Vehicle endurance planning method and device, vehicle and storage medium

Abstract

The invention relates to a vehicle endurance planning method and device, a vehicle and a storage medium, and the method comprises the steps: obtaining a destination of the vehicle, and determining an initial endurance planning strategy of the vehicle based on the destination, the current operation parameters of the vehicle and the power type of the vehicle; selecting a plurality of reference points between the current position of the vehicle and the destination according to a preset standard, and acquiring environment data and road condition data of each reference point; obtaining power consumption prediction data of the vehicle based on the environment data and the road condition data of the current position, the current operation parameters, the environment data and the road condition data of each reference point, the power type of the vehicle and a pre-constructed consumption model; and correcting the initial endurance planning strategy in combination with the power consumption prediction data and the endurance preference of the user to obtain an actual endurance planning strategy of the vehicle, and controlling the vehicle based on the actual endurance planning strategy until the vehicle travels to the destination. Therefore, the technical problem that the driving experience of the user is affected due to the fact that the endurance planning is difficult to meet the actual demand of the user because the influence of the real-time road condition is ignored and the driving preference and the refueling or charging habit of the user are not considered in the related technology is solved.

Description

Technical Field

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

[0002] With the development of intelligent transportation systems and technological advancement, vehicle intelligent endurance path planning has become one of the research hotspots. This technology aims to improve the energy efficiency of vehicles during driving by optimizing path selection, thereby achieving the goal of energy saving and emission reduction.

[0003] In the related art, the path planning method can be implemented based on map data, relying on static map data, such as road network information, traffic signs, etc., which are used to calculate the shortest path or fastest path between two points. However, this method ignores the impact of real-time road conditions, resulting in the actual planning not being consistent with the vehicle's energy consumption rate, thus affecting the user's driving experience.

[0004] The path planning method can also be implemented based on the energy consumption model. By constructing an energy consumption model of the vehicle under different working conditions, the energy consumption on a specific path is predicted, and then the path with the lowest energy consumption is selected. The mid-way refueling strategy is determined based on the energy consumption. However, it does not take into account the user's driving preferences, refueling or charging habits, which makes the planned refueling points deviate from the user's preferred refueling points, which is not conducive to improving the user's driving experience.

[0005] In summary, the relevant technologies ignore the impact of real-time road conditions and do not take into account the user's driving preferences, refueling or charging habits, making it difficult for the battery life planning to meet the user's actual needs, affecting the user's driving experience, and need to be improved. Summary of the invention

[0006] The present application provides a vehicle range planning method, device, vehicle and storage medium to solve the technical problem in the related art that the impact of real-time road conditions is ignored and the user's driving preferences, refueling or charging habits are not considered, making it difficult for the range planning to meet the user's actual needs, thereby affecting the user's driving experience.

[0007] The first aspect of the present application provides a vehicle range planning method, comprising the following steps: obtaining a destination of the vehicle, and determining an initial range planning strategy for the vehicle based on the destination, current operating parameters of the vehicle, and power type of the vehicle; selecting multiple reference points between the current position of the vehicle and the destination according to preset standards, and obtaining environmental data and road condition data for each reference point; obtaining power consumption prediction data for the vehicle based on the environmental data and road condition data of the current position, the current operating parameters, the environmental data and road condition data of each reference point, the power type of the vehicle, and a pre-constructed consumption model; correcting the initial range planning strategy in combination with the power consumption prediction data and the user's range preference to obtain an actual range planning strategy for the vehicle, so as to control the vehicle based on the actual range planning strategy until the vehicle reaches the destination.

[0008] Optionally, in one embodiment of the present application, the consumption model is trained by historical environmental parameters and road condition data, historical operating parameters and corresponding historical power consumption data in a historical endurance database of the vehicle.

[0009] Optionally, in one embodiment of the present application, after correcting the initial range planning strategy in combination with the power consumption prediction data and the user's range preference, it also includes: when the vehicle passes through any reference point, the environmental data and road condition data of any reference point and the power consumption data of the vehicle at any reference point are stored in the historical range database.

[0010] Optionally, in one embodiment of the present application, before correcting the initial range planning strategy in combination with the power consumption prediction data and the user's range preference, it also includes: obtaining the user's identity; using the identity as an index, matching the corresponding range preference in a preset database.

[0011] Optionally, in one embodiment of the present application, controlling the vehicle based on the actual endurance planning strategy until the vehicle reaches the destination includes: determining at least one power replenishment point based on the actual endurance planning strategy; and generating a power replenishment reminder when the distance between the vehicle and any power replenishment point is less than a preset distance threshold.

[0012] The second aspect of the present application provides a vehicle range planning device, including: a first acquisition module, used to acquire the destination of the vehicle, and determine the initial range planning strategy of the vehicle based on the destination, the current operating parameters of the vehicle and the power type of the vehicle; a second acquisition module, used to select multiple reference points between the current position of the vehicle and the destination according to preset standards, and acquire environmental data and road condition data for each reference point; a calculation module, used to obtain power consumption prediction data of the vehicle based on the environmental data and road condition data of the current position, the current operating parameters, the environmental data and road condition data of each reference point, the power type of the vehicle and a pre-constructed consumption model; a planning module, used to correct the initial range planning strategy in combination with the power consumption prediction data and the user's range preference, and obtain the actual range planning strategy of the vehicle, so as to control the vehicle based on the actual range planning strategy until the vehicle reaches the destination.

[0013] Optionally, in one embodiment of the present application, the consumption model is trained by historical environmental parameters and road condition data, historical operating parameters and corresponding historical power consumption data in a historical endurance database of the vehicle.

[0014] Optionally, in one embodiment of the present application, it also includes: a storage module, used to store the environmental data and road condition data of any reference point and the power consumption data of the vehicle at any reference point into the historical endurance database when the vehicle passes through any reference point.

[0015] Optionally, in an embodiment of the present application, it also includes: a third acquisition module, used to obtain the user's identity; a matching module, used to match the corresponding battery life preference in a preset database using the identity as an index.

[0016] Optionally, in one embodiment of the present application, the planning module includes: a determination unit for determining at least one power replenishment point based on the actual endurance planning strategy; and a reminder unit for generating a power replenishment reminder when the distance between the vehicle and any power replenishment point is less than a preset distance threshold.

[0017] The third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle range planning method as described in the above embodiment.

[0018] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the vehicle range planning method as described in the above embodiment.

[0019] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the vehicle range planning method as described above.

[0020] The embodiment of the present application can determine the initial cruising range planning strategy of the vehicle according to the destination of the vehicle, the current operating parameters of the vehicle and the power type of the vehicle, and select multiple reference points between the current position of the vehicle and the destination according to preset standards, and obtain the environmental data and road condition data of each reference point, so as to obtain the power consumption prediction data of the vehicle based on the environmental data and road condition data of the current position, the current operating parameters, the environmental data and road condition data of each reference point, the power type of the vehicle and the pre-built consumption model, and then correct the initial cruising range planning strategy in combination with the power consumption prediction data and the user's cruising range preference to obtain the actual cruising range planning strategy of the vehicle, so as to control the vehicle based on the actual cruising range planning strategy until the vehicle reaches the destination, predict the energy consumption through the road conditions, and make planning adjustments according to the user's cruising range preferences to ensure cruising range, improve driving efficiency and ensure driving safety. Thus, the technical problem that the impact of real-time road conditions is ignored in the related technology, and the driving preferences, refueling or charging habits of the user are not considered, making it difficult for the cruising range planning to meet the actual needs of the user, thereby affecting the user's driving experience, is solved.

[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A flowchart of a vehicle range planning method provided according to an embodiment of the present application;

[0024] Figure 2 A schematic diagram of the principle of a vehicle range planning method according to an embodiment of the present application;

[0025] Figure 3 This is a flow chart of a battery life planning method according to an embodiment of the present application;

[0026] Figure 4 A schematic diagram of the structure of a vehicle range planning device provided according to an embodiment of the present application;

[0027] Figure 5 It is a schematic diagram of the structure of a vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] The following describes the vehicle range planning method, device, vehicle and storage medium of an embodiment of the present application with reference to the accompanying drawings. In view of the technical problem that the related technologies mentioned in the above background technology ignore the influence of real-time road conditions and do not consider the user's driving preferences, refueling or charging habits, making it difficult for the cruising range planning to meet the actual needs of the user, thereby affecting the user's driving experience, the present application provides a vehicle cruising range planning method, in which the initial cruising range planning strategy of the vehicle can be determined according to the vehicle's destination, the vehicle's current operating parameters and the vehicle's power type, and multiple reference points are selected between the vehicle's current position and the destination according to preset standards, and the environmental data and road condition data of each reference point are obtained, so as to obtain the vehicle's power consumption prediction data based on the environmental data and road condition data of the current position, the current operating parameters, the environmental data and road condition data of each reference point, the vehicle's power type and a pre-built consumption model, and then combine the power consumption prediction data, the user's cruising range preferences and the user's revised initial cruising range planning strategy to obtain the vehicle's actual cruising range planning strategy, so as to control the vehicle based on the actual cruising range planning strategy until the vehicle reaches the destination, predict the energy consumption through the road conditions, and make planning adjustments according to the user's cruising range preferences to ensure cruising range, improve driving efficiency and ensure driving safety. This solves the technical problem in related technologies that ignores the impact of real-time road conditions and does not consider users' driving preferences, refueling or charging habits, making it difficult for battery life planning to meet users' actual needs, thereby affecting users' driving experience.

[0030] Specifically, Figure 1 A flowchart of a vehicle range planning method provided in an embodiment of the present application.

[0031] like Figure 1 As shown, the vehicle range planning method includes the following steps:

[0032] In step S101, the destination of the vehicle is obtained, and the initial cruising range planning strategy of the vehicle is determined based on the destination, the current operating parameters of the vehicle and the power type of the vehicle.

[0033] During actual execution, the embodiment of the present application can obtain the destination of the vehicle and generate path navigation according to the current position and destination of the vehicle.

[0034] The initial cruising range planning strategy of the vehicle is generated based on the route navigation, the current operating parameters of the vehicle (such as power, fuel, driving speed and other parameters) and the power type of the vehicle (such as pure electric type, pure fuel type or hybrid fuel-electric type, etc.), that is, based on the route navigation, it is determined whether the vehicle needs to be refueled midway, that is, whether it needs to be refueled or charged, and if midway refueling is required, a refueling point is selected in the route according to the current operating parameters and power type, or the route navigation is adjusted according to the refueling point, thereby obtaining the initial cruising range planning strategy.

[0035] In step S102, a plurality of reference points are selected between the current position of the vehicle and the destination according to a preset standard, and environmental data and road condition data of each reference point are obtained.

[0036] It is understandable that different environments and different road conditions have different impacts on the vehicle's energy consumption, especially for long-distance driving plans. The vehicle may pass through areas with various environmental changes during driving. Therefore, obtaining environmental data and road condition data for each reference point on the path is conducive to improving the accuracy of vehicle energy consumption prediction.

[0037] Among them, environmental data can be obtained from weather forecasts or from data uploaded by other vehicles, and road condition data can be obtained from a positioning system or from data uploaded by other vehicles.

[0038] In step S103, the power consumption prediction data of the vehicle is obtained based on the environmental data and road condition data of the current position, the current operating parameters, the environmental data and road condition data of each reference point, the power type of the vehicle and the pre-built consumption model.

[0039] The environmental data and road condition data of the current location may include but are not limited to temperature, humidity, wind speed, rainfall, road friction coefficient, traffic flow, etc. These data may be obtained through vehicle-mounted sensors or from a third-party service provider.

[0040] The current operating parameters may include, but are not limited to, vehicle speed, acceleration, battery charge, engine speed, etc. These data are usually collected by an electronic control unit (ECU) inside the vehicle.

[0041] Environmental data and road condition data for each reference point: For all key nodes that the vehicle will pass through, its environmental and road condition information also needs to be collected in order to predict the power consumption of future sections.

[0042] The type of power the vehicle uses: Different power types have different energy conversion efficiencies, so this needs to be taken into account when making predictions.

[0043] In combination with the above parameters, the embodiment of the present application can use a pre-built consumption model to predict the power consumption prediction data of the vehicle one by one, and update the power consumption prediction data of each reference point when the vehicle travels to the next reference point.

[0044] Optionally, in one embodiment of the present application, the consumption model is trained by historical environmental parameters and road condition data, historical operating parameters and corresponding historical power consumption data in the vehicle's historical endurance database.

[0045] As a possible implementation method, the embodiment of the present application can pre-process historical environmental parameters, road condition data, historical operating parameters and historical power consumption data, including but not limited to cleaning outliers, filling missing values, standardizing or normalizing numerical ranges, etc., to facilitate subsequent modeling and analysis.

[0046] Furthermore, by analyzing the original data, the embodiment of the present application can extract characteristic variables related to vehicle power consumption. For example, the average speed, average acceleration, fuel consumption per kilometer, etc. can be calculated as input features for model training.

[0047] Choose an appropriate machine learning or deep learning model based on the characteristics of the problem. Commonly used models include linear regression, decision tree, random forest, support vector machine (SVM), neural network, etc. Then use the historical data set to train the model so that the model learns to predict the output (i.e. power consumption) from the input features.

[0048] After the training is completed, the embodiment of the present application can use a validation data set that does not participate in the training to test the performance of the model, wherein the data in the validation data set also comes from historical data. Evaluation indicators can be mean square error (MSE), mean absolute error (MAE), etc., to measure the gap between the predicted value and the true value. If the model performs poorly, it is necessary to adjust the parameters or replace the model structure, or even re-select the features until a satisfactory prediction accuracy is achieved.

[0049] The embodiment of the present application can apply the finally trained model to the actual power consumption prediction, and regularly update and optimize the model according to the new data generated by the vehicle during the actual driving process to ensure that the model can always accurately reflect the current actual situation.

[0050] In step S104, the initial cruising range planning strategy is corrected in combination with the power consumption prediction data and the user's cruising range preference to obtain the vehicle's actual cruising range planning strategy, so as to control the vehicle based on the actual cruising range planning strategy until the vehicle reaches the destination.

[0051] In the actual implementation process, the embodiment of the present application can modify the battery life planning based on the prediction of power consumption and the user's battery life preference.

[0052] Among them, the endurance preference may include the user's driving style preference (such as power priority, endurance priority, stability priority, etc.) and the user's refueling or charging preference (such as a cautious preference to refuel when there is 30% oil or power remaining, an efficiency preference when there is 10% remaining, etc.).

[0053] For example, an embodiment of the present application can predict the power consumption of a vehicle when traveling to the next reference point, and update the predicted power consumption data in combination with the user's endurance preference and determine whether the current remaining oil or electricity level has reached or is about to reach the user's preferred refueling or charging threshold.

[0054] For example, when performing actual planning, the embodiment of the present application can calculate the farthest point that can be reached by the difference between the current remaining fuel / electricity and the lowest value in the energy replenishment preference, and then go back X kilometers to locate the red line point. The definition of the X value takes into account the density of gas stations / charging piles in different countries and regions and the user's endurance preferences. Among them, the actual fuel mileage can be obtained based on the environmental data and road condition data of each reference point and the user's driving preferences. Among them, the embodiment of the present application can also include the user's planning constraints and adjust the red line point floatingly according to the user's rest pattern.

[0055] Determine whether the nearest power replenishment point around the red line point can be found, such as a gas station / charging station. If not, the planning fails and the user voice and interface feedback is given. If yes, then based on the highest value in the endurance preference and the current actual remaining fuel / power difference, calculate whether it can continue to the first power replenishment point, that is, the energy replenishment point. If not, the planning fails and the user voice and interface feedback is given. If the judgment is yes, the difference is used to find the third and fourth ones in turn, until the difference can ensure that the destination is reached.

[0056] Starting from the second red line point, find the nearest power replenishment point around each red line point.

[0057] According to the power replenishment points that can be found in sequence at present, calculate the power replenishment points that can be reached in sequence.

[0058] Set the power replenishment points that can be reached sequentially and continuously as waypoints to carry out route planning.

[0059] Optionally, in one embodiment of the present application, after the initial range planning strategy is corrected in combination with the power consumption prediction data and the user's range preference, it also includes: when the vehicle passes any reference point, the environmental data and road condition data of any reference point and the power consumption data of the vehicle at any reference point are stored in the historical range database.

[0060] In some embodiments, the embodiments of the present application can store the actual environmental data and road condition data obtained at the reference point and the power consumption data of the vehicle at any reference point when the vehicle passes through the reference point, so as to optimize the consumption model.

[0061] Optionally, in one embodiment of the present application, before correcting the initial range planning strategy in combination with the power consumption prediction data and the user's range preference, it also includes: obtaining the user's identity; using the identity as an index, matching the corresponding range preference in a preset database.

[0062] In other embodiments, the battery life preference of the embodiments of the present application can be obtained from the user's identity. The embodiments of the present application can obtain the user's identity through facial feature recognition, voiceprint recognition, fingerprint recognition, etc., and determine the storage data corresponding to the user based on the identity, and then determine the user's battery life preference based on the storage data, thereby avoiding policy deviations caused by data mixing and avoiding the data of guest users from affecting the user's actual data.

[0063] Optionally, in one embodiment of the present application, the vehicle is controlled based on the actual endurance planning strategy until the vehicle reaches the destination, including: determining at least one power replenishment point based on the actual endurance planning strategy; generating a power replenishment reminder when the distance between the vehicle and any power replenishment point is less than a preset distance threshold.

[0064] As a possible implementation method, the embodiment of the present application can determine the power replenishment point in the actual cruising range planning strategy, and remind the user when the vehicle is about to reach the power replenishment point so that the user can make advance preparations, such as approaching the right turn lane in advance.

[0065] The distance threshold may be set by the user or by a person skilled in the art according to actual conditions, and no specific limitation is made here.

[0066] Combination Figure 2 and Figure 3 As shown, the working principle of the vehicle range planning method of the embodiment of the present application is described in detail with an embodiment of the present application.

[0067] The prerequisites for implementing the embodiments of the present application are: the vehicle power system is in the on state; the vehicle network system is in the awake state.

[0068] like Figure 2 As shown, the implementation steps of the embodiment of the present application may include:

[0069] 1. The user conducts long-distance navigation (based on the vehicle's current latitude and longitude coordinates, navigation destination coordinates, itinerary route, mileage and duration);

[0070] 2. Obtain the remaining fuel / power level on the instrument ICM (Integrated Control Module);

[0071] 3. Obtain the ICM mileage of the instrument;

[0072] 4. IHU (Infotainment Head Unit) map navigation locates the vehicle power replenishment points in the entire navigation route according to the real-time remaining fuel / power, cruising range, and user refueling / charging preferences to form an intelligent cruising path.

[0073] like Figure 3 As shown, the embodiment of the present application may include the following steps during the actual implementation process:

[0074] Step S1: The user initiates navigation and determines whether the current actual fuel mileage can support reaching the destination. If yes, perform regular navigation. If not, based on the difference between the current remaining fuel / power and the lowest value in the recharging preference, calculate the farthest point that the difference can reach, go back X kilometers, and locate the red line point. The definition of the X value takes into account the density of gas stations / charging piles in different countries and regions and the user's range preferences. Among them, the actual fuel mileage can be obtained based on the environmental data and road condition data of each reference point and the user's driving preferences.

[0075] Step S2: Determine whether the nearest power replenishment point around the red line point can be found, such as a gas station / charging station. If not, the planning fails and the user voice and interface feedback is given. If yes, calculate whether it can reach the first power replenishment point based on the highest value in the endurance preference and the current actual remaining fuel / power difference. If not, the planning fails and the user voice and interface feedback is given. If it is judged to be yes, the difference is used to find the third and fourth ones in turn, until the difference can ensure that the end point is reached.

[0076] Step S3: Starting from the second red line point, find the nearest power replenishment point around each red line point.

[0077] Step S4: Calculate the power replenishment points that can be reached in sequence according to the power replenishment points that can be found in sequence at present.

[0078] Step S5: Set the power replenishment points that can be reached sequentially and continuously as way points to perform route planning.

[0079] To summarize, the embodiments of the present application can eliminate the need for manual navigation when a user initiates long-distance navigation and needs to undergo several energy replenishment during driving. Instead, the vehicle system monitors the mileage in real time and plans the most reasonable power replenishment points for the user's vehicle throughout the journey based on the user's refueling / charging preferences, thereby solving the user's endurance path planning problem throughout the entire navigation cycle at the framework and perception levels.

[0080] According to the vehicle range planning method proposed in the embodiment of the present application, the initial range planning strategy of the vehicle can be determined according to the destination of the vehicle, the current operating parameters of the vehicle and the power type of the vehicle, and multiple reference points are selected between the current position of the vehicle and the destination according to preset standards, and the environmental data and road condition data of each reference point are obtained, so as to obtain the power consumption prediction data of the vehicle based on the environmental data and road condition data of the current position, the current operating parameters, the environmental data and road condition data of each reference point, the power type of the vehicle and the pre-built consumption model, and then the initial range planning strategy is corrected in combination with the power consumption prediction data and the user's range preference to obtain the actual range planning strategy of the vehicle, so as to control the vehicle based on the actual range planning strategy until the vehicle reaches the destination, predict the energy consumption through the road conditions, and make planning adjustments according to the user's range preferences to ensure range, improve driving efficiency and ensure driving safety. Thus, the technical problem that the impact of real-time road conditions is ignored in the related technology, and the driving preferences, refueling or charging habits of users are not considered, making it difficult for range planning to meet the actual needs of users, thereby affecting the driving experience of users is solved.

[0081] Next, a vehicle range planning device according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0082] Figure 4 It is a block diagram of a vehicle range planning device according to an embodiment of the present application.

[0083] like Figure 4 As shown, the vehicle range planning device 10 includes: a first acquisition module 100, a second acquisition module 200, a calculation module 300 and a planning module 400.

[0084] Specifically, the first acquisition module 100 is used to acquire the destination of the vehicle and determine the initial cruising range planning strategy of the vehicle based on the destination, the current operating parameters of the vehicle and the power type of the vehicle.

[0085] The second acquisition module 200 is used to select a plurality of reference points between the current position of the vehicle and the destination according to a preset standard, and to acquire environmental data and road condition data of each reference point.

[0086] The calculation module 300 is used to obtain the vehicle's power consumption prediction data based on the environmental data and road condition data of the current location, the current operating parameters, the environmental data and road condition data of each reference point, the vehicle's power type and a pre-built consumption model.

[0087] The planning module 400 is used to correct the initial cruising range planning strategy in combination with the power consumption prediction data and the user's cruising range preference to obtain the vehicle's actual cruising range planning strategy, so as to control the vehicle based on the actual cruising range planning strategy until the vehicle reaches the destination.

[0088] Optionally, in one embodiment of the present application, the consumption model is trained by historical environmental parameters and road condition data, historical operating parameters and corresponding historical power consumption data in the vehicle's historical endurance database.

[0089] Optionally, in one embodiment of the present application, the vehicle range planning device 10 further includes: a storage module.

[0090] Among them, the storage module is used to store the environmental data and road condition data of any reference point and the power consumption data of the vehicle at any reference point into the historical endurance database when the vehicle passes through any reference point.

[0091] Optionally, in one embodiment of the present application, the vehicle range planning device 10 further includes: a third acquisition module and a matching module.

[0092] The third acquisition module is used to obtain the user's identity.

[0093] The matching module is used to match the corresponding battery life preference in the preset database using the identity identifier as an index.

[0094] Optionally, in one embodiment of the present application, the planning module 400 includes: a determination unit and a reminder unit.

[0095] Among them, the determination unit is used to determine at least one power replenishment point based on the actual endurance planning strategy.

[0096] The reminder unit is used to generate a power replenishment reminder when the distance between the vehicle and any power replenishment point is less than a preset distance threshold.

[0097] It should be noted that the above explanation of the vehicle range planning method embodiment is also applicable to the vehicle range planning device of this embodiment, and will not be repeated here.

[0098] According to the vehicle range planning device proposed in the embodiment of the present application, the initial range planning strategy of the vehicle can be determined according to the destination of the vehicle, the current operating parameters of the vehicle and the power type of the vehicle, and multiple reference points are selected between the current position of the vehicle and the destination according to preset standards, and the environmental data and road condition data of each reference point are obtained, so as to obtain the power consumption prediction data of the vehicle based on the environmental data and road condition data of the current position, the current operating parameters, the environmental data and road condition data of each reference point, the power type of the vehicle and the pre-built consumption model, and then the initial range planning strategy is corrected in combination with the power consumption prediction data and the user's range preference to obtain the actual range planning strategy of the vehicle, so as to control the vehicle based on the actual range planning strategy until the vehicle reaches the destination, predict the energy consumption through the road conditions, and make planning adjustments according to the user's range preferences to ensure range, improve driving efficiency and ensure driving safety. Thus, the technical problem that the impact of real-time road conditions is ignored in the related technology, and the driving preferences, refueling or charging habits of users are not considered, making it difficult for range planning to meet the actual needs of users, thereby affecting the driving experience of users is solved.

[0099] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0100] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0101] When the processor 502 executes the program, the vehicle range planning method provided in the above embodiment is implemented.

[0102] Furthermore, the vehicle also includes:

[0103] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0104] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0105] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0106] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0107] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0108] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0109] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above vehicle range planning method is implemented.

[0110] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the vehicle range planning method provided by an embodiment of the present invention.

[0111] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0112] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0113] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0114] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0115] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0116] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0117] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0118] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A vehicle range planning method, characterized in that: The following steps are involved: Obtaining a destination of the vehicle, and determining an initial range planning strategy for the vehicle based on the destination, current operating parameters of the vehicle, and a power type of the vehicle; Selecting a plurality of reference points between the current position of the vehicle and the destination according to a preset standard, and acquiring environmental data and road condition data of each reference point; Obtaining power consumption prediction data of the vehicle based on the environmental data and road condition data of the current position, the current operating parameters, the environmental data and road condition data of each reference point, the power type of the vehicle, and a pre-built consumption model; The initial cruising range planning strategy is modified in combination with the power consumption prediction data and the user's cruising range preference to obtain the actual cruising range planning strategy of the vehicle, so as to control the vehicle based on the actual cruising range planning strategy until the vehicle reaches the destination.

2. The method according to claim 1, characterized in that The consumption model is trained by using historical environmental parameters and road condition data, historical operating parameters and corresponding historical power consumption data in the historical cruising range database of the vehicle.

3. The method according to claim 2, characterized in that After the initial cruising range planning strategy is modified in combination with the power consumption prediction data and the user's cruising range preference, the method further includes: When the vehicle passes through any reference point, the environment data and road condition data of the reference point and the power consumption data of the vehicle at the reference point are stored in the historical endurance database.

4. The method according to claim 1, characterized in that Before modifying the initial cruising range planning strategy in combination with the power consumption prediction data and the user's cruising range preference, the method further includes: Get the user's identity; The corresponding battery life preference is matched in a preset database using the identity identifier as an index.

5. The method according to claim 1, characterized in that The controlling the vehicle based on the actual cruising range planning strategy until the vehicle travels to the destination includes: Determining at least one power replenishment point based on the actual cruising range planning strategy; When the distance between the vehicle and any power replenishment point is less than a preset distance threshold, a power replenishment reminder is generated.

6. A vehicle range planning device, characterized in that: include: A first acquisition module, used to acquire a destination of the vehicle, and determine an initial cruising range planning strategy for the vehicle based on the destination, current operating parameters of the vehicle, and a power type of the vehicle; A second acquisition module, configured to select a plurality of reference points between the current position of the vehicle and the destination according to a preset standard, and acquire environmental data and road condition data of each reference point; a calculation module, configured to obtain power consumption prediction data of the vehicle based on the environmental data and road condition data of the current position, the current operating parameters, the environmental data and road condition data of each reference point, the power type of the vehicle, and a pre-built consumption model; A planning module is used to modify the initial cruising range planning strategy in combination with the power consumption prediction data and the user's cruising range preference to obtain the actual cruising range planning strategy of the vehicle, so as to control the vehicle based on the actual cruising range planning strategy until the vehicle reaches the destination.

7. The device according to claim 6, characterized in that The consumption model is trained by using historical environmental parameters and road condition data, historical operating parameters and corresponding historical power consumption data in the historical cruising range database of the vehicle.

8. The device according to claim 7, characterized in that Also includes: The storage module is used to store the environmental data and road condition data of any reference point and the power consumption data of the vehicle at any reference point into the historical endurance database when the vehicle passes through any reference point.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle range planning method as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the vehicle range planning method as described in any one of claims 1-5.

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