Method and device for predicting endurance mileage of vehicle and vehicle

Abstract

The invention relates to the technical field of vehicles, in particular to the technical field of new energy vehicles, in particular to an endurance mileage prediction method and device of a vehicle and the vehicle, and aims at conducting more accurate endurance mileage prediction.The method comprises the steps that energy consumption consumed when the vehicle passes through a target navigation path under the standard working condition is determined; based on the difference between the calibration value of the working condition parameter of the driving working condition corresponding to the vehicle and the working condition parameter of the standard working condition, a first energy consumption correction value of energy consumption is determined; wherein the working condition parameters comprise climate environment information, road surface information and vehicle operation information; determining a second energy consumption correction value of the energy consumption based on the difference between the calibration value and the actual value of the working condition parameter of the driving working condition; and based on the energy consumption, the first energy consumption correction value and the second energy consumption correction value, predicting the endurance mileage after the vehicle passes through the target navigation path, thereby more accurately and effectively predicting the endurance of the vehicle in combination with the road surface information, the climate environment information and the energy consumption of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more particularly to the field of new energy vehicle technology, specifically to a method, device, and vehicle for predicting the driving range of a vehicle. Background Technology

[0002] Currently, with the popularization of new energy vehicles, users' requirements for the driving range of new energy vehicles are increasing day by day. Therefore, the accuracy of the prediction of the driving range of new energy vehicles is also getting higher and higher.

[0003] In existing technologies, standardized laboratory conditions are usually used as the core basis for predicting vehicle range. However, the range of a vehicle changes during actual driving. Using standardized laboratory conditions as the basis for prediction is prone to large errors. Therefore, there is an urgent need for a more accurate and effective method to predict the range of a vehicle. Summary of the Invention

[0004] This application provides a method, apparatus, and vehicle for predicting vehicle driving range, to at least solve the technical problems of large deviations in energy consumption prediction and driving range prediction in related technologies. The technical solution adopted in this application is as follows: Firstly, this application provides a method for predicting the driving range of a vehicle, comprising: determining the energy consumption consumed by the vehicle traveling along a target navigation path under standard operating conditions; determining a first energy consumption correction value based on the difference between the calibration value of the operating condition parameters corresponding to the vehicle's driving conditions and the operating condition parameters of the standard operating conditions; wherein the operating condition parameters include climate environment information, road surface information, and vehicle operation information; determining a second energy consumption correction value based on the difference between the calibration value and the actual value of the operating condition parameters of the driving conditions; and predicting the driving range of the vehicle after traveling along the target navigation path based on the energy consumption, the first energy consumption correction value, and the second energy consumption correction value.

[0005] Based on the aforementioned technical means, this application corrects vehicle energy consumption by addressing the differences between the calibrated values ​​and standard operating conditions of vehicle climate environment information, road surface information, and vehicle operation information, as well as the differences between the calibrated values ​​and actual values ​​of these information. This corrected energy consumption is then used to predict the vehicle's driving range, effectively eliminating energy consumption deviations caused by environmental and road conditions, improving the accuracy and reliability of driving range prediction, and enhancing the user's travel experience and safety.

[0006] In one possible implementation, determining a second energy consumption correction value based on the difference between the calibration value and the actual value of the operating parameters under the driving conditions includes: dividing the target navigation path into multiple road segments; for each of the multiple road segments, determining the corresponding energy consumption correction value for the road segment based on the difference between the actual value and the calibration value of the operating parameters under the driving conditions under the road segment; and determining the second energy consumption correction value based on the respective energy consumption correction values ​​of the multiple road segments.

[0007] Based on the aforementioned technical means, this application divides the target navigation path of the vehicle into segments and determines the energy consumption correction value of each segment based on the difference between the actual value and the calibration value of the operating parameters of each segment, thereby obtaining the second energy consumption correction value of the target navigation path. This can improve the determination of vehicle energy consumption under different road conditions in different road segments and can more accurately reflect the vehicle's energy consumption.

[0008] In one possible implementation, the energy consumption correction value corresponding to the road segment is determined based on the difference between the actual and calibrated values ​​of the operating parameters under the driving conditions of the road segment. This includes: determining the operating condition correction coefficient corresponding to each operating parameter; wherein the operating condition correction coefficient is positively correlated with the degree of influence of the corresponding operating parameter on the vehicle's energy consumption; and determining the energy consumption correction value corresponding to the road segment based on the operating condition correction coefficient and the difference between the actual and calibrated values ​​of each operating parameter under the driving conditions of the road segment.

[0009] Based on the above technical means, this application determines the corresponding operating condition correction coefficient according to the operating condition parameters to correct the parameters, which can more accurately determine the impact of different parameters on energy consumption. Then, by comparing the actual value of the corrected operating condition parameters with the calibrated value, the accuracy of vehicle energy consumption correction can be improved.

[0010] In one possible implementation, the target navigation path is divided into multiple segments, including: identifying a first path region that meets a first preset condition and a second path region that does not meet the first preset condition in the target navigation path; wherein, the first preset condition is that the actual value of the operating parameters remains stable; dividing the first path region according to a first preset length; dividing the second path region according to a second preset length; wherein, the first preset length is greater than the second preset length.

[0011] Based on the above technical means, this application divides the stable path area in the target navigation path with a first preset length and the unstable path area with a second preset length. This can reduce the number of area divisions in stable road sections, thereby improving processing efficiency, and shorten the area length in fluctuating road sections, which can more accurately identify changes in operating conditions. This differentiated division method can more accurately match the operating condition characteristics under different paths.

[0012] In one possible implementation, the process of obtaining the actual values ​​of the operating parameters of the driving conditions under the road segment includes: for the first road segment divided into first path regions, based on the actual values ​​of the operating parameters of the driving conditions under the key path points on the first road segment, using interpolation to obtain the actual values ​​of the operating parameters of the driving conditions under each path point on the first road segment; for the second road segment divided into second path regions, obtaining the actual values ​​of the operating parameters of the driving conditions under each path point on the second road segment.

[0013] Based on the above technical means, this application estimates the actual values ​​of operating parameters for each road segment area by interpolation, which can more flexibly adapt to path areas with different division lengths and improve the rationality and accuracy of determining the actual values ​​of operating parameters.

[0014] In one possible implementation, the actual values ​​of climate and environmental information are obtained based on climate and environmental forecasting equipment; the actual values ​​of road surface information and vehicle operation information are obtained based on navigation equipment.

[0015] Based on the aforementioned technical means, this application obtains climate and road information of the target navigation path, and then optimizes the prediction of the vehicle's driving range based on the climate, road and vehicle operation information, thereby improving the accuracy of the vehicle's driving range prediction.

[0016] In one possible implementation, predicting the vehicle's range after traversing the target navigation path based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value further includes: determining the total driving energy consumption of the vehicle on the target navigation path based on energy consumption, the first energy consumption correction value, and the second energy consumption correction value; determining the vehicle's remaining available energy consumption; determining the energy consumption difference between the remaining available energy consumption and the total driving energy consumption if the remaining available energy consumption is greater than or equal to the total driving energy consumption; and determining the range based on the energy consumption difference.

[0017] Based on the aforementioned technical means, this application can more accurately determine the total driving energy consumption of a vehicle traveling on a target navigation path by using the energy consumption consumed by the vehicle under standard operating conditions, the first energy consumption correction value, and the second energy consumption correction value, thereby determining the driving range and making the prediction of the driving range more consistent with actual driving conditions.

[0018] In one possible implementation, the total driving energy consumption of the vehicle on the target navigation path is determined based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value. This includes: obtaining the vehicle's historical driving segments under driving conditions; determining the vehicle's corresponding driving behavior habits based on the actual values ​​of the operating condition parameters corresponding to the historical driving segments; determining the initial total driving energy consumption of the vehicle on the target navigation path by summing the energy consumption, the first energy consumption correction value, and the second energy consumption correction value; and correcting the initial total driving energy consumption based on the driving behavior habits to obtain the total driving energy consumption.

[0019] Based on the aforementioned technical means, this application obtains the vehicle's driving behavior habits and thereby corrects driving energy consumption, which can improve the driving stability of the vehicle on the target navigation path, reduce high-energy-consuming operations such as rapid acceleration and sudden braking, reduce vehicle energy consumption, and increase driving range.

[0020] In one possible implementation, the vehicle's remaining range after passing the target navigation path is predicted based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value. The method also includes: if the remaining available energy consumption is less than the total driving energy consumption, determining the vehicle's refueling planning information on the target navigation path based on the energy consumption difference, wherein the refueling planning information includes switching the target navigation path to a refueling navigation path that passes through a refueling location; and providing refueling planning information prompts.

[0021] Based on the aforementioned technical means, this application switches the target navigation path to a refueling navigation path when the remaining available energy consumption is less than the total driving energy consumption. This can guide users to replenish energy in a timely manner, avoid the vehicle from affecting the continuity of the journey due to insufficient energy, and improve the user's driving experience.

[0022] In one possible implementation, climate information includes: season, temperature, humidity, wind speed, light intensity, and rainfall; road surface information includes: road surface attributes, road surface topography, and road surface conditions; wherein, road surface attributes include: rural roads, ordinary urban roads, urban expressways, highways, tunnels, mountain roads, and underpasses; road surface topography includes elevation and slope; road surface conditions include congested roads and unobstructed roads; and vehicle operation information includes vehicle speed, acceleration, power battery state of charge, and load status.

[0023] Based on the aforementioned technical means, this application obtains climate environment information, road surface information, and vehicle operation information, thereby enabling a more accurate grasp of the vehicle's driving status on the target navigation path, in order to optimize vehicle energy consumption management and improve the user's driving experience.

[0024] Secondly, this application provides a vehicle range prediction device, comprising: an energy consumption determination module for determining the energy consumption consumed by the vehicle when traveling along a target navigation path under standard operating conditions; a first correction module for determining a first energy consumption correction value based on the difference between the calibration value of the operating condition parameters corresponding to the vehicle's driving conditions and the operating condition parameters of the standard operating conditions; wherein the operating condition parameters include climate environment information, road surface information, and vehicle operation information; a second correction module for determining a second energy consumption correction value based on the difference between the calibration value and the actual value of the operating condition parameters of the driving conditions; and a range prediction module for predicting the vehicle's range after traveling along the target navigation path based on the energy consumption, the first energy consumption correction value, and the second energy consumption correction value.

[0025] In one possible implementation, the second correction module is used to divide the target navigation path into multiple road segments; for each of the multiple road segments, based on the difference between the actual value and the calibration value of the operating condition parameters under the driving conditions of the road segment, the energy consumption correction value corresponding to the road segment is determined; based on the energy consumption correction values ​​of the multiple road segments, the second energy consumption correction value is determined.

[0026] In one possible implementation, the second correction module is further configured to determine the operating condition correction coefficient corresponding to each operating condition parameter; wherein the operating condition correction coefficient is positively correlated with the degree of influence of the corresponding operating condition parameter on the vehicle's energy consumption; and based on the operating condition correction coefficient and the difference between the actual value and the calibrated value of each operating condition parameter under the driving conditions of the road segment, the energy consumption correction value corresponding to the road segment is determined.

[0027] In one possible implementation, the second correction module is further configured to identify a first path region in the target navigation path that meets the first preset condition and a second path region that does not meet the first preset condition; wherein, the first preset condition is that the actual value of the operating parameters remains stable; the first path region is divided according to a first preset length; the second path region is divided according to a second preset length; wherein, the first preset length is greater than the second preset length.

[0028] In one possible implementation, the second correction module is further configured to, for the first road segment divided by the first path region, obtain the actual value of the operating condition parameter of the driving condition at each path point on the first road segment by interpolation based on the actual value of the operating condition parameter of the driving condition at each path point on the first road segment; and for the second road segment divided by the second path region, obtain the actual value of the operating condition parameter of the driving condition at each path point on the second road segment.

[0029] In one possible implementation, the actual values ​​of climate and environmental information are obtained based on climate and environmental forecasting equipment; the actual values ​​of road surface information and vehicle operation information are obtained based on navigation equipment.

[0030] In one possible implementation, the range prediction module is used to determine the total driving energy consumption of the vehicle on the target navigation path based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value; determine the remaining available energy consumption of the vehicle; if the remaining available energy consumption is greater than or equal to the total driving energy consumption, determine the energy consumption difference between the remaining available energy consumption and the total driving energy consumption; and determine the driving range based on the energy consumption difference.

[0031] In one possible implementation, the range prediction module is further used to obtain the vehicle's historical driving routes under driving conditions; determine the vehicle's corresponding driving behavior habits based on the actual values ​​of the operating parameters corresponding to the historical driving routes; determine the initial total driving energy consumption of the vehicle on the target navigation path by summing the energy consumption, the first energy consumption correction value, and the second energy consumption correction value; and correct the initial total driving energy consumption based on the driving behavior habits to obtain the total driving energy consumption.

[0032] In one possible implementation, the range prediction module is further configured to determine the vehicle's refueling planning information on the target navigation path based on the energy consumption difference when the remaining available energy consumption is less than the total driving energy consumption. The refueling planning information includes switching the target navigation path to a refueling navigation path that passes through the refueling location and providing refueling planning information prompts.

[0033] In one possible implementation, climate information includes: season, temperature, humidity, wind speed, light intensity, and rainfall; road surface information includes: road surface attributes, road surface topography, and road surface conditions; wherein, road surface attributes include: rural roads, ordinary urban roads, urban expressways, highways, tunnels, mountain roads, and underpasses; road surface topography includes elevation and slope; road surface conditions include congested roads and unobstructed roads; and vehicle operation information includes vehicle speed, acceleration, power battery state of charge, and load status.

[0034] Thirdly, this application provides a vehicle, including a vehicle electronic control unit (ECU), which uses the vehicle range prediction device of the second aspect to predict the vehicle's range.

[0035] Fourthly, this application provides an electronic device, including: a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the method described in the first aspect and any possible implementation thereof.

[0036] Fifthly, this application provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0037] In a sixth aspect, this application provides a computer program product comprising computer instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any of its possible implementations.

[0038] It should be noted that the technical effects of any of the implementation methods in aspects two through six can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0041] Figure 1 This is a schematic diagram of the structure of a vehicle shown in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an electronic control unit for a vehicle, as shown in an embodiment of this application; Figure 3 This is a flowchart illustrating a method for predicting the driving range of a vehicle according to an embodiment of this application; Figure 4 This is a schematic diagram illustrating the execution hierarchy of a range prediction method according to an embodiment of this application; Figure 5 This is a schematic diagram illustrating the execution of the correction layer in the execution hierarchy of the range prediction method shown in the embodiments of this application; Figure 6 This is a block diagram illustrating a vehicle range prediction device according to an embodiment of this application; Figure 7 This is a block diagram illustrating an electronic device according to an embodiment of this application. Detailed Implementation

[0042] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0043] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0044] In the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0045] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0046] The vehicle range prediction device provided in this application is used to predict the range of vehicles (especially intelligent driving vehicles). Vehicles can also be referred to as vehicles, mobile carriers, electric vehicles (EVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), fuel cell vehicles (FCVs), autonomous vehicles, intelligent and connected vehicles (ICVs), driverless vehicles, etc.

[0047] In this application, the vehicle can be a sedan, a sport utility vehicle (SUV), a truck, a special vehicle (such as an ambulance, fire truck, police car, etc.), a driverless taxi, an intelligent connected bus, an autonomous logistics vehicle, an electric truck, etc. Furthermore, this method is also applicable to various special-purpose vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, and port vehicles. This application does not impose specific limitations in this regard.

[0048] Figure 1 This is a schematic diagram of the structure of a vehicle shown in an embodiment of this application.

[0049] In one possible implementation, such as Figure 1 As shown, the vehicle 100 includes a vehicle range prediction device 101, a data acquisition device 102, and a display control device 103.

[0050] The data acquisition device 102 is used to collect the vehicle's operating parameters and transmit them to the vehicle's range prediction device 101. The operating parameters include climate environment information, road surface information and vehicle operation information.

[0051] The vehicle range prediction device 101 is used to determine the vehicle's energy consumption correction value based on the difference between the calibrated value of the operating condition parameter and the parameter value under standard operating conditions, and based on the difference between the calibrated value of the operating condition parameter and the actual value of the operating condition parameter.

[0052] The vehicle range prediction device 101 is also used to combine the parameter values ​​of the vehicle's operating parameters under standard operating conditions and the vehicle's energy consumption correction value to predict the vehicle's range.

[0053] The display control device 103 is used to display the vehicle's driving range obtained from the vehicle's driving range prediction device 101.

[0054] The display control device 103 is also used to display the target navigation path and / or refueling navigation path in the vehicle's range prediction device 101, and to provide refueling prompts for the vehicle.

[0055] In practical applications, the vehicle's range prediction device 101 can communicate with one or more data acquisition devices 102 and one or more display control devices 103.

[0056] For ease of understanding, this application uses the communication connection between a data acquisition device 102, a vehicle range prediction device 101, and a display control device 103 as an example for illustration.

[0057] As a feasible approach, Figure 1The vehicle range prediction device 101, data acquisition device 102, and display control device 103 can be installed inside the vehicle, or the vehicle range prediction device 101 can be installed outside the vehicle. The vehicle range prediction device 101, data acquisition device 102, and display control device 103 can be functional modules integrated into the same device, or they can be independently installed devices. This application does not impose any limitations on the comparison.

[0058] As an alternative approach, when the vehicle's range prediction device 101, data acquisition device 102, and display control device 103 are functional modules integrated into the same device, the communication method between the vehicle's range prediction device 101, data acquisition device 102, and display control device 103 is communication between modules within the device.

[0059] Alternatively, the vehicle's range prediction device 101, data acquisition device 102, and display control device 103 can be configured as independently independent devices. Figure 1 The vehicle range prediction device 101 can be installed on a terminal, a server, or other types of electronic devices.

[0060] When the vehicle range prediction device 101 is located at a terminal, the terminal can be a device providing data connectivity to vehicle users or owners, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The terminal can communicate with one or more core networks via a radio access network (RAN). The terminal can be a mobile terminal, such as a computer with a mobile terminal, or a mobile device that exchanges voice and / or data with the RAN, such as a mobile phone, tablet, laptop, netbook, or personal digital assistant (PDA). This application does not impose any limitations on this.

[0061] When the vehicle's range prediction device 101 is located on a server, the server can be a single server or a server cluster consisting of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. This application does not impose any limitations on this.

[0062] It should be noted that the structure illustrated in this application embodiment does not constitute a limitation on the vehicle range prediction device 101. It may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0063] Figure 2 This is a schematic diagram of the structure of an electronic control unit for a vehicle, as shown in an embodiment of this application. Figure 2 The electronic control unit 200 includes: a battery management system 201, a vehicle controller 202, and an energy management system 203.

[0064] Among them, the battery management system 201, the vehicle controller 202, and the energy management system 203 can be used to predict the driving range of the vehicle.

[0065] The battery management system 201 monitors the remaining battery power in real time. Taking into account the impact of the battery state of charge (SOC) on discharge performance, the dynamic control strategy of the battery management system 201 (such as the discharge limit coefficient set according to the environmental conditions) is converted into a functional equation. The vehicle controller 202 then inputs the power demand and energy consumption of the vehicle control strategy in each segment of the target navigation path to calculate the vehicle's energy consumption under the time sequence, and finally determines the vehicle's driving range.

[0066] For ease of understanding, the method for predicting the driving range of the vehicle provided in this application will be described in detail below with reference to the accompanying drawings.

[0067] Figure 3 This is a flowchart illustrating a method for predicting the driving range of a vehicle according to an embodiment of this application. (Refer to...) Figure 3 The method includes: S301. Determine the energy consumption of a vehicle traveling along a target navigation path under standard operating conditions.

[0068] The aforementioned standard operating conditions refer to pre-set standardized driving conditions used to evaluate vehicle energy consumption. These conditions are used to eliminate differences in vehicle energy consumption caused by parameters such as environment, road conditions, and driving habits during actual driving. The standard operating conditions are determined based on standards developed by vehicle regulatory agencies or standard testing methods. For example, the determination of standard operating conditions can be based on standard testing methods for each test cycle or driving cycle.

[0069] The aforementioned target navigation path refers to the specific driving route planned by the user through the navigation system from the input starting point to the input ending point.

[0070] The energy consumption mentioned above refers to the theoretical energy consumption required for a vehicle to travel along the target navigation path under standard operating conditions.

[0071] S302. Based on the difference between the calibration values ​​of the operating parameters corresponding to the vehicle's driving conditions and the operating parameters of the standard operating conditions, determine the first energy consumption correction value; wherein, the operating parameters include climate environment information, road surface information and vehicle operation information.

[0072] The aforementioned driving conditions refer to the driving scenarios or environments in which a vehicle is driven. For example, driving conditions can be divided into spring, summer, autumn, and winter according to the season. Each season corresponds to a different driving condition, and the driving conditions vary from season to season.

[0073] For example, if the driving condition is a spring driving condition, then under the current driving condition, the gradient is 0, the vehicle speed is the first speed, the vehicle load is the first load, and the remaining driving condition parameters remain unchanged based on the first parameter value; if the driving condition is a summer driving condition, the gradient is 0, the vehicle speed is the second speed, the vehicle load is the second load, and the remaining driving condition parameters remain unchanged based on the second parameter value. That is, under the same driving condition, the calibration values ​​of the driving condition parameters are consistent, while under different driving conditions, the calibration values ​​of the driving condition parameters are inconsistent.

[0074] The aforementioned operating parameters refer to the vehicle parameters estimated and determined by sensors or preset algorithms during vehicle operation.

[0075] The aforementioned climate and environmental information includes: season, temperature, humidity, wind speed, light intensity, and rainfall; road surface information includes: road surface attributes, road surface topography, and road surface conditions; among which, road surface attributes include: rural roads, ordinary urban roads, urban expressways, highways, tunnels, mountain roads, and underpasses; road surface topography includes elevation and slope; road surface conditions include congested roads and unobstructed roads; vehicle operation information includes vehicle speed, acceleration, power battery state of charge, and load status.

[0076] The aforementioned calibration values ​​refer to the specific values ​​of the vehicle's operating parameters estimated by a preset algorithm during vehicle operation. The calibration values ​​of the operating parameters are different under different driving conditions, while the calibration values ​​of the operating parameters under the same driving condition are consistent. However, the calibration values ​​of the operating parameters under different driving conditions are not the fixed values ​​of the operating parameters under standard conditions.

[0077] The aforementioned difference refers to the deviation between the calibrated values ​​of the operating parameters for the corresponding driving conditions of the vehicle and the fixed values ​​corresponding to the operating parameters for the standard operating conditions.

[0078] The aforementioned first energy consumption correction value refers to the theoretical correction value obtained based on the differences in operating parameters and the preset model or preset energy consumption algorithm, used to correct the energy consumption under standard operating conditions. For example, the preset model includes mechanism model, data-driven model and hybrid model.

[0079] The above-mentioned mechanism model is a model that calculates vehicle energy consumption by establishing mathematical equations through analyzing the physical processes of the vehicle's power system.

[0080] The aforementioned data-driven model refers to a model that uses historical vehicle data to train a preset algorithm to determine the mapping relationship between operating parameters and energy consumption.

[0081] The hybrid model mentioned above refers to a model that integrates mechanistic models and data-driven models. For example, a mechanistic model is used to calculate basic energy consumption, while a data-driven model is used to correct energy consumption errors.

[0082] S303. Based on the difference between the calibration value and the actual value of the operating parameters under the driving conditions, determine the second energy consumption correction value.

[0083] The actual values ​​of the above operating parameters refer to the parameter values ​​of the vehicle's operating parameters when the vehicle is actually driving on the target navigation path.

[0084] The aforementioned second energy consumption correction value refers to the theoretical correction value obtained by using a preset model or preset energy consumption algorithm to correct energy consumption under standard operating conditions, based on the difference between the calibrated value and the actual value of the operating parameters of the vehicle when driving on the target navigation path.

[0085] In one possible implementation, the actual values ​​of climate and environmental information are obtained based on climate and environmental forecasting equipment; the actual values ​​of road surface information and vehicle operation information are obtained based on navigation equipment.

[0086] The aforementioned climate and environmental forecasting equipment refers to meteorological observation equipment installed on vehicles, which is used to monitor meteorological data of the environment around the vehicle through multiple sensors. For example, meteorological data includes temperature, humidity, wind speed, wind direction, air pressure, and rainfall.

[0087] The actual values ​​of the aforementioned climate and environmental information can also be determined by obtaining weather forecasts from fixed meteorological observation equipment along the target navigation path via the vehicle-mounted terminal.

[0088] The aforementioned climate and environmental information also includes terrain and elevation information. Elevation data can be obtained from various open-source map data platforms and other methods for use in the next stage of grid division and data interpolation.

[0089] The aforementioned in-vehicle navigation equipment refers to driving assistance devices that utilize the Global Positioning System and electronic map technology to provide users with route planning, location positioning, and navigation guidance.

[0090] The aforementioned road surface information includes road attribute information, such as special types like ordinary urban roads, urban expressways, highways, mountain roads, rural roads, internal roads, tunnels, and bridges. Among these, special roads such as tunnels and bridges do not have the same elevation value as the terrain. When a vehicle is in a tunnel, its elevation cannot be directly interpolated from the terrain elevation data and must be separately marked and matched with the road attributes in the navigation base data.

[0091] S304. Based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value, the vehicle's remaining range after traversing the target navigation path is predicted.

[0092] The aforementioned driving range refers to the maximum distance a vehicle can travel continuously under different driving conditions based on its stored energy. It is usually measured in kilometers or miles. Driving range is one of the core indicators used to measure a vehicle's energy efficiency and practicality.

[0093] As an achievable approach, the driving range of gasoline vehicles is mainly affected by factors such as engine thermal efficiency and fuel tank capacity; the driving range of electric vehicles is mainly affected by factors such as battery capacity, motor efficiency, and energy recovery system; and the driving range of hydrogen fuel cell vehicles is mainly affected by factors such as hydrogen tank capacity and fuel cell efficiency.

[0094] Based on the aforementioned technical means, this application corrects vehicle energy consumption by addressing the differences between the calibrated values ​​and standard operating conditions of vehicle climate environment information, road surface information, and vehicle operation information, as well as the differences between the calibrated values ​​and actual values ​​of these information. This corrected energy consumption is then used to predict the vehicle's driving range, effectively eliminating energy consumption deviations caused by environmental and road conditions, improving the accuracy and reliability of driving range prediction, and enhancing the user's travel experience and safety.

[0095] In one possible implementation, a second energy consumption correction value is determined based on the difference between the calibration value and the actual value of the operating parameters under the driving conditions. This includes: dividing the target navigation path into multiple road segments; for each of the multiple road segments, determining the corresponding energy consumption correction value based on the difference between the actual value and the calibration value of the operating parameters under the driving conditions under the road segment; and determining the second energy consumption correction value based on the respective energy consumption correction values ​​of the multiple road segments, which can more accurately reflect the energy consumption of the vehicle.

[0096] The aforementioned road segments refer to the road segments obtained after dividing the target navigation path. The road segments can be divided based on distance, road curvature, traffic characteristics, and geographical landmarks.

[0097] The aforementioned energy consumption correction value refers to the energy consumption difference between the vehicle energy consumption value determined based on the road surface information, climate environment information, and actual vehicle operation information of each road segment, and the vehicle energy consumption value calculated based on the calibration value of the operating parameters.

[0098] The aforementioned second energy consumption correction value refers to the set of energy consumption correction values ​​determined by summarizing the energy consumption correction values ​​within each road segment, which is used to determine the energy consumption correction values ​​for the vehicle's driving on the target navigation path.

[0099] In one possible implementation, the energy consumption correction value corresponding to the road segment is determined based on the difference between the actual and calibrated values ​​of the operating parameters under the driving conditions of the road segment. This includes: determining the operating condition correction coefficient corresponding to each operating parameter; wherein the operating condition correction coefficient is positively correlated with the degree of influence of the corresponding operating parameter on the vehicle's energy consumption; and determining the energy consumption correction value corresponding to the road segment based on the operating condition correction coefficient and the difference between the actual and calibrated values ​​of each operating parameter under the driving conditions of the road segment can more accurately determine the impact of different parameters on energy consumption.

[0100] The above-mentioned operating condition correction factor is a dimensionless numerical factor. The operating condition correction factor is set according to the different degrees of influence of different operating condition parameters on the vehicle's energy consumption. The operating condition correction factor is usually determined based on experimental calibration or simulation analysis. The greater the degree of influence of the operating condition parameters on the vehicle's energy consumption, the higher the operating condition correction factor. The value range of the operating condition correction factor is (0,1).

[0101] The positive correlation mentioned above means that the greater the influence of operating parameters on vehicle energy consumption, the larger the operating condition correction coefficient will be, and vice versa. For example, the operating condition correction coefficient for vehicle speed is greater than that for gradient, and the operating condition correction coefficient for gradient is greater than that for load.

[0102] The aforementioned energy consumption correction value refers to the energy consumption correction value within the current road segment calculated based on the operating condition correction coefficient and the difference between the actual value and the calibrated value of each operating condition parameter under the corresponding road segment's driving conditions.

[0103] As an achievable method, operating condition correction factors include slope difference correction factors, temperature difference correction factors, load difference correction factors, etc.

[0104] For example, the slope difference correction factor is ,in, , This is the slope influence coefficient. This is the actual slope value. This is the slope calibration value.

[0105] In one possible implementation, the target navigation path is divided into multiple segments, including: identifying a first path region that meets a first preset condition and a second path region that does not meet the first preset condition in the target navigation path; wherein, the first preset condition is that the actual value of the operating condition parameter remains stable; dividing the first path region according to a first preset length; dividing the second path region according to a second preset length; wherein, the first preset length is greater than the second preset length, which allows for more detailed identification of changes in operating conditions.

[0106] The aforementioned first preset condition refers to the situation where, during vehicle operation, the changes in key operating parameters in time and / or space are less than a preset threshold. If the vehicle meets the first preset condition, it is determined that the vehicle is in a stable driving state.

[0107] The aforementioned key operating parameters include vehicle speed, acceleration, energy consumption, and torque. For example, if the standard deviation of vehicle speed is less than a preset vehicle speed threshold, and / or the absolute value of acceleration is less than a preset acceleration threshold, and / or the rate of change of vehicle energy consumption is less than a preset rate of change threshold, and / or the fluctuation of motor / engine output torque is less than a preset torque, it indicates that the vehicle is in a stable driving state. The preset vehicle speed threshold can be 5 km / h, the preset acceleration threshold can be 0.5 m / s², the preset rate of change threshold is less than 5%, and the preset torque is less than 10 Nm.

[0108] The second preset condition refers to a situation where, during vehicle operation, the change in key operating parameters in time and / or space dimensions is greater than or equal to a preset change threshold. If a vehicle meets the second preset condition, it is determined that the vehicle is in a state of frequent fluctuation.

[0109] The aforementioned first path region refers to a continuous sub-path segment in the target navigation path where the actual values ​​of the vehicle's operating parameters remain stable. For example, in the high-speed cruise segment, the vehicle's speed and energy consumption are stable and are classified as the first path region.

[0110] The aforementioned second path region refers to a continuous sub-path segment in the target navigation path where the vehicle's operating parameters meet the second preset condition. The vehicle driving scenarios corresponding to the second path region include urban congested road sections, curves, slopes, etc. For example, in urban road sections, vehicles frequently start and stop, and the vehicle speed fluctuates between 0 and 60 km / h, which is classified as the second path region.

[0111] The first preset length refers to the segmentation length for dividing the first path area into road segments; the second preset length refers to the segmentation length for dividing the second path area into road segments. The first preset length is greater than the second preset length because the operating parameters in the first path area change relatively slowly, and a longer road segment can cover more similar operating conditions; the operating parameters in the second path area change more rapidly, and a shorter road segment can more accurately capture the changes within the road segment.

[0112] In one possible implementation, the process of obtaining the actual values ​​of the operating parameters of the driving conditions under a road segment includes: for the first road segment divided into first path regions, based on the actual values ​​of the operating parameters of the driving conditions under key path points on the first road segment, using interpolation to obtain the actual values ​​of the operating parameters of the driving conditions under each path point on the first road segment; for the second road segment divided into second path regions, obtaining the actual values ​​of the operating parameters of the driving conditions under each path point on the second road segment, so as to improve the rationality and accuracy of the determination of the actual values ​​of the operating parameters.

[0113] The aforementioned first road segment refers to a road segment within the first path area. The first path area includes one or more first road segments. The more first road segments there are within the first path area, the more detailed the regional division of the first path area becomes.

[0114] The critical path points mentioned above refer to specific locations within each road segment in the region that exhibit significant changes in driving characteristics or affect vehicle operating conditions. Critical path points can be the start, end, or intermediate feature points of a road segment. Intermediate feature points are specific locations that exhibit significant changes in driving characteristics or affect vehicle operating conditions. For example, intermediate feature points include curve apex, tunnel entrance / exit, gradient change points, and speed extreme points.

[0115] The aforementioned critical path points also include key traffic points, such as traffic lights, toll booths, speed limit signs, and speed camera locations.

[0116] The aforementioned interpolation refers to the process of intensively processing discretely collected vehicle operation information within each road segment using mathematical methods to generate continuous vehicle state curves. These mathematical methods include linear interpolation, spline interpolation, and polynomial interpolation.

[0117] The aforementioned second road segment refers to a road segment within the second path area. The second path area includes one or more second road segments. The more second road segments there are within the second path area, the more detailed the regional division of the second path area becomes.

[0118] In one possible implementation, predicting the vehicle's remaining driving range after traversing the target navigation path based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value further includes: determining the total driving energy consumption of the vehicle on the target navigation path based on energy consumption, the first energy consumption correction value, and the second energy consumption correction value; determining the vehicle's remaining available energy consumption; determining the energy consumption difference between the remaining available energy consumption and the total driving energy consumption if the remaining available energy consumption is greater than or equal to the total driving energy consumption; and determining the driving range based on the energy consumption difference, which can more accurately determine the total driving energy consumption of the vehicle on the target navigation path.

[0119] The aforementioned total driving energy consumption refers to the driving energy consumption required for the vehicle to travel on the target navigation path, determined after correcting the vehicle's energy consumption based on the first energy consumption correction value and the second energy consumption correction value.

[0120] The aforementioned remaining available energy consumption refers to the total amount of energy that a vehicle can support for driving from the current moment until its energy consumption is exhausted, based on its current state. Specifically, for gasoline-powered vehicles, the remaining available energy consumption is obtained by the remaining fuel in the fuel tank and the current fuel consumption rate; for electric vehicles, the remaining available energy consumption is obtained by the remaining charge in the battery and the current energy consumption rate, typically expressed in kilowatt-hours (kWh).

[0121] The aforementioned energy consumption difference refers to the difference between the vehicle's remaining available energy consumption and the total driving energy consumption required for the target navigation path.

[0122] In one possible implementation, the total driving energy consumption of the vehicle on the target navigation path is determined based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value. This includes: obtaining the vehicle's historical driving segments under driving conditions; determining the vehicle's corresponding driving behavior habits based on the actual values ​​of the operating condition parameters corresponding to the historical driving segments; determining the initial total driving energy consumption of the vehicle on the target navigation path by summing the energy consumption, the first energy consumption correction value, and the second energy consumption correction value; and correcting the initial total driving energy consumption based on the driving behavior habits to obtain the total driving energy consumption, thereby reducing high-energy-consuming operations such as rapid acceleration and sudden braking, lowering vehicle energy consumption, and increasing driving range.

[0123] The aforementioned historical driving sections refer to the driving sections of a vehicle under current driving conditions, such as the historical driving sections of a vehicle under spring driving conditions.

[0124] The aforementioned driving habits refer to the relatively stable driving patterns and behavioral characteristics formed by users during the driving process, including driving operation habits, decision-making habits, and environmental adaptation habits. For example, operation habits include acceleration / braking methods, steering methods, and gear shifting timing; decision-making habits include following distance, lane changing frequency, and overtaking timing; and environmental adaptation habits include coping with rainy / snowy weather, congested road conditions, and nighttime driving.

[0125] The aforementioned initial total driving energy consumption refers to the total driving energy consumption of the vehicle on the target navigation path after correcting the energy consumption based on the first energy consumption correction value and the second energy consumption correction value.

[0126] The aforementioned total driving energy consumption refers to the driving energy consumption that is more consistent with the vehicle's driving conditions after adjusting the initial total driving energy consumption based on the driving habits of the vehicle's users.

[0127] As an achievable method, determining total driving energy consumption includes: building an energy consumption calculation model and sequentially substituting the first energy consumption correction value calculated from the calibrated values ​​of the operating parameters and the second energy consumption correction value calculated from the actual values ​​of the operating parameters into the final calculation.

[0128] First, the vehicle control strategy is broken down into its core computational units, with each road segment as the core unit. Taking the drive motor module control strategy as an example, the timing, hysteresis, mapping, and response actions of the various components within the drive motor module in coordinating with each other during vehicle operation in response to different scenarios such as slope, speed changes, and road condition changes are transformed into functional equations.

[0129] Next, based on the first and second energy consumption corrections calculated from the two corrections, the energy consumption corrections for each road segment are obtained by substituting them into the function equations.

[0130] Next, the differences between the actual values ​​of the operating parameters and the calibrated values ​​of the operating parameters in each road segment are analyzed, and the operating condition correction coefficient is calculated. The operating condition correction coefficient is then summed with the first energy consumption correction amount and the second energy consumption correction amount to obtain the road segment energy consumption value that is suitable for the actual operating conditions of each grid.

[0131] Finally, after calculating the actual energy consumption values ​​of the drive motor module, vehicle thermal management module, and other related electrical components using this method, the total driving energy consumption of the vehicle is calculated. Specifically, the power control of the thermal management module is calculated accurately using a correction factor based on the two revised climate parameters and the difference in heat generation power of the drive motor module, with the standard operating condition thermal management power as the benchmark. Other related electrical components, such as lights, are calculated similarly.

[0132] The energy consumption calculation method for each road segment is as follows: the travel time is obtained by combining the unit grid route length and the corresponding vehicle speed of the current road segment, and then the energy consumption is obtained by multiplying the corresponding power of the actions performed by each module in that grid with the travel time.

[0133] In one possible implementation, the vehicle's remaining range after passing the target navigation path is predicted based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value. The method further includes: if the remaining available energy consumption is less than the total driving energy consumption, determining the vehicle's refueling planning information on the target navigation path based on the energy consumption difference. This refueling planning information includes switching the target navigation path to a refueling navigation path that passes through a refueling location. The refueling planning information is then displayed to prevent the vehicle from affecting the continuity of the journey due to insufficient energy.

[0134] The aforementioned energy replenishment planning information refers to the energy replenishment information for vehicles within the range supported by the vehicle's remaining available energy consumption, determined based on the energy consumption difference between the remaining available energy consumption and the total driving energy consumption. This energy replenishment planning information includes information such as energy replenishment location, energy replenishment method, energy replenishment time, energy replenishment cost, energy replenishment navigation route, and energy replenishment prompts.

[0135] The aforementioned recharge navigation path refers to a driving path that can recharge the vehicle within the range of paths supported by the vehicle's remaining available energy, when the vehicle's remaining available energy does not support the vehicle's driving on the target navigation path.

[0136] As another feasible method, range prediction can be performed using a range prediction model, where the accuracy of the preset model is verified through real vehicle testing.

[0137] For example, using real-vehicle road test data as a benchmark, road tests are first conducted on typical routes with different seasons and terrains. Each route is tested a certain number of times, and the model's predicted energy consumption EX and the actual energy consumption E0 are recorded for each test. Then, multi-dimensional error indicators are calculated: correlation coefficient R, root mean square error, mean absolute error, and goodness of fit R². The model is optimized based on the error analysis results: if the error in a certain scenario exceeds a preset threshold, test data for that scenario is supplemented, and the corresponding parameters are adjusted. After optimization, road tests are conducted again for verification until the error indicators meet the preset accuracy requirements (e.g., RMSE ≤ RMSEX, R² ≥ R²X), forming a closed-loop optimized range prediction model.

[0138] As a feasible approach, Figure 4 This is a schematic diagram illustrating the execution hierarchy of a range prediction method according to an embodiment of this application; see reference. Figure 4 When a vehicle performs the aforementioned range prediction, the execution layers include the operation layer, data layer, grid layer, correction layer, and calculation layer.

[0139] The operation layer is used to receive user operation settings, process them at the vehicle terminal, and form input boundaries based on the processing results.

[0140] The input boundaries include routes and loads.

[0141] Pass the input boundaries to the data layer.

[0142] The data layer is used for data acquisition and data processing. Data acquisition includes collecting coordinates, road attributes, terrain elevation, climate environment, real-time traffic conditions, and other data. Data processing is used to standardize the format and classify the acquired data.

[0143] The data layer transmits the data processing results to the grid layer and the computation layer.

[0144] The grid layer is used to define the coverage area, calculate the grid size, and assign ID identifiers to the grid.

[0145] The correction layer is used to perform two corrections.

[0146] The first correction is used to fix the operating conditions and calculate the driving range under fixed operating conditions as a basic reference. The second correction is used to determine the correction coefficient for each grid covered by the vehicle driving path based on the preprocessed parameter dataset of the target grid. Then, the correction values ​​are calculated grid by grid and summed to obtain a comprehensive correction dataset that fits the actual scenario.

[0147] The calculation layer is used to input the comprehensive corrected dataset output by the correction layer and the data processing results transmitted by the data layer into the energy consumption calculation model, thereby calculating the driving range and outputting the driving range calculation results.

[0148] As one possible approach, the above-mentioned operation layer is implemented as follows: adopting a scenario-based integration strategy for function entry points, the control switch and parameter setting button of the energy consumption prediction function are embedded into the main interface of the vehicle navigation system, thereby realizing the scenario-based binding of this function with the navigation application and eliminating the operational redundancy of users switching between applications from the interaction path.

[0149] In the parameter configuration module, a "manual customization - automatic recognition - automatic learning" mode is constructed. The manual customization mode allows users to independently configure vehicle load parameters through preset categories (such as options for passenger and weight characteristics, trunk cargo categories and quality characteristics, and other item categories and quality characteristics). The automatic recognition mode relies on onboard sensors to achieve real-time collection and synchronization of load data. The automatic learning mode combines user manual setting habits, big data, and automatic recognition calibration. All of the above improves the convenience and accuracy of parameter acquisition while ensuring user control. The prediction trigger mechanism is designed with two options: "active trigger - automatic trigger". Users can actively start the calculation by clicking the switch button on the navigation interface, or they can pre-enable the automatic trigger mode so that the system automatically enters the background calculation process after the navigation route planning is completed, without manual intervention.

[0150] The output process employs a scenario-based feedback mechanism. If the remaining battery power is sufficient to reach the destination, a confirmation feedback containing post-arrival flight information will be displayed on the navigation interface. If the remaining battery power is insufficient, decision-making charging suggestions will be simultaneously output, including specific charging points, distances to those points, and navigation route switching options. The overall operation process is guided by the core need of users to know their battery range before traveling. Through optimized interactive paths and scenario-based feedback design, it achieves a synergistic improvement in both ease of operation and accuracy of information.

[0151] As one feasible approach, the above-mentioned grid layer is implemented as follows: when dividing the grid, the navigation route is the core, while taking into account both computational accuracy and efficiency.

[0152] After the user confirms the route in the vehicle navigation system, the system automatically triggers the grid division function of the target navigation route to delineate the area covered by the navigation route.

[0153] The area delineation depends on the largest rectangular area enclosed by the starting point, the ending point, and all waypoints. To ensure the integrity and accuracy of the data collected at the boundary points, a portion of the grid length is extended at the boundary points for interpolation and to reduce errors.

[0154] The grid size is dynamically adjusted based on factors such as the size of the coverage area, the complexity of the climate, terrain, altitude, and actual vehicle speed. For example, for urban roads, expressways, mountain roads, and rural roads, where some parameters of road conditions, environment, terrain, and altitude vary greatly and are dense, a smaller unit grid length is used, with a range of 100 to 1000 meters selected to consider model calculation efficiency. For flat highways, a moderately large unit grid length can be used.

[0155] After the grid is divided, a unique ID is assigned to each grid in a fixed order. The preprocessed environmental data, terrain data, road condition information and road surface attributes are associated with the grid ID through coordinate matching. Interpolation is then performed by directly interpolating the grid center or by averaging the interpolation from the four corners, so that each grid has its own exclusive data for the correction layer and the calculation layer to access.

[0156] As another feasible approach Figure 5 This is a schematic diagram illustrating the execution of the correction layer in the execution hierarchy of the range prediction method shown in the embodiments of this application.

[0157] Determine the operating parameters for the standard operating condition based on the operating parameter table.

[0158] The operating parameters of the standard operating condition are corrected first based on the calibration values ​​of the operating parameters.

[0159] Among them, the calibration values ​​of operating parameters include climate environment information (spring, summer, autumn, winter), slope calibration value, vehicle speed calibration value, vehicle load calibration value, and other condition calibration values.

[0160] Input the calibration values ​​of the operating conditions parameters into the calculation layer, and output the first energy consumption correction value corresponding to the calibration values ​​of the operating conditions parameters.

[0161] The calibration values ​​of the operating parameters are corrected a second time based on the actual values ​​of the operating parameters.

[0162] The actual values ​​of the operating parameters are injected into the grid to determine the operating condition correction coefficients.

[0163] Input the actual values ​​of the operating parameters and the operating correction coefficient into the calculation layer, and output the second energy consumption correction value corresponding to the actual values ​​of the operating parameters.

[0164] The actual values ​​of the operating parameters include actual values ​​of climate environment information, actual values ​​of slope, actual values ​​of vehicle speed, actual values ​​of vehicle load, and actual values ​​of other conditions.

[0165] Figure 6 This is a block diagram illustrating a vehicle range prediction device according to an embodiment of this application, with reference to... Figure 6 The vehicle range prediction device includes: an energy consumption determination module 601, a first correction module 602, a second correction module 603, and a range prediction module 604.

[0166] The energy consumption determination module 601 is used to determine the energy consumption of a vehicle traveling along a target navigation path under standard operating conditions. The first correction module 602 is used to determine a first energy consumption correction value based on the difference between the calibration value of the operating parameters of the corresponding driving conditions of the vehicle and the operating parameters of the standard operating conditions; wherein, the operating parameters include climate environment information, road surface information and vehicle operation information. The second correction module 603 is used to determine a second energy consumption correction value based on the difference between the calibration value and the actual value of the operating parameters of the driving condition. The range prediction module 604 is used to predict the range of the vehicle after it has traveled the target navigation path, based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value.

[0167] In one possible implementation, the second correction module is used to divide the target navigation path into multiple road segments; for each of the multiple road segments, based on the difference between the actual value and the calibration value of the operating condition parameters under the driving conditions of the road segment, the energy consumption correction value corresponding to the road segment is determined; based on the energy consumption correction values ​​of the multiple road segments, the second energy consumption correction value is determined.

[0168] In one possible implementation, the second correction module is further configured to determine the operating condition correction coefficient corresponding to each operating condition parameter; wherein the operating condition correction coefficient is positively correlated with the degree of influence of the corresponding operating condition parameter on the vehicle's energy consumption; and based on the operating condition correction coefficient and the difference between the actual value and the calibrated value of each operating condition parameter under the driving conditions of the road segment, the energy consumption correction value corresponding to the road segment is determined.

[0169] In one possible implementation, the second correction module is further configured to identify a first path region in the target navigation path that meets the first preset condition and a second path region that does not meet the first preset condition; wherein, the first preset condition is that the actual value of the operating parameters remains stable; the first path region is divided according to a first preset length; the second path region is divided according to a second preset length; wherein, the first preset length is greater than the second preset length.

[0170] In one possible implementation, the second correction module is further configured to, for the first road segment divided by the first path region, obtain the actual value of the operating condition parameter of the driving condition at each path point on the first road segment by interpolation based on the actual value of the operating condition parameter of the driving condition at each path point on the first road segment; and for the second road segment divided by the second path region, obtain the actual value of the operating condition parameter of the driving condition at each path point on the second road segment.

[0171] In one possible implementation, the actual values ​​of climate and environmental information are obtained based on climate and environmental forecasting equipment; the actual values ​​of road surface information and vehicle operation information are obtained based on navigation equipment.

[0172] In one possible implementation, the range prediction module is used to determine the total driving energy consumption of the vehicle on the target navigation path based on energy consumption, a first energy consumption correction value, and a second energy consumption correction value; determine the remaining available energy consumption of the vehicle; if the remaining available energy consumption is greater than or equal to the total driving energy consumption, determine the energy consumption difference between the remaining available energy consumption and the total driving energy consumption; and determine the driving range based on the energy consumption difference.

[0173] In one possible implementation, the range prediction module is further used to obtain the vehicle's historical driving routes under driving conditions; determine the vehicle's corresponding driving behavior habits based on the actual values ​​of the operating parameters corresponding to the historical driving routes; determine the initial total driving energy consumption of the vehicle on the target navigation path by summing the energy consumption, the first energy consumption correction value, and the second energy consumption correction value; and correct the initial total driving energy consumption based on the driving behavior habits to obtain the total driving energy consumption.

[0174] In one possible implementation, the range prediction module is further configured to determine the vehicle's refueling planning information on the target navigation path based on the energy consumption difference when the remaining available energy consumption is less than the total driving energy consumption. The refueling planning information includes switching the target navigation path to a refueling navigation path that passes through the refueling location and providing refueling planning information prompts.

[0175] In one possible implementation, climate information includes: season, temperature, humidity, wind speed, light intensity, and rainfall; road surface information includes: road surface attributes, road surface topography, and road surface conditions; wherein, road surface attributes include: rural roads, ordinary urban roads, urban expressways, highways, tunnels, mountain roads, and underpasses; road surface topography includes elevation and slope; road surface conditions include congested roads and unobstructed roads; and vehicle operation information includes vehicle speed, acceleration, power battery state of charge, and load status.

[0176] Regarding the apparatus in the above embodiments, the specific methods of execution of each module have been described in detail in the embodiments of the vehicle range prediction method, and will not be elaborated here.

[0177] Figure 7 This is a block diagram illustrating an electronic device according to an embodiment of this application. Figure 7 As shown, the electronic device includes, but is not limited to, a processor 701 and a memory 702.

[0178] The memory 702 described above is used to store the executable instructions of the processor 701. It is understood that the processor 701 is configured to execute instructions to implement the power control method of the heat pump air conditioner in the above embodiment.

[0179] It should be noted that those skilled in the art will understand that Figure 7 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 7 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0180] Processor 701 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 702, and by calling data stored in memory 702, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Processor 701 may include one or more processing units. Processor 701 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 701.

[0181] The memory 702 can be used to store software programs and various data. The memory 702 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as deterministic components, integrated components, etc.), etc. Furthermore, the memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0182] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 702 including instructions, which can be executed by a processor 701 of an electronic device to implement the methods in the above embodiments.

[0183] In actual implementation, Figure 6 The functions of the energy consumption determination module 601, the first correction module 602, the second correction module 603, and the range prediction module 604 can all be derived from... Figure 7The processor 701 calls the computer program stored in the memory 702 to implement the process. The specific execution process can be found in the description of the method section in the previous embodiment, and will not be repeated here.

[0184] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device. In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 701 of an electronic device to perform the methods in the above embodiments.

[0185] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of an electronic device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0186] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0187] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0190] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0191] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods described in the above method embodiments.

[0192] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method in the method flow shown in the above method embodiments.

[0193] The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a register, a hard disk, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC). In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0194] Since the vehicle range prediction device, computer-readable storage medium, and computer program product in the embodiments of this application can be applied to the above method, the technical effects that can be obtained can also be referred to the above method embodiments. The embodiments of this application will not be repeated here.

[0195] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the driving range of a vehicle, characterized in that, The method for predicting the driving range of the vehicle includes: Determine the energy consumption of the vehicle as it travels along the target navigation path under standard operating conditions; Based on the difference between the calibration values ​​of the operating parameters corresponding to the vehicle's driving conditions and the operating parameters of the standard operating conditions, a first energy consumption correction value is determined; wherein, the operating parameters include climate environment information, road surface information, and vehicle operation information; Based on the difference between the calibration value and the actual value of the operating parameters of the driving condition, a second energy consumption correction value is determined for the energy consumption. Based on the energy consumption, the first energy consumption correction value, and the second energy consumption correction value, the driving range of the vehicle after passing the target navigation path is predicted.

2. The vehicle range prediction method according to claim 1, characterized in that, Determining the second energy consumption correction value based on the difference between the calibration value and the actual values ​​of the operating parameters under the driving conditions includes: The target navigation path is divided into multiple road segments; For each of the multiple road segments, based on the difference between the actual value and the calibration value of the operating parameters under the driving conditions of the road segment, the corresponding energy consumption correction value for the road segment is determined; The second energy consumption correction value is determined based on the energy consumption correction values ​​of each of the multiple road segments.

3. The vehicle range prediction method according to claim 2, characterized in that, The determination of the energy consumption correction value corresponding to the road segment based on the difference between the actual and calibrated values ​​of the operating parameters under the driving conditions of the road segment includes: Determine the operating condition correction coefficient corresponding to each operating condition parameter; wherein the operating condition correction coefficient is positively correlated with the degree of influence of the corresponding operating condition parameter on the energy consumption of the vehicle; Based on the operating condition correction coefficient and the difference between the actual value and the calibration value of each operating condition parameter under the driving conditions of the road segment, the energy consumption correction value corresponding to the road segment is determined.

4. The vehicle range prediction method according to claim 2, characterized in that, The process of dividing the target navigation path into multiple road segments includes: Identify a first path region in the target navigation path that meets a first preset condition and a second path region that does not meet the first preset condition; wherein, the first preset condition is that the actual value of the operating condition parameter remains stable; The first path region is divided according to a first preset length; The second path region is divided according to a second preset length; Wherein, the first preset length is greater than the second preset length.

5. The vehicle range prediction method according to claim 4, characterized in that, The process of obtaining the actual values ​​of the operating parameters under the driving conditions of the aforementioned road section includes: For the first road segment of the first path region, based on the actual values ​​of the operating condition parameters of the driving conditions at the key path points on the first road segment, an interpolation method is used to obtain the actual values ​​of the operating condition parameters of the driving conditions at each path point on the first road segment. For the second road segment divided by the second path region, obtain the actual values ​​of the driving condition parameters for each path point on the second road segment.

6. The method for predicting the driving range of a vehicle according to any one of claims 1-5, characterized in that, The actual values ​​of the climate and environmental information are obtained based on climate and environmental forecasting equipment; the actual values ​​of the road surface information and the vehicle operation information are obtained based on navigation equipment.

7. The method for predicting the driving range of a vehicle according to claim 1, characterized in that, The method of predicting the vehicle's remaining range after traversing the target navigation path based on the energy consumption, the first energy consumption correction value, and the second energy consumption correction value further includes: Based on the energy consumption, the first energy consumption correction value, and the second energy consumption correction value, the total driving energy consumption of the vehicle on the target navigation path is determined. Determine the remaining available energy consumption of the vehicle; If the remaining available energy consumption is greater than or equal to the total driving energy consumption, the energy consumption difference between the remaining available energy consumption and the total driving energy consumption is determined. The driving range is determined based on the energy consumption difference.

8. The method for predicting the driving range of a vehicle according to claim 7, characterized in that, Determining the total driving energy consumption of the vehicle on the target navigation path based on the energy consumption, the first energy consumption correction value, and the second energy consumption correction value includes: Obtain the historical travel routes of the vehicle under the stated driving conditions; Based on the actual values ​​of the operating parameters corresponding to the historical driving segments, the driving behavior habits corresponding to the vehicle are determined. The sum of the energy consumption, the first energy consumption correction value, and the second energy consumption correction value is determined as the initial total driving energy consumption of the vehicle traveling on the target navigation path; Based on the driving behavior habits, the initial total driving energy consumption is corrected to obtain the total driving energy consumption.

9. The method for predicting the driving range of a vehicle according to claim 8, characterized in that, The method of predicting the vehicle's remaining range after traversing the target navigation path based on the energy consumption, the first energy consumption correction value, and the second energy consumption correction value further includes: If the remaining available energy consumption is less than the total driving energy consumption, the vehicle's refueling planning information on the target navigation path is determined based on the energy consumption difference, wherein the refueling planning information includes switching the target navigation path to a refueling navigation path that passes through a refueling location; The prompt indicates the energy replenishment planning information.

10. The method for predicting the driving range of a vehicle according to claim 5, characterized in that, The climate environment information includes: season, temperature, humidity, wind speed, light intensity, and rainfall; the road surface information includes: road surface attributes, road surface topography, and road surface conditions; wherein, the road surface attributes include: rural roads, urban ordinary roads, urban expressways, highways, tunnels, mountain roads, and underpasses; the road surface topography includes altitude and slope; the road surface conditions include congested roads and unobstructed roads; the vehicle operation information includes vehicle speed, acceleration, power battery state of charge, and load status.

11. A vehicle range prediction device, characterized in that, The vehicle's range prediction device includes: The energy consumption determination module is used to determine the energy consumption of a vehicle traveling along a target navigation path under standard operating conditions. The first correction module is used to determine a first energy consumption correction value for the energy consumption based on the difference between the calibration value of the operating condition parameters corresponding to the vehicle's driving condition and the operating condition parameters of the standard operating condition; wherein, the operating condition parameters include climate environment information, road surface information and vehicle operation information. The second correction module is used to determine a second energy consumption correction value for the energy consumption based on the difference between the calibration value and the actual value of the operating parameters of the driving condition. The range prediction module is used to predict the range of the vehicle after it has traveled the target navigation path, based on the energy consumption, the first energy consumption correction value, and the second energy consumption correction value.

12. A vehicle, characterized in that, The vehicle includes a vehicle electronic control unit (ECU), which uses the vehicle range prediction device as described in claim 11 to predict the vehicle's range.

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