A method and device for predicting cruising range

Abstract

The present application provides a method and device for predicting the cruising range, which are applied to the field of vehicle technology. The method includes predicting a first driving path according to the target positioning signal of the vehicle; matching the first driving path with multiple common driving paths stored in the common path library respectively to determine the target common driving path; determining a first group of energy consumption influencing factors according to the cloud map; determining the energy consumption corresponding to the first group of energy consumption influencing factors according to the basic energy consumption of the vehicle under the first driving path and the weighting coefficients of the respective energy consumption influencing factors in the first group of energy consumption influencing factors; and predicting the cruising range according to the energy consumption and the remaining discharge capacity of the vehicle battery. By introducing the common path library to predict the cruising range of the vehicle, this method can predict the cruising range of the vehicle even when the vehicle does not turn on the navigation, thereby expanding the application scenario of the cruising range prediction method.

Description

Technical Field

[0001] The present application relates to the technical field of vehicles, and in particular, to a method and device for predicting driving range. Background Art

[0002] The driving range, also known as the endurance capacity, refers to the total mileage that vehicles such as automobiles and ships can continuously travel with the maximum fuel reserve. The driving range of an electric vehicle refers to the mileage traveled by the power battery on the electric vehicle from the fully charged state to the end of the standard-specified test. It is an important performance indicator of electric vehicles. In the existing driving range prediction methods, mainly when the user turns on the navigation, rich road information is obtained within the navigation using the path planned by the user, and then the driving range is predicted. However, this prediction method is limited to when the user turns on the navigation. When the user does not turn on the navigation, the driving range cannot be effectively predicted. It can be seen that the application scenarios of the existing driving range prediction methods are relatively limited. Summary of the Invention

[0003] Embodiments of the present application provide a method and device for predicting driving range to solve the problem that the application scenarios of the existing driving range prediction methods are relatively limited.

[0004] To solve the above technical problems, the present application is implemented as follows:

[0005] In a first aspect, embodiments of the present application provide a method for predicting driving range, the method including:

[0006] Predicting a first driving path of the vehicle according to the target positioning signal of the vehicle;

[0007] Matching the first driving path with multiple common driving paths of the vehicle stored in the common path library respectively to determine a target common driving path, where the target common driving path is a common driving path that matches the first driving path among the multiple common driving paths of the vehicle, and the target common driving path corresponds to at least one energy consumption influencing factor group, and each energy consumption influencing factor group includes at least one energy consumption influencing factor;

[0008] Determining a first energy consumption influencing factor group according to the cloud map, where the first energy consumption influencing factor group is an energy consumption influencing factor group corresponding to the actual road conditions of the first driving path among the multiple energy consumption influencing factor groups of the target common driving path;

[0009] Determining the energy consumption corresponding to the first energy consumption influencing factor group according to the basic energy consumption of the vehicle on the first driving path and the weighting coefficients of the respective energy consumption influencing factors in the first energy consumption influencing factor group;

[0010] Predict the cruising range of the vehicle according to the energy consumption corresponding to the first energy consumption impact factor group and the remaining discharge capacity of the vehicle battery.

[0011] Optionally, before predicting the first driving path of the vehicle according to the target positioning signal of the vehicle, the method further includes:

[0012] Collect a plurality of positioning signals of the vehicle;

[0013] Determine the driving path of the vehicle and the energy consumption impact factor group corresponding to the driving path according to the plurality of positioning signals of the vehicle and the cloud map;

[0014] Store the driving path, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path into the common path library.

[0015] Optionally, after storing the driving path, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path into the common database, the method further includes:

[0016] When the common path library stores a plurality of energy consumption impact factor groups corresponding to the driving path, determine the weighting coefficients of the respective energy consumption impact factors in the energy consumption impact factor group according to the energy consumption corresponding to the plurality of energy consumption impact factor groups;

[0017] Store the weighting coefficients into the common path library.

[0018] Optionally, predicting the first driving path of the vehicle according to the target positioning signal of the vehicle includes:

[0019] When the navigation system of the vehicle is turned off, predict the first driving path of the vehicle according to the target positioning signal of the vehicle;

[0020] The method further includes:

[0021] When the navigation system is started, obtain a second energy consumption impact factor group according to the in-vehicle map corresponding to the navigation system, where the second energy consumption impact factor group is the energy consumption impact factor group of the second driving path of the vehicle, and the second driving path is the driving path of the vehicle planned by the navigation system;

[0022] Predict the cruising range of the vehicle according to the energy consumption corresponding to the second energy consumption impact factor group and the remaining discharge capacity of the vehicle battery.

[0023] Optionally, after predicting the cruising range of the vehicle based on the energy consumption corresponding to the first energy consumption influencing factor group and the remaining discharge capacity of the vehicle battery, the method further includes:

[0024] When the first driving path fails to match any of the multiple common driving paths of the vehicle and the historical driving mileage of the vehicle is greater than or equal to the first driving mileage, predict the cruising range of the vehicle based on the energy consumption corresponding to the historical driving mileage of the vehicle and the remaining discharge capacity of the vehicle battery.

[0025] Optionally, the method further includes:

[0026] When the historical driving mileage of the vehicle is less than the first driving mileage, predict the cruising range of the vehicle based on the standard energy consumption of the vehicle and the remaining discharge capacity of the vehicle battery.

[0027] In a second aspect, an embodiment of the present application further provides a cruising range prediction device, and the cruising range prediction device includes:

[0028] A first prediction module, configured to predict a first driving path of the vehicle according to a target positioning signal of the vehicle;

[0029] A first determination module, configured to match the first driving path with multiple common driving paths of the vehicle stored in a common path library respectively to determine a target common driving path, where the target common driving path is a common driving path that matches the first driving path among the multiple common driving paths of the vehicle, and at least one energy consumption influencing factor group corresponds to the target common driving path, and each energy consumption influencing factor group includes at least one energy consumption influencing factor;

[0030] A second determination module, configured to determine a first energy consumption influencing factor group according to a cloud map, where the first energy consumption influencing factor group is an energy consumption influencing factor group corresponding to the actual road conditions of the first driving path among multiple energy consumption influencing factor groups of the target common driving path;

[0031] A third determination module, configured to determine the energy consumption corresponding to the first energy consumption influencing factor group according to the basic energy consumption of the vehicle on the first driving path and the weighting coefficients of the respective energy consumption influencing factors in the first energy consumption influencing factor group;

[0032] A second prediction module, configured to predict the cruising range of the vehicle according to the energy consumption corresponding to the first energy consumption influencing factor group and the remaining discharge capacity of the vehicle battery.

[0033] Optionally, the device further includes:

[0034] A first collection module, configured to collect a plurality of positioning signals of the vehicle;

[0035] A fourth determination module, configured to determine a driving path of the vehicle and an energy consumption impact factor group corresponding to the driving path according to the plurality of positioning signals of the vehicle and the cloud map;

[0036] A first storage module, configured to store the driving path, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path into a common path library.

[0037] Optionally, the device further includes:

[0038] A fifth determination module, configured to, when the common path library stores a plurality of energy consumption impact factor groups corresponding to the driving path, determine a weighting coefficient of each energy consumption impact factor in the energy consumption impact factor group according to the energy consumption corresponding to the plurality of energy consumption impact factor groups;

[0039] A second storage module, configured to store the weighting coefficient into the common path library.

[0040] Optionally, the first prediction module includes:

[0041] A first prediction unit, configured to predict a first driving path of the vehicle according to a target positioning signal of the vehicle when the navigation system of the vehicle is turned off;

[0042] The device further includes:

[0043] A first acquisition module, configured to, when the navigation system is started, acquire a second energy consumption impact factor group according to an in-vehicle map corresponding to the navigation system, where the second energy consumption impact factor group is an energy consumption impact factor group of a second driving path of the vehicle, and the second driving path is a driving path of the vehicle planned by the navigation system;

[0044] A third prediction module, configured to predict a cruising range of the vehicle according to the energy consumption corresponding to the second energy consumption impact factor group and a remaining discharge capacity of the vehicle battery.

[0045] Optionally, the device further includes:

[0046] A fourth prediction module, configured to, when the first driving path fails to match any of the plurality of common driving paths of the vehicle and the historical driving mileage of the vehicle is greater than or equal to a first driving mileage, predict the cruising range of the vehicle according to the energy consumption corresponding to the historical driving mileage of the vehicle and the remaining discharge capacity of the vehicle battery.

[0047] Optionally, the device further includes:

[0048] A fifth prediction module, configured to predict the cruising range of the vehicle according to the standard energy consumption of the vehicle and the remaining discharge capacity of the vehicle battery when the historical driving mileage of the vehicle is less than the first driving mileage.

[0049] In a third aspect, an embodiment of the present application further provides a cruising range prediction device, including a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the above-mentioned cruising range prediction method are implemented.

[0050] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned cruising range prediction method are implemented.

[0051] The cruising range prediction method according to the embodiment of the present application includes predicting a first driving path of the vehicle according to a target positioning signal of the vehicle; matching the first driving path with multiple common driving paths of the vehicle stored in a common path library respectively to determine a target common driving path; determining a first energy consumption influence factor group according to a cloud map; and determining the energy consumption corresponding to the first energy consumption influence factor group according to the basic energy consumption of the vehicle on the first driving path and the weighting coefficients of the respective energy consumption influence factors in the first energy consumption influence factor group. By determining the target common driving path in the common path library that matches the first driving path, and then predicting the cruising range of the vehicle according to the energy consumption corresponding to the first energy consumption influence factor of the target common driving path, the cruising range of the vehicle can be predicted even if the vehicle does not turn on the navigation, thereby expanding the application scenario of the cruising range prediction method. Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0053] Figure 1 is one of the flowcharts of the cruising range prediction method provided by the embodiment of the present application;

[0054] Figure 2 is the second flowchart of the cruising range prediction method provided by the embodiment of the present application;

[0055] Figure 3 It is a structural diagram of a remaining driving range prediction device provided by another embodiment of the present application;

[0056] Figure 4 It is a structural diagram of an electronic device provided by another embodiment of the present application. Specific embodiments

[0057] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0058] The embodiments of the present application provide a method for predicting the remaining driving range. Refer to Figure 1 , Figure 1 It is a flowchart of the method for predicting the remaining driving range provided by the embodiments of the present application. As Figure 1 shown, it includes the following steps:

[0059] Step 101: Predict the first driving path of the vehicle according to the target positioning signal of the vehicle;

[0060] In this step, the target positioning signal of the vehicle is obtained through the Beidou positioning system or the GPS positioning system. According to the target positioning signal of the vehicle, the approximate initial motion trajectory of the vehicle can be determined, and then the first driving path of the vehicle can be predicted according to the approximate initial motion trajectory of the vehicle. Exemplarily, according to the target positioning signal of the vehicle, the first driving path of the vehicle can be predicted as from the company to home. It should be noted that if it is later shown according to the target positioning signal of the vehicle that the vehicle is not from the company to home, but from the company to the mall, the first driving path needs to be switched from the company to home to from the company to the mall.

[0061] Step 102: Match the first driving path with multiple common driving paths of the vehicle stored in the common path library respectively to determine the target common driving path. The target common driving path is the common driving path that matches successfully with the first driving path among the multiple common driving paths of the vehicle. The target common driving path corresponds to at least one energy consumption influence factor group, and each energy consumption influence factor group includes at least one energy consumption influence factor;

[0062] In this step, the aforementioned common path library stores multiple common driving paths of the vehicle. These common driving paths may be the driving paths from the company to home or from home to the mall for the vehicle user. Exemplarily, the first driving path is respectively matched with multiple common driving paths of the vehicle stored in the common path library, and it is determined that the target common driving path is the road from the company to home. The aforementioned energy consumption impact factors may include, but are not limited to, environmental temperature, distribution of road traffic lights, weather, and accessory opening conditions (such as whether the air conditioner is turned on). The target common driving path corresponds to at least one group of energy consumption impact factors. Exemplarily, it can be understood that when driving from the company to home on a rainy day with traffic congestion, then rainy day and traffic congestion form a group of energy consumption impact factors; when driving from the company to home on a rainy day with the air conditioner on, then rainy day and air conditioner on form a group of energy consumption impact factors; when driving from the company to home with the air conditioner on and passing through many red lights, then air conditioner on and many red lights form a group of energy consumption impact factors.

[0063] Step 103: Determine a first group of energy consumption impact factors according to the cloud map. The first group of energy consumption impact factors is the group of energy consumption impact factors corresponding to the actual road conditions of the first driving path among the multiple groups of energy consumption impact factors of the target common driving path.

[0064] In this step, the aforementioned cloud map is a public map, and the map information it contains is relatively rough compared with the in-vehicle map. However, it can generally determine some basic actual road conditions when driving from the company to home at this time. Then, according to these basic actual road conditions, a first group of energy consumption impact factors is determined. Exemplarily, it can be determined that the first group of energy consumption impact factors is rainy day and traffic congestion.

[0065] Step 104: Determine the energy consumption corresponding to the first group of energy consumption impact factors according to the basic energy consumption of the vehicle on the first driving path and the weighting coefficients of the respective energy consumption impact factors in the first group of energy consumption impact factors.

[0066] In this step, the basic energy consumption of the vehicle under the first driving path can be understood as the minimum energy consumption required for the vehicle to complete the mileage of the first driving path without being affected by the above-mentioned various energy consumption influencing factors. Exemplarily, the energy consumption required for the vehicle to complete the mileage of the first driving path on a sunny day and without road congestion can be used as the basic energy consumption of the vehicle. For example, when the basic energy consumption of the vehicle from the company to home is 12.7 kWh, if the above-mentioned first energy consumption influencing factor group includes two energy consumption influencing factors, namely rainy days and road congestion, and the weighting coefficient for rainy days can be 1.07 and the weighting coefficient for road congestion can be 1.3, then it is determined that the energy consumption from the company to home on a rainy day and with road congestion is 12.7*(1 + 0.3 + 0.07).

[0067] Step 105: Predict the cruising range of the vehicle according to the energy consumption corresponding to the first energy consumption influencing factor group and the remaining discharge capacity of the vehicle battery.

[0068] In this step, according to the energy consumption corresponding to the first energy consumption influencing factor group and the remaining discharge capacity of the vehicle battery, the cruising range of the vehicle can be predicted. The aforementioned cruising range can be understood as the maximum distance that the vehicle can still travel.

[0069] The cruising range prediction method of the embodiments of the present application determines the target common driving path in the common path library that matches the first driving path, and then predicts the cruising range of the vehicle according to the energy consumption corresponding to the first energy consumption influencing factor group of the target common driving path. Even if the vehicle does not turn on the navigation, the cruising range of the vehicle can be predicted, thereby expanding the application scenarios of the cruising range prediction method.

[0070] Optionally, before predicting the first driving path of the vehicle according to the target positioning signal of the vehicle when the navigation system of the vehicle is turned off, the method further includes:

[0071] Collect a plurality of positioning signals of the vehicle;

[0072] Determine the driving path of the vehicle and the energy consumption influencing factor group corresponding to the driving path according to the plurality of positioning signals of the vehicle and the cloud map;

[0073] Store the driving path, the energy consumption influencing factor group corresponding to the driving path, and the energy consumption under the energy consumption influencing factor group corresponding to the driving path into the common path library.

[0074] In the driving range prediction method of the embodiment of the present application, it is necessary to form a common path library according to the driving records of the vehicle. First, continuously collect multiple positioning signals uploaded by the vehicle to the cloud. According to the multiple positioning signals of the vehicle, determine the driving path of the vehicle at a certain time, and then determine the energy consumption impact factor group corresponding to the driving path according to the map information of the cloud map. Then store the driving path, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path into the common path library. Exemplarily, the driving path of the vehicle this time is from home to the mall, and the energy consumption impact factor group corresponding to this time from home to the mall can be that there are many red lights, the air conditioner is on, and the road is congested. After the vehicle completes this driving path, record the energy consumption of the vehicle under this energy consumption impact factor group. By storing the driving path of the vehicle, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path into the common path library, when the vehicle experiences the same driving process again, the energy consumption of the vehicle can be predicted more accurately.

[0075] Optionally, after storing the driving path, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path into the common database, the method further includes:

[0076] When multiple energy consumption impact factor groups corresponding to the driving path are stored in the common path library, determine the weighting coefficients of each energy consumption impact factor in the energy consumption impact factor group respectively according to the energy consumption corresponding to the multiple energy consumption impact factor groups;

[0077] Store the weighting coefficients into the common path library.

[0078] In the driving range prediction method of the embodiment of the present application, if a certain driving path is stored in the common path library multiple times, it means that the vehicle has a high driving frequency on this driving path. Then this driving path needs to be analyzed emphatically. Each time the vehicle drives on this driving path, it will obtain a corresponding energy consumption impact factor group. By respectively determining the energy consumption corresponding to multiple energy consumption impact factor groups, the weighting coefficients of each energy consumption impact factor in the energy consumption impact factor group are determined.

[0079] Exemplarily, by the energy consumption from the company to home on a rainy day with road congestion, the energy consumption from the company to home on a rainy day with the vehicle air conditioner on, and the energy consumption from the company to home with the air conditioner on and passing through many red lights, the weighting factor for rainy days, the weighting factor for road congestion, the weighting factor for air conditioner turning on, and the weighting factor for passing through many red lights can be determined. At the same time, it should be noted that the weight of this driving route can also be increased during route matching by recording the number of times this driving route is traveled in the common route library. The remaining driving range prediction method in the embodiments of the present application performs weighted statistics on each energy consumption impact factor in the energy consumption factor group, which is beneficial to improving the accuracy of the next energy consumption prediction for the vehicle driving route.

[0080] Optionally, the predicting the first driving route of the vehicle according to the target positioning signal of the vehicle includes:

[0081] When the navigation system of the vehicle is turned off, predicting the first driving route of the vehicle according to the target positioning signal of the vehicle;

[0082] The method further includes:

[0083] When the navigation system is started, obtaining a second energy consumption impact factor group according to the in-vehicle map corresponding to the navigation system, where the second energy consumption impact factor group is the energy consumption impact factor group of the second driving route of the vehicle, and the second driving route is the driving route of the vehicle planned by the navigation system;

[0084] Predicting the remaining driving range of the vehicle according to the energy consumption corresponding to the second energy consumption impact factor group and the remaining discharge capacity of the vehicle battery.

[0085] In the remaining driving range prediction method in the embodiments of the present application, when the navigation system of the vehicle is turned off, the specific driving route of the vehicle cannot be known through the navigation system, so the first driving route of the vehicle can be predicted according to the target positioning signal of the vehicle; when the navigation system of the vehicle is turned on, the driving route planned by the navigation system for the vehicle can be directly determined as the second driving route, and then the second energy consumption factor group corresponding to the second driving route can be determined through the in-vehicle map corresponding to the navigation system. Then, according to the energy consumption corresponding to the second energy consumption impact factor group and the remaining discharge capacity of the vehicle, the remaining driving range of the vehicle is predicted. The remaining driving range prediction method in the embodiments of the present application directly determines the energy consumption of the vehicle through the navigation system, which is beneficial to quickly predicting the remaining driving range of the vehicle.

[0086] Optionally, after predicting the remaining driving range of the vehicle according to the energy consumption corresponding to the first energy consumption impact factor group and the remaining discharge capacity of the vehicle battery, the method further includes:

[0087] When the first driving route fails to match all of the multiple common driving routes of the vehicle, and the historical driving mileage of the vehicle is greater than or equal to the first driving mileage, the remaining driving mileage of the vehicle is predicted according to the energy consumption corresponding to the historical driving mileage of the vehicle and the remaining discharge capacity of the vehicle battery.

[0088] In the remaining driving mileage prediction method of the embodiments of the present application, if the first driving route fails to match all of the multiple common driving routes of the vehicle, then it is impossible to predict the energy consumption of the vehicle through the common route library, and further predict the remaining driving mileage of the vehicle. At this time, if the vehicle has traveled a certain mileage, the future energy consumption of the vehicle can be predicted through the energy consumption of the vehicle during the historical driving mileage. It should be noted that, in order to make the energy consumption of the vehicle during the historical driving mileage have certain reference, the historical driving mileage of the vehicle can be restricted to be greater than or equal to the first driving mileage, and the specific value of the first driving mileage can be determined according to the actual application situation. The remaining driving mileage prediction method of the embodiments of the present application predicts the future energy consumption of the vehicle through the energy consumption of the vehicle during the historical driving mileage, which is beneficial to further expanding the application scenarios of the remaining driving mileage prediction method.

[0089] Optionally, the method further includes:

[0090] When the historical driving mileage of the vehicle is less than the first driving mileage, the remaining driving mileage of the vehicle is predicted according to the standard energy consumption of the vehicle and the remaining discharge capacity of the vehicle battery.

[0091] In the remaining driving mileage prediction method of the embodiments of the present application, when the historical driving mileage of the vehicle is less than the first driving mileage, it indicates that the historical driving mileage of the vehicle has no reference, then the remaining driving mileage of the vehicle is predicted according to the standard energy consumption of the vehicle. The aforementioned standard energy consumption of the vehicle refers to the energy consumption of the vehicle under the specified vehicle speed according to the performance of the vehicle itself. The remaining driving mileage prediction method of the embodiments of the present application predicts the remaining driving mileage of the vehicle according to the standard energy consumption of the vehicle, which is beneficial to further expanding the application scenarios of the remaining driving mileage prediction method.

[0092] See Figure 2, in the remaining driving range prediction method of the embodiments of the present application, first, if the navigation is turned on, the remaining driving range of the vehicle is predicted by the energy consumption of the second driving path planned by the navigation; if the navigation is not turned on, first, the driving path of the vehicle is predicted as the first driving path, then a target common driving path that matches the first driving path is found from the common path library, and then the remaining driving range of the vehicle is predicted by the energy consumption of the target common driving path; if no matching information is found in the common path library, the remaining driving range of the vehicle is predicted by the energy consumption of the vehicle's historical driving mileage; if the energy consumption of the vehicle's historical driving mileage is not referenceable, the remaining driving range of the vehicle is predicted by the standard energy consumption of the vehicle. The remaining driving range prediction method of the embodiments of the present application takes into account various situations that occur during prediction, further expanding the application scenarios of the remaining driving range prediction method.

[0093] See Figure 3 , Figure 3 is the structural diagram of the remaining driving range prediction device provided by another embodiment of the present application.

[0094] As Figure 3 shown, the remaining driving range prediction device 300 includes:

[0095] A first prediction module 301, configured to predict the first driving path of the vehicle according to the target positioning signal of the vehicle;

[0096] A first determination module 302, configured to match the first driving path with multiple common driving paths of the vehicle stored in the common path library respectively, and determine a target common driving path, where the target common driving path is a common driving path that matches the first driving path successfully among the multiple common driving paths of the vehicle, and the target common driving path corresponds to at least one energy consumption influence factor group, and each energy consumption influence factor group includes at least one energy consumption influence factor;

[0097] A second determination module 303, configured to determine a first energy consumption influence factor group according to the cloud map, where the first energy consumption influence factor group is an energy consumption influence factor group corresponding to the actual road conditions of the first driving path among the multiple energy consumption influence factor groups of the target common driving path;

[0098] A third determination module 304, configured to determine the energy consumption corresponding to the first energy consumption influence factor group according to the basic energy consumption of the vehicle under the first driving path and the weighting coefficients of the respective energy consumption influence factors in the first energy consumption influence factor group;

[0099] The second prediction module 305 is configured to predict the driving range of the vehicle according to the energy consumption corresponding to the first energy consumption impact factor group and the remaining discharge capacity of the vehicle battery.

[0100] Optionally, the device further includes:

[0101] The first collection module is configured to collect a plurality of positioning signals of the vehicle;

[0102] The fourth determination module is configured to determine the driving path of the vehicle and the energy consumption impact factor group corresponding to the driving path according to the plurality of positioning signals of the vehicle and the cloud map;

[0103] The first storage module is configured to store the driving path, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path into the common path library.

[0104] Optionally, the device further includes:

[0105] The fifth determination module is configured to determine the weighting coefficients of the respective energy consumption impact factors in the energy consumption impact factor group according to the energy consumption corresponding to the plurality of energy consumption impact factor groups when the common path library stores the plurality of energy consumption impact factor groups corresponding to the driving path;

[0106] The second storage module is configured to store the weighting coefficients into the common path library.

[0107] Optionally, the first prediction module includes:

[0108] The first prediction unit is configured to predict the first driving path of the vehicle according to the target positioning signal of the vehicle when the navigation system of the vehicle is turned off;

[0109] The device further includes:

[0110] The first acquisition module is configured to obtain a second energy consumption impact factor group according to the in-vehicle map corresponding to the navigation system when the navigation system is started, where the second energy consumption impact factor group is the energy consumption impact factor group of the second driving path of the vehicle, and the second driving path is the driving path of the vehicle planned by the navigation system;

[0111] The third prediction module is configured to predict the driving range of the vehicle according to the energy consumption corresponding to the second energy consumption impact factor group and the remaining discharge capacity of the vehicle battery.

[0112] Optionally, the device further includes:

[0113] A fourth prediction module, configured to predict the remaining driving range of the vehicle according to the energy consumption corresponding to the historical driving mileage of the vehicle and the remaining discharge capacity of the vehicle battery when the first driving path fails to match all of the multiple common driving paths of the vehicle and the historical driving mileage of the vehicle is greater than or equal to a first driving mileage.

[0114] Optionally, the device further includes:

[0115] A fifth prediction module, configured to predict the remaining driving range of the vehicle according to the standard energy consumption of the vehicle and the remaining discharge capacity of the vehicle battery when the historical driving mileage of the vehicle is less than the first driving mileage.

[0116] See Figure 4 , Figure 4 is a structural diagram of an electronic device provided in another embodiment of the present application. As Figure 4 shown, the electronic device includes: a processor 401, a communication interface 402, a communication bus 404, and a memory 403. Among them, the processor 401, the communication interface 402, and the memory 403 complete mutual interaction through the communication bus 404.

[0117] Among them, the memory 403 is used to store a computer program; the processor 401 is configured to execute the program stored on the memory 403. When the calculator program is executed by the processor 401: predicting a first driving path of the vehicle according to a target positioning signal of the vehicle; matching the first driving path with multiple common driving paths of the vehicle stored in a common path library respectively to determine a target common driving path, where the target common driving path is a common driving path that matches the first driving path among the multiple common driving paths of the vehicle, and at least one energy consumption influence factor group corresponds to the target common driving path, and each energy consumption influence factor group includes at least one energy consumption influence factor; determining a first energy consumption influence factor group according to a cloud map, where the first energy consumption influence factor group is an energy consumption influence factor group corresponding to the actual road conditions of the first driving path among the multiple energy consumption influence factor groups of the target common driving path; determining the energy consumption corresponding to the first energy consumption influence factor group according to the basic energy consumption of the vehicle on the first driving path and the weighting coefficients of the respective energy consumption influence factors in the first energy consumption influence factor group; predicting the remaining driving range of the vehicle according to the energy consumption corresponding to the first energy consumption influence factor group and the remaining discharge capacity of the vehicle battery.

[0118] Optionally, the processor 401 is further configured to:

[0119] Collect multiple positioning signals of the vehicle;

[0120] Determine the driving path of the vehicle and the energy consumption impact factor group corresponding to the driving path according to multiple positioning signals of the vehicle and the cloud map;

[0121] Store the driving path, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path in the common path library.

[0122] Optionally, the processor 401 is further configured to:

[0123] When multiple energy consumption impact factor groups corresponding to the driving path are stored in the common path library, determine the weighting coefficients of each energy consumption impact factor in the energy consumption impact factor group according to the energy consumption corresponding to the multiple energy consumption impact factor groups;

[0124] Store the weighting coefficients in the common path library.

[0125] Optionally, the processor 401 is specifically configured to:

[0126] When the navigation system of the vehicle is turned off, predict the first driving path of the vehicle according to the target positioning signal of the vehicle;

[0127] The processor 401 is further configured to:

[0128] When the navigation system is started, obtain a second energy consumption impact factor group according to the in-vehicle map corresponding to the navigation system, where the second energy consumption impact factor group is the energy consumption impact factor group of the second driving path of the vehicle, and the second driving path is the driving path of the vehicle planned by the navigation system;

[0129] Predict the cruising range of the vehicle according to the energy consumption corresponding to the second energy consumption impact factor group and the remaining discharge capacity of the vehicle battery.

[0130] Optionally, the processor 401 is further configured to:

[0131] When the first driving path fails to match all the common driving paths of the vehicle and the historical driving mileage of the vehicle is greater than or equal to the first driving mileage, predict the cruising range of the vehicle according to the energy consumption corresponding to the historical driving mileage of the vehicle and the remaining discharge capacity of the vehicle battery.

[0132] Optionally, the processor 401 is further configured to:

[0133] When the historical driving mileage of the vehicle is less than the first driving mileage, the cruising range of the vehicle is predicted according to the standard energy consumption of the vehicle and the remaining discharge capacity of the vehicle battery.

[0134] The communication bus 404 mentioned in the above electronic device may be an external device interconnect standard (Peripheral Component Interconnect, PCT) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. This communication bus 404 can be divided into an address bus, a data bus, a control bus, etc. For the sake of easy identification, only a thick line is used in the figure to represent it, but it does not mean that there is only one bus or one data type.

[0135] The communication interface 402 is used for communication between the above terminal and other devices.

[0136] The memory 403 may include a random access memory (Random Access Memory, RAM), and may also include a non-volatile memory, such as at least one disk memory. Optionally, the memory 403 may also be at least one storage device located far from the aforementioned processor 401. The above-mentioned processor 401 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; it may also be a digital signal processor (Digital Signal Processing, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0137] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes each process of the above embodiment of the cruising range prediction method and can achieve the same technical effect. To avoid repetition, it will not be elaborated here. Among them, the computer-readable storage medium, such as a read-only memory (Read-Only Memory, abbreviated as ROM), a random access memory (Random Access Memory, abbreviated as RAM), a magnetic disk or an optical disc, etc.

[0138] It should be noted that in this text, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or device comprising that element.

[0139] From the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0140] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present application and without departing from the spirit and scope protected by the claims of the present application, can also make many forms, all of which fall within the protection scope of the present application.

Claims

1. A method for predicting the cruising range, characterized in that, The method includes: Predicting a first driving path of the vehicle according to a target positioning signal of the vehicle; Matching the first driving path with multiple common driving paths of the vehicle stored in a common path library respectively to determine a target common driving path, where the target common driving path is a common driving path that matches the first driving path among the multiple common driving paths of the vehicle, and at least one energy consumption impact factor group corresponds to the target common driving path, and each energy consumption impact factor group includes at least one energy consumption impact factor; Determining a first energy consumption impact factor group according to a cloud map, where the first energy consumption impact factor group is an energy consumption impact factor group corresponding to the actual road condition of the first driving path among the multiple energy consumption impact factor groups of the target common driving path; Determining the energy consumption corresponding to the first energy consumption impact factor group according to the basic energy consumption of the vehicle on the first driving path and the weighting coefficients of the respective energy consumption impact factors in the first energy consumption impact factor group; Predicting the cruising range of the vehicle according to the energy consumption corresponding to the first energy consumption impact factor group and the remaining discharge capacity of the vehicle battery.

2. The method for predicting the cruising range according to claim 1, wherein Before predicting the first driving path of the vehicle according to the target positioning signal of the vehicle, the method further includes: Collecting multiple positioning signals of the vehicle; Determining the driving path of the vehicle and the energy consumption impact factor group corresponding to the driving path according to the multiple positioning signals of the vehicle and the cloud map; Storing the driving path, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path into the common path library.

3. The method for predicting the cruising range according to claim 2, wherein After storing the driving path, the energy consumption impact factor group corresponding to the driving path, and the energy consumption under the energy consumption impact factor group corresponding to the driving path into the common database, the method further includes: When multiple energy consumption impact factor groups corresponding to the driving path are stored in the common path library, determining the weighting coefficients of the respective energy consumption impact factors in the energy consumption impact factor group according to the energy consumption corresponding to the multiple energy consumption impact factor groups; Storing the weighting coefficients into the common path library.

4. The method for predicting the cruising range according to claim 1, wherein Predicting the first driving path of the vehicle according to the target positioning signal of the vehicle includes: Predicting the first driving path of the vehicle according to the target positioning signal of the vehicle when the navigation system of the vehicle is closed; The method further includes: When the navigation system is started, obtaining a second energy consumption impact factor group according to the in-vehicle map corresponding to the navigation system, where the second energy consumption impact factor group is the energy consumption impact factor group of a second driving path of the vehicle, and the second driving path is the driving path of the vehicle planned by the navigation system; Predicting the cruising range of the vehicle according to the energy consumption corresponding to the second energy consumption impact factor group and the remaining discharge capacity of the vehicle battery.

5. The method for predicting the cruising range according to claim 1, wherein After predicting the cruising range of the vehicle based on the energy consumption corresponding to the first energy consumption influencing factor group and the remaining discharge capacity of the vehicle battery, the method further includes: When the first driving path fails to match any of the multiple common driving paths of the vehicle and the historical driving mileage of the vehicle is greater than or equal to the first driving mileage, predict the cruising range of the vehicle based on the energy consumption corresponding to the historical driving mileage of the vehicle and the remaining discharge capacity of the vehicle battery.

6. The method for predicting cruising range according to claim 5, characterized in that, The method further includes: When the historical driving mileage of the vehicle is less than the first driving mileage, predict the cruising range of the vehicle based on the standard energy consumption of the vehicle and the remaining discharge capacity of the vehicle battery.

7. A remaining driving range prediction device, characterized in that, The device includes: A first prediction module, configured to predict a first driving path of the vehicle according to a target positioning signal of the vehicle; A first determination module, configured to match the first driving path with multiple common driving paths of the vehicle stored in a common path library respectively to determine a target common driving path, where the target common driving path is a common driving path that matches the first driving path among the multiple common driving paths of the vehicle, and at least one energy consumption influencing factor group corresponds to the target common driving path, and each energy consumption influencing factor group includes at least one energy consumption influencing factor; A second determination module, configured to determine a first energy consumption influencing factor group according to a cloud map, where the first energy consumption influencing factor group is an energy consumption influencing factor group corresponding to the actual road conditions of the first driving path among the multiple energy consumption influencing factor groups of the target common driving path; A third determination module, configured to determine the energy consumption corresponding to the first energy consumption influencing factor group according to the basic energy consumption of the vehicle on the first driving path and the weighting coefficients of the respective energy consumption influencing factors in the first energy consumption influencing factor group; A second prediction module, configured to predict the cruising range of the vehicle according to the energy consumption corresponding to the first energy consumption influencing factor group and the remaining discharge capacity of the vehicle battery.

8. The remaining driving range prediction device according to claim 7, characterized in that, The device further includes: A first collection module, configured to collect multiple positioning signals of the vehicle; A fourth determination module, configured to determine the driving path of the vehicle and the energy consumption influencing factor group corresponding to the driving path according to the multiple positioning signals of the vehicle and the cloud map; A first storage module, configured to store the driving path, the energy consumption influencing factor group corresponding to the driving path, and the energy consumption under the energy consumption influencing factor group corresponding to the driving path into the common path library.

9. An electronic device apparatus, characterized in that, It includes a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the cruising range prediction method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, the steps of the cruising range prediction method according to any one of claims 1 to 6 are implemented.

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