Method for determining range, computing device and readable storage medium

Abstract

The application discloses a method for determining a driving range, a computing device and a computer readable storage medium. The method comprises: obtaining target driving condition information of a future driving section, the target driving condition information comprising a road condition type and / or at least one driving condition characteristic under the road condition type; determining a target unit mileage energy consumption matched with the target driving condition information of the future driving section; and obtaining the driving range according to the target unit mileage energy consumption and a vehicle energy reserve. The technical solution of the application fully considers the influence of various driving condition information 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 when driving on the future driving section, and the driving range determined based on the target unit mileage energy consumption and the vehicle energy reserve is also closer to the actual driving range, thereby achieving the purpose of improving user experience.

Description

Technical Field

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

[0002] Electric vehicles, as representatives of new energy vehicles, are gradually becoming a development trend in the automotive industry. However, the "range anxiety" caused by their limited driving range has always been one of the key factors restricting their market promotion and user acceptance. Therefore, research on methods for estimating vehicle driving range is of great significance to promoting the development of the electric vehicle industry. Providing accurate driving range estimates can help users better understand the performance of electric vehicles and alleviate their "range anxiety."

[0003] Most existing methods for estimating driving range directly calculate the range based on the nominal range and the state of charge (SOC) of the battery, without considering the impact of various factors (such as road traffic conditions and related driving conditions) on the energy consumption of electric vehicles. This results in a large error between the driving range calculated by existing methods and the actual driving range, leading to low user trust in the driving range displayed on electric vehicles and a poor user experience. Summary of the Invention

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

[0005] To achieve the above objectives:

[0006] In a first aspect, embodiments of this application provide a method for determining driving range, comprising: obtaining target driving condition information for a future driving segment, the target driving condition information including 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 for the future driving segment; and obtaining the driving range based on the target energy consumption per unit mileage and the vehicle's remaining energy.

[0007] In one embodiment, before the step of obtaining target driving condition information of future driving segments, the method includes: obtaining a navigation route in response to a navigation operation; dividing the navigation route into at least one future driving segment according to a preset division rule, wherein the preset division rule includes at least one of division by distance rule and division by road condition type.

[0008] In one embodiment, the step of determining the target energy consumption per unit mileage that matches the target driving condition information of the future driving segment includes: determining the target energy consumption per unit mileage of the future driving segment based on the target driving condition information of the future driving segment and a pre-built energy consumption prediction model. The energy consumption prediction model is obtained by building a model based on the mapping relationship information that characterizes the correspondence between sample driving condition information and energy consumption per unit mileage.

[0009] In one implementation, the future travel route is obtained by dividing the navigation route.

[0010] In one embodiment, the step of obtaining the driving range based on the target energy consumption per unit mileage and the vehicle's remaining energy includes: obtaining the predicted driving energy consumption of the future driving segment based on the target energy consumption per unit mileage of the future driving segment; determining the total mileage energy consumption corresponding to the navigation route based on the predicted driving energy consumption corresponding to each of the future driving segments of the navigation route; determining the average energy consumption per unit mileage based on the total mileage energy consumption and the future driving range corresponding to the navigation route; and determining the driving range based on the vehicle's remaining energy and the average energy consumption per unit mileage.

[0011] In one embodiment, the step of obtaining the predicted driving energy consumption of a future driving segment based on the target energy consumption per unit mileage of the future driving segment includes: determining the initial driving energy consumption based on the target energy consumption per unit mileage of the future driving segment and the driving mileage corresponding to the future driving segment; and performing energy consumption correction processing on the initial driving energy consumption based on the geographical environmental factors corresponding to the future driving segment to obtain the predicted driving energy consumption, wherein the geographical environmental factors include at least one of altitude information and road segment ambient temperature information.

[0012] In one embodiment, when the geographical environmental factors include altitude information, the step of performing energy consumption correction processing on the initial driving energy consumption based on the geographical environmental factors corresponding to the future driving segment to obtain the predicted driving energy consumption includes: determining a first compensation energy consumption based on the altitude information when the altitude information corresponding to the future driving segment meets the uphill condition; or determining a second compensation energy consumption based on the altitude information when the altitude information corresponding to the future driving segment meets the downhill condition; and obtaining the predicted driving energy consumption based on the initial driving energy consumption and the first compensation energy consumption or the second compensation energy consumption.

[0013] In one embodiment, the altitude information includes the altitude of the first and second boundary points of the future travel route.

[0014] In one embodiment, determining the first compensation energy consumption based on altitude information includes: calculating the first compensation energy consumption based on the altitude information and a calculation formula for the first compensation energy consumption; wherein the calculation formula for the first compensation energy consumption is:

[0015]

[0016] in, This indicates the first compensation energy consumption. Indicates the elevation of the first boundary point. Indicates the elevation of the second boundary point. This indicates the vehicle's curb weight. It represents the acceleration due to gravity.

[0017] In one embodiment, determining the second compensation energy consumption based on altitude information includes: calculating the second compensation energy consumption based on the altitude information and a calculation formula for the second compensation energy consumption; wherein the calculation formula for the second compensation energy consumption is:

[0018]

[0019] in, This indicates the second compensation energy consumption. Indicates the elevation of the first boundary point. Indicates the elevation of the second boundary point. This indicates the vehicle's curb weight. Represents gravitational acceleration. This indicates the energy recovery coefficient calibrated by the vehicle's energy recovery system.

[0020] In one embodiment, the step of determining the total mileage energy consumption of the navigation route based on the predicted driving energy consumption corresponding to each of the future driving segments of the navigation route includes: summarizing the predicted driving energy consumption corresponding to each of the future driving segments to obtain the basic energy consumption of the total mileage of the navigation route; obtaining the total energy consumption of the accessories corresponding to the navigation route; and obtaining the total mileage energy consumption based on the basic energy consumption of the total mileage and the total energy consumption of the accessories.

[0021] In one embodiment, the step of obtaining the total energy consumption of accessories corresponding to the navigation route includes: obtaining the energy consumption of a first low-voltage accessory corresponding to a normally activated low-voltage accessory based on the driving time corresponding to the navigation route and the normally activated low-voltage accessory; and / or, obtaining the energy consumption of a second low-voltage accessory corresponding to a conditionally activated low-voltage accessory based on the first estimated activation time corresponding to the navigation route and the conditionally activated low-voltage accessory; obtaining the energy consumption of a first high-voltage accessory corresponding to a normally activated high-voltage accessory based on the driving time corresponding to the navigation route and the normally activated high-voltage accessory; and / or, obtaining the energy consumption of a second high-voltage accessory corresponding to a conditionally activated high-voltage accessory based on the second estimated activation time corresponding to the navigation route and the conditionally activated high-voltage accessory; obtaining the total energy consumption of low-voltage accessories corresponding to the navigation route based on the energy consumption of the first low-voltage accessory and / or the energy consumption of the second low-voltage accessory; obtaining the total energy consumption of high-voltage accessories corresponding to the navigation route based on the energy consumption of the first high-voltage accessory and / or the energy consumption of the second high-voltage accessory; and obtaining the total energy consumption of accessories corresponding to the navigation route based on the total energy consumption of low-voltage accessories and the total energy consumption of high-voltage accessories.

[0022] In one embodiment, the step of determining the average energy consumption per unit mileage based on the total mileage energy consumption and the future mileage corresponding to the navigation route includes: calculating the average energy consumption per unit mileage based on the total 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:

[0023]

[0024] in, This represents the average energy consumption per unit distance. Indicates energy consumption over the entire distance. This indicates the future driving distance corresponding to the navigation route.

[0025] In one embodiment, the step of determining the driving range based on the vehicle's remaining energy and average energy consumption per unit mile includes: calculating and outputting the driving range based on the vehicle's remaining energy, average energy consumption per unit mile, and a formula for calculating the driving range, wherein the formula for calculating the driving range is:

[0026]

[0027] in, Indicates driving range, Indicates the vehicle's remaining energy. This represents the average energy consumption per unit distance.

[0028] Secondly, embodiments of this application provide a computing device, including: a processor and a memory storing a computer program, wherein when the processor runs the computer program, it implements the steps of the method for determining the driving range as described in any of the preceding claims.

[0029] Thirdly, embodiments of this application provide a computer-readable storage medium, in one embodiment of which stores a computer program that, when executed by a processor, implements the steps of the method for determining the driving range as described in any of the preceding claims.

[0030] The method, computing device, and computer-readable storage medium for determining driving range provided in this application include: obtaining target driving condition information for a future driving segment, the target driving condition information including road condition type and / or at least one driving condition feature under the road condition type; determining a target unit mileage energy consumption matching the target driving condition information for the future driving segment; and obtaining the driving range based on the target unit mileage energy consumption and the vehicle's remaining energy. Thus, the technical solution of this application, when determining the target unit mileage energy consumption for calculating the driving range, can fully consider the impact of various driving condition information (such as road condition type and / or specific driving conditions under that road condition type) on the unit mileage energy consumption for the future driving segment. This makes the determined target unit mileage energy consumption closer to the actual unit mileage energy consumption while driving on the future driving segment. Consequently, the driving range determined based on the target unit mileage energy consumption and the vehicle's remaining energy can also be closer to the actual driving range. Therefore, the technical solution of this application can achieve the goal of improving user experience. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0032] Figure 1 This is a flowchart illustrating the method for determining driving range provided in the embodiments of this application.

[0033] Figure 2 This is a schematic diagram illustrating the segmentation of navigation routes as exemplified in an embodiment of this application.

[0034] Figure 3 This is a speed-time correspondence diagram illustrating the classification of driving state types, as exemplified in an embodiment of this application.

[0035] Figure 4 This is a schematic diagram illustrating the division of uphill and downhill working conditions in an example embodiment of this application.

[0036] Figure 5 This is a flowchart illustrating a method for determining the basic energy consumption of a navigation route over its entire length, as exemplified in an embodiment of this application.

[0037] Figure 6 This is a flowchart illustrating how to determine the total energy consumption of an accessory, as exemplified in this embodiment.

[0038] Figure 7 This is a flowchart illustrating an example of estimating driving range in this embodiment.

[0039] Figure 8 This is a schematic diagram of the structure of the computing device provided in the embodiments of this application.

[0040] The realization of the objectives, functional features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and textual descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0041] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0042] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0043] It should be understood that although the terms first, second, third, etc., may be used herein to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if," as used herein, can be interpreted as "when," "when," or "in response to determination." Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprising," "including," indicate the presence of the stated feature, step, operation, element, component, item, kind, and / or group, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms "or" and "and / or" as used herein are to be interpreted as inclusive, or mean any one or any combination thereof. Therefore, "A, B, or C" or "A, B, and / or C" means "any one of the following: A; B; C; A and B; A and C; B and C; A, B, and C". Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0044] It should be understood that although the steps in the flowcharts of this application's embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0045] It should be noted that step designations such as S11 and S12 are used in this document for the purpose of more clearly and concisely describing the corresponding content, and do not constitute a substantial limitation on the order. In specific implementation, those skilled in the art may execute S12 first and then S11, etc., but these should all be within the protection scope of this application.

[0046] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0047] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0048] See Figure 1 This application provides a method for determining driving range, which can be executed by a computing device provided in this application. This device can be implemented in software and / or hardware. Optionally, the computing device may be, for example, a server, an in-vehicle terminal, etc.

[0049] This embodiment provides a method for determining driving range, including (for example, steps S11 to S13):

[0050] Step S11: Obtain the target driving condition information for the future driving segment. The target driving condition information includes the road condition type and / or at least one driving condition feature under the road condition type.

[0051] In one embodiment, the future travel segment can represent the road segment that the vehicle will travel on. Optionally, the future travel segment can be determined through the online map function provided by a navigation map platform, and the future travel segment can be associated with navigation segment data.

[0052] In one embodiment, navigation segment data can characterize vehicle-related data and road traffic condition information estimated by the online map function of a navigation map platform during driving on a segment. Thus, the technical solution of this embodiment can determine the road condition type of the future driving segment and / or at least one driving condition characteristic under the road condition type based on the navigation segment data, to obtain the target driving condition information for the future driving segment.

[0053] In one embodiment, road condition types can be used to distinguish different road traffic conditions. Optionally, road condition types can correspond to road traffic condition information, wherein the road traffic condition information includes at least one of road grade information, congestion level information, and other information reflecting road traffic conditions. Optionally, road traffic condition information can be included in navigation segment 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 level information in its corresponding navigation segment data.

[0054] In one embodiment, the road classification information in this embodiment can refer to the definition rules of road classification in currently mature navigation map platforms, such as including unnamed roads, general roads, main roads, expressways, urban expressways, national highways, and expressways.

[0055] In one embodiment, the congestion level information in this embodiment can refer to the definition rules of congestion levels in currently mature navigation map platforms, such as smooth traffic, light congestion, moderate congestion, severe congestion, and complete congestion.

[0056] In one embodiment, actual research shows that the energy consumption per unit mileage of a vehicle varies under different road conditions. Therefore, the technical solution of this embodiment can fully consider the impact of various driving conditions on the energy consumption per unit mileage by carefully classifying road conditions.

[0057] In one embodiment, to facilitate understanding of the definition of road condition types in this embodiment, this embodiment exemplifies a method of classifying road condition types according to road grade and congestion level. The following table 1 illustrates the road condition types:

[0058] Table 1:

[0059]

[0060] In one embodiment, the driving condition characteristics under a road condition type can characterize various driving parameters of the vehicle on a future driving segment of a road condition type. Optionally, the driving condition characteristics may include, for example, average acceleration, average speed, mileage on the future driving segment, driving time on the future driving segment, and ambient temperature on the future driving segment.

[0061] In one embodiment, before obtaining the target driving condition information of the future driving segment in step S11, the method includes: obtaining a navigation route in response to a navigation operation; dividing the navigation route into at least one future driving segment according to a preset division rule, wherein the preset division rule includes at least one of division by distance rule and division by road condition type.

[0062] In one embodiment, the step of obtaining a navigation route in response to a navigation operation may include: after setting the destination through an online map function, obtaining the navigation route and its corresponding navigation route data based on the current vehicle location and the destination.

[0063] In one embodiment, navigation route data can represent vehicle-related data and road traffic information estimated by the online map function of the navigation map platform during a route journey.

[0064] In one embodiment, the preset division rules can characterize various rules for dividing the navigation route into at least one future travel segment. In other words, multiple future travel segments can be multiple short segments divided by the navigation route.

[0065] In one embodiment, the division according to distance rules includes, but is not limited to, one of the following:

[0066] The navigation route is divided according to a preset distance to obtain at least one future driving segment, and / or if the distance of the last segment of the divided navigation route is less than the preset distance, the last segment is taken as a future driving segment;

[0067] Divide the navigation route into at least one future driving segment that is equidistant from each other.

[0068] In one embodiment, the classification based on road condition type includes, but is not limited to, at least one of the following:

[0069] Based on the navigation route data, the road segments in the navigation route are divided into different road condition types;

[0070] The road segments classified by road condition type will be used as future driving routes;

[0071] For each road condition type, long road segments exceeding the distance threshold are divided into at least two short road segments according to distance rules, so that the short road segments can be used as future travel segments.

[0072] The technical solution of this embodiment can divide the navigation route into one or more suitable future driving segments, so that the energy consumption per unit mileage corresponding to each future driving segment can be more closely close to the actual energy consumption per unit mileage.

[0073] Based on the aforementioned technical concept of dividing navigation routes, see [link to relevant documentation]. Figure 2 Here is an example of a navigation route division: the entire navigation route is divided into segments, with each fixed distance (i.e., a preset distance) forming a future driving segment (if the mileage of the last segment of the navigation route is insufficient, it is also considered as a separate future driving segment), resulting in 14 future driving segments.

[0074] In one embodiment, step S11, which involves obtaining target driving condition information for a future driving segment, may include: determining navigation segment data for at least one future driving segment based on navigation route data; and determining target driving condition information for the future driving segment based on the navigation segment data.

[0075] Step S12: Determine the target energy consumption per unit mileage that matches the target driving condition information of the future driving segment.

[0076] In one embodiment, step S12: determining the target unit mileage energy consumption that matches the target driving condition information of the future driving segment may include: determining the target unit mileage energy consumption of the future driving segment based on the target driving condition information of the future driving segment and the pre-built unit energy consumption prediction model. The unit energy consumption prediction model is obtained by building a model based on the mapping relationship information that characterizes the correspondence between sample driving condition information and unit mileage energy consumption.

[0077] Thus, 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 conditions under that road condition type) on energy consumption per unit mileage. It pre-associates accurate energy consumption per unit mileage for various sample driving condition information to obtain one or more mapping relationship information. When building a model using one or more mapping relationship information, it learns the energy consumption per unit mileage under different driving conditions (for example, learning the energy consumption per unit mileage under specific driving conditions of different road condition types, and / or learning the energy consumption per unit mileage under different specific driving conditions of the same road condition type) to obtain a unit energy consumption prediction model. Subsequently, when determining the target driving condition information of the current driving segment or the future driving segment, the unit energy consumption prediction model can 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.

[0078] In one embodiment, the steps for building a unit energy consumption prediction model include: obtaining one or more mapping relationship information; and building a model based on one or more mapping relationship information to obtain a unit energy consumption prediction model.

[0079] In one embodiment, the unit energy consumption prediction model can be built 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 trees: using a tree diagram to represent data features and decision rules, used for classification and regression problems; support vector machine regression (SVR): a supervised learning technique for regression analysis that can predict continuous values; random forests: integrating multiple decision trees to improve prediction accuracy; neural networks: simulating how the human brain processes information, suitable for prediction of complex datasets; ensemble learning: combining multiple learners to improve prediction performance, such as XGBoost, LightGBM, CatBoost, etc.

[0080] In one embodiment, the unit energy consumption prediction model of this embodiment can also be a mapping diagram (MAP). Optionally, the mapping diagram can be obtained by interpolating based on one or more mapping relationship information. Optionally, the mapping diagram is, for example, a "speed-acceleration-unit mileage energy consumption" MAP associated with each road condition type; another example is a "road condition type-unit mileage energy consumption" MAP; yet another example is a "driving state type-unit mileage energy consumption" MAP associated with each road condition type.

[0081] In one implementation, the sample driving condition information can correspond to historical driving data.

[0082] In one embodiment, the historical driving data can be at least one of the following: historical driving data of this vehicle, historical driving data of other vehicles of the same model as this vehicle, etc. Optionally, the sample driving condition information in this embodiment can be obtained by analyzing the driving condition characteristics in the historical driving data of this vehicle, and the energy consumption per unit mile corresponding to the sample driving condition information can be calculated based on the driving condition characteristics in the historical driving data of this vehicle corresponding to the sample driving condition information.

[0083] In one embodiment, the historical driving data of the vehicle is obtained by actually controlling the vehicle based on the driving habits of the vehicle user and the explicit and / or implicit performance of the vehicle. Therefore, the technical solution of this embodiment is based on the sample driving condition information determined by the historical driving data of the vehicle and its corresponding energy consumption per unit mileage, and builds a model to obtain a unit energy consumption prediction model. This model can adapt to the driving habits of the vehicle user and the energy consumption pattern of the explicit and / or implicit performance of the vehicle, so that the target unit mileage energy consumption predicted by the unit energy consumption prediction model is closer to the actual unit mileage energy consumption when the vehicle user drives the vehicle in real-world driving.

[0084] In one embodiment, the sample driving condition information can also characterize the road condition type of the previous driving segment, and / or at least one driving condition feature under the road condition type.

[0085] In one embodiment, the steps for building a per-unit energy consumption prediction model include: obtaining one or more mapping relationship information, which may include: determining at least one sample driving condition information based on acquired historical driving data; calculating unit energy consumption based on the sample driving condition information to obtain the corresponding unit mileage energy consumption; and forming at least one mapping relationship information based on the unit mileage energy consumption corresponding to each of the at least one sample driving condition information. Thus, 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.

[0086] In one embodiment, before determining at least one sample driving condition information based on the acquired historical driving data, the process 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 for at least one road condition type.

[0087] In one embodiment, real-vehicle driving data can characterize vehicle-related data and road traffic conditions during the actual driving process.

[0088] In one embodiment, the real-vehicle driving data can be a set of driving data corresponding to road segments representing various road condition types. Optionally, the driving condition features (or driving condition parameters) in the real-vehicle driving data may include vehicle parameters and road parameters.

[0089] In one embodiment, the vehicle parameters can be various parameters corresponding to the vehicle's operation, such as vehicle speed, time, battery voltage, motor torque, motor speed, and mileage. Optionally, the vehicle parameters can be recorded in association with a time axis. For example, recording vehicle speed in association with a time axis can obtain a vehicle speed-time correspondence, and recording mileage in association with a time axis can obtain a mileage-time correspondence.

[0090] In one embodiment, road parameters can characterize various parameters related to the driving environment of a road segment, such as road grade information, congestion grade information, average road speed, altitude information, and road segment ambient temperature information.

[0091] In one implementation, to facilitate understanding of real-vehicle driving data, the following table 2 illustrates the driving condition characteristics collected at a sampling time:

[0092] Table 2:

[0093]

[0094] In one embodiment, the preprocessing strategy corresponding to the data preprocessing includes at least one of the following:

[0095] Delete driving data segments from the real vehicle driving data that meet the exclusion criteria;

[0096] Perform duplicate value deletion on the actual vehicle driving data;

[0097] Missing values ​​were filled in the actual vehicle driving data;

[0098] Perform outlier repair on real vehicle driving data.

[0099] In one embodiment, driving data segments that meet the exclusion criteria can be driving data segments corresponding to unconventional driving conditions that enable the calculation of energy consumption per unit mileage.

[0100] In one embodiment, driving data segments that meet the exclusion criteria include, but are not limited to, at least one of the following:

[0101] The driving data segment corresponds to a driving condition where the parking time is greater than or equal to the preset time.

[0102] The driving data segment corresponds to a driving condition where the maximum gradeability is greater than or equal to the preset gradeability.

[0103] In one implementation, the preset duration can be the upper limit of the parking duration corresponding to a road segment of a certain traffic condition type.

[0104] In one embodiment, the preset slope can be set to any slope value according to actual needs.

[0105] In one embodiment, the maximum gradeability can be determined based on altitude information from real-world vehicle driving data. For example, the altitude difference between the starting point and the ending point of a road segment of a certain road condition type in the real-world driving data can be determined, and the maximum gradeability can be determined based on the altitude difference.

[0106] In one embodiment, during the real vehicle driving data collection process, data anomalies may occur due to driver misoperation or communication issues with the collection equipment. Therefore, it is necessary to exclude driving data segments that meet the exclusion criteria from the real vehicle driving data. Furthermore, the collected real vehicle driving data needs to be deleting duplicate values, processing abnormal values, and supplementing missing values ​​so that the historical driving data obtained based on the real vehicle driving data can achieve more accurate calculation or prediction of energy consumption per unit mileage.

[0107] In one embodiment, considering that the longest traffic light duration in urban areas is generally 120 seconds, the preset duration can be appropriately extended to 150 seconds. Therefore, driving data segments with continuous parking durations exceeding 150 seconds are considered long-term parking segments (driving data segments that meet the exclusion criteria). Optionally, considering that road gradient has a direct impact on driving energy consumption, this embodiment mainly considers flat road conditions when building the model, so that the target energy consumption per unit mile output by the completed model can be further corrected based on the gradient in subsequent applications. Therefore, when determining historical driving data based on real vehicle driving data, it is necessary to reduce the impact of gradient on energy consumption. Based on road design specifications, the maximum gradeability for cars that frequently drive in urban areas and on good highways is around 10°. Therefore, driving data segments with a maximum gradeability greater than 10° (i.e., the preset gradient) are considered excessively gradient segments (driving data segments that meet the exclusion criteria). Optionally, to improve the effectiveness of historical driving data, it is necessary to delete the aforementioned excessively gradient segments and long-term parking segments.

[0108] In one embodiment, real vehicle driving data is preprocessed and classified according to road conditions to obtain historical driving data for at least one road condition type.

[0109] In one embodiment, the operating condition classification processing may be to classify the actual vehicle driving data after data preprocessing according to a preset operating condition classification rule.

[0110] In one embodiment, the preset operating condition classification rules include, but are not limited to, at least one of the following:

[0111] Extract the first driving data of the first journey segment that meets the road condition type requirements from the actual vehicle driving data;

[0112] Extract second driving data from the first driving data that meets the requirements of the second travel segment of the driving state type;

[0113] Use the first driving data as historical driving data, and / or use the second driving data as historical driving data.

[0114] In one implementation, the road condition type requirement can characterize the types of road conditions required, such as the 30 road condition types in Table 1.

[0115] 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 using the first driving data as historical driving data and building a model based on the mapping relationship information (correspondence between road condition type and energy consumption per unit mileage) determined by the historical driving data, the influence law of road condition type on energy consumption per unit mileage can be learned, so that the model obtained later can input the road condition type and output the corresponding target energy consumption per unit mileage.

[0116] In one embodiment, the first driving data of the first trip segment is, for example, the driving data corresponding to the interval from one idling stop state to the next idling stop state according to the time axis; or, for example, the driving data corresponding to a preset mileage interval under a road condition type.

[0117] In one embodiment, the driving state type requirement represents at least one required driving state type. Optionally, the driving state type requirement may include at least one of acceleration driving state, constant speed driving state, deceleration driving state, etc.

[0118] In one embodiment, the second driving data, which is extracted from the first driving data to satisfy the driving state type requirements, includes, but is not limited to, at least one of the following:

[0119] Second driving data is extracted from the first driving data to represent the second travel segment of the acceleration state;

[0120] Second driving data is extracted from the first driving data to represent a second travel segment in a constant-speed driving state;

[0121] The second driving data is extracted from the first driving data, representing the second travel segment of the deceleration state.

[0122] In one embodiment, see Figure 3 Taking the first driving data of the first trip segment under a road condition type as an example, the speed-time correspondence included in the first driving data is used to demonstrate how the first driving data can be segmented into driving data of acceleration driving state, driving data of constant speed driving state, and driving data of deceleration driving state according to the driving state type.

[0123] In one embodiment, this embodiment can determine the driving state type represented by the segment based on 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:

[0124]

[0125] in, It represents the average acceleration of a segment of travel.

[0126] It should be understood that The corresponding acceleration threshold can be, but is not limited to, 1 / 2. It can also be set to any other value according to actual needs; The corresponding acceleration threshold can be, but is not limited to, 1 / 2. It can also be set to any other value according to actual needs; The corresponding upper limit threshold for acceleration can be, but is not limited to, 1 / 2. It can also be set to any other value according to actual needs, and The corresponding lower limit threshold for acceleration can be, but is not limited to, 1 / 2. It can also be set to any other value according to actual needs.

[0127] In one embodiment, the second travel data of the second trip segment can represent the travel data corresponding to a short trip under a specific travel state type of a road condition. Optionally, when using the second travel data as historical travel data and building a model based on the mapping relationship information determined by the historical travel data (the correspondence between a specific travel state type under a road condition and energy consumption per unit mile), it is possible to learn the road condition type and the influence of the travel state type under the road condition type on energy consumption per unit mile. Thus, the subsequent model can take the road condition type and the specific travel state type as input to output the corresponding target energy consumption per unit mile.

[0128] In one embodiment, the technical solution of this embodiment divides continuous real vehicle driving data or first driving data into driving data representing independent travel segments. This allows for better calculation or analysis of the relationship between various driving conditions and energy consumption through driving data representing independent travel segments. It facilitates 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. This makes it easier to build a more accurate unit energy consumption prediction model based on the obtained mapping relationship information.

[0129] In one embodiment, historical driving data can characterize vehicle-related data and road traffic information during the actual driving process of a vehicle on a road segment with a certain road condition, or characterize vehicle-related data and road traffic information during the actual driving process when driving a vehicle in a specific driving state on a road segment with a certain road condition.

[0130] In one embodiment, historical driving data may include at least one of the following: vehicle speed-time correspondence, road parameters, vehicle speed sampling duration on a road segment, and mileage on a road segment. Optionally, road parameters may include at least one of the following: road type, road grade information, congestion grade information, average road speed, altitude information, and road segment ambient temperature information.

[0131] In one embodiment, the step of determining at least one sample driving condition information based on acquired historical driving data includes, but is not limited to, one of the following:

[0132] Based on the vehicle speed-time correspondence, determine the vehicle speed at multiple time points, and based on the vehicle speed at multiple time points and the corresponding road condition type, determine multiple sample driving condition information, which includes road condition type and vehicle speed at time point.

[0133] 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 road condition type. The sample driving condition information includes road condition type and average vehicle speed.

[0134] Based on the speed-time correspondence and the formula for calculating average speed, the average speed is calculated. Based on the formulas for calculating average speed and acceleration, the average acceleration is calculated. Based on the road condition type, average acceleration, and average speed, a sample driving condition information is determined. The sample driving condition information includes road condition type, average speed, and average acceleration.

[0135] Based on the vehicle speed-time correspondence and the average acceleration calculation strategy, the average acceleration is calculated, and the vehicle speed at multiple time points is determined according to the vehicle speed-time correspondence. Based on the road condition type, average acceleration, and vehicle speed at multiple time points, multiple sample driving condition information is determined. The sample driving condition information includes road condition type, vehicle speed at time point, and average acceleration.

[0136] The average acceleration is calculated based on the vehicle speed-time correspondence and the average acceleration calculation strategy. The driving state type is determined based on the average acceleration. The vehicle speed at multiple times is determined based on the vehicle speed-time correspondence. Based on the road condition type, driving state type and multiple times of vehicle speed, multiple sample driving condition information is determined. The sample driving condition information includes road condition type, time-of-time vehicle speed and driving state type.

[0137] Based on the speed-time correspondence and the formula for calculating average speed, the average speed is calculated. Then, based on the formulas for calculating average speed and acceleration, the average acceleration is calculated. The driving state type is determined based on the average acceleration. Based on the road condition type, driving state type, and average speed, a sample driving condition information is determined. The sample driving condition information includes the road condition type, average speed, and driving state type.

[0138] In one embodiment, multiple vehicle speeds at different times are determined based on the vehicle speed-time correspondence. Based on these multiple speeds and corresponding road condition types, multiple sample driving condition information is determined. This allows for the determination of multiple different sample driving condition information from historical driving data, facilitating the subsequent determination of the energy consumption per unit mile for each sample driving condition. This enables the determination of multiple different mapping relationships from historical driving data, allowing for the rapid acquisition of a large amount of mapping relationship information to build a unit energy consumption prediction model and improving the model's ability to predict energy consumption per unit mile. Furthermore, the sample driving condition information obtained in this method mainly includes road condition type and vehicle speed at different times. Therefore, the mapping relationship information obtained based on this sample driving condition information is a correspondence between road condition type, vehicle speed at different times, and energy consumption per unit mile. The unit energy consumption prediction model built according to this mapping relationship information can output the target energy consumption per unit mile based on the input target road condition type and target vehicle speed.

[0139] In one embodiment, the average acceleration is calculated based on the vehicle speed-time correspondence and the average acceleration calculation strategy. Multiple vehicle speeds at various time points are determined according to the vehicle speed-time correspondence. Based on road condition type, average acceleration, and multiple vehicle speeds at various time points, multiple sample driving condition information is determined. This enables the determination of multiple different sample driving condition information from historical driving data, facilitating the subsequent determination of the energy consumption per unit mileage corresponding to each sample driving condition. This allows for the determination of multiple different mapping relationship information from historical driving data, enabling the rapid acquisition of a large amount of mapping relationship information to build a unit energy consumption prediction model and improving the unit energy consumption prediction model's ability to predict energy consumption per unit mileage. Furthermore, the sample driving condition information obtained in this way mainly includes road condition type, vehicle speed at various time points, and average acceleration. Therefore, the mapping relationship information obtained based on this sample driving condition information is a correspondence between road condition type, vehicle speed at various time points, average acceleration, and energy consumption per unit mileage. The unit energy consumption prediction model built according to this mapping relationship information can output the target energy consumption per unit mileage based on the input target road condition type, vehicle speed, and average acceleration.

[0140] In one embodiment, average acceleration is calculated based on the vehicle speed-time correspondence and an average acceleration calculation strategy. The driving state type is determined based on the average acceleration, and multiple vehicle speeds at different times are determined based on the vehicle speed-time correspondence. This allows for the determination of multiple sample driving condition information based on road condition type, driving state type, and multiple vehicle speeds at different times. This enables the determination of multiple different sample driving condition information from historical driving data, facilitating the subsequent determination of the energy consumption per unit mileage corresponding to each sample driving condition. This achieves the determination of multiple different mapping relationship information from historical driving data, enabling the rapid acquisition of a large amount of mapping relationship information to build a unit energy consumption prediction model and improving the unit energy consumption prediction model's ability to predict energy consumption per unit mileage. Furthermore, the sample driving condition information obtained in this way mainly includes road condition type, vehicle speed at different times, and driving state type. Therefore, the mapping relationship information obtained based on this sample driving condition information is a correspondence between road condition type, vehicle speed at different times, driving state type, and energy consumption per unit mileage. The unit energy consumption prediction model built according to this mapping relationship information can output the target energy consumption per unit mileage based on the input target road condition type, vehicle speed, and driving state type.

[0141] 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 road condition type. This enables the determination of the most representative sample driving condition information from historical driving data, thereby enabling the rapid determination of the unit mileage energy consumption corresponding to the most representative sample driving condition information with relatively low computing power, thus obtaining the most representative mapping relationship information. Building a unit energy consumption prediction model based on the most representative mapping relationship information under various road condition types has the advantages of high efficiency and low computing power consumption. The unit energy consumption prediction model built according to this mapping relationship information can output the target unit mileage energy consumption based on the input target road condition type and vehicle speed (e.g., average vehicle speed).

[0142] In one embodiment, the average vehicle speed is calculated based on the vehicle speed-time correspondence and the formula for calculating the average vehicle speed. The average acceleration is then calculated using the formulas for calculating the average vehicle speed and acceleration. Based on the road condition type, average acceleration, and average vehicle speed, a sample driving condition information is determined. This allows for the identification of the most representative sample driving condition information from historical driving data, enabling the rapid determination of the unit mileage energy consumption corresponding to the most representative sample driving condition information with relatively low computational power, thus obtaining the most representative mapping relationship information. Building a unit energy consumption prediction model based on the most representative mapping relationship information under various road condition types has advantages such as high efficiency and low computational power consumption. The unit energy consumption prediction model built according to this mapping relationship information can output the target unit mileage energy consumption based on the input target road condition type, vehicle speed (e.g., average vehicle speed), and average acceleration.

[0143] In one embodiment, the average vehicle speed is calculated based on the vehicle speed-time correspondence and the formula for calculating average vehicle speed. The average acceleration is then calculated using the formulas for calculating average vehicle speed and acceleration. The driving state type is determined based on the average acceleration. This allows for the determination of a sample driving condition based on road condition type, driving state type, and average vehicle speed. This enables the identification of a most representative sample driving condition based on historical driving data, thus allowing for the rapid determination of the unit mileage energy consumption corresponding to the most representative sample driving condition with relatively low computational power, resulting in a most representative mapping relationship. Building a unit energy consumption prediction model based on the most representative mapping relationship information under various road condition types offers advantages such as high efficiency and low computational power consumption. The unit energy consumption prediction model built according to this mapping relationship information can output the target unit mileage energy consumption based on the input target road condition type, vehicle speed (e.g., average vehicle speed), and driving state type.

[0144] In one embodiment, the step of calculating unit energy consumption based on sample driving condition information to obtain the corresponding unit mileage energy consumption includes: calculating unit energy consumption based on driving condition characteristics and vehicle performance information in the sample driving condition information to obtain the corresponding unit mileage energy consumption. Thus, the technical solution of this embodiment, when determining unit mileage energy consumption, not only fully considers the influence of driving behavior (e.g., acceleration) and traffic conditions (e.g., road type, road slope) based on historical driving data, but also fully considers the vehicle's own performance. Therefore, the unit mileage energy consumption determined by the technical solution of this embodiment is closer to the actual unit mileage energy consumption. Consequently, the technical solution of this embodiment can increase the trust of new energy vehicle users in the unit mileage energy consumption and other related energy consumption data displayed in the vehicle, achieving the goal of improving user experience.

[0145] In one embodiment, the driving condition characteristics include, but are not limited to, average acceleration, mileage, and average vehicle speed / time-of-flight speed, and the vehicle performance information includes, but is not limited to, vehicle mechanical parameters and energy conversion efficiency parameters.

[0146] In one embodiment, the step of calculating unit energy consumption based on driving condition characteristics and vehicle performance information in sample driving condition information to obtain the corresponding unit mileage energy consumption includes: calculating the driving force using the driving force calculation formula based on average vehicle speed / instantaneous vehicle speed, average acceleration, and vehicle mechanical parameters; calculating the driving energy consumption using the driving energy, average vehicle speed / instantaneous vehicle speed, and energy conversion efficiency parameters; and calculating the unit mileage energy consumption using the unit mileage energy consumption calculation formula based on the driving energy consumption and the mileage traveled. Thus, the technical solution of this embodiment, when determining unit mileage energy consumption, not only fully considers the influence of driving behavior (e.g., acceleration) and traffic conditions (e.g., road type, road gradient) based on historical driving data, but also fully combines vehicle mechanical parameters and energy conversion efficiency parameters. Therefore, the unit mileage energy consumption determined by the technical solution of this embodiment is closer to the actual unit mileage energy consumption.

[0147] In one embodiment, the formula for calculating the average vehicle speed is:

[0148]

[0149] in, Indicates average vehicle speed. This represents the vehicle speed at a given moment, determined from the speed-time correspondence. Indicates the vehicle speed sampling duration; and / or,

[0150] The formula for calculating acceleration is:

[0151]

[0152] in, This represents the average acceleration in the sample driving condition information. This represents the vehicle speed at a given moment, determined from the speed-time correspondence. Indicates the vehicle speed sampling duration; and / or,

[0153] The formula for calculating driving force is:

[0154]

[0155] in, Indicates driving force. This represents the average vehicle speed / vehicle speed at any given moment. This represents the average acceleration and vehicle mechanical parameters in the sample driving condition information. Indicates the vehicle's curb weight. Indicates the rolling resistance coefficient. Indicates the air drag coefficient. Indicates the windward area. Indicates the rotating mass conversion factor; and / or,

[0156] The formula for calculating drive energy consumption is:

[0157]

[0158] in, Indicates drive energy consumption. Energy conversion efficiency parameter, representing average vehicle speed / vehicle speed at any given moment: Indicates drive efficiency. Indicates motor efficiency. Indicates battery efficiency; and / or,

[0159] The formula for calculating energy consumption per unit distance is:

[0160]

[0161] in, Indicates energy consumption per unit distance. Indicates drive energy consumption. This indicates the mileage in the sample driving condition information.

[0162] In one embodiment, the vehicle's speed and acceleration directly affect the vehicle's energy consumption. Therefore, the technical solution of this embodiment enables the unit energy consumption prediction model to fully learn the changes in unit mileage energy consumption under different vehicle speeds and acceleration conditions in various road conditions.

[0163] In one embodiment, the driving condition characteristics in the target driving condition information of the future driving segment may include average vehicle speed and acceleration.

[0164] In one embodiment, the average vehicle speed corresponding to the future travel segment can be determined based on navigation segment data. That is, the online map function can predict the average vehicle speed of traffic flow on the future travel segment. Therefore, the navigation segment data includes the average vehicle speed of traffic flow on the future travel segment, so the average vehicle speed of traffic flow can be used as the average vehicle speed under the specific road condition type corresponding to the future travel segment.

[0165] In one embodiment, the average acceleration corresponding to the future travel segment can be determined based on navigation segment data, or it can be calculated by using the average speed of adjacent future travel segments as the speed at the intersection of each future travel segment and then using the speeds of the two adjacent intersections. Optionally, when the average speed corresponding to the future travel segment is determined based on navigation segment data, that is, when the online map function can predict the average acceleration of traffic flow in the future travel segment, the navigation segment data includes the average acceleration of traffic flow in the future travel segment, and thus this average acceleration can be used as the average acceleration under a specific road condition type corresponding to the future travel segment. Optionally, the average acceleration corresponding to the future travel segment is calculated by using the average speed of adjacent future travel segments as the speed at the intersection of each future travel segment and then using the speeds of the two adjacent intersections, for example, see [link to relevant documentation]. Figure 2 When determining the average acceleration corresponding to the future travel segment 2, the average speed of the vehicle in the future travel segment 1 is used as the first speed at the intersection with the future travel segment 2, and the average speed of the vehicle in the future travel segment 3 is used as the second speed at the intersection with the future travel segment 2. The average acceleration of the intermediate future travel segment 2 is calculated based on the first speed and the second speed.

[0166] The technical solution of this embodiment can divide the navigation route data fed back by the online map function into future driving segments for the entire mileage after the user determines the destination through the online map function. It can also determine the average vehicle speed, average acceleration, road grade information, congestion level information (or road condition type), and driving distance in the navigation segment data corresponding to each future driving segment. Subsequently, the target driving condition information, including road condition type, average vehicle speed, and average acceleration, can be determined based on the navigation segment data corresponding to the future driving segment. Then, the "speed-acceleration-energy consumption per unit mile" MAP associated with the corresponding road condition type can be determined based on the target driving condition information corresponding to the future driving segment. Finally, the target energy consumption per unit mile of the future driving segment can be calculated by combining the determined "speed-acceleration-energy consumption per unit mile" MAP with the average vehicle speed and average acceleration in the target driving condition information corresponding to the future driving segment. Furthermore, the driving energy consumption of each future driving segment can be calculated by combining the target energy consumption per unit mile and the driving distance of each future driving segment of the navigation route.

[0167] Step S13: Obtain the driving range based on the target energy consumption per unit mileage and the vehicle's remaining energy.

[0168] In one embodiment, when the vehicle is an electric vehicle, the vehicle energy reserve can represent the remaining battery power; when the vehicle is a new energy vehicle other than an electric vehicle, the vehicle energy reserve can represent the corresponding new energy supply reserve.

[0169] In one implementation, the driving range can characterize the maximum distance the vehicle can travel continuously in the future.

[0170] In one implementation, the future driving route can be obtained by dividing the entire mileage of the navigation route.

[0171] In one embodiment, step S13, obtaining the remaining driving range based on the target unit mileage energy consumption and the vehicle's remaining energy, may include: obtaining the predicted driving energy consumption of the future driving segment based on the target unit mileage energy consumption of the future driving segment; determining the total mileage energy consumption corresponding to the navigation route based on the predicted driving energy consumption corresponding to each of the future driving segments of the navigation route; determining the average unit mileage energy consumption based on the total mileage energy consumption and the future driving mileage corresponding to the navigation route; and determining the remaining driving range based on the vehicle's remaining energy and the average unit mileage energy consumption.

[0172] Thus, the technical solution of this embodiment can determine the predicted driving energy consumption corresponding to each future driving segment of the navigation route after determining the target unit mileage energy consumption for each future driving segment, thereby determining the total mileage energy consumption for the navigation route. Finally, the driving range is determined based on the vehicle's remaining energy and average unit mileage energy consumption. Since the target unit mileage energy consumption for each future driving segment is close to the actual unit mileage energy consumption when actually driving on that future driving segment, the driving range determined based on the target unit mileage energy consumption for each future driving segment can also be closer to the actual driving range.

[0173] In one embodiment, the step of obtaining the predicted driving energy consumption of a future driving segment based on the target energy consumption per unit mileage of the future driving segment includes: determining the initial driving energy consumption based on the target energy consumption per unit mileage of the future driving segment and the driving mileage corresponding to the future driving segment; and performing energy consumption correction processing on the initial driving energy consumption based on the geographical environmental factors corresponding to the future driving segment to obtain the predicted driving energy consumption.

[0174] In one embodiment, geographical environmental factors can characterize various geographical environmental information that can affect the driving energy consumption of a vehicle. Optionally, geographical environmental factors may include at least one of the following: altitude information, road segment ambient temperature information, etc. Optionally, geographical environmental factors may be obtainable through online map functions and / or sensing devices in the vehicle.

[0175] In one embodiment, when the geographical environmental factors include road segment temperature information, the step of performing energy consumption correction processing on the initial driving energy consumption based on the geographical environmental factors corresponding to the future driving road segment to obtain the predicted driving energy consumption includes: determining an energy consumption correction coefficient based on the road segment ambient temperature information corresponding to the future driving road segment; and correcting the initial driving energy consumption based on the energy consumption correction coefficient. The technical solution of this embodiment can consider the impact of ambient temperature on the energy consumption of the vehicle's battery, 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 road segment, making each value close to the actual driving energy consumption during actual driving on the future driving road segment.

[0176] In one embodiment, while or after correcting the initial drive energy consumption based on ambient temperature information, further corrections can be made after determining compensation based on geographical environmental factors.

[0177] In one embodiment, when the geographical environmental factors include altitude information, the step of performing energy consumption correction processing on the initial driving energy consumption based on the geographical environmental factors corresponding to the future driving segment to obtain the predicted driving energy consumption includes: determining a first compensation energy consumption based on the altitude information when the altitude information corresponding to the future driving segment meets the uphill condition; or determining a second compensation energy consumption based on the altitude information when the altitude information corresponding to the future driving segment meets the downhill condition; and obtaining the predicted driving energy consumption based on the initial driving energy consumption and the first compensation energy consumption or the second compensation energy consumption.

[0178] In one embodiment, the altitude information corresponding to the future travel segment may include the altitude of the first boundary point (or the altitude of the starting point of the segment) and the altitude of the second boundary point (or the altitude of the ending point of the segment).

[0179] In one implementation, the uphill condition can be characterized by the elevation of the first boundary point being lower than that of the second boundary point.

[0180] In one embodiment, the downhill condition can be characterized by the elevation of the first boundary point being greater than the elevation of the second boundary point.

[0181] In one embodiment, determining the first or second compensation energy consumption based on altitude information can be achieved by subtracting the altitude of the first boundary point from the altitude of the second boundary point.

[0182] In one embodiment, see Figure 4 An example of an uphill / downhill driving condition is provided to help understand the elevation of the first and second junctions of a future driving segment.

[0183] 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 take into account the impact of slope on driving energy consumption when analyzing the future driving energy consumption of the vehicle. In conceiving this technical solution, we considered that real road conditions are highly complex, especially in mountainous areas where it is difficult to directly obtain the gradient changes of future driving segments. Therefore, we can use the altitude information provided by the online map function to determine the gradient changes. Furthermore, since roads are uneven in actual conditions, there may be multiple uphill and downhill sections between the start and end points of the navigation route. If we ignore the changes in uphill and downhill conditions of each future driving segment within the navigation route's driving range and directly consider the altitude difference between the start and end points of the navigation route for energy consumption analysis, there will be a large error. Therefore, based on the above concept, we divide the entire navigation route into future driving segments, and each future driving segment can represent one type of uphill or downhill condition (e.g., uphill condition, downhill condition, or flat condition). This allows for better compensation and correction of the driving energy consumption of future driving segments based on the uphill and downhill conditions.

[0184] In one embodiment, the essence of the vehicle's driving in uphill and downhill conditions is the conversion of the vehicle's gravitational potential energy. In uphill conditions, 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 downhill conditions, the vehicle's energy recovery strategy converts the vehicle's gravitational potential energy into the chemical energy inside the battery, and the vehicle can consume less of the energy converted from gravitational potential energy.

[0185] In one embodiment, based on the aforementioned principle, when the altitude information corresponding to the future driving segment matches 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 segment matches the downhill condition, the second compensation energy consumption is determined according to the altitude information, which can be achieved through the following formula:

[0186]

[0187] in, This represents the energy consumption for slope compensation (characterized by the first compensation energy consumption in both uphill and flat conditions). The second compensation energy consumption is characterized during downhill operation. ), This indicates the elevation of the first boundary point (or the elevation of the starting point of the road segment). This indicates the elevation of the second boundary point (or the elevation of the end of the road segment). This indicates the energy recovery coefficient calibrated by the vehicle's energy recovery system. This indicates the vehicle's curb weight. It represents the acceleration due to gravity.

[0188] in, It can characterize the altitude difference determined based on altitude information.

[0189] In downhill driving, the vehicle's energy recovery strategy converts the vehicle's gravitational potential energy into chemical energy within the battery, reducing the energy consumed by the vehicle. However, in reality, it's difficult to recover all gravitational potential energy. Therefore, the energy recovery coefficient is calibrated using the energy recovery system of electric vehicles. The recoverable feedback energy can be estimated.

[0190] In one embodiment, in the step of obtaining the predicted drive energy consumption based on the initial drive energy consumption and either the first compensation energy consumption or the second compensation energy consumption, the predicted drive energy consumption is: (in, Indicates initial drive power consumption. (This indicates the energy consumption for slope compensation).

[0191] In one embodiment, the step of determining the total mileage energy consumption of the navigation route based on the predicted driving energy consumption corresponding to each of the future driving segments of the navigation route includes: summarizing the predicted driving energy consumption corresponding to each of the future driving segments to obtain the basic energy consumption of the total mileage of the navigation route; obtaining the total energy consumption of the accessories corresponding to the navigation route; and obtaining the total mileage energy consumption based on the basic energy consumption of the total mileage and the total energy consumption of the accessories.

[0192] In one embodiment, the step of aggregating the predicted driving energy consumption corresponding to each of the future driving segments to obtain the basic energy consumption for the entire navigation route may include: aggregating the predicted driving energy consumption corresponding to each of the future driving segments of the navigation route according to the formula for calculating the basic energy consumption for the entire mileage, thereby obtaining the basic energy consumption for the entire navigation route. The formula for calculating the basic energy consumption for the entire mileage is as follows:

[0193]

[0194] in, This represents the predicted base energy consumption (kWh) for the entire journey. This represents the predicted driving energy consumption (kWh) for the i-th future driving segment of the navigation route. This represents the gradient compensation energy consumption (kWh) for the i-th future driving segment of the navigation route.

[0195] The technical solution of this embodiment can comprehensively consider the slope factors of each future driving segment to obtain the predicted driving energy consumption of each future driving segment. Then, by summing the predicted driving energy consumption for each future driving segment, the predicted base energy consumption for the entire journey can be obtained. .

[0196] In one embodiment, the navigation information (which can represent navigation route data) of the online map function is updated in real time during actual use. Therefore, the predicted driving energy consumption can be updated in real time with the changes in road traffic conditions in the online map function, thereby realizing the online update of the predicted driving energy consumption.

[0197] In one embodiment, based on the inventive concept of the aforementioned technical solution, see [link to inventive concept]. Figure 5 The following is a flowchart illustrating a method for determining the base energy consumption over the entire mileage of a navigation route, for reference:

[0198] 1. Respond to navigation operations and determine navigation information.

[0199] Optionally, the navigation information includes navigation route, mileage, travel time, road traffic conditions, altitude, and vehicle speed.

[0200] 2. Divide the driving route into segments based on the navigation route in the navigation information to determine n future driving segments, and determine the navigation segment data for each future driving segment based on other information in the navigation information.

[0201] Optionally, the navigation route data for future driving segments may include average vehicle speed, average acceleration, elevation of the intersection of the route segments, and road condition type.

[0202] 3. Based on the navigation segment data of the future driving segment, determine the altitude difference at the boundary point and the target driving condition information (including road condition type, average acceleration, and average vehicle speed) of the future driving segment; and determine the target energy consumption per unit mile based on the target driving condition information and the "speed-acceleration-energy consumption per unit mile" MAP associated with a road condition type; and determine the predicted driving energy consumption of the future driving segment based on the altitude difference at the boundary point and the target energy consumption per unit mile.

[0203] 4. The predicted driving energy consumption of all future driving segments of the navigation route is summarized to obtain the basic energy consumption for the entire mileage.

[0204] In one embodiment, the power battery, as the sole energy source for the electric vehicle, powers not only the drive motor but also other accessories on the vehicle. Therefore, the energy consumption of these accessories directly affects the overall vehicle energy consumption, and the impact of accessory energy consumption on overall vehicle energy consumption needs to be considered when predicting the energy consumption of the electric vehicle. Therefore, this embodiment proposes an accessory energy consumption compensation scheme (i.e., obtaining the total energy consumption over the entire mileage based on the base energy consumption and the total energy consumption of the accessories). Electric vehicle accessories can be divided into two main categories based on their voltage type: high-voltage accessories and low-voltage accessories. High-voltage accessories include, but are not limited to, Battery Management System (BMS), Power Distribution Unit (PDU), electric air conditioning, and PTC (Positive Temperature Coefficient) heaters. Low-voltage accessories include, but are not limited to, the vehicle's lighting system, infotainment system, wipers, and washing system. Optionally, the classification of high-voltage and low-voltage accessories for electric vehicles can refer to currently established voltage classification rules.

[0205] In one embodiment, the step of obtaining the total energy consumption of accessories corresponding to the navigation route may include: obtaining the first low-voltage accessory energy consumption corresponding to the normally activated low-voltage accessory based on the driving time corresponding to the normally activated low-voltage accessory and the navigation route; and / or, obtaining the second low-voltage accessory energy consumption corresponding to the conditionally activated low-voltage accessory based on the first estimated activation time corresponding to the navigation route; obtaining the first high-voltage accessory energy consumption corresponding to the normally activated high-voltage accessory based on the driving time corresponding to the normally activated high-voltage accessory and the navigation route; and / or, obtaining the second high-voltage accessory energy consumption corresponding to the conditionally activated high-voltage accessory based on the second estimated activation time corresponding to the navigation route; obtaining the total energy consumption of low-voltage accessories corresponding to the navigation route based on the first low-voltage accessory energy consumption and / or the second low-voltage accessory energy consumption; obtaining the total energy consumption of high-voltage accessories corresponding to the navigation route based on the first high-voltage accessory energy consumption and / or the second high-voltage accessory energy consumption; and obtaining the total energy consumption of accessories corresponding to the navigation route based on the total energy consumption of low-voltage accessories and the total energy consumption of high-voltage accessories.

[0206] In one embodiment, the average power of each accessory can be predetermined in the technical solution of this embodiment. Optionally, the average power can be calculated by testing the steady-state current of the accessories of the electric vehicle to obtain the steady-state current of each accessory during operation.

[0207] In one embodiment, there are many types of low-voltage accessories in electric vehicles. Based on their opening conditions, they can be roughly divided into low-voltage accessories that are normally open (such as sensors, instrument panels and display systems) and low-voltage accessories that are conditionally open (such as lighting systems and blowers).

[0208] In one embodiment, the low-voltage accessory that is conditionally activated is activated by default until the end of the entire navigation route, i.e., energy consumption is calculated using the average power and the remaining time of the current mileage of the navigation route (i.e., the first estimated activation duration) fed back by the online map function.

[0209] In one implementation, the low-voltage accessory, which is normally on, calculates energy consumption directly based on the total travel time of the navigation route fed back by the online map function (e.g., the travel time corresponding to the total travel time of the navigation route updated in real time according to vehicle movement, or the travel time corresponding to the total travel time of the initial navigation route triggered by navigation operation).

[0210] In one embodiment, the step of obtaining the first low-voltage accessory energy consumption corresponding to the normally activated low-voltage accessory based on the driving time corresponding to the normally activated low-voltage accessory and the navigation route; and / or, obtaining the second low-voltage accessory energy consumption corresponding to the conditionally activated low-voltage accessory based on the first estimated activation time corresponding to the conditionally activated low-voltage accessory and the navigation route, includes one of the following:

[0211] In response to the activation operation of the conditionally activated low-voltage accessory, determine the current remaining mileage of the navigation route and the conditionally activated low-voltage accessory (or target condition accessory); based on the normally activated low-voltage accessory and the driving time corresponding to the navigation route, obtain the first low-voltage accessory energy consumption corresponding to the normally activated low-voltage accessory, and based on the first estimated activation time corresponding to the conditionally activated low-voltage accessory and the navigation route, obtain the second low-voltage accessory energy consumption corresponding to the conditionally activated low-voltage accessory.

[0212] In response to the closing operation of a conditionally activated low-voltage accessory that has already been activated, and when it is detected that there is still a conditionally activated low-voltage accessory that is activated, the remaining time of the current mileage of the navigation route and the conditionally activated low-voltage accessory (or target condition accessory) are determined; based on the normally activated low-voltage accessory and the driving time corresponding to the navigation route, the energy consumption of the first low-voltage accessory corresponding to the normally activated low-voltage accessory is obtained, and based on the first estimated activation time corresponding to the conditionally activated low-voltage accessory and the navigation route, the energy consumption of the second low-voltage accessory corresponding to the conditionally activated low-voltage accessory is obtained;

[0213] If the low-pressure accessory that is conditionally activated is not activated, the energy consumption of the first low-pressure accessory corresponding to the normally activated low-pressure accessory is obtained based on the driving time corresponding to the normally activated low-pressure accessory and the navigation route.

[0214] In one embodiment, the total energy consumption of low-voltage accessories corresponding to the navigation route is obtained based on the energy consumption of the first low-voltage accessory and / or the energy consumption of the second low-voltage accessory. The following formula for calculating the total energy consumption of low-voltage accessories can be used as a reference:

[0215]

[0216] in, This indicates the total energy consumption (kWh) of the low-voltage accessories corresponding to the navigation route. This represents the average power (kW) of all normally operational low-voltage accessories. This indicates the total travel time for the entire navigation route. This represents the average power (kW) of all conditionally activated low-voltage accessories. This indicates the remaining time (i.e., the first estimated activation duration) of the navigation route at the time when an activation or deactivation operation for a low-voltage accessory under certain conditions is received.

[0217] Thus, the technical solution of this embodiment can perform an energy consumption prediction update calculation when the low-pressure accessory is turned on, and also perform an energy consumption prediction update calculation when it is turned off (such as subsequent update calculations of the total energy consumption of the low-pressure accessory, update calculations of the total energy consumption of the accessory, and update calculations of the energy consumption over the entire mileage).

[0218] In other implementations, the first estimated activation duration can be based on feedback from an online map function. Optionally, the online map function can predict the conditions under which low-voltage accessories need to be activated and their activation duration during navigation.

[0219] 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 significant impact on energy consumption and driving range. Therefore, the technical solution of this embodiment can mainly consider the impact of these two high-voltage accessories on energy consumption.

[0220] In one embodiment, there are many types of high-voltage accessories in electric vehicles. Based on their opening conditions, they can be roughly divided into high-voltage accessories that are normally open (such as BMS, PDU, etc.) and high-voltage accessories that are conditionally open (such as PTC heaters, electric air conditioners, etc.).

[0221] In one embodiment, the high-voltage accessory that is conditionally activated is activated by default until the end of the entire navigation route, that is, the energy consumption is calculated using the average power and the remaining time of the current mileage of the navigation route (i.e., the second estimated activation duration) fed back by the online map function.

[0222] In one implementation, the high-voltage accessory that is normally in operation directly calculates energy consumption based on the total travel time of the navigation route fed back by the online map function (e.g., the travel time corresponding to the total travel time of the navigation route updated in real time according to vehicle movement, or the travel time corresponding to the total travel time of the initial navigation route triggered by navigation operation).

[0223] In one embodiment, the step of obtaining the first high-voltage accessory energy consumption corresponding to the normally activated high-voltage accessory based on the driving time corresponding to the normally activated high-voltage accessory and the navigation route; and / or, obtaining the second high-voltage accessory energy consumption corresponding to the conditionally activated high-voltage accessory based on the second estimated activation time corresponding to the conditionally activated high-voltage accessory and the navigation route, includes one of the following:

[0224] In response to the activation operation of the conditionally activated high-voltage accessory, determine the current remaining mileage of the navigation route and the conditionally activated high-voltage accessory (or target condition accessory); based on the normally activated high-voltage accessory and the driving time corresponding to the navigation route, obtain the first high-voltage accessory energy consumption corresponding to the normally activated high-voltage accessory, and based on the first estimated activation time corresponding to the conditionally activated high-voltage accessory and the navigation route, obtain the second high-voltage accessory energy consumption corresponding to the conditionally activated high-voltage accessory.

[0225] In response to the closing operation of a conditionally activated high-voltage accessory that has already been activated, and when it is detected that there is still a conditionally activated high-voltage accessory that is activated, the remaining time of the current mileage of the navigation route and the conditionally activated high-voltage accessory (or target condition accessory) are determined; based on the normally activated high-voltage accessory and the driving time corresponding to the navigation route, the first high-voltage accessory energy consumption corresponding to the normally activated high-voltage accessory is obtained, and based on the conditionally activated high-voltage accessory and the first estimated activation time corresponding to the navigation route, the second high-voltage accessory energy consumption corresponding to the conditionally activated high-voltage accessory is obtained;

[0226] If the high-voltage accessory that is normally activated is not activated, the energy consumption of the first high-voltage accessory corresponding to the normally activated high-voltage accessory is obtained based on the driving time corresponding to the normally activated high-voltage accessory and the navigation route.

[0227] In one embodiment, the total energy consumption of the high-voltage accessories corresponding to the navigation route is obtained based on the energy consumption of the first high-voltage accessory and / or the energy consumption of the second high-voltage accessory. The following formula for calculating the total energy consumption of the high-voltage accessories can be used as a reference:

[0228] (4)

[0229] in, This represents the total energy consumption (kWh) of the high-voltage accessories corresponding to the navigation route. This represents the average power (kW) of all normally operational high-voltage accessories. This indicates the total travel time for the entire navigation route. This represents the average power (kW) of all activated high-voltage accessories. This indicates the remaining time (i.e., the second estimated activation duration) of the current mileage of the navigation route at the time when an activation or deactivation operation for a high-voltage accessory under certain conditions is received.

[0230] Thus, the technical solution of this embodiment can perform an energy consumption prediction update calculation when the high-voltage accessory is turned on, and also perform an energy consumption prediction update calculation when it is turned off (such as subsequent update calculation of the total energy consumption of the high-voltage accessory, update calculation of the total energy consumption of the accessory, and update calculation of the energy consumption over the entire mileage).

[0231] In other implementations, the second estimated activation duration can be based on feedback from an online map function. Optionally, the online map function can predict the conditions under which high-voltage accessories need to be activated and their activation duration during navigation.

[0232] In one embodiment, in the step of obtaining the total energy consumption of accessories corresponding to the navigation route based on the total energy consumption of low-voltage accessories and the total energy consumption of high-voltage accessories, the formula for calculating the total energy consumption of accessories corresponding to the navigation route can be:

[0233]

[0234] in, This represents the total energy consumption (kWh) of the accessories corresponding to the navigation route. This represents the total energy consumption (kWh) of the high-voltage accessories corresponding to the navigation route. This indicates the total energy consumption (kWh) of the low-voltage accessories corresponding to the navigation route.

[0235] See Figure 6 This embodiment illustrates a flowchart for determining the total energy consumption of an accessory, in order to help understand the inventive concept of the above-described technical solution for determining the total energy consumption of an accessory.

[0236] In one embodiment, in the step of obtaining the total mileage energy consumption based on the base energy consumption and the total energy consumption of accessories, the formula for calculating the total mileage energy consumption can be:

[0237]

[0238] in, This indicates the total energy consumption (kWh) for the entire navigation route. This represents the predicted base energy consumption (kWh) for the entire journey. This represents the total energy consumption (kWh) of the accessories corresponding to the navigation route.

[0239] In one embodiment, the vehicle's overall energy consumption is not only affected by road conditions, vehicle speed, and acceleration, but also by factors such as ambient temperature, low-voltage accessories, and high-voltage accessories. Therefore, the technical solution of this embodiment can correct energy consumption based on geographical environmental factors, low-voltage accessories, and high-voltage accessories, thereby enabling the final driving range to be closer to the actual driving range.

[0240] In one embodiment, the step of determining the average energy consumption per unit mileage based on the total mileage energy consumption and the future mileage corresponding to the navigation route may include: calculating the average energy consumption per unit mileage based on the total 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:

[0241]

[0242] in, This represents the average energy consumption per unit distance. Indicates energy consumption over the entire distance. This indicates the driving distance corresponding to the navigation route (e.g., the remaining driving distance to the end of the navigation route).

[0243] In one embodiment, the step of determining the driving range based on the vehicle's remaining energy and average energy consumption per unit mile may include: calculating and outputting the driving range based on the vehicle's remaining energy, average energy consumption per unit mile, and a formula for calculating the driving range, wherein the formula for calculating the driving range is:

[0244]

[0245] in, Indicates driving range, This indicates the vehicle's remaining energy capacity (e.g., remaining battery charge, in kWh). This represents the average energy consumption per unit distance.

[0246] In one embodiment, when the technical solution of this embodiment is applied to an electric vehicle, the remaining battery power can be determined by obtaining the SOE (State of Energy).

[0247] See Figure 7 This embodiment illustrates a flowchart for estimating driving range, in order to help understand the inventive concept of the above-described technical solution for estimating driving range.

[0248] The technical solution of this embodiment can perform real-time energy consumption prediction based on navigation information provided by 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 vehicle information in real time and calculates the driving mileage corresponding to the navigation route. This allows for real-time prediction of driving range, and the predicted driving range is close to the actual driving range under the current operating conditions.

[0249] The method for determining the driving range provided in this embodiment includes: Step S11: Obtaining target driving condition information for future driving segments, the target driving condition information including 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 for future driving segments; Step S13: Obtaining the driving range based on the target unit mileage energy consumption and the vehicle's remaining energy. Thus, the technical solution of this embodiment, when determining the target unit mileage energy consumption for calculating the driving range, can fully consider the impact of various driving condition information (such as road condition type and / or specific driving conditions under that road condition type) on the unit mileage energy consumption for future driving segments. This makes the determined target unit mileage energy consumption closer to the actual unit mileage energy consumption while driving on future driving segments. Consequently, the driving range determined based on the target unit mileage energy consumption and the vehicle's remaining energy can also be closer to the actual driving range. Therefore, the technical solution of this embodiment can achieve the goal of improving user experience.

[0250] The technical solution of this embodiment can fully consider the different energy consumption performance of new energy vehicles (such as electric vehicles) under different operating conditions. It can combine multiple energy consumption influencing factors such as road level, congestion level, and road slope to build a unit energy consumption prediction model to predict the basic unit mileage energy consumption of future driving conditions, and make a prediction of the driving range based on the basic unit mileage energy consumption, so as to provide users with a more accurate and reliable driving range prediction service.

[0251] The technical solution of this embodiment takes into account that the driving range is affected by multiple factors, including the vehicle's driving state type, driving habits, road conditions, etc. (traditional driving range methods do not fully consider these relationships). The technical solution of this embodiment can establish a unit energy consumption prediction model that comprehensively considers multiple energy consumption influencing factors such as road level and congestion level based on the vehicle's real driving data and road traffic information provided by the navigation map platform (online map function). By establishing the unit energy consumption prediction model, the energy consumption per unit mileage can be predicted and output based on the input road condition type of the future driving segment and / or the driving condition characteristics under a certain road condition type, so as to support the calculation of the driving range. Therefore, the obtained driving range has fully considered factors such as the vehicle's driving state type, driving habits, and road conditions, so that the obtained driving range is closer to the actual driving range.

[0252] Based on the same inventive concept as the foregoing embodiments, this application 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 diagram does not indicate that there is only one processor 310, but only indicates the positional relationship of the processor 310 relative to other devices. In practical applications, there can be one or more processors 310; similarly, Figure 8 The memory 311 shown in the diagram has the same meaning, that is, it is only used to indicate the positional relationship of memory 311 relative to other devices. In practical applications, there can be one or more memories 311. When the processor 310 runs the computer program, it implements a method for determining the driving range of the above-mentioned device.

[0253] The device may also include at least one network interface 312. The various components of the device are coupled together via a bus system 313. It is understood that the bus system 313 is used to implement communication between these components. In addition to a data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 8 The general designated all buses as Bus System 313.

[0254] The memory 311 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but 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), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 311 described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0255] The memory 311 in this embodiment is used to store various types of data to support the operation of the device. Examples of this data include any computer programs used to operate on the device, such as operating systems and applications. The operating system includes various system programs, such as framework layers, core library layers, and driver layers, used to implement various basic business functions and handle hardware-based tasks. Applications can include various applications, such as media players and browsers, used to implement various application services. Here, the program implementing the method of this embodiment can be included in the application.

[0256] Based on the same inventive concept as the foregoing embodiments, this embodiment also provides a computer-readable storage medium storing a computer program. The computer-readable storage medium can be a magnetic 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), etc.; it can also be various devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc. When the computer program stored in the computer-readable storage medium is executed by a processor, it implements the above-described method for determining the driving range. For the specific steps implemented when the computer program is executed by the processor, please refer to [link to relevant documentation]. Figure 1 The description of the illustrated embodiments will not be repeated here.

[0257] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above 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.

[0258] In this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, which includes not only the elements listed but also other elements not expressly listed.

[0259] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of determining a range, characterized by The method comprises the following steps: obtaining target driving condition information of a future driving section, the target driving condition information comprising a road condition type and / or at least one driving condition characteristic under the road condition type, the future driving section being obtained by dividing a navigation route; determining a target unit energy consumption that matches the target driving condition information of the future driving section; obtaining the cruising range according to the target unit energy consumption and a vehicle energy reserve; wherein the step of obtaining the cruising range according to the target unit energy consumption and the vehicle energy reserve comprises: obtaining a predicted driving energy consumption of the future driving section according to the target unit energy consumption of the future driving section, the predicted driving energy consumption being obtained by performing energy consumption correction processing on an initial driving energy consumption according to a geographical environmental factor corresponding to the future driving section; determining a total energy consumption corresponding to the navigation route according to the predicted driving energy consumptions respectively corresponding to all the future driving sections of the navigation route; determining an average unit energy consumption according to the total energy consumption and a future driving distance corresponding to the navigation route; determining the cruising range according to the vehicle energy reserve and the average unit energy consumption; wherein the predicted driving energy consumption is obtained by performing energy consumption correction processing on the initial driving energy consumption according to the geographical environmental factor corresponding to the future driving section, and the step comprises: obtaining the predicted driving energy consumption according to the initial driving energy consumption and a first compensation energy consumption or a second compensation energy consumption, the first compensation energy consumption being determined according to altitude information corresponding to the future driving section and meeting uphill condition, and the second compensation energy consumption being determined according to altitude information corresponding to the future driving section and meeting downhill condition.

2. The method of claim 1, wherein, Before the step of obtaining the target driving condition information of the future driving section, the method comprises the following steps: in response to a navigation operation, obtaining the navigation route; dividing the navigation route according to a preset division rule to divide the navigation route into at least one future driving section, the preset division rule comprising at least one of a distance-based division rule and a road condition type-based division rule.

3. The method of claim 1, wherein, The step of determining the target unit energy consumption that matches the target driving condition information of the future driving section comprises: determining the target unit energy consumption of the future driving section according to 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 model building based on mapping relationship information representing a corresponding relationship between sample driving condition information and unit energy consumption.

4. The method of claim 1, wherein, The step of obtaining the predicted driving energy consumption of the future driving section according to the target unit energy consumption of the future driving section comprises: determining the initial driving energy consumption according to the target unit energy consumption of the future driving section and a driving distance corresponding to the future driving section; performing energy consumption correction processing on the initial driving energy consumption according to a geographical environmental factor corresponding to the future driving section to obtain the predicted driving energy consumption, the geographical environmental factor comprising at least one of altitude information and road section environmental temperature information.

5. The method of claim 4, wherein, The altitude information includes a first junction altitude and a second junction altitude of the future driving section; The first compensation energy consumption is determined according to the altitude information, including: The first compensation energy consumption is calculated 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: wherein denotes the first compensation energy consumption, denotes the first intersection altitude, denotes the second intersection altitude, denotes the kerb mass of the vehicle, denotes the gravitational acceleration; The second compensation energy consumption is determined according to the altitude information, including: The second compensation energy consumption is calculated 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: wherein, represents the second compensation energy consumption, represents the first interface altitude, represents the second interface altitude, represents the curb weight of the vehicle, represents the gravitational acceleration, represents the energy recovery coefficient of the energy recovery system calibration of the vehicle.

6. The method according to any one of claims 1 to 5, characterized in that, The step of determining the full mileage energy consumption corresponding to the navigation route according to the predicted driving energy consumption corresponding to all the future driving sections of the navigation route, including: The predicted driving energy consumption corresponding to all the future driving sections is summarized to obtain a full mileage basic energy consumption corresponding to the navigation route; An accessory total energy consumption corresponding to the navigation route is obtained; The full mileage energy consumption is obtained according to the full mileage basic energy consumption and the accessory total energy consumption.

7. The method of claim 6, wherein, The step of obtaining the accessory total energy consumption corresponding to the navigation route, including: A first low-voltage accessory energy consumption corresponding to a low-voltage accessory normally turned on is obtained according to the low-voltage accessory normally turned on and a driving time length corresponding to the navigation route; and / or, a second low-voltage accessory energy consumption corresponding to a low-voltage accessory conditionally turned on is obtained according to the low-voltage accessory conditionally turned on and a first estimated turning-on time length corresponding to the navigation route; A first high-voltage accessory energy consumption corresponding to a high-voltage accessory normally turned on is obtained according to the high-voltage accessory normally turned on and a driving time length corresponding to the navigation route; and / or, a second high-voltage accessory energy consumption corresponding to a high-voltage accessory conditionally turned on is obtained according to the high-voltage accessory conditionally turned on and a second estimated turning-on time length corresponding to the navigation route; The low-voltage accessory total energy consumption corresponding to the navigation route is obtained according to the first low-voltage accessory energy consumption and / or the second low-voltage accessory energy consumption; The high-voltage accessory total energy consumption corresponding to the navigation route is obtained according to the first high-voltage accessory energy consumption and / or the second high-voltage accessory energy consumption; The accessory total energy consumption corresponding to the navigation route is obtained according to the low-voltage accessory total energy consumption and the high-voltage accessory total energy consumption.

8. The method of claim 1, wherein, The step of determining the average unit mileage energy consumption according to the full mileage energy consumption and a future driving mileage corresponding to the navigation route, including: The average unit mileage energy consumption is calculated according to the full mileage energy consumption, the future driving mileage corresponding to the navigation route and an average energy consumption calculation formula, wherein the average energy consumption calculation formula is: wherein, represents the average unit-mileage energy consumption, represents the total mileage energy consumption, represents the future driving distance corresponding to the navigation route; and / or, The step of determining the cruising range according to the vehicle energy remaining amount and the average unit mileage energy consumption, including: The cruising range is calculated and output according to the vehicle energy remaining amount, the average unit mileage energy consumption and a cruising range calculation formula, wherein the cruising range calculation formula is: wherein, represents the range, represents the vehicle energy reserve, represents the average energy consumption per unit of range.

9. A computing device, comprising: including: A processor and a memory storing a computer program, when the processor runs the computer program, the steps of the method for determining the cruising range in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored, and when executed by a processor, implements the steps of the method for determining the range according to any one of claims 1 to 8.

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