calculating the driving mileage is determined, the influence of various kinds of driving working condition information of the future driving road section on the unit mileage energy consumption is fully considered, so that the determined target unit mileage energy consumption is closer to the actual unit mileage energy consumption of driving on the future driving road section; therefore, the driving range determined based on the target unit mileage energy and the vehicle energy margin can be closer to the actual driving range, and the purpose of improving the user experience is achieved.

Description

Technical Field

[0001] The present application relates to the field of terminal technology, and in particular to a method for determining a driving range, a computing device, and a computer-readable storage medium. Background Art

[0002] As a representative of new energy vehicles, electric vehicles are gradually becoming a development trend in the automotive industry. However, the "range anxiety" problem caused by its range problem has always been one of the key factors restricting its market promotion and user acceptance. Therefore, conducting research on vehicle range estimation methods is of great significance to promoting the development of the electric vehicle industry. Providing accurate range estimation can help users better understand the performance of electric vehicles and alleviate users' "range anxiety" problem.

[0003] Most existing methods for estimating the driving range directly convert the nominal driving range and the state of charge (SOC) of the power battery, without considering that the energy consumption of electric vehicles is affected by various factors (such as road traffic conditions and related driving conditions). As a result, there is a large error between the driving range calculated by the existing driving range estimation methods and the actual driving range, which makes electric vehicle users have low trust in the driving range displayed in the electric vehicles and the user experience is poor. Summary of the invention

[0004] The purpose of this application is to provide a method for determining a driving range, a computing device, and a computer-readable storage medium, which can more accurately estimate the driving range so that the estimated driving range can be closer to the actual driving range, further achieving the purpose of improving user experience.

[0005] To achieve the above objectives: In a first aspect, an embodiment of the present application provides a method for determining a driving range, comprising: obtaining target driving condition information for a future driving section, the target driving condition information including a road condition type and / or at least one driving condition feature under the road condition type; determining a target unit mileage energy consumption that matches the target driving condition information for the future driving section; and obtaining the driving range based on the target unit mileage energy consumption and the vehicle's energy reserve.

[0006] In one embodiment, before the step of obtaining target driving condition information of a future driving section, the step includes: obtaining a navigation route in response to a navigation operation; dividing the navigation route according to a preset division rule to divide the navigation route into at least one future driving section, wherein the preset division rule includes at least one of division by distance rule and division by road condition type.

[0007] In one embodiment, the step of determining a target unit mileage energy consumption that matches the target driving condition information of a future driving section includes: determining the target unit mileage energy consumption of the future driving section based on the target driving condition information of the future driving section and a pre-built unit energy consumption estimation model, wherein the unit energy consumption estimation model is obtained by modeling the mapping relationship information that characterizes the corresponding relationship between the sample driving condition information and the unit mileage energy consumption.

[0008] In one implementation, the future driving section is obtained by dividing the navigation route.

[0009] In one embodiment, the step of obtaining the driving range based on the target unit mileage energy consumption and the vehicle energy reserve includes: obtaining the predicted driving energy consumption of the future driving section based on the target unit mileage energy consumption of the future driving section; determining the full mileage energy consumption corresponding to the navigation route based on the predicted driving energy consumption corresponding to all future driving sections of the navigation route; determining the average unit mileage energy consumption based on the full mileage energy consumption and the future driving mileage corresponding to the navigation route; and determining the driving range based on the vehicle energy reserve and the average unit mileage energy consumption.

[0010] In one embodiment, the step of obtaining the predicted driving energy consumption of the future driving section according to the target unit mileage energy consumption of the future driving section includes: determining the initial driving energy consumption according to the target unit mileage energy consumption of the future driving section and the mileage corresponding to the future driving section; performing energy consumption correction processing on the initial driving energy consumption according to the geographical environment factors corresponding to the future driving section to obtain the predicted driving energy consumption, the geographical environment factors including at least one of the altitude information and the section ambient temperature information.

[0011] In one embodiment, when the geographical environmental factors include altitude information, the initial driving energy consumption is energy-corrected according to the geographical environmental factors corresponding to the future driving section to obtain the steps of predicted driving energy consumption, including: when the altitude information corresponding to the future driving section meets the uphill condition, determining the first compensation energy consumption according to the altitude information; or, when the altitude information corresponding to the future driving section meets the downhill condition, determining the second compensation energy consumption according to the altitude information; obtaining the predicted driving energy consumption according to the initial driving energy consumption and the first compensation energy consumption or the second compensation energy consumption.

[0012] In one embodiment, the altitude information includes the altitude of a first junction point and the altitude of a second junction point of the future driving segment.

[0013] In one embodiment, determining the first compensation energy consumption according to the altitude information includes: calculating the first compensation energy consumption according to the altitude information and a calculation formula of the first compensation energy consumption; wherein the calculation formula of the first compensation energy consumption is:

[0014] in, represents the first compensation energy consumption, Indicates the altitude of the first junction point, Indicates the altitude of the second junction point, Indicates the vehicle's curb weight. Represents the acceleration due to gravity.

[0015] In one embodiment, determining the second compensation energy consumption according to the altitude information includes: calculating the second compensation energy consumption according to the altitude information and a calculation formula of the second compensation energy consumption; wherein the calculation formula of the second compensation energy consumption is:

[0016] in, represents the second compensation energy consumption, Indicates the altitude of the first junction point, Indicates the altitude of the second junction point, Indicates the vehicle's curb weight. represents the acceleration due to gravity, Indicates the energy recovery coefficient calibrated by the vehicle's energy recovery system.

[0017] In one embodiment, the step of determining the full mileage energy consumption corresponding to the navigation route based on the predicted driving energy consumption corresponding to all future driving sections of the navigation route includes: summarizing the predicted driving energy consumption corresponding to all future driving sections to obtain the full mileage basic energy consumption corresponding to the navigation route; obtaining the total energy consumption of accessories corresponding to the navigation route; and obtaining the full mileage energy consumption based on the full mileage basic energy consumption and the total energy consumption of accessories.

[0018] In one embodiment, the step of obtaining the total energy consumption of accessories corresponding to the navigation route includes: obtaining a first low-pressure accessory energy consumption corresponding to the normally turned-on low-pressure accessory based on the normally turned-on low-pressure accessory and the driving time corresponding to the navigation route; and / or obtaining a second low-pressure accessory energy consumption corresponding to the conditionally turned-on low-pressure accessory based on the conditionally turned-on low-pressure accessory and the first estimated turning-on time corresponding to the navigation route; obtaining a first high-pressure accessory energy consumption corresponding to the normally turned-on high-pressure accessory based on the normally turned-on high-pressure accessory and the driving time corresponding to the navigation route; and / or obtaining a second high-pressure accessory energy consumption corresponding to the conditionally turned-on high-pressure accessory based on the conditionally turned-on high-pressure accessory and the second estimated turning-on time corresponding to the navigation route; obtaining the total energy consumption of the low-pressure accessories corresponding to the navigation route based on the first low-pressure accessory energy consumption and / or the second low-pressure accessory energy consumption; obtaining the total energy consumption of the high-pressure accessories corresponding to the navigation route based on the first high-pressure accessory energy consumption and / or the second high-pressure accessory energy consumption; obtaining the total energy consumption of the accessories corresponding to the navigation route based on the total energy consumption of the low-pressure accessories and the total energy consumption of the high-pressure accessories.

[0019] In one embodiment, the step of determining the average energy consumption per unit mileage according to the full mileage energy consumption and the future driving mileage corresponding to the navigation route includes: calculating the average energy consumption per unit mileage according to the full mileage energy consumption, the future driving mileage corresponding to the navigation route and the average energy consumption calculation formula, wherein the average energy consumption calculation formula is:

[0020] in, Represents the average energy consumption per unit mileage, Indicates the full mileage energy consumption, Indicates the future driving distance corresponding to the navigation route.

[0021] In one embodiment, the step of determining the driving range according to the remaining energy of the vehicle and the average energy consumption per unit mileage includes: calculating and outputting the driving range according to the calculation formula of the remaining energy of the vehicle, the average energy consumption per unit mileage and the driving range, wherein the calculation formula of the driving range is:

[0022] in, Indicates the driving range. Indicates the remaining energy of the vehicle. Indicates the average energy consumption per unit mileage.

[0023] In a second aspect, an embodiment of the present application provides a computing device, comprising: a processor and a memory storing a computer program, and when the processor runs the computer program, the steps of the method for determining the driving range as described in any of the above items are implemented.

[0024] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which, in one implementation, stores a computer program. When the computer program is executed by a processor, the steps of the method for determining the driving range as described in any of the above items are implemented.

[0025] The method for determining the driving range, the computing device and the computer-readable storage medium provided in the embodiment of the present application, the method for determining the driving range includes: obtaining the target driving condition information of the future driving section, the target driving condition information includes the road condition type and / or at least one driving condition feature under the road condition type; determining the target unit mileage energy consumption matching the target driving condition information of the future driving section; and obtaining the driving range according to the target unit mileage energy consumption and the vehicle energy reserve. In this way, when determining the target unit mileage energy consumption for calculating the driving range, the technical solution of the present application can fully consider the influence of various driving condition information of the future driving section (such as the road condition type and / or the specific driving condition under the road condition type) on the unit mileage energy consumption, so that the determined target unit mileage energy consumption is closer to the actual unit mileage energy consumption of driving on the future driving section, and then the driving range determined based on the target unit mileage energy and the vehicle energy reserve can also be closer to the actual driving range, so the technical solution of the present application can achieve the purpose of improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0027] Figure 1 It is a flowchart of a method for determining a driving range provided in an embodiment of the present application.

[0028] Figure 2 It is a schematic diagram of dividing navigation segments according to an example of an embodiment of the present application.

[0029] Figure 3 It is a speed-time correspondence diagram for classifying driving state types according to an example of an embodiment of the present application.

[0030] Figure 4 It is a schematic diagram of the division of uphill and downhill working conditions in an example of an embodiment of the present application.

[0031] Figure 5 It is a flowchart of a method for determining the full-mileage basic energy consumption of a navigation route according to an example embodiment of the present application.

[0032] Figure 6 This is a flowchart of determining the total energy consumption of an accessory according to an example of this embodiment.

[0033] Figure 7 This is a flowchart of estimating the driving range according to an example of this embodiment.

[0034] Figure 8 It is a schematic diagram of the structure of the computing device provided in an embodiment of the present application.

[0035] The realization of the purpose, functional features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The above-mentioned drawings have shown clear embodiments of this application, which will be described in more detail later. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0036] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0037] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0038] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of..." or "when..." or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising", "including" indicate that there are described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Thus, “A, B, or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will occur only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0039] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0040] It should be noted that, in this article, step codes such as S11, S12, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing it specifically, those skilled in the art may execute S12 first and then S11, etc., but these should all be within the scope of protection of this application.

[0041] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0043] See also Figure 1 , is a method for determining a driving range provided in an embodiment of the present application, which can be executed by a computing device provided in an embodiment of the present application, and the device can be implemented in software and / or hardware. Optionally, the computing device is, for example, a server, a vehicle-mounted terminal, etc.

[0044] This embodiment provides a method for determining a driving range, including (for example, steps S11 to S13): Step S11: acquiring target driving condition information of a future driving section, wherein the target driving condition information includes a road condition type and / or at least one driving condition feature under the road condition type.

[0045] In one embodiment, the future driving segment may represent the segment that the vehicle will travel on. Optionally, the future driving segment may be determined by an online map function provided by a navigation map platform, and the future driving segment may be associated with navigation segment data.

[0046] In one embodiment, the navigation segment data may represent vehicle-related data and road traffic condition information estimated by the online map function of the navigation map platform when traveling on a segment. In this way, the technical solution of this embodiment can determine the road condition type of the future driving segment and / or at least one driving condition feature under the road condition type based on the navigation segment data to obtain the target driving condition information of the future driving segment.

[0047] In one embodiment, the road condition type can be used to distinguish different road traffic conditions. Optionally, the road condition type can correspond to road traffic condition information, wherein the road traffic condition information includes at least one of road grade information, congestion grade information, and other information reflecting the road traffic condition. Optionally, the road traffic condition information can be included in the navigation section data. Optionally, the road condition type in the target driving condition information of this embodiment can be determined based on the road grade information and congestion grade information in the corresponding navigation section data.

[0048] In one implementation, the road grade information in this embodiment can refer to the definition rules of road grades in the current mature navigation map platform, such as unnamed roads, general roads, main roads, expressways, urban expressways, national highways, expressways, etc.

[0049] In one implementation, the congestion level information in this embodiment may refer to the definition rules of congestion levels in currently mature navigation map platforms, such as unimpeded traffic, light congestion, moderate congestion, severe congestion, complete congestion, etc.

[0050] In one embodiment, according to actual research, the energy consumption per unit mileage of a vehicle under different road conditions may vary. Therefore, the technical solution of this embodiment can fully consider the impact of various driving condition information of future driving sections on the energy consumption per unit mileage by dividing the road conditions into detailed types.

[0051] In one implementation, in order to facilitate understanding of the definition of the road condition type in this embodiment, this embodiment illustrates a method of classifying the road condition type according to the road grade and the congestion grade. The road condition type is exemplified in Table 1 below: Table 1:

[0052] In one embodiment, the driving condition characteristics under the road condition type can represent various driving parameters of the vehicle on a future driving section of the road condition type. Optionally, the driving condition characteristics are, for example, average acceleration, average vehicle speed, mileage on the future driving section, driving time on the future driving section, and section ambient temperature on the future driving section.

[0053] In one embodiment, step S11: before obtaining the target driving condition information of the future driving section, includes: obtaining a navigation route in response to a navigation operation; dividing the navigation route according to a preset division rule to divide the navigation route into at least one future driving section, and the preset division rule includes at least one of division by distance rule and division by road condition type.

[0054] In one embodiment, in response to the navigation operation, the step of obtaining the navigation route may include: after setting the destination of the travel through the online map function, obtaining the navigation route and its corresponding navigation route data according to the current vehicle position and the destination.

[0055] In one embodiment, the navigation route data may represent vehicle-related data and road traffic condition information estimated by an online map function of a navigation map platform when traveling on a route.

[0056] In one embodiment, the preset division rule may represent various rules for dividing the navigation route into at least one future driving segment. In other words, the multiple future driving segments may be multiple short segments divided by the navigation route.

[0057] In one implementation, the division by distance rule includes but is not limited to one of the following situations: Dividing the navigation route according to a preset distance to obtain at least one future driving segment, and / or when the distance of the last segment of the divided navigation route is less than the preset distance, using the last segment as a future driving segment; The navigation route is equally divided into at least one future driving segment of equal distance.

[0058] In one embodiment, the classification by road condition type includes but is not limited to at least one of the following situations: Dividing the road sections of each road condition type in the navigation route according to the navigation route data of the navigation route; Use the road sections with the divided road condition types as future driving sections; The long road sections exceeding the distance threshold in the road sections of each road condition type are divided into at least two short road sections according to the distance rule, so that the short road sections are used as future driving sections.

[0059] The technical solution of this embodiment can divide the navigation route into one or more suitable future driving sections, so that the target unit mileage energy consumption corresponding to each future driving section obtained later can be closer to the actual unit mileage energy consumption.

[0060] Based on the above technical concept of dividing navigation routes, see Figure 2 , an example of dividing a navigation route is given: the entire navigation route is divided into sections, and each fixed distance (i.e., preset distance) is divided into a future driving section (when the mileage of the last section of the navigation route is insufficient, it is also regarded as a future driving section separately), so as to obtain 14 future driving sections.

[0061] In one embodiment, step S11: the step of obtaining target driving condition information of a future driving section may include: determining navigation section data of at least one future driving section based on navigation route data of the navigation route; and determining target driving condition information of the future driving section based on the navigation section data of the future driving section.

[0062] Step S12: Determine a target unit mileage energy consumption that matches the target driving condition information of the future driving section.

[0063] In one embodiment, step S12: determining the target unit mileage energy consumption that matches the target driving condition information of the future driving section may include: determining the target unit mileage energy consumption of the future driving section based on the target driving condition information of the future driving section and a pre-built unit energy consumption estimation model, the unit energy consumption estimation model being obtained by modeling the mapping relationship information that characterizes the correspondence between the sample driving condition information and the unit mileage energy consumption.

[0064] In this way, the technical solution of this embodiment can fully consider the impact of various driving condition information (such as road condition type and / or specific driving condition under the road condition type) on unit mileage energy consumption, and pre-associate accurate unit mileage energy consumption for various sample driving condition information to obtain one or more mapping relationship information, so that when building a model through one or more mapping relationship information, the unit mileage energy consumption law under different driving conditions is learned (for example, the unit mileage energy consumption law under specific driving conditions of different road condition types is learned, and / or the unit mileage energy consumption law under different specific driving conditions of the same road condition type is learned) to obtain a unit energy consumption estimation model, and then when determining the target driving condition information of the current driving section or the future driving section, the unit mileage energy consumption estimation model can be used to output a target unit mileage energy consumption that is close to the actual unit mileage energy consumption of the relevant real driving scenario for application.

[0065] In one embodiment, the step of building a unit energy consumption estimation model includes: obtaining one or more mapping relationship information; and building a model according to the one or more mapping relationship information to obtain the unit energy consumption estimation model.

[0066] In one embodiment, the unit energy consumption estimation model can be obtained by building a model based on a machine learning prediction model and one or more mapping relationship information. Optionally, the machine learning prediction model includes but is not limited to: decision tree: a tree diagram is used to represent data features and decision rules for classification and regression problems; support vector machine regression (SVR): a supervised learning technique for regression analysis that can predict continuous values; random forest: integrating multiple decision trees to improve prediction accuracy; neural network: simulating the way the human brain processes information, suitable for the prediction of complex data sets; ensemble learning: combining multiple learners to improve prediction performance, such as XGBoost, LightGBM, CatBoost, etc.

[0067] In one implementation, the unit energy consumption estimation model of this embodiment may also be a mapping map (MAP for short). Optionally, the mapping map may be obtained by sorting and interpolating one or more mapping relationship information. Optionally, the mapping map may be, for example, a "speed-acceleration-unit mileage energy consumption" MAP associated with each road condition type; the mapping map may be, for example, a "road condition type-unit mileage energy consumption" MAP; the mapping map may also be, for example, a "driving state type-unit mileage energy consumption" MAP associated with each road condition type.

[0068] In one implementation, the sample driving condition information may correspond to historical driving data.

[0069] In one embodiment, the historical driving data may be at least one of the historical driving data of the vehicle, the historical driving data of other vehicles of the same model as the vehicle, etc. Optionally, the sample driving condition information in this embodiment may be obtained by analyzing the driving condition characteristics in the historical driving data of the vehicle, and the energy consumption per unit mileage corresponding to the sample driving condition information may be obtained by calculating the driving condition characteristics in the historical driving data of the vehicle corresponding to the sample driving condition information.

[0070] In one embodiment, the historical driving data of the vehicle is data obtained after actual operation of the vehicle based on the driving habits of the user of the vehicle and the explicit performance and / or implicit performance of the vehicle. Therefore, the technical solution of this embodiment is based on the sample driving condition information and its corresponding unit mileage energy consumption determined by the historical driving data of the vehicle, and a unit energy consumption estimation model is constructed to adapt to the driving habits of the user of the vehicle and the energy consumption rules of the explicit performance and / or implicit performance of the vehicle, so that the target unit mileage energy consumption estimated by the unit energy consumption estimation model is closer to the actual unit mileage energy consumption of the user of the vehicle when driving the vehicle in actual driving.

[0071] In one embodiment, the sample driving condition information may also represent the road condition type of the previous driving section and / or at least one driving condition feature under the road condition type.

[0072] In one embodiment, the step of building a bit energy consumption estimation model: obtaining one or more mapping relationship information may include: determining at least one sample driving condition information based on the historical driving data obtained; calculating the unit energy consumption based on the sample driving condition information to obtain the corresponding unit mileage energy consumption; forming at least one mapping relationship information based on the unit mileage energy consumption corresponding to the at least one sample driving condition information. In this way, the technical solution of this embodiment can determine one or more sample driving condition information and the unit mileage energy consumption corresponding to each sample driving condition information through historical driving data, thereby obtaining one or more mapping relationship information for subsequent model building.

[0073] In one embodiment, before the step of determining at least one sample driving condition information based on the acquired historical driving data, it may include: collecting actual vehicle driving data; performing data preprocessing and condition classification processing on the actual vehicle driving data to obtain historical driving data of at least one road condition type.

[0074] In one implementation, the actual vehicle driving data may represent vehicle-related data and road traffic condition information during the actual driving process of the vehicle.

[0075] In one embodiment, the actual vehicle driving data may represent a set of driving data corresponding to road sections of various road condition types. Optionally, the driving condition characteristics (or driving condition parameters) in the actual vehicle driving data may include vehicle parameters and road parameters.

[0076] In one embodiment, the vehicle parameters may be various parameters corresponding to the vehicle when it is traveling, such as vehicle speed, time, battery voltage, motor torque, motor speed, mileage, etc. Optionally, the vehicle parameters may be recorded in association with a time axis, for example, the vehicle speed may be recorded in association with the time axis to obtain a vehicle speed-time correspondence relationship, and the mileage may be recorded in association with the time axis to obtain a mileage-time correspondence relationship.

[0077] In one embodiment, the road parameters may represent various parameters related to the driving environment of a road section, such as road grade information, congestion grade information, average road speed, altitude information, road section ambient temperature information, etc.

[0078] In one implementation, in order to facilitate understanding of actual vehicle driving data, the following driving condition characteristics collected at a sampling time are illustrated in Table 2: Table 2:

[0079] In one embodiment, the preprocessing strategy corresponding to the data preprocessing includes at least one of the following: Deleting driving data segments that meet the exclusion conditions in the actual vehicle driving data; Delete duplicate values ​​from real vehicle driving data; Supplement missing values ​​in real vehicle driving data; Perform outlier repair processing on real vehicle driving data.

[0080] In one embodiment, the driving data segments meeting the exclusion condition may represent driving data segments corresponding to unconventional driving conditions that may affect the calculation result of energy consumption per mileage.

[0081] In one embodiment, the driving data segments that meet the exclusion conditions include, but are not limited to, at least one of the following: The driving data segment corresponds to a driving condition in which the parking time is greater than or equal to the preset time; The driving data segment corresponds to a driving condition in which the maximum climbing gradient is greater than or equal to a preset gradient.

[0082] In one implementation, the preset duration may be an upper limit of the parking duration corresponding to a road section of a certain road condition type.

[0083] In one embodiment, the preset slope can be set to any slope value according to actual needs.

[0084] In one embodiment, the maximum climbing grade may be determined based on altitude information in the actual vehicle driving data. For example, the altitude information of the starting point of a road section of a road condition type in the actual vehicle driving data and the altitude information of the end point of the road section determine the altitude difference, thereby determining the maximum climbing grade based on the altitude difference.

[0085] In one embodiment, during the actual vehicle driving data collection process, data anomalies may occur due to driver misoperation and collection equipment communication, and therefore it is necessary to exclude driving data segments that meet the exclusion conditions in the actual vehicle driving data, and the collected actual vehicle driving data needs to delete duplicate values, process abnormal values, and supplement missing values, so that the historical driving data obtained based on the actual vehicle driving data can achieve more accurate calculation or prediction of energy consumption per unit mileage.

[0086] In one embodiment, considering that the longest duration of traffic lights in urban roads is generally 120s, the preset duration can be appropriately extended to 150s based on this, so the driving data segments with continuous parking duration exceeding 150s are regarded as long-term parking segments (driving data segments that meet the exclusion conditions). Optionally, considering that the road slope has a direct impact on driving energy consumption, the present embodiment mainly considers the flat road surface when building the model, so that the target unit mileage energy consumption output by the built model can be corrected based on the slope in subsequent applications. Therefore, when determining historical driving data based on actual vehicle driving data, it is necessary to reduce the impact of the slope on energy consumption. Based on the road design specifications, for cars that often travel in cities and good roads, the maximum climbing gradient is about 10°. Because the driving data segments with a maximum climbing gradient greater than 10° (i.e., the preset slope) are regarded as excessively steep segments (driving data segments that meet the exclusion conditions). Optionally, in order to improve the validity of historical driving data, it is necessary to delete the aforementioned excessively steep segments and long-term parking segments.

[0087] In one embodiment, data preprocessing and working condition classification processing are performed on the actual vehicle driving data to obtain historical driving data of at least one road condition type. In one embodiment, the operating condition classification process may be to classify the actual vehicle driving data after data preprocessing according to a preset operating condition classification rule.

[0088] In one embodiment, the preset operating condition classification rules include but are not limited to at least one of the following: Extracting first driving data of a first trip segment that meets the road condition type requirement from the actual vehicle driving data; Extracting second driving data of a second trip segment that meets the driving state type requirement from the first driving data; The first driving data is used as the historical driving data, and / or the second driving data is used as the historical driving data.

[0089] In one implementation, the road condition type requirement may represent the types of road condition types required, such as the 30 road condition types in Table 1.

[0090] In one embodiment, the first driving data of the first trip segment can represent the driving data corresponding to a short trip of a road condition type. Optionally, when the first driving data is used as historical driving data and a model is built based on the mapping relationship information (the correspondence between road condition type and unit mileage energy consumption) determined based on the historical driving data, the influence of the road condition type on the unit mileage energy consumption can be learned, so that the subsequently obtained model can input the road condition type and output the corresponding target unit mileage energy consumption.

[0091] In one embodiment, the first driving data of the first trip segment is, for example, driving data corresponding to an interval from an idle stop state to a next idle stop state according to a time axis; or driving data corresponding to a preset mileage interval under a road condition type.

[0092] In one embodiment, the driving state type requirement represents at least one driving state type required. Optionally, the driving state type requirement may include at least one of an accelerating driving state, a constant speed driving state, a decelerating driving state, and the like.

[0093] In one embodiment, the second driving data of the second trip segment that meets the driving state type requirement is intercepted from the first driving data, including but not limited to at least one of the following: Extracting second driving data of a second travel segment representing an accelerated driving state from the first driving data; Extracting second driving data representing a second travel segment in a constant speed driving state from the first driving data; Second driving data representing a second journey segment in a deceleration driving state is extracted from the first driving data.

[0094] In one embodiment, see Figure 3 Taking the first driving data of the first trip segment under a road condition type as an example, through the speed-time correspondence included in the first driving data, it is exemplarily demonstrated that the first driving data is intercepted according to the driving state type into driving data of an accelerated driving state, driving data of a uniform speed driving state, and driving data of a decelerated driving state.

[0095] In one embodiment, the present embodiment can determine the driving state type that forms the segment characterization according to the average acceleration of the travel segment. Optionally, the driving state type is determined based on the average acceleration of the travel segment, for example:

[0096] in, Indicates the average acceleration of a travel segment.

[0097] It should be understood that The corresponding acceleration threshold can be, but is not limited to, , and can also be any other value set according to actual needs; The corresponding acceleration threshold can be, but is not limited to, , and can also be any other value set according to actual needs; The corresponding acceleration upper limit threshold can be but is not limited to , and can also be any other value set according to actual needs, and The corresponding lower acceleration threshold can be, but is not limited to, , and can also be any other value set according to actual needs.

[0098] In one embodiment, the second driving data of the second trip segment may represent driving data corresponding to a short trip of a specific driving state type under a road condition type. Optionally, when the second driving data is used as historical driving data and a model is built based on mapping relationship information (a corresponding relationship between a specific driving state type under a road condition type and energy consumption per unit mileage) determined by the historical driving data, the influence of the road condition type and the driving state type under the learned road condition type on the energy consumption per unit mileage can be learned, so that the model obtained subsequently can input the road condition type and the specific driving state type to output the corresponding target energy consumption per unit mileage.

[0099] In one embodiment, the technical solution of this embodiment divides the continuous real vehicle driving data or the first driving data into driving data representing independent travel segments, so as to better calculate or analyze the relationship between various driving conditions and energy consumption through the driving data representing independent travel segments, thereby facilitating the determination and association of sample unit mileage energy consumption that is closer to the actual unit mileage energy consumption for various driving conditions to obtain mapping relationship information, thereby facilitating the construction of a unit energy consumption estimation model that can estimate performance more accurately based on the obtained mapping relationship information.

[0100] In one embodiment, the historical driving data may represent vehicle-related data and road traffic condition information during the actual driving process of the vehicle on a road section of a road condition type, or represent vehicle-related data and road traffic condition information during the actual driving process when the vehicle is driven in a specific driving state on a road section of a road condition type.

[0101] In one embodiment, the historical driving data may include at least one of the vehicle speed-time correspondence, road parameters, vehicle speed sampling duration on a road segment, and mileage of a road segment. Optionally, the road parameters may include at least one of the road type, road grade information, congestion grade information, average road speed, altitude information, and road segment ambient temperature information.

[0102] In one embodiment, the step of determining at least one sample driving condition information based on the acquired historical driving data includes but is not limited to one of the following: Determine the vehicle speeds at multiple times according to the vehicle speed-time correspondence, and determine multiple sample driving condition information according to the vehicle speeds at multiple times and the corresponding road condition types, wherein the sample driving condition information includes the road condition type and the vehicle speed at the time; Determine the average vehicle speed according to the vehicle speed-time correspondence, and determine a sample driving condition information according to the average vehicle speed and the road condition type, the sample driving condition information including the road condition type and the average vehicle speed; The average vehicle speed is calculated according to the vehicle speed-time correspondence relationship and the calculation formula of the average vehicle speed, and the average acceleration is calculated according to the calculation formula of the average vehicle speed and acceleration, so as to determine a sample driving condition information according to the road condition type, the average acceleration and the average vehicle speed, and the sample driving condition information includes the road condition type, the average vehicle speed and the average acceleration; According to the vehicle speed-time correspondence relationship and the calculation strategy of the average acceleration, the average acceleration is calculated, and the vehicle speeds at multiple moments are determined according to the vehicle speed-time correspondence relationship, so as to determine multiple sample driving condition information according to the road condition type, the average acceleration and the vehicle speeds at multiple moments, wherein the sample driving condition information includes the road condition type, the vehicle speed at the moment and the average acceleration; The average acceleration is calculated according to the vehicle speed-time correspondence and the average acceleration calculation strategy, and the driving state type is determined according to the average acceleration, and the vehicle speeds at multiple moments are determined according to the vehicle speed-time correspondence, so as to determine multiple sample driving condition information according to the road condition type, the driving state type and the vehicle speeds at multiple moments, wherein the sample driving condition information includes the road condition type, the vehicle speed at the moment and the driving state type; The average vehicle speed is calculated according to the vehicle speed-time correspondence and the calculation formula of the average vehicle speed, and the average acceleration is calculated according to the calculation formula of the average vehicle speed and acceleration, and the driving state type is determined according to the average acceleration, so as to determine a sample driving condition information according to the road condition type, the driving state type and the average vehicle speed, and the sample driving condition information includes the road condition type, the average vehicle speed and the driving state type.

[0103] In one embodiment, multiple vehicle speeds at different times are determined based on the vehicle speed-time correspondence, and multiple sample driving condition information is determined based on the multiple vehicle speeds at different times and the corresponding road condition types, so that multiple different sample driving condition information can be determined based on a historical driving data, so as to facilitate the subsequent determination of the unit mileage energy consumption corresponding to each sample driving condition information, thereby determining multiple different mapping relationship information based on a historical driving data, so as to quickly obtain a large amount of mapping relationship information to build a unit energy consumption estimation model, and improve the unit mileage energy consumption estimation capability of the unit energy consumption estimation model. In addition, the sample driving condition information obtained in this way mainly includes the road condition type and the vehicle speed at different times. Therefore, the mapping relationship information obtained based on the sample driving condition information is the corresponding relationship between the road condition type-the vehicle speed at different times-the unit mileage energy consumption. The unit energy consumption estimation model built according to the mapping relationship information can output the target unit mileage energy consumption according to the input target road condition type and target vehicle speed.

[0104] In one embodiment, according to the calculation strategy of the vehicle speed-time correspondence and the average acceleration, the average acceleration is calculated, and the vehicle speeds at multiple times are determined according to the vehicle speed-time correspondence, so as to determine multiple sample driving condition information according to the road condition type, the average acceleration and the vehicle speeds at multiple times, so as to achieve the determination of multiple different sample driving condition information according to a historical driving data, so as to facilitate the subsequent determination of the unit mileage energy consumption corresponding to each sample driving condition information, thereby achieving the determination of multiple different mapping relationship information according to a historical driving data, so as to achieve the rapid acquisition of a large amount of mapping relationship information to build a unit energy consumption estimation model, and improve the estimation ability of the unit mileage energy consumption of the unit energy consumption estimation model. In addition, the sample driving condition information obtained in this way mainly includes the road condition type, the vehicle speed at the time and the average acceleration. Therefore, the mapping relationship information obtained based on the sample driving condition information is the corresponding relationship of the road condition type-the vehicle speed at the time-the average acceleration-the unit mileage energy consumption. The unit energy consumption estimation model built according to the mapping relationship information can output the target unit mileage energy consumption according to the input target road condition type, vehicle speed and average acceleration.

[0105] In one embodiment, the average acceleration is calculated according to the speed-time correspondence and the calculation strategy of the average acceleration, and the driving state type is determined according to the average acceleration, and the vehicle speeds at multiple times are determined according to the speed-time correspondence, so as to determine multiple sample driving condition information according to the road condition type, the driving state type and the vehicle speeds at multiple times, so as to achieve the determination of multiple different sample driving condition information according to a historical driving data, so as to facilitate the subsequent determination of the unit mileage energy consumption corresponding to each sample driving condition information, thereby achieving the determination of multiple different mapping relationship information according to a historical driving data, so as to achieve the rapid acquisition of a large amount of mapping relationship information to build a unit energy consumption estimation model, and improve the estimation ability of the unit mileage energy consumption of the unit energy consumption estimation model. In addition, the sample driving condition information obtained in this way mainly includes the road condition type, the vehicle speed at the time and the driving state type, so that the mapping relationship information obtained based on the sample driving condition information is the corresponding relationship of the road condition type-the vehicle speed at the time-the driving state type-the unit mileage energy consumption, and the unit energy consumption estimation model built according to the mapping relationship information can output the target unit mileage energy consumption according to the input target road condition type, vehicle speed and driving state type.

[0106] In one embodiment, the average vehicle speed is determined based on the vehicle speed-time correspondence, and a sample driving condition information is determined based on the average vehicle speed and the road condition type, so that the most representative sample driving condition information can be determined based on historical driving data, so that the unit mileage energy consumption corresponding to the most representative sample driving condition information can be quickly determined with less computing power to obtain the most representative mapping relationship information. The unit energy consumption estimation model is built based on the most representative mapping relationship information under various road condition types, which has the advantages of high efficiency and low computing power consumption. The unit energy consumption estimation model built according to the mapping relationship information can output the target unit mileage energy consumption according to the input target road condition type and vehicle speed (such as average vehicle speed).

[0107] In one embodiment, the average vehicle speed is calculated according to the vehicle speed-time correspondence and the calculation formula of the average vehicle speed, and the average acceleration is calculated according to the calculation formula of the average vehicle speed and acceleration, so as to determine a sample driving condition information according to the road condition type, the average acceleration, and the average vehicle speed, so as to achieve the most representative sample driving condition information according to the historical driving data, so as to quickly determine the unit mileage energy consumption corresponding to the most representative sample driving condition information with less computing power, so as to obtain the most representative mapping relationship information. The unit energy consumption estimation model is constructed based on the most representative mapping relationship information under various road condition types, which has the advantages of high efficiency and low computing power occupation. The unit energy consumption estimation model constructed according to the mapping relationship information can output the target unit mileage energy consumption according to the input target road condition type, vehicle speed (such as average vehicle speed), and average acceleration.

[0108] In one embodiment, the average vehicle speed is calculated according to the vehicle speed-time correspondence and the calculation formula of the average vehicle speed, and the average acceleration is calculated according to the calculation formula of the average vehicle speed and acceleration, and the driving state type is determined according to the average acceleration, so as to determine a sample driving condition information according to the road condition type, the driving state type, and the average vehicle speed, so as to achieve the most representative sample driving condition information according to a historical driving data, so as to quickly determine the unit mileage energy consumption corresponding to the most representative sample driving condition information with less computing power, so as to obtain the most representative mapping relationship information. The unit energy consumption estimation model is constructed based on the most representative mapping relationship information under various road condition types, which has the advantages of high efficiency and low computing power occupation. The unit energy consumption estimation model constructed according to the mapping relationship information can output the target unit mileage energy consumption according to the input target road condition type, vehicle speed (such as average vehicle speed), and driving state type.

[0109] In one embodiment, the step of calculating the unit energy consumption according to the sample driving condition information to obtain the corresponding unit mileage energy consumption includes: calculating the unit energy consumption according to the driving condition characteristics and vehicle performance information in the sample driving condition information to obtain the corresponding unit mileage energy consumption. In this way, when determining the unit mileage energy consumption, the technical solution of this embodiment can not only fully consider the influence of driving behavior (such as acceleration), traffic road conditions and environmental conditions (such as road condition type, road slope) based on historical driving data, but also fully combine the performance of the vehicle itself. Therefore, the unit mileage energy consumption determined by the technical solution of this embodiment can be closer to the actual unit mileage energy consumption, and then the technical solution of this embodiment can improve the trust of users of new energy vehicles in the unit mileage energy consumption displayed in the vehicle and other related energy consumption data, thereby achieving the purpose of improving user experience.

[0110] In one embodiment, the driving condition characteristics include but are not limited to average acceleration, mileage and average vehicle speed / speed at any given moment, and the vehicle performance information includes but is not limited to vehicle mechanical parameters and energy conversion efficiency parameters.

[0111] In one embodiment, the step of calculating unit energy consumption according to the driving condition characteristics and vehicle performance information in the sample driving condition information to obtain the corresponding unit mileage energy consumption includes: calculating the driving force using the driving force calculation formula according to the average vehicle speed / momentary vehicle speed, the average acceleration and the vehicle mechanical parameters; calculating the driving energy consumption using the driving energy consumption calculation formula according to the driving force, the average vehicle speed / momentary vehicle speed and the energy conversion efficiency parameters; calculating the unit mileage energy consumption using the unit mileage energy consumption calculation formula according to the driving energy consumption and the mileage. In this way, when determining the unit mileage energy consumption, the technical solution of this embodiment can not only fully consider the influence of driving behavior (such as acceleration), traffic road environment conditions (such as road condition type, road slope) and the like according to historical driving data, but also fully combine the vehicle mechanical parameters and energy conversion efficiency parameters. Therefore, the unit mileage energy consumption determined by the technical solution of this embodiment can be closer to the actual unit mileage energy consumption.

[0112] In one embodiment, the average vehicle speed is calculated as follows:

[0113] in, represents the average vehicle speed, represents the vehicle speed at a certain moment determined from the vehicle speed-time correspondence, Indicates the vehicle speed sampling duration; and / or, The acceleration is calculated as:

[0114] in, represents the average acceleration in the sample driving condition information, represents the vehicle speed at a certain moment determined from the vehicle speed-time correspondence, Indicates the vehicle speed sampling duration; and / or, The formula for calculating the driving force is:

[0115] in, Indicates driving force, Indicates average speed / speed at any given moment. Represents the average acceleration and vehicle mechanical parameters in the sample driving condition information: Indicates the vehicle's curb weight. is the rolling resistance coefficient, is the air resistance coefficient, represents the windward area, represents the rotational mass conversion factor; and / or, The calculation formula of driving energy consumption is:

[0116] in, Indicates the drive energy consumption, Indicates average vehicle speed / speed at any given moment, energy conversion efficiency parameters: represents the driving efficiency, represents the motor efficiency, represents the battery efficiency; and / or, The formula for calculating energy consumption per unit mileage is:

[0117] in, Indicates the energy consumption per unit mileage, Indicates the drive energy consumption, Indicates the mileage in the sample driving condition information.

[0118] In one embodiment, the speed and acceleration of the vehicle will have a direct impact on the vehicle's energy consumption. Therefore, the technical solution of this embodiment enables the unit energy consumption estimation model to fully learn the change law of unit mileage energy consumption under different vehicle speeds and different acceleration conditions under various road conditions.

[0119] In one implementation, the driving condition characteristics in the target driving condition information of the future driving section may include average vehicle speed and acceleration.

[0120] In one embodiment, the average vehicle speed corresponding to the future driving section can be determined based on the navigation section data, that is, the online map function can predict the average traffic flow speed of the future driving section. Therefore, the navigation section data includes the average traffic flow speed of the future driving section. Therefore, the average traffic flow speed can be used as the average vehicle speed under the specific road condition type corresponding to the future driving section.

[0121] In one embodiment, the average acceleration corresponding to the future driving section may be determined based on the navigation section data, or may be calculated by taking the average vehicle speed of adjacent future driving sections as the speed of the junction of each future driving section, and then calculating the average acceleration through the speeds of two adjacent junctions. Optionally, when the average vehicle speed corresponding to the future driving section is determined based on the navigation section data, that is, the online map function can predict the average acceleration of the traffic flow of the future driving section, the navigation section data includes the average acceleration of the traffic flow of the future driving section, and thus the average acceleration of the traffic flow can be used as the average acceleration under the specific road condition type corresponding to the future driving section. Optionally, the average acceleration corresponding to the future driving section is calculated by taking the average vehicle speed of adjacent future driving sections as the speed of the junction of each future driving section, and then calculating the average acceleration through the speeds of two adjacent junctions. For example, see Figure 2When determining the average acceleration corresponding to the future driving section 2, the average vehicle speed of the future driving section 1 is used as the first vehicle speed at the intersection with the future driving section 2, and the average vehicle speed of the future driving section 3 is used as the second vehicle speed at the intersection with the future driving section 2. The average acceleration of the intermediate future driving section 2 is calculated based on the first vehicle speed and the second vehicle speed.

[0122] The technical solution of this embodiment can divide the navigation route data of the navigation route fed back by the online map function into future driving sections of full mileage after the user determines the driving destination through the online map function, and determine the average vehicle speed, average acceleration, road grade information, congestion grade information (or road condition type), driving mileage, etc. in the navigation section data corresponding to each future driving section, so that the target driving condition information including the road condition type, average vehicle speed, and average acceleration can be determined according to the navigation section data corresponding to the future driving section, and then the "speed-acceleration-unit mileage energy consumption" MAP associated with the corresponding road condition type can be determined according to the target driving condition information corresponding to the future driving section. The "speed-acceleration-unit mileage energy consumption" MAP finally determined can be combined with the average vehicle speed and average acceleration in the target driving condition information corresponding to the future driving section to obtain the target unit mileage energy consumption of the future driving section, and the target unit mileage energy consumption and driving mileage of each future driving section of the navigation route can be further calculated to calculate the driving energy consumption of each future driving section.

[0123] Step S13: Obtain the driving range according to the target unit mileage energy consumption and the vehicle energy reserve.

[0124] In one embodiment, when the vehicle is an electric vehicle, the vehicle energy reserve may represent the remaining battery power; when the vehicle is a new energy vehicle other than an electric vehicle, the vehicle energy reserve may represent the corresponding new energy supply reserve.

[0125] In one implementation, the driving range may represent a maximum distance that the vehicle can continuously travel in the future.

[0126] In one implementation, the future driving segment may be obtained by dividing the full mileage of the navigation route.

[0127] In one embodiment, step S13: obtaining the driving range based on the target unit mileage energy consumption and the vehicle energy reserve may include: obtaining the predicted driving energy consumption of the future driving section based on the target unit mileage energy consumption of the future driving section; determining the full mileage energy consumption corresponding to the navigation route based on the predicted driving energy consumption corresponding to all future driving sections of the navigation route; determining the average unit mileage energy consumption based on the full mileage energy consumption and the future driving mileage corresponding to the navigation route; and determining the driving range based on the vehicle energy reserve and the average unit mileage energy consumption.

[0128] In this way, the technical solution of this embodiment can determine the predicted driving energy consumption corresponding to all future driving sections of the navigation route after determining the target unit mileage energy consumption corresponding to all future driving sections of the navigation route, thereby determining the full mileage energy consumption corresponding to the navigation route, and finally determining the cruising range based on the vehicle's energy reserve and the average unit mileage energy consumption. Since the target unit mileage energy consumption corresponding to each future driving section is close to the actual unit mileage energy consumption when actually driving on the future driving section, the cruising range determined based on the target unit mileage energy corresponding to each future driving section can also be closer to the actual cruising range.

[0129] In one embodiment, the step of obtaining the predicted driving energy consumption of the future driving section according to the target unit mileage energy consumption of the future driving section includes: determining the initial driving energy consumption according to the target unit mileage energy consumption of the future driving section and the mileage corresponding to the future driving section; and performing energy consumption correction processing on the initial driving energy consumption according to the geographical environment factors corresponding to the future driving section to obtain the predicted driving energy consumption.

[0130] In one embodiment, the geographical environment factor may represent various geographical environment information that can affect the driving energy consumption of the vehicle. Optionally, the geographical environment factor may include at least one of altitude information, road section ambient temperature information, etc. Optionally, the geographical environment factor may be obtained through an online map function and / or a sensor device in the vehicle.

[0131] In one embodiment, when the geographical environmental factors include road section temperature information, the initial driving energy consumption is corrected according to the geographical environmental factors corresponding to the future driving section to obtain the step of predicting the driving energy consumption, including: determining the energy consumption correction coefficient according to the road section environmental temperature information corresponding to the future driving section; and correcting the initial driving energy consumption according to the energy consumption correction coefficient. The technical solution of this embodiment can take into account the influence of the ambient temperature on the energy consumption of the battery of the vehicle, and can set the same or different energy consumption correction coefficients for different ambient temperatures to correct the driving energy consumption of the future driving section so that each is close to the actual driving energy consumption when actually driving on the future driving section.

[0132] In one embodiment, while or after the initial driving energy consumption is corrected based on the ambient temperature information, it may be corrected again after compensation is determined based on geographical environmental factors.

[0133] In one embodiment, when the geographical environmental factors include altitude information, the initial driving energy consumption is energy-corrected according to the geographical environmental factors corresponding to the future driving section to obtain the steps of predicted driving energy consumption, including: when the altitude information corresponding to the future driving section meets the uphill condition, determining the first compensation energy consumption according to the altitude information; or, when the altitude information corresponding to the future driving section meets the downhill condition, determining the second compensation energy consumption according to the altitude information; obtaining the predicted driving energy consumption according to the initial driving energy consumption and the first compensation energy consumption or the second compensation energy consumption.

[0134] In one implementation, the altitude information corresponding to the future driving section may include the altitude of the first junction point (or the altitude of the starting point of the section) and the altitude of the second junction point (or the altitude of the end point of the section).

[0135] In one implementation, the uphill condition may be characterized by the first junction point having an altitude lower than the second junction point.

[0136] In one implementation, the downhill condition may be characterized by a first junction point having an altitude greater than a second junction point.

[0137] In one implementation, the first compensation energy consumption or the second compensation energy consumption is determined according to the altitude information, and the corresponding first compensation energy consumption or the second compensation energy consumption may be determined according to the difference between the altitude of the second junction point and the altitude of the first junction point.

[0138] In one embodiment, see Figure 4 , an uphill and downhill condition is given as an example to help understand the elevation of the first junction point and the elevation of the second junction point of the future driving section.

[0139] In one embodiment, slope resistance is an important part of the vehicle's driving resistance and has a direct impact on the vehicle's driving energy consumption. Therefore, the technical solution of this embodiment can consider the impact of the slope on the driving energy consumption when analyzing the future driving energy consumption of the vehicle. When the technical solution of this embodiment was conceived, it was considered that the actual road undulations are very complicated, especially for roads in mountainous areas, where it is difficult to directly obtain the road slope changes in the future driving sections. Therefore, the road slope changes can be judged by the altitude information fed back by the online map function. In addition, due to the undulations of the actual road conditions, there may be multiple uphill and downhill sections in the interval from the start point to the end point of the navigation route. If the changes in the uphill and downhill conditions of each future driving section within the driving interval of the navigation route are ignored, there will be a large error in the energy consumption analysis by directly considering the altitude difference between the start point and the end point of the navigation route. Therefore, based on the above conception, the entire navigation route is divided into future driving sections, and each future driving section can represent an uphill and downhill condition (for example, one of an uphill condition, a downhill condition, and a flat condition). In this way, the compensation correction of the driving energy consumption of the future driving section can be better achieved based on the uphill and downhill conditions.

[0140] In one embodiment, the driving of the vehicle in uphill and downhill conditions is essentially a conversion of the vehicle's gravitational potential energy. In the uphill condition, the chemical energy inside the battery is converted into the gravitational potential energy of the electric vehicle, and the vehicle needs to consume additional energy to convert it into gravitational potential energy. In the downhill condition, the vehicle's energy recovery strategy is used to convert the vehicle's gravitational potential energy into chemical energy inside the battery, and the vehicle can consume less energy converted from gravitational potential energy.

[0141] In one embodiment, based on the above principle, when the altitude information corresponding to the future driving section meets the uphill condition, the first compensation energy consumption is determined according to the altitude information; or when the altitude information corresponding to the future driving section meets the downhill condition, the second compensation energy consumption is determined according to the altitude information, which can be achieved by the following formula:

[0142] in, Indicates the slope compensation energy consumption (indicates the first compensation energy consumption in uphill and flat conditions) ; Characterize the second compensation energy consumption in downhill conditions ), Indicates the altitude of the first junction (or the altitude of the starting point of the section). Indicates the altitude of the second junction (or the end point of the section). Indicates the energy recovery coefficient calibrated by the vehicle's energy recovery system, Indicates the vehicle's curb weight. Represents the acceleration due to gravity.

[0143] in, The altitude difference determined based on the altitude information may be represented.

[0144] Among them, when driving downhill, the vehicle's energy recovery strategy converts the vehicle's gravitational potential energy into chemical energy inside the battery, so the vehicle can consume less energy converted from gravitational potential energy. However, it is difficult to recover all the gravitational potential energy in actual situations. Therefore, the energy recovery coefficient is calibrated using the energy recovery system of electric vehicles. , the recoverable feedback energy can be estimated.

[0145] In one embodiment, in the step of obtaining the predicted driving energy consumption according to the initial driving energy consumption and the first compensation energy consumption or the second compensation energy consumption, the predicted driving energy consumption is (in, represents the initial driving energy consumption, represents the slope compensation energy consumption).

[0146] In one embodiment, the step of determining the full mileage energy consumption corresponding to the navigation route based on the predicted driving energy consumption corresponding to all future driving sections of the navigation route includes: summarizing the predicted driving energy consumption corresponding to all future driving sections to obtain the full mileage basic energy consumption corresponding to the navigation route; obtaining the total energy consumption of accessories corresponding to the navigation route; and obtaining the full mileage energy consumption based on the full mileage basic energy consumption and the total energy consumption of accessories.

[0147] In one embodiment, the step of summarizing the predicted driving energy consumption corresponding to all future driving sections to obtain the full mileage basic energy consumption corresponding to the navigation route may include: summarizing the predicted driving energy consumption corresponding to all future driving sections of the navigation route according to the calculation formula of the full mileage basic energy consumption to obtain the full mileage basic energy consumption corresponding to the navigation route. The calculation formula of the full mileage basic energy consumption is:

[0148] in, represents the predicted full-mileage basic energy consumption (kWh), represents the predicted driving energy consumption (kWh) of the ith future driving segment of the navigation route, Represents the slope compensation energy consumption (kWh) of the i-th future driving segment of the navigation route.

[0149] The technical solution of this embodiment can comprehensively consider the slope factors of each future driving section to obtain the predicted driving energy consumption of each future driving section: , and then add the predicted driving energy consumption of each future driving section to get the predicted full mileage basic energy consumption .

[0150] In one embodiment, during actual use, the navigation information of the online map function (which may represent navigation route data) will be updated in real time, so the predicted driving energy consumption can be updated in real time as the road traffic condition information in the online map function changes, thereby achieving online update of the predicted driving energy consumption.

[0151] In one embodiment, based on the inventive concept of the aforementioned technical solution, see Figure 5 The following is an example of a method for determining the full mileage basic energy consumption of a navigation route for reference: 1. In response to a navigation operation, determine navigation information.

[0152] Optionally, the navigation information includes navigation route, mileage, driving time, road traffic condition information, altitude information, vehicle speed information, etc.

[0153] 2. Divide the driving sections based on the navigation route in the navigation information to determine n future driving sections, and determine the navigation section data of each future driving section based on other information in the navigation information.

[0154] Optionally, the navigation section data of the future driving section includes average vehicle speed, average acceleration, altitude of the junction point of the section, road condition type, etc.

[0155] 3. Determine the altitude difference of the junction point and the target driving condition information of the future driving section (including road condition type, average acceleration, and average vehicle speed) based on the navigation section data of the future driving section; and determine the target unit mileage energy consumption based on the target driving condition information and the "speed-acceleration-unit mileage energy consumption" MAP associated with the road condition type; and determine the predicted driving energy consumption of the future driving section based on the altitude difference of the junction point and the target unit mileage energy consumption.

[0156] 4. Summarize the predicted driving energy consumption of all future driving sections of the navigation route to obtain the full mileage basic energy consumption.

[0157] In one embodiment, the power battery, as the only energy source of the electric vehicle, not only supplies power to the driving motor of the electric vehicle, but also supplies power to other accessories on the electric vehicle. Therefore, the power consumption of the accessories will also directly affect the energy consumption of the whole vehicle. When predicting the energy consumption of the electric vehicle, it is necessary to consider the impact of the energy consumption of the accessories on the energy consumption of the whole vehicle. Therefore, the technical solution of this embodiment proposes an energy consumption compensation scheme for accessories (i.e., obtaining the full mileage energy consumption based on the full mileage basic energy consumption and the total energy consumption of the accessories). The accessories of the electric vehicle can be divided into two categories according to the voltage type used, namely, high-voltage accessories and low-voltage accessories. The high-voltage accessories include but are not limited to the battery management system (Battery Management System, BMS), high-voltage power distribution unit (Power Distribution Unit, PDU), electric air conditioner, PTC (Positive Temperature Coefficient) heater, etc., and the low-voltage accessories include but are not limited to the vehicle's lighting system, infotainment system, wiper and washing system, etc. Optionally, the division method of the high-voltage accessories and low-voltage accessories of the electric vehicle can refer to the current mature voltage division rules.

[0158] In one embodiment, the step of obtaining the total energy consumption of accessories corresponding to the navigation route may include: obtaining a first low-pressure accessory energy consumption corresponding to the normally turned-on low-pressure accessory based on the normally turned-on low-pressure accessory and the driving time corresponding to the navigation route; and / or obtaining a second low-pressure accessory energy consumption corresponding to the conditionally turned-on low-pressure accessory based on the conditionally turned-on low-pressure accessory and the first estimated turning-on time corresponding to the navigation route; obtaining a first high-pressure accessory energy consumption corresponding to the normally turned-on high-pressure accessory based on the normally turned-on high-pressure accessory and the driving time corresponding to the navigation route; and / or obtaining a second high-pressure accessory energy consumption corresponding to the conditionally turned-on high-pressure accessory based on the conditionally turned-on high-pressure accessory and the second estimated turning-on time corresponding to the navigation route; obtaining the total energy consumption of the low-pressure accessories corresponding to the navigation route based on the first low-pressure accessory energy consumption and / or the second low-pressure accessory energy consumption; obtaining the total energy consumption of the high-pressure accessories corresponding to the navigation route based on the first high-pressure accessory energy consumption and / or the second high-pressure accessory energy consumption; obtaining the total energy consumption of the accessories corresponding to the navigation route based on the total energy consumption of the low-pressure accessories and the total energy consumption of the high-pressure accessories.

[0159] In one implementation, the technical solution of this embodiment may predetermine the average power of each accessory. Optionally, the steady-state current of the accessories of the electric vehicle is tested to obtain the steady-state current of each accessory when it is working, thereby calculating its average power.

[0160] In one embodiment, there are many types of low-voltage accessories in electric vehicles, which can be roughly divided into normally turned-on low-voltage accessories (sensors, instrument panels and display systems, etc.) and conditionally turned-on low-voltage accessories (lighting systems, blowers, etc.) according to their activation conditions.

[0161] In one embodiment, the conditionally enabled low-voltage accessory is enabled by default until the full mileage of the navigation route is completed, that is, the energy consumption is calculated using the average power and the current mileage remaining time of the navigation route fed back by the online map function (i.e., the first estimated activation time).

[0162] In one embodiment, the normally turned-on low-voltage accessory directly calculates energy consumption through the full-mileage driving time of the navigation route fed back by the online map function (for example, the driving time corresponding to the full-mileage of the navigation route updated in real time according to the movement of the vehicle, or the driving time corresponding to the full-mileage of the initial navigation route triggered by the navigation operation).

[0163] In one embodiment, the step of obtaining a first low-voltage accessory energy consumption corresponding to the normally-activated low-voltage accessory and the driving duration corresponding to the navigation route, and / or obtaining a second low-voltage accessory energy consumption corresponding to the conditionally-activated low-voltage accessory and the first estimated activation duration corresponding to the conditionally-activated low-voltage accessory and the navigation route, includes one of the following situations: In response to the opening operation of the conditionally opened low-voltage accessory, the current mileage remaining time of the navigation route and the conditionally opened low-voltage accessory (or target conditional accessory) are determined; according to the driving time corresponding to the normally opened low-voltage accessory and the navigation route, a first low-voltage accessory energy consumption corresponding to the normally opened low-voltage accessory is obtained, and according to the first estimated opening time corresponding to the conditionally opened low-voltage accessory and the navigation route, a second low-voltage accessory energy consumption corresponding to the conditionally opened low-voltage accessory is obtained; In response to a closing operation for a conditionally turned-on low-voltage accessory that has been turned on, and when it is detected that there is still a conditionally turned-on low-voltage accessory that is turned on, the current mileage remaining time of the navigation route and the conditionally turned-on low-voltage accessory (or target conditional accessory) are determined; based on the driving time corresponding to the normally turned-on low-voltage accessory and the navigation route, a first low-voltage accessory energy consumption corresponding to the normally turned-on low-voltage accessory is obtained, and based on the first estimated turning-on time corresponding to the conditionally turned-on low-voltage accessory and the navigation route, a second low-voltage accessory energy consumption corresponding to the conditionally turned-on low-voltage accessory is obtained; In response to the conditionally enabled low-voltage accessory not being enabled, a first low-voltage accessory energy consumption corresponding to the normally enabled low-voltage accessory is obtained according to the normally enabled low-voltage accessory and the driving duration corresponding to the navigation route.

[0164] In one embodiment, the total energy consumption of the low-voltage accessories corresponding to the navigation route is obtained according to the energy consumption of the first low-voltage accessory and / or the energy consumption of the second low-voltage accessory, and the following calculation formula for the total energy consumption of the low-voltage accessories can be referred to:

[0165] in, Indicates the total energy consumption (kWh) of the low-voltage accessories corresponding to the navigation route. Indicates the average power (kW) of all normally-on low-voltage accessories. Indicates the driving time corresponding to the full mileage of the navigation route. Indicates the average power (kW) of all low-voltage accessories that are enabled. It indicates the current mileage remaining time (i.e., the first estimated opening duration) of the corresponding navigation route each time an opening operation or a closing operation for a conditionally opened low-voltage accessory is received.

[0166] In this way, the technical solution of this embodiment can perform an updated calculation of the energy consumption forecast when the conditionally turned-on low-voltage accessory is turned on, and also perform an updated calculation of the energy consumption forecast when it is turned off (for example, the subsequent updated calculation of the total energy consumption of the low-voltage accessories, the updated calculation of the total energy consumption of the accessories, and the updated calculation of the full mileage energy consumption).

[0167] In other embodiments, the first estimated activation duration may be based on feedback from an online map function. Optionally, the online map function may predict the conditionally activated low-voltage accessories that need to be activated and their activation durations during navigation.

[0168] In one embodiment, among the high-voltage accessories of an electric vehicle, in addition to the drive motor, the PTC heater and the electric air-conditioning system have a greater impact on energy consumption and driving range, so the technical solution of this embodiment can mainly consider the impact of these two high-voltage accessories on energy consumption.

[0169] In one embodiment, there are many types of high-voltage accessories in electric vehicles, which can be roughly divided into normally turned-on high-voltage accessories (such as BMS, PDU, etc.) and conditionally turned-on high-voltage accessories (such as PTC heaters, electric air conditioners, etc.) according to their opening conditions.

[0170] In one embodiment, the conditionally enabled high-voltage accessory is enabled by default until the full mileage of the navigation route is completed, that is, the energy consumption is calculated using the average power and the current mileage remaining time of the navigation route fed back by the online map function (i.e., the second estimated activation time).

[0171] In one embodiment, the normally turned-on high-voltage accessory calculates energy consumption directly through the full-mileage driving time of the navigation route fed back by the online map function (for example, the driving time corresponding to the full-mileage of the navigation route updated in real time according to the movement of the vehicle, or the driving time corresponding to the full-mileage of the initial navigation route triggered by the navigation operation).

[0172] In one embodiment, the step of obtaining a first high-voltage accessory energy consumption corresponding to the normally turned-on high-voltage accessory and the driving duration corresponding to the navigation route; and / or the step of obtaining a second high-voltage accessory energy consumption corresponding to the conditionally turned-on high-voltage accessory and the navigation route according to the second estimated turning-on duration corresponding to the conditionally turned-on high-voltage accessory includes one of the following situations: In response to the activation operation of the conditionally activated high-voltage accessory, the current mileage remaining time of the navigation route and the conditionally activated high-voltage accessory (or target conditional accessory) are determined; based on the driving time corresponding to the normally activated high-voltage accessory and the navigation route, a first high-voltage accessory energy consumption corresponding to the normally activated high-voltage accessory is obtained, and based on the first estimated activation time corresponding to the conditionally activated high-voltage accessory and the navigation route, a second high-voltage accessory energy consumption corresponding to the conditionally activated high-voltage accessory is obtained; In response to a closing operation of a conditionally turned-on high-voltage accessory that has been turned on, and when it is detected that there are still turned-on conditionally turned-on high-voltage accessories, the current mileage remaining time of the navigation route and the conditionally turned-on high-voltage accessories (or target conditional accessories) are determined; based on the driving time corresponding to the normally turned-on high-voltage accessories and the navigation route, a first high-voltage accessory energy consumption corresponding to the normally turned-on high-voltage accessories is obtained, and based on the first estimated turning-on time corresponding to the conditionally turned-on high-voltage accessories and the navigation route, a second high-voltage accessory energy consumption corresponding to the conditionally turned-on high-voltage accessories is obtained; In response to the conditionally enabled high-voltage accessory not being enabled, a first high-voltage accessory energy consumption corresponding to the normally enabled high-voltage accessory is obtained according to the normally enabled high-voltage accessory and the driving duration corresponding to the navigation route.

[0173] In one embodiment, the total energy consumption of the high-voltage accessories corresponding to the navigation route is obtained according to the energy consumption of the first high-voltage accessory and / or the energy consumption of the second high-voltage accessory, and the following calculation formula for the total energy consumption of the high-voltage accessories can be referred to: (4) in, Indicates the total energy consumption (kWh) of the high-voltage accessories corresponding to the navigation route. Indicates the average power (kW) of all normally-on high-voltage accessories. Indicates the driving time corresponding to the full mileage of the navigation route. Indicates the average power (kW) of all high-voltage accessories that are enabled. It indicates the current mileage remaining time (i.e., the second estimated opening duration) of the corresponding navigation route each time an opening operation or a closing operation for a conditionally opened high-voltage accessory is received.

[0174] In this way, the technical solution of this embodiment can perform an updated calculation of the energy consumption forecast when the conditionally turned-on high-voltage accessory is turned on, and also perform an updated calculation of the energy consumption forecast when it is turned off (for example, the subsequent updated calculation of the total energy consumption of the high-voltage accessories, the updated calculation of the total energy consumption of the accessories, and the updated calculation of the full mileage energy consumption).

[0175] In other embodiments, the second estimated activation time may be based on feedback from an online map function. Optionally, the online map function may predict the conditionally activated high-voltage accessories that need to be activated and their activation time during navigation.

[0176] In one embodiment, in the step of obtaining the total energy consumption of the accessories corresponding to the navigation route according to the total energy consumption of the low-voltage accessories and the total energy consumption of the high-voltage accessories, the calculation formula of the total energy consumption of the accessories corresponding to the navigation route may be:

[0177] in, Indicates the total energy consumption of the accessories corresponding to the navigation route (kWh), Indicates the total energy consumption (kWh) of the high-voltage accessories corresponding to the navigation route. Indicates the total energy consumption (kWh) of low-voltage accessories corresponding to the navigation route.

[0178] See also Figure 6 , a flowchart of determining the total energy consumption of an accessory is provided as an example in this embodiment, so as to facilitate understanding of the inventive concept of the above-mentioned technical solution for determining the total energy consumption of an accessory.

[0179] In one embodiment, in the step of obtaining the full mileage energy consumption according to the full mileage basic energy consumption and the total energy consumption of accessories, the calculation formula of the full mileage energy consumption may be:

[0180] in, Indicates the full mileage energy consumption (kWh) corresponding to the navigation route. represents the predicted full-mileage basic energy consumption (kWh), Indicates the total energy consumption (kWh) of accessories corresponding to the navigation route.

[0181] In one embodiment, the comprehensive energy consumption of a vehicle is not only affected by the road condition type, vehicle speed and acceleration, but also by energy consumption influencing factors such as ambient temperature, low-pressure accessories, high-pressure accessories, etc. Therefore, the technical solution of this embodiment can correct the energy consumption according to geographical environmental factors, low-pressure accessories, and high-pressure accessories, so that the final cruising range can be closer to the actual cruising range.

[0182] In one embodiment, the step of determining the average energy consumption per unit mileage according to the full mileage energy consumption and the future driving mileage corresponding to the navigation route may include: calculating the average energy consumption per unit mileage according to the full mileage energy consumption, the future driving mileage corresponding to the navigation route and the average energy consumption calculation formula, wherein the average energy consumption calculation formula is:

[0183] in, Represents the average energy consumption per unit mileage, Indicates the full mileage energy consumption, Indicates the driving distance corresponding to the navigation route (for example, the remaining driving distance to the end point of the navigation route).

[0184] In one embodiment, the step of determining the driving range according to the remaining energy of the vehicle and the average energy consumption per unit mileage may include: calculating and outputting the driving range according to the calculation formula of the remaining energy of the vehicle, the average energy consumption per unit mileage and the driving range, wherein the calculation formula of the driving range is:

[0185] in, Indicates the driving range. Indicates the remaining energy of the vehicle (e.g. remaining battery power, in kWh), Indicates the average energy consumption per unit mileage.

[0186] In one implementation, when the technical solution of this embodiment is applied to an electric vehicle, the remaining battery power can be determined by obtaining SOE (State of Energy).

[0187] See also Figure 7 , a flowchart of estimating the driving range is provided in this embodiment to help understand the inventive concept of the above-mentioned technical solution for estimating the driving range.

[0188] The technical solution of this embodiment can perform real-time energy consumption prediction based on the navigation information provided by the online map function to predict the future energy consumption corresponding to the navigation route in real time. At the same time, the online map function updates the current information of the vehicle in real time to calculate the mileage corresponding to the navigation route. , so that real-time mileage prediction can be achieved, and the predicted mileage can be close to the actual mileage when driving according to the current working conditions.

[0189] The method for determining the driving range provided in this embodiment includes: step S11: obtaining target driving condition information of the future driving section, the target driving condition information including the road condition type and / or at least one driving condition feature under the road condition type; step S12: determining the target unit mileage energy consumption that matches the target driving condition information of the future driving section; step S13: obtaining the driving range according to the target unit mileage energy consumption and the remaining energy of the vehicle. In this way, the technical solution of this embodiment can fully consider the influence of various driving condition information (such as the road condition type and / or the specific driving condition under the road condition type) of the future driving section on the unit mileage energy consumption when determining the target unit mileage energy consumption for calculating the driving range, so that the determined target unit mileage energy consumption is closer to the actual unit mileage energy consumption of driving on the future driving section, and then the driving range determined based on the target unit mileage energy and the remaining energy of the vehicle can also be closer to the actual driving range, so the technical solution of this embodiment can achieve the purpose of improving user experience.

[0190] The technical solution of this embodiment can fully consider the different energy consumption performances of new energy vehicles (such as electric vehicles) under different types of operating conditions. It can combine multiple energy consumption influencing factors such as road grade, congestion level, road slope, etc. to build a unit energy consumption estimation model to predict the basic unit mileage energy consumption of future driving conditions, and estimate the driving range based on the basic unit mileage energy consumption, so as to provide users with more accurate and reliable driving range prediction services.

[0191] The technical solution of this embodiment takes into account that the driving range is jointly affected by multiple factors, including the vehicle's driving state type, driving habits, road condition type, etc. (the traditional driving range method does not fully consider these relationships). The technical solution of this embodiment can be based on the vehicle's actual driving data and the road traffic condition information provided by the navigation map platform (online map function), consider the different energy consumption performance of the vehicle under different road condition types or the energy consumption performance of different driving conditions under different road condition types, and establish a unit energy consumption estimation model that comprehensively considers multiple energy consumption influencing factors such as road grade and congestion level. By establishing the unit energy consumption estimation model, the unit mileage energy consumption can be estimated and output based on the road condition type of the future driving section and / or the driving condition characteristics under one road condition type to support the calculation of the driving range. Therefore, the obtained driving range has fully considered the vehicle's driving state type, driving habits, road condition type and other factors, so that the obtained driving range can be closer to the actual driving range.

[0192] Based on the same inventive concept as the above embodiments, the present application embodiment provides a computing device, such as Figure 8 As shown, the device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 8The processor 310 shown in the figure is not used to indicate that the number of the processor 310 is one, but is only used to indicate the positional relationship of the processor 310 relative to other devices. In actual applications, the number of the processor 310 may be one or more; similarly, Figure 8 The memory 311 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 311 relative to other devices. In practical applications, the number of memories 311 can be one or more. When the processor 310 runs the computer program, the method for determining the driving range applied to the above device is implemented.

[0193] The device may also include: at least one network interface 312. The various components in the device are coupled together via a bus system 313. It is understood that the bus system 313 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 8 Various buses are labeled as bus system 313.

[0194] The memory 311 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 311 described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memories.

[0195] The memory 311 in the embodiment of the present application is used to store various types of data to support the operation of the device. Examples of these data include: any computer program used to operate on the device, such as an operating system and an application program. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and handle hardware-based tasks. The application program may include various applications, such as a media player (MediaPlayer), a browser (Browser), etc., which are used to implement various application services. Here, the program that implements the method of the embodiment of the present application may be included in the application program.

[0196] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer-readable storage medium, in which a computer program is stored. The computer-readable storage medium may be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); or it may be various devices including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer-readable storage medium is executed by the processor, the above-mentioned method for determining the driving range is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 The description of the illustrated embodiment will not be repeated here.

[0197] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0198] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0199] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present 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 method for determining a driving range, characterized in that: include: Acquire target driving condition information of a future driving section, wherein the target driving condition information includes a road condition type and / or at least one driving condition feature under the road condition type; Determining a target energy consumption per unit mileage that matches the target driving condition information of the future driving section; The driving range is obtained according to the target energy consumption per unit mileage and the remaining energy of the vehicle.

2. The method according to claim 1, characterized in that Before the step of obtaining the target driving condition information of the future driving section, the method includes: In response to the navigation operation, obtaining a navigation route; The navigation route is divided according to a preset division rule to divide the navigation route into at least one future driving section, wherein the preset division rule includes at least one of division according to a distance rule and division according to a road condition type.

3. The method according to claim 1 or 2, characterized in that: The step of determining a target energy consumption per unit mileage that matches the target driving condition information of the future driving section comprises: The target unit mileage energy consumption of the future driving section is determined based on the target driving condition information of the future driving section and a pre-built unit energy consumption estimation model. The unit energy consumption estimation model is obtained by building a model based on mapping relationship information that characterizes the corresponding relationship between sample driving condition information and unit mileage energy consumption.

4. The method according to claim 3, characterized in that The future driving section is obtained by dividing the navigation route; The step of obtaining the driving range according to the target energy consumption per unit mileage and the remaining energy of the vehicle comprises: Acquiring predicted driving energy consumption of the future driving section according to the target unit mileage energy consumption of the future driving section; Determining the full mileage energy consumption corresponding to the navigation route according to the predicted driving energy consumptions corresponding to all the future driving sections of the navigation route; Determine the average energy consumption per unit mileage according to the full mileage energy consumption and the future driving mileage corresponding to the navigation route; The driving range is determined according to the vehicle energy reserve and the average energy consumption per unit mileage.

5. The method according to claim 4, characterized in that The step of obtaining the predicted driving energy consumption of the future driving section according to the target unit mileage energy consumption of the future driving section comprises: determining an initial driving energy consumption according to the target unit mileage energy consumption of the future driving section and the driving mileage corresponding to the future driving section; The initial driving energy consumption is subjected to energy consumption correction processing according to the geographical environment factors corresponding to the future driving section to obtain the predicted driving energy consumption, wherein the geographical environment factors include at least one of altitude information and section ambient temperature information.

6. The method according to claim 5, characterized in that When the geographical environment factor includes the altitude information, the step of performing energy consumption correction processing on the initial driving energy consumption according to the geographical environment factor corresponding to the future driving section to obtain the predicted driving energy consumption includes: When the altitude information corresponding to the future driving section meets the uphill condition, determining the first compensation energy consumption according to the altitude information; or, When the altitude information corresponding to the future driving section meets the downhill condition, determining the second compensation energy consumption according to the altitude information; The predicted driving energy consumption is acquired according to the initial driving energy consumption and the first compensation energy consumption or the second compensation energy consumption.

7. The method according to claim 6, characterized in that The altitude information includes the altitude of the first junction point and the altitude of the second junction point of the future driving section; Determining a first compensation energy consumption according to the altitude information includes: The first compensation energy consumption is calculated according to the altitude information and the calculation formula of the first compensation energy consumption; wherein the calculation formula of the first compensation energy consumption is: in, represents the first compensation energy consumption, represents the altitude of the first junction point, represents the altitude of the second junction point, Indicates the vehicle's curb weight. represents the acceleration due to gravity; Determining a second compensation energy consumption according to the altitude information includes: The second compensation energy consumption is calculated according to the altitude information and the calculation formula of the second compensation energy consumption; wherein the calculation formula of the second compensation energy consumption is: in, represents the second compensation energy consumption, represents the altitude of the first junction point, represents the altitude of the second junction point, Indicates the vehicle's curb weight. represents the acceleration due to gravity, Indicates the energy recovery coefficient calibrated by the vehicle's energy recovery system.

8. The method according to any one of claims 4 to 7, characterized in that: The step of determining the full mileage energy consumption corresponding to the navigation route according to the predicted driving energy consumptions corresponding to all the future driving sections of the navigation route respectively comprises: Summarizing the predicted driving energy consumptions corresponding to all the future driving sections respectively to obtain the full mileage basic energy consumption corresponding to the navigation route; Obtaining the total energy consumption of the accessories corresponding to the navigation route; The full mileage energy consumption is obtained according to the full mileage basic energy consumption and the total energy consumption of the accessories.

9. The method according to claim 8, characterized in that The step of obtaining the total energy consumption of the accessories corresponding to the navigation route includes: According to the normally turned-on low-voltage accessory and the driving duration corresponding to the navigation route, a first low-voltage accessory energy consumption corresponding to the normally turned-on low-voltage accessory is obtained; and / or according to the conditionally turned-on low-voltage accessory and the first estimated turning-on duration corresponding to the navigation route, a second low-voltage accessory energy consumption corresponding to the conditionally turned-on low-voltage accessory is obtained; According to the normally turned-on high-voltage accessory and the driving duration corresponding to the navigation route, a first high-voltage accessory energy consumption corresponding to the normally turned-on high-voltage accessory is obtained; and / or according to the conditionally turned-on high-voltage accessory and the second estimated turning-on duration corresponding to the navigation route, a second high-voltage accessory energy consumption corresponding to the conditionally turned-on high-voltage accessory is obtained; According to the energy consumption of the first low-voltage accessory and / or the energy consumption of the second low-voltage accessory, acquiring the total energy consumption of the low-voltage accessories corresponding to the navigation route; According to the first high-voltage accessory energy consumption and / or the second high-voltage accessory energy consumption, acquiring the total energy consumption of the high-voltage accessories corresponding to the navigation route; The total energy consumption of the accessories corresponding to the navigation route is obtained according to the total energy consumption of the low-voltage accessories and the total energy consumption of the high-voltage accessories.

10. The method according to claim 4, characterized in that The step of determining the average energy consumption per unit mileage according to the full mileage energy consumption and the future driving mileage corresponding to the navigation route comprises: The average energy consumption per unit mileage is calculated according to the full mileage energy consumption, the future mileage corresponding to the navigation route and the average energy consumption calculation formula, wherein the average energy consumption calculation formula is: in, represents the average energy consumption per unit mileage, represents the full mileage energy consumption, Indicates the future driving distance corresponding to the navigation route; and / or, The step of determining the driving range according to the vehicle energy reserve and the average energy consumption per unit mileage comprises: The driving range is calculated and output according to the vehicle energy reserve, the average energy consumption per unit mileage and the calculation formula of the driving range, wherein the calculation formula of the driving range is: in, Indicates the driving range, represents the remaining energy of the vehicle, Represents the average energy consumption per unit mileage.

11. A computing device, characterized in that: include: A processor and a memory storing a computer program, wherein when the processor runs the computer program, the steps of the method for determining the driving range according to any one of claims 1 to 10 are implemented.

12. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the steps of the method for determining the driving range according to any one of claims 1 to 10 are implemented.

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