Path recommendation method and device based on road endurance planning, vehicle and medium

Abstract

The invention relates to a path recommendation method and device based on road endurance planning, a vehicle and a medium, and the method comprises the steps: determining at least one navigation path according to a current position and a target position, and carrying out the road working condition segment division of each navigation path, and obtaining a plurality of driving segments corresponding to each navigation path; based on the current vehicle operation information and the plurality of driving segments corresponding to each navigation path, matching an energy consumption prediction value of each driving segment of each navigation path from a preset database; and obtaining the total energy consumption of each navigation path according to the energy consumption predicted value of each driving segment of each navigation path, and generating a travel suggestion according to the total energy consumption of each navigation path. Therefore, the problems of low data processing efficiency and real-time performance, inaccurate predicted energy consumption result and the like in related technologies are solved, the data processing efficiency and real-time performance are improved, the energy consumption prediction accuracy is greatly improved, and the endurance experience feeling of a user is improved.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a path recommendation method, device, vehicle and medium based on road endurance planning. Background Art

[0002] In the related art, when predicting the energy consumption of a vehicle, it is generally necessary to measure at least one energy consumption of at least one other vehicle traveling along a road section, and predict the energy consumption of the vehicle required to travel along the road section based on at least one measured energy consumption of the at least one other vehicle.

[0003] However, since the energy consumption of pure electric vehicles depends largely on the user's personal usage, and the energy consumption prediction method in related technologies is based on third-party data, resulting in low data processing efficiency and real-time performance, and the predicted energy consumption results are inaccurate, which needs to be solved urgently. Summary of the invention

[0004] The present application provides a route recommendation method, device, vehicle and medium based on road endurance planning to solve the problems of low data processing efficiency and real-time performance and inaccurate predicted energy consumption results in related technologies. Energy consumption prediction is performed based on a local database, which not only improves the efficiency and real-time performance of data processing, but also greatly improves the accuracy of energy consumption prediction and enhances the user's endurance experience.

[0005] The first embodiment of the present application provides a path recommendation method based on road endurance planning, comprising the following steps:

[0006] Get the vehicle's current location, target location, and current vehicle operation information;

[0007] Determine at least one navigation path according to the current position and the target position, and divide each navigation path into road condition segments to obtain a plurality of driving segments corresponding to each navigation path;

[0008] Based on the current vehicle operation information and the multiple driving segments corresponding to each navigation route, matching the energy consumption prediction value of each driving segment of each navigation route from a preset database;

[0009] The total energy consumption of each navigation path is obtained according to the energy consumption prediction value of each driving segment of each navigation path, and a travel suggestion is generated according to the total energy consumption of each navigation path.

[0010] Optionally, before matching the energy consumption prediction value of each driving segment of each navigation path from a preset database, the method further includes:

[0011] Acquiring historical data of the vehicle, and determining a plurality of navigation learning paths and vehicle operation information corresponding to each navigation learning path according to the historical data;

[0012] Based on a preset highway speed limit level, the plurality of navigation learning paths are respectively divided into road condition segments to obtain a plurality of driving segments corresponding to each navigation learning path, and each driving segment corresponding to each navigation learning path is numbered as a road segment;

[0013] Based on the vehicle operation information corresponding to each navigation learning path, calculating the average power consumption of the air conditioner, the average power consumption of the electronic load, the energy consumption of the drive motor and the energy utilization efficiency of the battery for each driving segment corresponding to each navigation learning path;

[0014] The preset database is generated according to the multiple navigation learning paths, the vehicle operation information corresponding to each navigation learning path, the number of each driving segment corresponding to each navigation learning path, and the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor and the battery energy utilization efficiency of each driving segment corresponding to each navigation learning path.

[0015] Optionally, the calculating, based on the vehicle operation information corresponding to each navigation learning path, the average power consumption of the air conditioner, the average power consumption of the electronic load, the energy consumption of the drive motor, and the energy utilization efficiency of the battery for each driving segment corresponding to each navigation learning path includes:

[0016] When the air conditioner is in the on state, based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed of the road section, the energy consumption of the air conditioner compressor, the PTC energy consumption and the DCDC average energy consumption of each driving segment corresponding to each navigation learning path are calculated, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path are obtained, and the average power consumption of the air conditioner is obtained according to the average vehicle speed of the road section, the energy consumption of the air conditioner compressor, the PTC energy consumption and the DCDC average energy consumption of each driving segment corresponding to each navigation learning path, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path;

[0017] When the air conditioner is in the off state, based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed and the average DCDC energy consumption of each driving segment corresponding to each navigation learning path are calculated, and the air conditioning and DC / DC energy consumption secondary index parameters of each driving segment corresponding to each navigation learning path are obtained, and the average consumption of the electronic load is obtained according to the average vehicle speed and the average DCDC energy consumption of each driving segment corresponding to each navigation learning path, and the energy consumption interference parameter of each driving segment corresponding to each navigation learning path;

[0018] Based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path are calculated, and the drive motor energy consumption index parameter of each driving segment corresponding to each navigation learning path is obtained, and the drive motor energy consumption is obtained according to the average vehicle speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path, and the drive motor energy consumption index parameter of each driving segment corresponding to each navigation learning path;

[0019] Based on the vehicle operation information corresponding to each navigation learning path, the average discharge efficiency of each driving segment corresponding to each navigation learning path is calculated, and the battery energy consumption parameters of each driving segment corresponding to each navigation learning path are obtained, and the battery energy consumption utilization efficiency is obtained according to the average discharge efficiency and battery energy consumption parameters of each driving segment corresponding to each navigation learning path.

[0020] Optionally, matching the energy consumption prediction value of each driving segment of each navigation path from a preset database based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path includes:

[0021] Based on the preset database, determining an energy consumption prediction function for each driving segment corresponding to each navigation learning path;

[0022] Based on the current vehicle operation information, the multiple driving segments corresponding to each navigation path and the number of each driving segment corresponding to each navigation path, a plurality of target driving segments with the highest matching degree with the multiple driving segments corresponding to each navigation path are sequentially screened out from the multiple driving segments corresponding to each navigation learning path, and an energy consumption prediction value of each driving segment of each navigation path is calculated according to an energy consumption prediction function of each target driving segment.

[0023] Optionally, generating a travel suggestion according to the total energy consumption of each navigation path includes:

[0024] A target navigation path with the lowest total energy consumption is selected from the total energy consumption of the multiple navigation paths, and the target navigation path is used as a recommended travel path.

[0025] The second aspect of the present application provides a route recommendation device based on road endurance planning, including:

[0026] The acquisition module is used to obtain the current position, target position and current vehicle operation information of the vehicle;

[0027] a determination module, configured to determine at least one navigation path according to the current position and the target position, and to divide each navigation path into road condition segments to obtain a plurality of driving segments corresponding to each navigation path;

[0028] a matching module, configured to match the energy consumption prediction value of each driving segment of each navigation path from a preset database based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path;

[0029] The control module is used to obtain the total energy consumption of each navigation path according to the energy consumption prediction value of each driving segment of each navigation path, and generate travel suggestions according to the total energy consumption of each navigation path.

[0030] Optionally, before matching the energy consumption prediction value of each driving segment of each navigation path from a preset database, the matching module is further configured to:

[0031] Acquiring historical data of the vehicle, and determining a plurality of navigation learning paths and vehicle operation information corresponding to each navigation learning path according to the historical data;

[0032] Based on a preset highway speed limit level, the plurality of navigation learning paths are respectively divided into road condition segments to obtain a plurality of driving segments corresponding to each navigation learning path, and each driving segment corresponding to each navigation learning path is numbered as a road segment;

[0033] Based on the vehicle operation information corresponding to each navigation learning path, calculating the average power consumption of the air conditioner, the average power consumption of the electronic load, the energy consumption of the drive motor and the energy utilization efficiency of the battery for each driving segment corresponding to each navigation learning path;

[0034] The preset database is generated according to the multiple navigation learning paths, the vehicle operation information corresponding to each navigation learning path, the number of each driving segment corresponding to each navigation learning path, and the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor and the battery energy utilization efficiency of each driving segment corresponding to each navigation learning path.

[0035] Optionally, the control module is specifically used to:

[0036] When the air conditioner is in the on state, based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed of the road section, the energy consumption of the air conditioner compressor, the PTC energy consumption and the DCDC average energy consumption of each driving segment corresponding to each navigation learning path are calculated, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path are obtained, and the average power consumption of the air conditioner is obtained according to the average vehicle speed of the road section, the energy consumption of the air conditioner compressor, the PTC energy consumption and the DCDC average energy consumption of each driving segment corresponding to each navigation learning path, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path;

[0037] When the air conditioner is in the off state, based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed and the average DCDC energy consumption of each driving segment corresponding to each navigation learning path are calculated, and the air conditioning and DC / DC energy consumption secondary index parameters of each driving segment corresponding to each navigation learning path are obtained, and the average consumption of the electronic load is obtained according to the average vehicle speed and the average DCDC energy consumption of each driving segment corresponding to each navigation learning path, and the energy consumption interference parameter of each driving segment corresponding to each navigation learning path;

[0038] Based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path are calculated, and the drive motor energy consumption index parameter of each driving segment corresponding to each navigation learning path is obtained, and the drive motor energy consumption is obtained according to the average vehicle speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path, and the drive motor energy consumption index parameter of each driving segment corresponding to each navigation learning path;

[0039] Based on the vehicle operation information corresponding to each navigation learning path, the average discharge efficiency of each driving segment corresponding to each navigation learning path is calculated, and the battery energy consumption parameters of each driving segment corresponding to each navigation learning path are obtained, and the battery energy consumption utilization efficiency is obtained according to the average discharge efficiency and battery energy consumption parameters of each driving segment corresponding to each navigation learning path.

[0040] Optionally, the matching module is specifically used to:

[0041] Based on the preset database, determining an energy consumption prediction function for each driving segment corresponding to each navigation learning path;

[0042] Based on the current vehicle operation information, the multiple driving segments corresponding to each navigation path and the number of each driving segment corresponding to each navigation path, a plurality of target driving segments with the highest matching degree with the multiple driving segments corresponding to each navigation path are sequentially screened out from the multiple driving segments corresponding to each navigation learning path, and an energy consumption prediction value of each driving segment of each navigation path is calculated according to an energy consumption prediction function of each target driving segment.

[0043] Optionally, the control module is specifically used to:

[0044] A target navigation path with the lowest total energy consumption is selected from the total energy consumption of the multiple navigation paths, and the target navigation path is used as a recommended travel path.

[0045] The third aspect of the present application provides a vehicle, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the path recommendation method based on road endurance planning as described in the above embodiment.

[0046] The fourth aspect of the present application provides a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement the path recommendation method based on road endurance planning as described in the above embodiment.

[0047] The fifth aspect of the present application provides a computer program product, which stores a computer program. When the program is executed by a processor, it implements the path recommendation method based on road endurance planning as described in the above embodiment.

[0048] Thus, the current position, target position and current vehicle operation information of the vehicle are obtained, and at least one navigation path is determined according to the current position and target position, and each navigation path is divided into road condition segments to obtain multiple driving segments corresponding to each navigation path. Based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path, the energy consumption prediction value of each driving segment of each navigation path is matched from a preset database, and the total energy consumption of each navigation path is obtained to generate travel suggestions. Thus, the problems of low data processing efficiency and real-time performance and inaccurate predicted energy consumption results in the related technology are solved. Energy consumption prediction based on the local database not only improves the efficiency and real-time performance of data processing, but also greatly improves the accuracy of energy consumption prediction and improves the user's endurance experience.

[0049] 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

[0050] 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:

[0051] Figure 1 A flowchart of a route recommendation method based on road endurance planning provided according to an embodiment of the present application;

[0052] Figure 2 A schematic block diagram of a navigation terminal display according to an embodiment of the present application;

[0053] Figure 3 A flowchart of establishing a preset database according to an embodiment of the present application;

[0054] Figure 4 A block diagram of a road endurance planning calculation and prediction system according to an embodiment of the present application;

[0055] Figure 5 A block diagram of a vehicle system according to an embodiment of the present application;

[0056] Figure 6 This is a flow chart of a path recommendation method based on road endurance planning according to a specific embodiment of the present application;

[0057] Figure 7 A schematic diagram of data transmission involved in a path recommendation method based on road endurance planning according to an embodiment of the present application;

[0058] Figure 8 A schematic diagram of a route recommendation device based on road endurance planning according to an embodiment of the present application;

[0059] Fig. 9 It is a block diagram of a vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0060] 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.

[0061] The following describes the path recommendation method, device, vehicle and medium based on road endurance planning of the embodiment of the present application with reference to the accompanying drawings. In view of the problems that the data processing efficiency and real-time performance of the related technologies mentioned in the above background technology are both low, and the predicted energy consumption results are inaccurate, the present application provides a path recommendation method based on road endurance planning, in which the current position, target position and current vehicle operation information of the vehicle are obtained, and at least one navigation path is determined according to the current position and the target position, and each navigation path is divided into road condition segments to obtain multiple driving segments corresponding to each navigation path, and based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path, the energy consumption prediction value of each driving segment of each navigation path is matched from the preset database, and the total energy consumption of each navigation path is obtained, and travel suggestions are generated. Therefore, the problems that the data processing efficiency and real-time performance of the related technologies are both low, and the predicted energy consumption results are inaccurate are solved, and the energy consumption prediction based on the local database not only improves the efficiency and real-time performance of data processing, but also greatly improves the accuracy of energy consumption prediction, and improves the endurance experience of users.

[0062] Specifically, Figure 1 A flowchart of a route recommendation method based on road endurance planning provided in an embodiment of the present application.

[0063] like Figure 1 As shown, the path recommendation method based on road endurance planning includes the following steps:

[0064] In step S101, the current position, target position and current vehicle operation information of the vehicle are obtained.

[0065] Among them, the current location of the vehicle refers to the specific geographical location where the vehicle is currently located; the target location is the destination location that the user wants to reach.

[0066] Specifically, the current position of the vehicle is usually determined by a satellite navigation receiver such as GPS or Beidou, which receives satellite signals in real time and calculates the precise position of the vehicle; in addition to satellite navigation, the vehicle can also match the high-precision map through on-board sensors (such as wheel speed sensors, steering wheel angle sensors, etc.) to further confirm the vehicle's position. The user can enter the destination address through the on-board infotainment system or navigation system, and the embodiment of the present application will convert it into the target location in latitude and longitude coordinates or other formats; some vehicles support voice control functions, and the user can enter the target location through voice commands. The current vehicle operation information includes but is not limited to real-time data such as vehicle speed, battery status (such as remaining power), air conditioning usage, electronic load, etc., which helps to more accurately predict energy consumption. Based on these three pieces of information, the embodiment of the present application can further plan the navigation path, and perform subsequent energy consumption predictions and mileage calculations.

[0067] It is understandable that there are multiple ways to obtain the current vehicle operation information. In some embodiments, various sensors installed on the vehicle, such as vehicle speed sensors, engine speed sensors, battery power sensors, etc., can monitor and transmit the vehicle's operating status information in real time; modern vehicles generally use CAN (Controller Area Network) bus technology to connect various control units on the vehicle (such as engine control unit, brake control unit, etc.) to achieve data sharing and communication. By reading the data on the CAN bus, the vehicle's real-time operation information can be obtained; for some vehicles, the vehicle's fault code, operating status and other information can also be read through the OBD-II (On-Board Diagnostics II) interface.

[0068] In step S102, at least one navigation path is determined according to the current position and the target position, and each navigation path is divided into road condition segments to obtain a plurality of driving segments corresponding to each navigation path.

[0069] Among them, driving segments refer to the entire navigation route being divided into multiple small segments according to road conditions and speed limit levels.

[0070] Specifically, after the present application embodiment obtains the current position and target position of the vehicle, a path planning algorithm is used to determine at least one navigation path from the current position to the target position. These algorithms can consider factors such as road networks, traffic rules, obstacles, etc. to find the best or suboptimal path. For each determined navigation path, it is necessary to collect relevant road information, including road types (such as cities, highways, winding roads, hilly roads, etc.), road surface types (such as paved roads, gravel roads, etc.), environmental conditions (such as temperature, wind speed, precipitation, etc.) and traffic conditions (such as current average speed, whether there is traffic jam, etc.). Based on the collected road information, the present application embodiment can divide the navigation path into multiple driving segments, and each driving segment can be defined based on the change of road type, the change of environmental conditions or the significant difference of traffic conditions. For example, a driving segment may include a section of urban road, and another segment may include a section of highway.

[0071] In step S103, based on the current vehicle operation information and a plurality of driving segments corresponding to each navigation route, the energy consumption prediction value of each driving segment of each navigation route is matched from a preset database.

[0072] The energy consumption prediction value refers to the power consumption corresponding to each driving segment.

[0073] Specifically, a large amount of historical data is stored in the preset database, which includes various parameters under different driving segments (such as average power consumption of air conditioners, average consumption of electronic loads, energy consumption of drive motors, battery energy consumption utilization efficiency, etc.) and their corresponding energy consumption values. Based on the current vehicle operation information and the driving segments of each navigation path, the closest historical data is searched from the database; this process may involve single-parameter retrieval matching, such as matching the energy consumption data at the same or similar speed in history according to the current vehicle speed; or involve dual-parameter matching, combining two related parameters, such as vehicle speed and ambient temperature, to find the closest situation in historical data; or involve multi-parameter fuzzy matching, when there is no completely matching data, fuzzy matching technology is used to consider the combination of multiple parameters to find the closest historical data cluster. The energy consumption prediction value of each driving segment is calculated using the historical data in the database and the corresponding energy consumption prediction function (such as a multivariate linear regression model).

[0074] It should be noted that if enough historical data has been stored in the database, and these data have been used to train reliable energy consumption prediction functions, then these prediction functions can be directly called to calculate the energy consumption prediction value of each driving segment. Specifically, relevant features (such as vehicle speed, temperature, wind speed, road type, etc.) are extracted from the current vehicle operation information and multiple driving segments corresponding to each navigation path, and the extracted features are matched with the historical data in the database to find the closest historical data segment, and the existing energy consumption prediction function is used to input the current feature value to calculate the energy consumption prediction value of each driving segment; if there is not enough historical data in the database, or the existing data is not enough to cover all possible driving conditions, it is necessary to collect more data through actual driving or simulated driving, clean and preprocess the newly collected data to ensure the quality and consistency of the data, extract key features such as vehicle speed, temperature, wind speed, road type, etc., and use machine learning methods (such as multivariate linear regression, decision tree, random forest, support vector machine, neural network, etc.) to train new energy consumption prediction functions.

[0075] Optionally, in some embodiments, based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path, the energy consumption prediction value of each driving segment of each navigation path is matched from a preset database, including: determining the energy consumption prediction function of each driving segment corresponding to each navigation learning path based on the preset database; based on the current vehicle operation information, the multiple driving segments corresponding to each navigation path and the number of each driving segment corresponding to each navigation path, selecting multiple target driving segments with the highest matching degree with the multiple driving segments corresponding to each navigation path from the multiple driving segments corresponding to each navigation learning path in turn, and calculating the energy consumption prediction value of each driving segment of each navigation path according to the energy consumption prediction function of each target driving segment.

[0076] It can be understood that in actual applications, when the vehicle starts to plan a driving route, the embodiment of the present application will obtain the current vehicle operation information, such as vehicle speed, battery level, air conditioning usage status, etc., and combine the multiple driving segments corresponding to each navigation path and their numbers, and screen out multiple target driving segments with the highest matching degree with the current situation from the preset database; this screening process is based on comprehensive consideration of multiple dimensions, including but not limited to the road type, traffic conditions, weather conditions, etc. of the driving segment, to ensure that the selected target driving segment can truly reflect the energy consumption under the current driving conditions; after screening out the target driving segment, the embodiment of the present application uses the previously determined energy consumption prediction function to calculate the energy consumption prediction value of each driving segment under each navigation path, and these prediction values ​​can help the driver or vehicle system to more accurately understand the energy consumption under different driving paths, so as to make more reasonable driving decisions.

[0077] In step S104, the total energy consumption of each navigation route is obtained according to the energy consumption prediction value of each driving segment of each navigation route, and a travel suggestion is generated according to the total energy consumption of each navigation route.

[0078] Specifically, for each driving segment in each navigation path, the energy consumption prediction value obtained by matching previously is used, and the energy consumption prediction values ​​of all driving segments in the same navigation path are added together to obtain the total energy consumption of the path. It is checked whether the current remaining battery power of the vehicle is sufficient to support the total energy consumption of the selected navigation path. If there are multiple alternative navigation paths, their total energy consumption is compared to determine which path is the most energy-efficient or most in line with the user's other travel preferences (such as shortest time, lowest cost, etc.). Real-time factors can also be considered, and energy consumption can be fine-tuned in combination with real-time traffic information, weather conditions, and driving habits. Based on the total energy consumption, Recommend the optimal route to the user; provide energy-saving driving suggestions to the user, such as reasonably controlling the vehicle speed, reducing sudden acceleration and braking, adjusting the air-conditioning temperature, etc. If the energy consumption of a certain route exceeds the remaining battery power, it is recommended to change the route or arrange charging on the way; if the prediction result shows that charging is required on the way, the location and estimated arrival time of the charging stations along the way are provided; the embodiment of the present application provides the user with a decision support system, which displays information such as energy consumption, estimated arrival time, and charging requirements of different routes; monitors energy consumption and updates predictions in real time during the journey, and dynamically adjusts travel suggestions according to actual conditions; records actual energy consumption data for future optimization of energy consumption prediction models.

[0079] Optionally, in some embodiments, based on the vehicle operation information corresponding to each navigation learning path, the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor and the battery energy consumption utilization efficiency of each driving segment corresponding to each navigation learning path are calculated, including: when the air conditioner is in the on state, based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed of the road section, the energy consumption of the air conditioner compressor, the PTC energy consumption and the average energy consumption of the DCDC of each driving segment corresponding to each navigation learning path are calculated, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path are obtained, and the average power consumption of the air conditioner is obtained according to the average vehicle speed of the road section, the energy consumption of the air conditioner compressor, the PTC energy consumption and the average energy consumption of the DCDC of each driving segment corresponding to each navigation learning path, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path; when the air conditioner is in the off state, based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed of the road section and the average energy consumption of the DCDC of each driving segment corresponding to each navigation learning path are calculated, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path are obtained. The secondary index parameters of air conditioning and DC / DC energy consumption are obtained, and the average consumption of the electronic load is obtained according to the average vehicle speed and DCDC average energy consumption of each driving segment corresponding to each navigation learning path, and the energy consumption interference parameters of each driving segment corresponding to each navigation learning path; based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path are calculated, and the drive motor energy consumption index parameters of each driving segment corresponding to each navigation learning path are obtained, and the drive motor energy consumption is obtained according to the average vehicle speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path, and the drive motor energy consumption index parameters of each driving segment corresponding to each navigation learning path; based on the vehicle operation information corresponding to each navigation learning path, the average discharge efficiency of each driving segment corresponding to each navigation learning path is calculated, and the battery energy consumption parameters of each driving segment corresponding to each navigation learning path are obtained, and the battery energy consumption utilization efficiency is obtained according to the average discharge efficiency and battery energy consumption parameters of each driving segment corresponding to each navigation learning path.

[0080] It is understandable that the comprehensive energy consumption evaluation system accurately calculates the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor and the energy efficiency of the battery in each driving segment by analyzing the vehicle operation information under each navigation learning path; this system not only helps the driver or the automatic driving system to optimize the driving strategy, reduce energy consumption, improve energy efficiency and endurance, but also promotes green travel and sustainable development. At the same time, it enhances environmental awareness, promotes the innovation and development of related technologies, and provides strong support for energy conservation, emission reduction and green development in the transportation industry.

[0081] Optionally, in some embodiments, generating travel suggestions based on the total energy consumption of each navigation path includes: selecting a target navigation path with the minimum total energy consumption from the total energy consumption of multiple navigation paths, and using the target navigation path as the recommended travel path.

[0082] It is understandable that for each navigation path, the total energy consumption is calculated using a comprehensive energy consumption assessment system, which includes factors such as the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor, and the efficiency of the battery energy consumption in different driving segments of the vehicle; from all calculated total energy consumptions of the navigation paths, the path with the lowest total energy consumption is selected, and this path will be used as the recommended travel path. The selected target navigation path is presented to the driver as a recommended travel path. The suggestion may include the estimated driving time, remaining range, key locations along the way (such as charging stations, rest areas, etc.), and any special precautions that may affect the journey; in addition to the recommended travel path, other useful information may also be provided, such as average vehicle speed recommendations on the path, energy-saving driving tips, etc., to help the driver further reduce energy consumption during actual driving, wherein the embodiments of the present application may be implemented through, for example, Figure 2 The navigation terminal display shown is displayed.

[0083] It should be noted that if the driver changes the destination or encounters unforeseen circumstances (such as traffic congestion, weather changes, etc.) during driving, the embodiment of the present application should be able to dynamically adjust the recommended travel route to ensure that the optimal energy consumption solution is always provided.

[0084] Thus, by obtaining the current position, target position and current vehicle operation information of the vehicle, and determining at least one navigation path according to the current position and target position, each navigation path is divided into road condition segments to obtain multiple driving segments corresponding to each navigation path, and based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path, the energy consumption prediction value of each driving segment of each navigation path is matched from a preset database, and the total energy consumption of each navigation path is obtained to generate travel suggestions. Thus, the problems of low data processing efficiency and real-time performance and inaccurate predicted energy consumption results in the related technology are solved, and energy consumption prediction based on the local database not only improves the efficiency and real-time performance of data processing, but also greatly improves the accuracy of energy consumption prediction and improves the user's endurance experience.

[0085] To facilitate understanding of the generation of the preset database, the following describes how to generate the preset database in conjunction with a possible implementation method.

[0086] Optionally, in some embodiments, before matching the predicted energy consumption value of each driving segment of each navigation path from a preset database, it also includes: obtaining historical data of the vehicle, and determining multiple navigation learning paths and vehicle operation information corresponding to each navigation learning path based on the historical data; based on a preset highway speed limit level, dividing the multiple navigation learning paths into road condition segments to obtain multiple driving segments corresponding to each navigation learning path, and numbering each driving segment corresponding to each navigation learning path; based on the vehicle operation information corresponding to each navigation learning path, calculating the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor and the battery energy consumption utilization efficiency of each driving segment corresponding to each navigation learning path; generating a preset database based on the multiple navigation learning paths, the vehicle operation information corresponding to each navigation learning path, the number of each driving segment corresponding to each navigation learning path and the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor and the battery energy consumption utilization efficiency of each driving segment corresponding to each navigation learning path.

[0087] Specifically, the embodiment of the present application can obtain the historical data of the vehicle, and divide the road sections into highways, expressways, urban roads and other sections according to the highway speed limit level based on the navigation data in the historical data of the vehicle, and distinguish the driving direction of the sections; among them, for highways and expressways, the navigation segment from the first entrance to the next exit is used for data analysis; such as G425-001; for cities and other roads, the starting point-end point is numerically numbered according to the road number: such as X22-001, X22-002, and the above intervals can be further spliced ​​and fitted according to all segments to obtain the full road section data, so as to obtain multiple driving segments corresponding to each navigation learning path.

[0088] Furthermore, the embodiments of the present application can set statistical indicators according to road driving segments, and thus combine the multiple driving segments corresponding to each navigation learning path to calculate the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor, and the battery energy utilization efficiency of each driving segment corresponding to each navigation learning path, and then generate a preset database according to the multiple navigation learning paths, the vehicle operation information corresponding to each navigation learning path, the number of each driving segment corresponding to each navigation learning path, and the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor, and the battery energy utilization efficiency of each driving segment corresponding to each navigation learning path.

[0089] Among them, Figure 3 As shown, Figure 3 This is a flowchart of establishing a preset database according to an embodiment of the present application. The establishment of the preset database mainly includes the following parts:

[0090] S21, condition judgment: after entering the self-learning road section, obtain the current road segment number, monitor the air conditioning operation status, and judge the vehicle speed status;

[0091] S22, data indicator statistics: cleaning, deletion, conversion preprocessing of raw data, filtering out abnormal mutation data, filtering out default signal virtual values ​​in bus signals according to rules such as constraint condition temperature T∈[-40℃,150℃], and performing smooth linear interpolation processing on the raw signals at the analog signal loss points.

[0092] If the air conditioner is running, calculate the total mileage, average air conditioner power consumption, DC / DC average energy consumption, PTC average energy consumption, operating time, and average vehicle speed under this state; if the air conditioner is not in working state, calculate the DC / DC average power consumption and the average vehicle speed of the section.

[0093] The energy consumption statistics of the driving motor are used to determine whether the vehicle is in the driving process by the vehicle speed. If the vehicle speed is not 0, the average driving speed is calculated, and the average driving speed, average driving energy consumption, and average recovered energy are calculated. The battery energy consumption statistics SOC change theoretical average energy output and actual discharge power average;

[0094] The first-level index includes road sections, data generation time mark, travel time, average ambient temperature, average driving speed, average speed of the route, and energy consumption data. The endurance planning calculation module establishes the above data retrieval index. The drive motor energy consumption and average driving speed are used as the clustering coordinate axis of the working condition clustering feature 1, the average driving speed and the energy consumption of electronic accessories are used as the clustering coordinate axis of the clustering feature 2, and the battery uses the relationship between the actual discharge efficiency and the average battery temperature as the clustering coordinate axis of the clustering feature 3.

[0095] Secondary index parameters: actual energy consumption is affected by user parameter settings, driving habits, environmental factors and other conditions. These quantities cross-affect the energy consumption results. This index parameter provides the section average of the associated influencing parameters for calculating each classified energy consumption indicator, providing a basis for further executing the associated function operation.

[0096] Air conditioning and DC / DC energy consumption secondary index parameters. The external sources of air conditioning energy consumption interference are air temperature and humidity, wind speed (vehicle speed), and solar radiation. Solar radiation is related to declination angle, time difference line, etc. The internal sources are accessories in the car and passenger heat radiation. Read user-set data such as air conditioning gear position, air conditioning temperature setting, damper mode, seat sensor information, and record lights, wipers, electronic water pumps, and environmental parameters.

[0097] Secondary index parameters of drive motor energy consumption: read driving mode, motor temperature, motor torque, motor speed, acceleration, recovery energy, seat sensor, starting and braking times, recovery level and other classified driving styles. Changes in the above parameters affect the motor transmission efficiency and driving resistance.

[0098] The secondary index parameter of battery output energy consumption is established, as shown in the following formula. The battery has nonlinear characteristics, and the actual user battery model cannot accurately reflect the energy consumption of the road section.

[0099]

[0100] Where -R batt is the battery internal resistance and U ocv is the battery open circuit voltage, which is obtained by looking up the SOC and T battery temperature;

[0101] C is the battery capacity, SOC0 is the initial battery capacity, P batt is the actual discharge power of the battery;

[0102] The average discharge efficiency is obtained based on the actual SOC drop and actual output capacity of the road section, and the battery SOH, internal resistance, average cell temperature, and SOC average are read.

[0103] S23, output and store road section data and labels: The database stores data and records the original data according to the following rules, the first-level index = {road label, date and time, interval travel distance, average driving speed, average speed of the road section, ambient temperature and humidity, {average power consumption of air conditioning system, air conditioning system secondary index 1}, {average power consumption of electronic load, DC / DC system secondary index 2}, {average power consumption of drive motor, drive motor secondary index 3}, {average discharge efficiency of battery system, battery system secondary index 4}}

[0104] Air conditioning system secondary index 1 = {travel time, outside temperature, inside temperature, air conditioning gear, damper mode, circulation mode, headlight gear, wiper gear, electronic water pump duty cycle, seat sensor, weather information}

[0105] DC / DC system secondary index 2 = {blower gear, damper mode, circulation mode, headlight gear, wiper gear, electronic water pump duty cycle, cooling fan speed, battery voltage}

[0106] Drive motor secondary index 3 = {average driving speed and standard deviation, driving mode, average acceleration, average deceleration, average energy recovery, average driving energy consumption, seat sensor, energy recovery level, vehicle start and stop times, tire pressure, average motor temperature}

[0107] Battery system secondary index 4 = {battery internal resistance, module average temperature, SOC, battery voltage, battery current}

[0108] The above parameters are converted digitally, such as the damper mode and air conditioner gear position, into digital quantities, and the first and second level index sample parameters are combined to obtain the initial array:

[0109] {[x11 、x 12 ,…x 1n ],[x 21 、x 22 ,…x 2n ],…[x m1 、x m2 ,…x mn ]}, assuming n parameters, m groups of data, m>n.

[0110] It should be noted that Figure 3 The average driving speed shown in refers to the average speed of the vehicle under speed conditions, which is strongly related to the consumption of the drive motor; the average speed of the section is obtained by the actual travel distance and time, and the working energy consumption of the section air-conditioning system is related to the average speed of the section.

[0111] Therefore, by obtaining the vehicle's historical data and determining the navigation learning path and its corresponding vehicle operation information, the road condition segmentation is further divided based on the highway speed limit level and the detailed energy consumption parameters of each driving segment are calculated, and finally a preset database containing rich information is constructed, which provides a solid foundation for subsequent energy consumption prediction and travel suggestions. Using this database, the energy consumption prediction value of the driving segment of each navigation path can be accurately matched, thereby generating more scientific and reasonable travel suggestions, which not only improves the driving experience, but also promotes energy conservation and emission reduction.

[0112] To facilitate those skilled in the art to further understand the path recommendation method based on road endurance planning in the embodiment of the present application, the following is a description of the path recommendation method based on road endurance planning in the present application. Figures 4 to 7 The specific embodiments shown are introduced in detail below: the road endurance planning calculation prediction system and vehicle system involved in the path recommendation method based on road endurance planning in an embodiment of the present application, as well as the path recommendation method based on road endurance planning in a specific embodiment of the present application.

[0113] Specifically, Figure 4 As shown, Figure 4 This is a block diagram of a road cruising range planning calculation and prediction system according to an embodiment of the present application. The road cruising range planning calculation and prediction system 10 includes a cruising range planning calculation module 1, a communication module 2 and a storage module 3.

[0114] It should be noted that the cruising range planning calculation module 1 is a vehicle-grade controller that can calculate the user's actual energy consumption through programming, process the collected road data in real time, perform data classification and index calculation, and retrieve historical data to plan navigation section operation parameters; the storage module 3 is used to store all road segments, energy consumption information, data feature labels and other fitting curve results. The communication module 2 can perform CAN / CANFD / LIN and other bus transceiver functions, receive vehicle bus information, and output cruising range planning results.

[0115] It solves the problems of low data processing efficiency and real-time performance in related technologies, and inaccurate predicted energy consumption results. Energy consumption prediction based on local database not only improves data processing efficiency and real-time performance, but also greatly improves the accuracy of energy consumption prediction, and improves the user's endurance experience. Figure 5 As shown, Figure 5 This is a block diagram of a vehicle system according to an embodiment of the present application. The vehicle system 100 includes a road endurance planning calculation and prediction system 10, a navigation interface 101, an on-board bus 102, and a display terminal 103.

[0116] Among them, the navigation interface 101 includes node mileage, road node information, weather information, estimated travel time, vehicle speed, etc. Considering that the vehicle displayed speed deviates from the actual speed during actual driving, all the following speed descriptions preferably use the navigation interface speed as the calculated value; the on-board bus 102 provides the vehicle operation mode, battery real-time current, battery real-time voltage, battery average temperature, drive motor current, drive motor speed, motor temperature, air conditioning gear, blower gear, DC / DC power, heating PTC and compressor current and voltage, ambient temperature, weather information, time, battery SOH, battery SOC, cab temperature, seat sensor, driver setting information, automatic cruise status, driving mode, battery voltage and current, etc. in a CAN / CANFD bus manner; the display terminal 103 displays weather information including temperature, humidity, precipitation, wind speed, light, etc. of each navigation section, and the driver setting information includes blower gear, ambient temperature, air conditioning gear, air conditioning temperature setting, headlight gear, wiper gear, in-vehicle entertainment setting, etc.

[0117] The embodiment of the present application solves the problems of low data processing efficiency and real-time performance in the related technology, and inaccurate predicted energy consumption results. Energy consumption prediction based on the local database not only improves the efficiency and real-time performance of data processing, but also greatly improves the accuracy of energy consumption prediction, and improves the user's endurance experience. Furthermore, based on Figure 4 and Figure 5 The path recommendation method based on road endurance planning in the embodiment of the present application can be as follows: Figure 6 As shown, the path recommendation method based on road endurance planning includes the following steps:

[0118] S1: Plan driving segments according to the navigation path.

[0119] S2: When the vehicle enters the area, calculate and store the energy consumption of high-voltage electrical components, define labels for the road sections, set the road section number, weather, settings, and vehicle information retrieval, and self-learn to reduce the priority of parameters with low correlation with energy changes.

[0120] S3: Call the navigation again, retrieve the road label, call up the historical data, perform fuzzy matching of the label definition conditions on the road section, and find the closest unit.

[0121] Specifically, the embodiment of the present application can call a preset database, match the database label parameters, learn the blank points for fitting. Data training is to fit the best prediction function, self-learn the historical data, and assign it to each blank road section as the initialization calculation condition, and gradually fit the energy consumption prediction function of the new road section as the data accumulates.

[0122] S4: Generate energy consumption of combined route segments, intelligently predict driving range, push energy-saving driving routes, and shut down devices to indicate the impact on driving range.

[0123] Specifically, it plans the best endurance path, completes user parameter recommendations, and updates endurance data in real time. It calculates the energy consumption of the entire path and estimates the battery power E at the end. end The planning results and navigation information are displayed together on the vehicle navigation system.

[0124] It should be noted that the following formula can be used to generate the energy consumption of the path combination fragment:

[0125]

[0126] Where n is the road segment combination sequence number;

[0127] E start -Battery current at the start time, in kwh;

[0128] P 1,i -The predicted energy consumption of the air conditioning system on section i, in kw;

[0129] P 2,i -The predicted value of energy consumption at the DC / DC high voltage end of section i, in kw;

[0130] E 1,i - Prediction of motor drive energy consumption on road section i, in kwh;

[0131] t 1,i -Estimated navigation travel time for section i, in seconds;

[0132] η 1,i - Actual utilization efficiency of battery energy on section i, %;

[0133] k-weighted correction adjustment factor for each road section. The actual total power value released by the battery is not equal to the sum of the power values ​​of all high-voltage devices due to sensor accuracy and temperature drift issues. It is obtained from multiple learning data. The best historical data of each road section is called during driving to remind users to set in-car parameters and driving habits to improve endurance.

[0134] It should be noted that the data transmission involved in the path recommendation method based on road endurance planning in the embodiment of the present application can be as follows: Figure 7 shown.

[0135] Specifically, the main factors affecting the endurance of pure electric vehicles include high-voltage consumption, air conditioning system consumption, DC / DC terminal consumption, drive motor consumption, and battery discharge capacity. The embodiment of this application records and calculates the above energy consumption data separately. In actual driving processes, such as high-speed driving, the energy consumption of the whole vehicle is closely related to the actual driving speed; while in urban travel, there are often situations such as parking, waiting for red and green lights, and traffic jams. The proportion of air conditioning system consumption has increased, and the correlation with the actual driving time is relatively high. Based on the above characteristics, the following steps are used to separately calculate the energy consumption indicators with high and low correlation with time and speed.

[0136] According to the route recommendation method based on road endurance planning proposed in the embodiment of the present application, the current position, target position and current vehicle operation information of the vehicle are obtained, and at least one navigation path is determined based on the current position and target position, and each navigation path is divided into road condition segments to obtain multiple driving segments corresponding to each navigation path. Based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path, the energy consumption prediction value of each driving segment of each navigation path is matched from a preset database, and the total energy consumption of each navigation path is obtained to generate travel suggestions. Thus, the problems of low data processing efficiency and real-time performance and inaccurate predicted energy consumption results in the related technology are solved. Energy consumption prediction based on the local database not only improves the efficiency and real-time performance of data processing, but also greatly improves the accuracy of energy consumption prediction and improves the user's endurance experience.

[0137] Next, a path recommendation device based on road endurance planning proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0138] Figure 8 is a block diagram of a route recommendation device based on road endurance planning according to an embodiment of the present application. Figure 8 As shown, the route recommendation device 20 based on road endurance planning includes: an acquisition module 200 , a determination module 300 , a matching module 400 , and a control module 500 .

[0139] Wherein, the acquisition module 200 is used to acquire the current position, target position and current vehicle operation information of the vehicle;

[0140] A determination module 300 is used to determine at least one navigation path according to the current position and the target position, and to divide each navigation path into road condition segments to obtain a plurality of driving segments corresponding to each navigation path;

[0141] A matching module 400, for matching the energy consumption prediction value of each driving segment of each navigation path from a preset database based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path;

[0142] The control module 500 is used to obtain the total energy consumption of each navigation path according to the energy consumption prediction value of each driving segment of each navigation path, and generate travel suggestions according to the total energy consumption of each navigation path.

[0143] Optionally, before matching the predicted energy consumption value of each driving segment of each navigation path from a preset database, the matching module 400 is further used to: obtain historical data of the vehicle, and determine multiple navigation learning paths and vehicle operation information corresponding to each navigation learning path based on the historical data; based on a preset highway speed limit level, divide the multiple navigation learning paths into road condition segments to obtain multiple driving segments corresponding to each navigation learning path, and number each driving segment corresponding to each navigation learning path; based on the vehicle operation information corresponding to each navigation learning path, calculate the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor, and the battery energy consumption utilization efficiency of each driving segment corresponding to each navigation learning path; generate a preset database based on the multiple navigation learning paths, the vehicle operation information corresponding to each navigation learning path, the number of each driving segment corresponding to each navigation learning path, and the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor, and the battery energy consumption utilization efficiency of each driving segment corresponding to each navigation learning path.

[0144] Optionally, the control module 500 is specifically used to: when the air conditioner is in the on state, based on the vehicle operation information corresponding to each navigation learning path, calculate the average vehicle speed of each driving segment corresponding to each navigation learning path, the air conditioner compressor energy consumption, the PTC energy consumption and the DCDC average energy consumption, and obtain the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path, and obtain the average power consumption of the air conditioner according to the average vehicle speed of each driving segment corresponding to each navigation learning path, the air conditioner compressor energy consumption, the PTC energy consumption and the DCDC average energy consumption, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path; when the air conditioner is in the off state, based on the vehicle operation information corresponding to each navigation learning path, calculate the average vehicle speed of each driving segment corresponding to each navigation learning path and the DCDC average energy consumption, and obtain the secondary index parameters of the air conditioner and DC / DC energy consumption for each driving segment corresponding to each navigation learning path, and obtain the secondary index parameters of the air conditioner and DC / DC energy consumption for each driving segment corresponding to each navigation learning path according to the average vehicle speed of each driving segment corresponding to each navigation learning path, the air conditioner compressor energy consumption, the PTC energy consumption and the DCDC average energy consumption, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path. The average consumption of the electronic load is obtained based on the average vehicle speed and DCDC average energy consumption of the road section, and the energy consumption interference parameter of each driving segment corresponding to each navigation learning path; based on the vehicle operation information corresponding to each navigation learning path, the average driving speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path are calculated, and the drive motor energy consumption index parameter of each driving segment corresponding to each navigation learning path is obtained, and the drive motor energy consumption is obtained according to the average vehicle speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path, and the drive motor energy consumption index parameter of each driving segment corresponding to each navigation learning path; based on the vehicle operation information corresponding to each navigation learning path, the average discharge efficiency of each driving segment corresponding to each navigation learning path is calculated, and the battery energy consumption parameter of each driving segment corresponding to each navigation learning path is obtained, and the battery energy consumption utilization efficiency is obtained according to the average discharge efficiency and battery energy consumption parameter of each driving segment corresponding to each navigation learning path.

[0145] Optionally, the matching module 400 is specifically used to: determine the energy consumption prediction function of each driving segment corresponding to each navigation learning path based on a preset database; based on current vehicle operation information, multiple driving segments corresponding to each navigation path and the number of each driving segment corresponding to each navigation path, sequentially select multiple target driving segments with the highest matching degree with the multiple driving segments corresponding to each navigation path from the multiple driving segments corresponding to each navigation learning path, and calculate the energy consumption prediction value of each driving segment of each navigation path according to the energy consumption prediction function of each target driving segment.

[0146] Optionally, the control module 500 is specifically configured to: select a target navigation path with the minimum total energy consumption from the total energy consumption of the multiple navigation paths, and use the target navigation path as the recommended travel path.

[0147] It should be noted that the above explanation of the embodiment of the route recommendation method based on road endurance planning is also applicable to the route recommendation device based on road endurance planning in this embodiment, and will not be repeated here.

[0148] According to the route recommendation device based on road endurance planning proposed in the embodiment of the present application, the current position, target position and current vehicle operation information of the vehicle are obtained, and at least one navigation path is determined according to the current position and target position, and each navigation path is divided into road condition segments to obtain multiple driving segments corresponding to each navigation path, and based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path, the energy consumption prediction value of each driving segment of each navigation path is matched from a preset database, and the total energy consumption of each navigation path is obtained to generate travel suggestions. Thus, the problems of low data processing efficiency and real-time performance and inaccurate predicted energy consumption results in the related technology are solved, and energy consumption prediction based on the local database not only improves the efficiency and real-time performance of data processing, but also greatly improves the accuracy of energy consumption prediction and improves the user's endurance experience.

[0149] Fig. 9 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0150] A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0151] When the processor 902 executes the program, the path recommendation method based on road endurance planning provided in the above embodiment is implemented.

[0152] Furthermore, the vehicle also includes:

[0153] The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0154] The memory 901 is used to store computer programs that can be executed on the processor 902 .

[0155] The memory 901 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.

[0156] If the memory 901, the processor 902 and the communication interface 903 are implemented independently, the communication interface 903, the memory 901 and the processor 902 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, Fig. 9 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.

[0157] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0158] The processor 902 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.

[0159] This embodiment also provides a computer-readable storage medium on which a computer program is stored, characterized in that when the program is executed by a processor, the path recommendation method based on road endurance planning as described above is implemented.

[0160] An embodiment of the present invention further provides a computer program product, which stores a computer program. When the program is executed by a processor, the above-mentioned path recommendation method based on road endurance planning is implemented.

[0161] 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.

[0162] 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.

[0163] 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 more 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.

[0164] 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.

[0165] 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.

Claims

1. A route recommendation method based on road endurance planning, characterized in that: The following steps are involved: Get the vehicle's current location, target location, and current vehicle operation information; Determine at least one navigation path according to the current position and the target position, and divide each navigation path into road condition segments to obtain a plurality of driving segments corresponding to each navigation path; Based on the current vehicle operation information and the multiple driving segments corresponding to each navigation route, matching the energy consumption prediction value of each driving segment of each navigation route from a preset database; The total energy consumption of each navigation path is obtained according to the energy consumption prediction value of each driving segment of each navigation path, and a travel suggestion is generated according to the total energy consumption of each navigation path.

2. The method according to claim 1, characterized in that Before matching the energy consumption prediction value of each driving segment of each navigation path from a preset database, the method further includes: Acquiring historical data of the vehicle, and determining a plurality of navigation learning paths and vehicle operation information corresponding to each navigation learning path according to the historical data; Based on a preset highway speed limit level, the plurality of navigation learning paths are respectively divided into road condition segments to obtain a plurality of driving segments corresponding to each navigation learning path, and each driving segment corresponding to each navigation learning path is numbered as a road segment; Based on the vehicle operation information corresponding to each navigation learning path, calculating the average power consumption of the air conditioner, the average power consumption of the electronic load, the energy consumption of the drive motor and the energy utilization efficiency of the battery for each driving segment corresponding to each navigation learning path; The preset database is generated according to the multiple navigation learning paths, the vehicle operation information corresponding to each navigation learning path, the number of each driving segment corresponding to each navigation learning path, and the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor and the battery energy utilization efficiency of each driving segment corresponding to each navigation learning path.

3. The method according to claim 2, characterized in that The calculating, based on the vehicle operation information corresponding to each navigation learning path, the average power consumption of the air conditioner, the average power consumption of the electronic load, the energy consumption of the drive motor, and the energy utilization efficiency of the battery for each driving segment corresponding to each navigation learning path includes: When the air conditioner is in the on state, based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed of the road section, the energy consumption of the air conditioner compressor, the PTC energy consumption and the DCDC average energy consumption of each driving segment corresponding to each navigation learning path are calculated, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path are obtained, and the average power consumption of the air conditioner is obtained according to the average vehicle speed of the road section, the energy consumption of the air conditioner compressor, the PTC energy consumption and the DCDC average energy consumption of each driving segment corresponding to each navigation learning path, and the driving habit parameters and environmental parameters of each driving segment corresponding to each navigation learning path; When the air conditioner is in the off state, based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed and the average DCDC energy consumption of each driving segment corresponding to each navigation learning path are calculated, and the air conditioning and DC / DC energy consumption secondary index parameters of each driving segment corresponding to each navigation learning path are obtained, and the average consumption of the electronic load is obtained according to the average vehicle speed and the average DCDC energy consumption of each driving segment corresponding to each navigation learning path, and the energy consumption interference parameter of each driving segment corresponding to each navigation learning path; Based on the vehicle operation information corresponding to each navigation learning path, the average vehicle speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path are calculated, and the drive motor energy consumption index parameter of each driving segment corresponding to each navigation learning path is obtained, and the drive motor energy consumption is obtained according to the average vehicle speed, average driving energy and average recovery energy of each driving segment corresponding to each navigation learning path, and the drive motor energy consumption index parameter of each driving segment corresponding to each navigation learning path; Based on the vehicle operation information corresponding to each navigation learning path, the average discharge efficiency of each driving segment corresponding to each navigation learning path is calculated, and the battery energy consumption parameters of each driving segment corresponding to each navigation learning path are obtained, and the battery energy consumption utilization efficiency is obtained according to the average discharge efficiency and battery energy consumption parameters of each driving segment corresponding to each navigation learning path.

4. The method according to claim 3, characterized in that The matching of the energy consumption prediction value of each driving segment of each navigation path from a preset database based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path includes: Based on the preset database, determining an energy consumption prediction function for each driving segment corresponding to each navigation learning path; Based on the current vehicle operation information, the multiple driving segments corresponding to each navigation path and the number of each driving segment corresponding to each navigation path, a plurality of target driving segments with the highest matching degree with the multiple driving segments corresponding to each navigation path are sequentially screened out from the multiple driving segments corresponding to each navigation learning path, and an energy consumption prediction value of each driving segment of each navigation path is calculated according to an energy consumption prediction function of each target driving segment.

5. The method according to claim 1, characterized in that The generating of travel suggestions according to the total energy consumption of each navigation path comprises: A target navigation path with the smallest total energy consumption is selected from the total energy consumption of the multiple navigation paths, and the target navigation path is used as a recommended travel path.

6. A route recommendation device based on road endurance planning, characterized in that: The following steps are involved: The acquisition module is used to obtain the current position, target position and current vehicle operation information of the vehicle; a determination module, configured to determine at least one navigation path according to the current position and the target position, and to divide each navigation path into road condition segments to obtain a plurality of driving segments corresponding to each navigation path; a matching module, configured to match the energy consumption prediction value of each driving segment of each navigation path from a preset database based on the current vehicle operation information and the multiple driving segments corresponding to each navigation path; The control module is used to obtain the total energy consumption of each navigation path according to the energy consumption prediction value of each driving segment of each navigation path, and generate travel suggestions according to the total energy consumption of each navigation path.

7. According to the route recommendation device based on road endurance planning in claim 6, before matching the energy consumption prediction value of each driving segment of each navigation route from the preset database, the matching module is further used to: Acquiring historical data of the vehicle, and determining a plurality of navigation learning paths and vehicle operation information corresponding to each navigation learning path according to the historical data; Based on a preset highway speed limit level, the plurality of navigation learning paths are respectively divided into road condition segments to obtain a plurality of driving segments corresponding to each navigation learning path, and each driving segment corresponding to each navigation learning path is numbered as a road segment; Based on the vehicle operation information corresponding to each navigation learning path, calculating the average power consumption of the air conditioner, the average power consumption of the electronic load, the energy consumption of the drive motor and the energy utilization efficiency of the battery for each driving segment corresponding to each navigation learning path; The preset database is generated according to the multiple navigation learning paths, the vehicle operation information corresponding to each navigation learning path, the number of each driving segment corresponding to each navigation learning path, and the average power consumption of the air conditioner, the average consumption of the electronic load, the energy consumption of the drive motor and the battery energy utilization efficiency of each driving segment corresponding to each navigation learning path.

8. 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 path recommendation method based on road endurance planning as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a path recommendation method based on road endurance planning as described in any one of claims 1 to 5.

10. A computer program product, wherein the computer program product stores a computer program, characterized in that: When the program is executed by a processor, a path recommendation method based on road endurance planning as described in any one of claims 1 to 5 is implemented.

Images