A method, apparatus and vehicle for determining range

Abstract

The application provides a kind of driving range determination method, device and vehicle, the method comprises: obtaining the first path from the current position of vehicle to reach first destination;According to the first path, determine the first unit distance energy consumption of the first path;According to the first unit distance energy consumption and historical unit distance energy consumption, determine the second unit distance energy consumption;According to the remaining available power of vehicle and the second unit distance energy consumption, determine the driving range of the vehicle. Through the method provided by the application, the driving range of the vehicle can be determined more accurately when the navigation is not turned on.

Description

Technical Field

[0001] This application relates to the field of vehicles, specifically to a method, apparatus, and vehicle for determining driving range. Background Technology

[0002] With the development of new energy technologies, more and more people are choosing electric vehicles. However, due to limitations in the charging power and number of charging stations, users need to know the driving range of electric vehicles; therefore, accurately determining the driving range of an electric vehicle is crucial.

[0003] Currently, there are three main ways to determine the driving range of electric vehicles. The first is to determine it based on the average energy consumption per unit distance in history, but this method has a large margin of error. The second is to determine it based on the starting point, destination, and road condition information determined by navigation or intelligent transportation, but this method cannot determine the driving range when navigation is not turned on. The third is to determine the driving range of the vehicle based on its current location, but due to the complexity of road routes, this method also cannot accurately determine the driving range.

[0004] Therefore, a method is needed that can accurately determine the vehicle's remaining range even when navigation is not enabled. Summary of the Invention

[0005] This application provides a method, device, and vehicle for determining driving range, which can accurately determine the driving range of a vehicle without turning on navigation.

[0006] A first aspect of this application provides a method for determining driving range, comprising: obtaining a first path from the vehicle's current location to a first destination; determining a first unit distance energy consumption based on the first path; determining a second unit distance energy consumption based on the first unit distance energy consumption and historical unit distance energy consumption; and determining the vehicle's driving range based on the vehicle's remaining available battery power and the second unit distance energy consumption.

[0007] By determining the energy consumption per unit distance along the first path from the vehicle's current location to the first destination, and by determining the energy consumption per unit distance based on the first unit distance energy consumption and historical unit distance energy consumption, the vehicle's remaining range can be determined relatively accurately without navigation being activated.

[0008] In one possible implementation, obtaining the path from the vehicle's current location to the first destination specifically includes: obtaining the vehicle's current location information and environmental information; and determining the first destination and the first path based on the location information and environmental information.

[0009] In one possible implementation, determining the first destination specifically includes: determining the second destination and the probability of their corresponding values ​​based on location information and environmental information; when the maximum value of the probabilities is less than a first preset value, outputting at least one of the second destinations based on the probabilities; and determining the first destination from the at least one of the output second destinations in response to the user's selection.

[0010] With the above settings, even when the probability of a second destination being the correct one is low, at least one destination can be output from the second destination for the user to select or confirm, thereby improving the accuracy of destination determination and thus improving the accuracy of vehicle range determination.

[0011] In one possible implementation, determining the first destination specifically includes: determining the second destination and the probability corresponding to the second destination based on location information and environmental information; when the remaining available battery power of the vehicle is less than a second preset value, outputting at least one of the second destinations according to the probability; and determining the first destination from the at least one output second destination in response to the user's selection.

[0012] With the above settings, even when the vehicle's remaining available battery power is low, at least one destination can be output from the second destination for the user to select or confirm, improving the accuracy of destination determination and thus improving the accuracy of vehicle range determination.

[0013] In one possible implementation, the probability of a second destination corresponding to a second destination is determined based on location information and environmental information. Specifically, this includes: inputting location information and environmental information into a first model to obtain the probability of a second destination corresponding to a second destination, wherein the first model is trained based on path information and environmental information from historical trips.

[0014] In one possible implementation, the first model includes the random forest model.

[0015] The first model enables a relatively accurate prediction of the second destination and its corresponding probability, thereby improving the accuracy of determining the driving range.

[0016] In one possible implementation, determining the first unit distance energy consumption of the first path based on the first path specifically includes: obtaining multiple unit distance energy consumptions of the first path; and calculating the first unit distance energy consumption based on the multiple unit distance energy consumptions.

[0017] For example, if there is only one primary path from the starting point to a destination, the energy consumption per unit distance of the primary path can be determined by the ratio of the average historical energy consumption of multiple trips along the primary path to the length of the primary path.

[0018] In one possible implementation, determining the first unit distance energy consumption of the first path based on the first path specifically includes: obtaining the average unit distance energy consumption of the first path; for the first destination, determining the average unit distance energy consumption to the first destination based on the average unit distance energy consumption of the first path to the first destination; and determining the first unit distance energy consumption based on the average unit distance energy consumption to the first destination.

[0019] In one possible implementation, determining the average energy consumption per unit distance to the first destination, based on the average energy consumption per unit distance along the first path, specifically includes: The average energy consumption per unit distance to the first destination is determined based on the energy consumption per unit distance of each first path to the first destination and the first weight corresponding to each first path; wherein, the first weight is calculated based on the length of the first path and / or based on the selection probability of each first path.

[0020] For example, when a vehicle's first path from its current location to a destination includes multiple paths (e.g., ), the weight of each first path can be determined in the manner described above, thereby calculating the average energy consumption per unit distance to reach that destination.

[0021] In one possible implementation, the first unit distance energy consumption is calculated based on the average energy consumption per unit distance to each first destination, including: calculating the first unit distance energy consumption based on the average energy consumption per unit distance to each first destination and the second weight corresponding to each first destination; wherein the second weight is calculated based on the path length or equivalent path length to each first destination and / or based on the selection probability of each first destination.

[0022] For example, if the first path to a destination includes multiple routes and the vehicle may reach multiple destinations, the average energy consumption per unit distance to each destination can be determined using the above method, and then the energy consumption per unit distance can be calculated.

[0023] In one possible implementation, the equivalent path length to the first destination is calculated based on the length of each first path to that first destination and / or based on the selection probability of each first path.

[0024] In one possible implementation, the second unit distance energy consumption is determined based on the first unit distance energy consumption and the historical unit distance energy consumption, specifically including: determining the weight based on the historical unit distance energy consumption, the remaining available power, and the first path; and determining the second unit distance energy consumption based on the first unit distance energy consumption, the historical unit distance energy consumption, and the weight.

[0025] In one possible implementation, the weights are determined based on historical energy consumption per unit distance, remaining available power, and a first path. Specifically, this includes: determining a rough estimate of the driving range based on historical energy consumption per unit distance and remaining available power; determining the energy consumption prediction accuracy based on the first path and its correspondence with a preset energy consumption prediction accuracy; determining the credibility of the first destination; and determining the weights based on the ratio of the first path to the rough estimate of the driving range, the credibility of the first destination, and the predicted energy consumption accuracy.

[0026] By calculating the weights, the accuracy of determining the energy consumption per unit distance can be improved, thereby improving the accuracy of determining the driving range.

[0027] A second aspect of this application provides a driving range determination device, comprising: a transceiver module for acquiring a first path from the vehicle's current location to a first destination; a first determination module for determining a first unit distance energy consumption based on the first path; a second determination module for determining a second unit distance energy consumption based on the first unit distance energy consumption and historical unit distance energy consumption; and a third determination module for determining the vehicle's driving range based on the vehicle's remaining available battery power and the second unit distance energy consumption.

[0028] In one possible implementation, the transceiver module is specifically used to: acquire the vehicle's current location information and environmental information; and determine the first destination and the first path based on the location information and environmental information.

[0029] In one possible implementation, the transceiver module is specifically used to: determine the probability of a second destination and a second destination corresponding to each other based on location information and environmental information; when the maximum value of the probability is less than a first preset value, output at least one of the second destinations based on the probability; and determine the first destination from the at least one output second destination in response to the user's selection.

[0030] In one possible implementation, the transceiver module is specifically used to: determine the probability of a second destination and the second destination corresponding to the location information and environmental information; when the remaining available battery power of the vehicle is less than a second preset value, output at least one of the second destinations according to the probability; and determine the first destination from the at least one output second destination in response to the user's selection.

[0031] In one possible implementation, the transceiver module is specifically used to: input location information and environmental information into the first model to obtain the probability of the second destination and the second destination, wherein the first model is trained based on the path information of the historical trip and the environmental information.

[0032] In one possible implementation, the first model includes the random forest model.

[0033] In one possible implementation, the first determining module is specifically used to: obtain the average energy consumption per unit distance of the first path; for the first destination, determine the average energy consumption per unit distance to the first destination based on the average energy consumption per unit distance of the first path to the first destination; and determine the first unit distance energy consumption based on the average energy consumption per unit distance to the first destination.

[0034] In one possible implementation, the second determining module is specifically used to: determine the weight based on historical unit distance energy consumption, remaining available power, and the first path; and determine the second unit distance energy consumption based on the first unit distance energy consumption, historical unit distance energy consumption, and the weight.

[0035] In one possible implementation, the second determining module is specifically used to: determine a rough estimated driving range based on historical energy consumption per unit distance and remaining available power; determine the energy consumption prediction accuracy based on the first path and the correspondence between the first path and the preset energy consumption prediction accuracy; determine the credibility of the first destination; and determine the weight based on the ratio of the first path to the rough estimated driving range, the credibility of the first destination, and the predicted energy consumption accuracy.

[0036] The technical effects of the driving range determination device provided by the second aspect of the embodiments of this application and any possible implementation thereof correspond to the technical effects of the driving range determination method provided by the first aspect of the embodiments of this application and any possible implementation thereof. For the sake of brevity, they will not be repeated here.

[0037] A third aspect of the embodiments of this application provides a computer-readable storage medium storing a driving range determination method provided by the first aspect of the embodiments of this application and its possible implementations, which enables a computer to execute the method.

[0038] A fourth aspect of the present application provides a computing device including a processor and a memory, wherein the memory stores a program, and the processor executes the driving range determination method provided by the first aspect of the present application and its possible implementations.

[0039] A fifth aspect of the embodiments of this application provides a computer program product that, when the computer program is run on a computer, causes the computer to execute the driving range determination method provided by the first aspect of the embodiments of this application and its possible implementations.

[0040] A sixth aspect of the embodiments of this application provides a vehicle, including a driving range determination device provided by the second aspect of the embodiments of this application and its possible implementations.

[0041] Through the above aspects of the embodiments of this application, the remaining driving range of a vehicle can be determined more accurately when the vehicle navigation is not turned on, avoiding inconvenience to users caused by inaccurate driving range prediction and improving user experience.

[0042] By calculating the energy consumption per unit distance from the vehicle's current location to the first destination (e.g., a destination selected by the user or calculated by the model), and by calculating the energy consumption per unit distance based on the energy consumption per unit distance and historical energy consumption per unit distance, the vehicle's remaining driving range can be determined more accurately. Attached Figure Description

[0043] The various features of the present invention and the relationships between them are further explained below with reference to the accompanying drawings. The drawings are exemplary; some features are not shown to scale, and some drawings may omit conventional features in the field of this application that are not essential to this application, or additional features that are not essential to this application may be shown. The combination of features shown in the drawings is not intended to limit the present application. Furthermore, throughout this specification, the same reference numerals refer to the same things. Specific descriptions of the drawings are as follows: Figure 1A This is a flowchart of a method for determining driving range provided in one embodiment of this application; Figures 1B-1F yes Figure 1A The following is a flowchart of a method for determining driving range provided in one embodiment of this application; Figure 2A This is a flowchart of a method for determining driving range provided in another embodiment of this application; Figures 2B-2D yes Figure 2A A sub-flowchart of a driving range determination method provided in another embodiment of this application is shown; Figure 3A This is a schematic diagram of a specific example of a decision tree model for endpoint prediction; Figure 3B This is a schematic diagram of a specific example of a road network node decision tree model; Figure 4 This is a schematic diagram of the routes taken by vehicles from the starting point to destinations A, B, and C respectively. Figure 5 This is a schematic diagram illustrating the relationship between equivalent path length (referred to as navigation path length when using navigation) and energy consumption prediction accuracy. Figure 6 This is a diagram of the destination option pop-up window; Figure 7 This is a map example provided in one embodiment of this application; Figure 8 This is a schematic diagram of a driving range determination device provided in one embodiment of this application; Figure 9 This is a schematic diagram of the computing device provided in the embodiments of this application. Detailed Implementation

[0044] The driving range determination method provided in this application embodiment can be applied to scenarios involving the determination of the driving range of electric vehicles.

[0045] Figures 1A-1F A flowchart illustrating the driving range determination method provided in an embodiment of this application is shown. The driving range determination method in this embodiment can be executed by a terminal, such as a smart vehicle or in-vehicle device, or by an electronic device applied within the terminal, such as a system-on-a-chip or a general-purpose chip. Figure 1A As shown, the driving range determination method provided in this application embodiment may include the following steps S100-S400: Step S100: Obtain the first path from the vehicle's current location to the first destination.

[0046] When a user activates navigation, the navigation system can obtain the first route from the vehicle's current location to the first destination. Specifically, with navigation enabled, the navigation system can obtain the first destination by receiving user input.

[0047] When the user has not enabled navigation, such as Figure 1B As shown, the first path from the vehicle's current location to the first destination can be obtained through the following steps S110-S120: Step S110: Obtain the vehicle's current location information and environmental information.

[0048] The vehicle's current location information can be obtained through a positioning device. Such devices include those based on the Global Positioning System (GPS), BeiDou Navigation Satellite System, or Galileo satellite positioning system, as well as those based on base station positioning. The vehicle's current environmental information can include weather and road condition information. Road condition information can include real-time traffic flow information for various road segments (such as traffic congestion information, road construction information, etc.), which can be obtained by receiving information from traffic information dissemination systems or from roadside equipment.

[0049] Step S120: Determine the first destination and the first route based on the location information and environmental information.

[0050] In some embodiments, such as Figure 1C As shown, determining the first destination may include the following steps S121-S123: Step S121: Determine the probability of the second destination corresponding to the second destination based on the location information and environmental information. There can be multiple second destinations.

[0051] In step S121, determining the probability of the second destination corresponding to the second destination may include: inputting location information and environmental information into the first model to obtain the probability of the second destination corresponding to the second destination.

[0052] In some embodiments, the first model is trained based on path information and environmental information from historical trips.

[0053] In some embodiments, the first model may include a random forest model. The random forest model may include a destination prediction decision tree model and a road network node decision pair tree model.

[0054] Among them, the destination prediction decision tree model is used to determine the predicted destination and the probability of the predicted destination based on the current environmental information and location information.

[0055] In some embodiments, the endpoint prediction decision tree model can have the starting point as the root node, and the week time, time period of day and weather as nodes in sequence, with the endpoint as the leaf (i.e. the output). Figure 3A A schematic diagram illustrating a specific example of a decision tree model for endpoint prediction is shown, combined with... Figure 7 The map example shown shows that when the starting point S is Garden Square, the week is Monday, the time period is Monday morning from 6:00 to 9:00, and the weather is sunny, the ending point Y is Tianpingqiao Park; when the starting point S is Garden Square, the week is Monday, the time period is Monday morning from 6:00 to 9:00, and the weather is rainy, the ending point Z is Huangpu Center Tower.

[0056] When training the endpoint prediction decision tree model, the starting point, endpoint, weather, and time of the historical trip can be used as samples to input the endpoint prediction decision tree model, and the probability of the number of endpoints relative to the total number of endpoints in its output can be summarized to obtain the probability corresponding to the endpoint.

[0057] The road network node decision tree model is used to determine the probability of each route at each intersection based on the current environmental information.

[0058] In some embodiments, the road network node decision tree model can take the intersection (including the starting point) as the root node, and take the week time, time period of day, weather and road conditions as nodes in sequence, and the route as the leaf (i.e. the output). Figure 3B A schematic diagram illustrating a specific example of a road network node decision tree model is shown, combined with... Figure 7The map example shown illustrates the following routes: when the intersection is intersection 1, the week is Monday, the time period is Monday morning from 6:00 to 9:00, the weather is sunny, and the traffic is smooth, the route is route 1a; when the intersection is intersection 1, the week is Monday, the time period is Monday morning from 6:00 to 9:00, the weather is sunny, and the traffic is congested, the route is route 2a; when the intersection is intersection 1, the week is Monday, the time period is Monday morning from 6:00 to 9:00, and the weather is rainy, the route is route 2a.

[0059] When training the road network node decision tree model, weather information, road condition information, trajectory information and time from historical trips can be used as samples to input the road network node decision tree model, and the probability of the number of routes in its output relative to the total number of routes can be summarized to obtain the probability corresponding to the route.

[0060] In some embodiments, location information and environmental information are input into the first model to obtain the second destination and the probabilities corresponding to the second destination, as shown in Table 1. Table 1

[0061] In some embodiments, location information and environmental information are input into a first model to obtain the probability of each route at each intersection. For example... Figure 7 As shown, the two arrows at intersection 1 represent two different routes. In other embodiments, the first model can also be implemented using a deep neural network, where a classification network can be used to predict the destination or path. Examples of classification networks include Convolutional Neural Networks (CNNs), Fully Connected Networks (FCNs), and Graph Convolutional Networks (GCNs). When training the classification network, the sampled data can include current environmental information and location information, with the data labeled as destination and path. The trained classification network can then output predicted destinations and paths based on the current environmental and location information during the inference phase, and the corresponding confidence level (e.g., softmax value) at the time of output represents the corresponding probability. Furthermore, when time is incorporated into the sampled data, the trained classification network can then output predicted destinations and paths, along with their respective probabilities, based on the current environmental information, location information, and time during the inference phase.

[0062] Step S122: When the maximum value of the probability corresponding to the second destination is less than the first preset value, output a portion of the second destinations according to the order of the probability values.

[0063] In other embodiments, step S122 may also be, or may further include: when the remaining available battery power of the vehicle is less than a second preset value, outputting the partial second destination according to the probability values.

[0064] The first preset value can be selected within the range of 50% to 90%, and the second preset value can be selected within the range of 1% to 40%. However, this application is not limited to these ranges, and the first or second preset value can be set as needed. The second destination output based on the probability can be a pop-up display of the top three second destinations corresponding to the probabilities, allowing the user to select one. Figure 2C As shown. In some embodiments, second destinations corresponding to probability values ​​greater than a third preset value (the third preset value is, for example, 60%, where the third preset value is less than the first preset value) may be displayed for the user to select.

[0065] Of course, the way to output the second destination is not limited to displaying a pop-up window on the screen; it can also be output through voice broadcast for users to choose from.

[0066] Step S123: In response to the user's selection, determine the first destination from the output second destination.

[0067] This can be achieved by receiving user input on the screen or by receiving voice commands. For example... Figure 6 As shown, users can choose one of the multiple secondary destinations displayed on the screen as their primary destination, or they can select "None of them". In this case, users can further manually or by voice input their desired primary destination.

[0068] When no user selection is received, for example, if the user does not respond to the displayed content, all displayed second destinations can be designated as the first destination. For instance, if the output includes destinations A, B, and C, and the user does not respond, then destinations A, B, and C are all designated as the first destination. In some other embodiments, the first destination may be the one with the highest probability value among the displayed second destinations.

[0069] In some embodiments, after determining the first destination, a first path from the current location to the first destination can be determined based on the first destination. There can be multiple first paths to the first destination, and the probability of each first path can be determined based on the output of the network node decision tree model, with a subset of first paths selected according to the probability ranking.

[0070] The first path may include: the path from the current location to the first destination in the vehicle's historical journey; wherein, the path may be obtained based on the road network node decision tree model in the first model described in step S121 above.

[0071] In some embodiments, when the navigation system is enabled, the first path may include a path recommended by the navigation system from the current location to the first destination.

[0072] In other embodiments, for step S122 above, when the maximum value of the probability corresponding to the second destination is greater than the first preset value, it indicates that the accuracy of the second destination determined by the first model is high, and in this case, the second destination can be directly determined as the first destination without being output to the user. Alternatively, when the remaining available battery power of the vehicle is greater than the second preset value, it indicates that the error of the second destination determined by the first model has little impact on the user's experience, and in this case, the second destination can be directly determined as the first destination without being output to the user.

[0073] like Figure 1A As shown, step S200: Based on the first path, determine the first unit distance energy consumption of the first path. The first unit distance energy consumption is also referred to as the known travel unit distance energy consumption in the following embodiments.

[0074] In some embodiments, the following situations may be included: 1) When there is one first destination and only one first path, the historical energy consumption record of the first path can be obtained. The historical energy consumption record contains the average energy consumption per unit distance of the first path, which is used as the first unit distance energy consumption.

[0075] In some embodiments, when there are multiple historical energy consumption records for the first path, the average of the historical energy consumption records for the first path can be taken as the first unit distance energy consumption. For example, if there are 5 energy consumption records for the first path on Monday, Tuesday, Wednesday, Thursday, and Friday, then the average of the unit distance energy consumption in these 5 energy consumption records can be taken as the first unit distance energy consumption for the first path.

[0076] 2) When there is one primary destination and multiple primary routes.

[0077] 3) There are multiple first destinations and multiple first paths, with at least one first path corresponding to each first destination.

[0078] For case 1) above, the energy consumption per unit distance of the first path can be determined as described above. For cases 2) and 3), both involve multiple first paths. In some embodiments, such as... Figure 1DAs shown, in these two cases, the step of determining the energy consumption per unit distance in step S200 may include the following sub-steps S210-S220: Step S210: Obtain the average energy consumption per unit distance for each first path. The method for obtaining this energy consumption can be the same as described above, based on historical energy consumption records, and will not be repeated here.

[0079] Step S220: For each first destination, calculate the average energy consumption per unit distance to that first destination.

[0080] When there is only one first path to a certain first destination, the average energy consumption per unit distance of the first path is the average energy consumption per unit distance to the first destination.

[0081] by Figure 4 Taking the scenario shown as an example, if there is only one path from the starting point to destination B, and only one path from the starting point to destination C, then the average energy consumption per unit distance for the vehicle to travel from the current location to destination B is the average energy consumption per unit distance of the first path to destination B; similarly, the average energy consumption per unit distance for the vehicle to travel from the current location to destination C is the average energy consumption per unit distance of the first path to destination C. The calculation methods can be found in the description of the formulas for calculating E2 and E3 in step S63 of this application.

[0082] When there are multiple first paths to a certain first destination, the average energy consumption per unit distance of each first path is first obtained. Then, the average energy consumption per unit distance of these multiple first paths is calculated by weighting the average energy consumption per unit distance of each first path, and this average energy consumption per unit distance is used as the average energy consumption per unit distance to the first destination.

[0083] Still with Figure 4 Taking the scenario shown as an example, there are three paths from the starting point to destination A. The average energy consumption per unit distance for each of these three paths is calculated (see step S63). , 2. 3) Perform weighted calculations to obtain the average energy consumption per unit distance to destination A (see E1 in step S63).

[0084] In some embodiments, the weights used in the weighted calculation may be calculated based on the length of each first path, or / and based on the selection probability of each first path.

[0085] by Figure 4 Taking the scenario shown as an example, referring to the formula for calculating E1 in step S63, the length of each first path from the starting point to destination A is... The probability of choosing each first path is: , , .

[0086] Step S230: Determine the first unit distance energy consumption based on the average energy consumption per unit distance to each first destination.

[0087] For example, when there are multiple first destinations and the first path for a vehicle to reach at least one of the first destinations includes multiple routes, the energy consumption per unit distance to each first destination is calculated based on the average energy consumption per unit distance to each first destination and the weight corresponding to each first destination.

[0088] In some embodiments, the weights used in the weighted calculation may be calculated based on the path length to each first destination, or the equivalent path length, and the probability of each selected first destination.

[0089] Still with Figure 4 Taking the scenario shown as an example, there are three paths from the starting point to destination A, one path from the starting point to destination B, and one path from the starting point to destination C. The calculated average energy consumption per unit distance to destination A, destination B, and destination C (e.g., E1, E2, and E3 in step S63) can be weighted. See step S63 for details on calculation. As can be seen from the formula, the weights in the weighted calculation can be determined based on the path lengths to each of the first destinations B and C. Equivalent path length to destination A The probabilities P1, P2, and P3 of choosing each first destination A, B, and C are calculated.

[0090] In some embodiments, the equivalent path length to a first destination (e.g., destination A) The value can be calculated based on the length of each first path to the first destination and the probability of selecting each first path. A specific implementation of the calculation method can be found in the formula for calculating L1 in step S61.

[0091] like Figure 1A As shown, step S300: Determine the second unit distance energy consumption based on the first unit distance energy consumption and the historical unit distance energy consumption.

[0092] In some embodiments, such as Figure 1E As shown, step S300 may include the following sub-steps S310-S320: Step S310: Determine the weights based on historical energy consumption per unit distance, remaining available power, and the first path.

[0093] In some embodiments, such as Figure 1F As shown, step S310 includes the following sub-steps S311-S314: Step S311: Determine a rough driving range based on historical energy consumption per unit distance and remaining available power.

[0094] In some embodiments, historical energy consumption per unit distance can be determined based on the ratio of historical total power consumption (i.e., historical total energy consumption) to the length of historical cumulative mileage; and the estimated driving range can be determined based on the ratio of remaining available power to historical energy consumption per unit distance.

[0095] Step S312: Determine the energy consumption prediction accuracy based on the first path and the correspondence between the first path and the energy consumption prediction accuracy.

[0096] When the navigation path is too short, the randomness of the energy consumption prediction model will affect the accuracy of energy consumption prediction; when the navigation path is too long, the accuracy of traffic prediction will also decrease. Therefore, the first path is related to the accuracy of energy consumption prediction.

[0097] In some embodiments, the correspondence between the first path and the accuracy of energy consumption prediction can be established by fitting, such as... Figure 5 An example is shown showing the correspondence between the length or equivalent length of the fitted first path (referred to as the navigation path length when using navigation) and the accuracy of energy consumption prediction.

[0098] Step S313: Determine the credibility of the first destination.

[0099] In some embodiments, the confidence level of a first destination can be based on the probability of traveling from the current location to each first destination (see [reference]). Figure 4 For example, the probability of arriving at destination A, destination B, and destination B. , , And the probability of each path in each first path from the current location to each first destination. , , Confirmed. The method for confirming this can be found in the description of step S61, which will not be repeated here for the sake of brevity.

[0100] Step S314: Determine the weights based on the ratio of the first route to the estimated driving range, the credibility of the first destination, and the accuracy of the predicted energy consumption.

[0101] The method for determining the weights can be found in the description of steps S71-S74, and will not be repeated here for the sake of brevity.

[0102] like Figure 1E As shown, step S320: Determine the second unit distance energy consumption based on the first unit distance energy consumption, historical unit distance energy consumption, and weight.

[0103] In some embodiments, the second unit distance energy consumption can be calculated using the following formula:

[0104] in, This refers to the energy consumption per unit distance. Historical energy consumption per unit distance; Energy consumption per unit distance; As weight.

[0105] like Figure 1A As shown, step S400: Determine the vehicle's driving range based on the vehicle's remaining available battery power and the energy consumption per unit distance.

[0106] In some embodiments, the driving range can be determined based on the ratio of remaining available power to the energy consumption per unit distance.

[0107] Figure 2A A flowchart of the driving range determination method provided in the embodiments of this application is shown, such as... Figure 2A As shown, the driving range determination method provided in this application embodiment includes the following steps S1-S9: Step S1: Determine if navigation is enabled.

[0108] When navigation is enabled, execute step S2: obtain the navigation destination and navigation path.

[0109] See Figure 7 As shown in the example, a user starts from Garden Square at point S and travels to Huangpu Center Tower at point Z. The navigation function can determine the navigation route.

[0110] Step S3: Determine the energy consumption per unit distance of the known journey based on the navigation path.

[0111] In this application, the known travel unit distance energy consumption is also referred to as the first unit distance energy consumption. The known travel is the path from the vehicle's current location to the predicted destination, the navigation destination, or the final destination selected by the user based on the predicted destination.

[0112] When navigation is not enabled, proceed to step S4: obtain trip information.

[0113] The trip information includes: the vehicle's current location information and environmental information. The environmental information may include current weather information and current road condition information.

[0114] Step S5: Determine the final path from the current location to the final destination based on the trip information and road network model.

[0115] In this application, the road network model is also referred to as the first model. For a description of the road network model, please refer to the description of the first model in step S121 of the above embodiments of this application. For the sake of brevity, it will not be repeated here.

[0116] like Figure 2B As shown, step S5 may include the following sub-steps S51-S56: Step S51: Based on the current location information, environmental information, and road network model, determine the predicted destination, the probability corresponding to the predicted destination, and the probability of each route at each intersection from the current location to the predicted destination.

[0117] In this application, the predicted destination is also referred to as the second destination. See also Figure 7 The example shown could predict destinations such as: Xiangyangming Junior High School (destination X), Tianpingqiao Park (destination Y), and Huangpu Center Building (destination Z); intersections include intersection 1, intersection 2, and intersection 3, with arrows indicating different routes 1a and 2a from intersection 1. It should be noted that, for simplicity... Figure 7 The intersections and destinations shown are only schematically represented in three different ways, but this application is not limited to these.

[0118] In some embodiments, the predicted destination and the probability corresponding to the predicted destination are determined based on the current location information, environmental information and road network model, as shown in Table 2.

[0119] Table 2

[0120] Step S52: Determine whether the maximum value in the probability is less than the first preset value or determine whether the vehicle's SOC (State of Charge, battery state of charge) is less than the second preset value.

[0121] The first preset value can be selected from a range of 50% to 90%, but this application is not limited to this; the first preset value can also be set as needed. The second preset value can be selected from a range of 1% to 40%, and this application is also not limited to this; the second preset value can be set as needed.

[0122] When the maximum value in the probability is less than the first preset value or when the vehicle's SOC is less than the second preset value Step S53: Based on the predicted probability of the destination, a destination confirmation window will pop up on the display screen.

[0123] One or more destination options will be displayed in the destination confirmation window.

[0124] In some embodiments, the top three destinations corresponding to the probability values ​​can be displayed for the user to select, such as... Figure 6 As shown. In some embodiments, destinations with a probability value greater than a third preset value (e.g., 60%) may be displayed for the user to select.

[0125] The destination confirmation window displays a preset time by default, such as 10 seconds. The destination confirmation window can be manually closed. Optionally, the destination confirmation window will only pop up once per drive.

[0126] In some embodiments, one or more destination options may be announced via voice for the user to choose from.

[0127] When the maximum value of the probability is greater than the first preset value or when the vehicle's SOC is greater than the second preset value Step S54: Use the predicted destination as the final destination.

[0128] In this application, the final destination is also referred to as the first destination.

[0129] When the maximum value in the probability is greater than the first preset value, the accuracy of the predicted destination determined by the road network model is considered to be high, so the destination confirmation window does not need to pop up; or, when the vehicle's SOC is greater than the second preset value, the remaining battery power of the vehicle is considered to be high, so the destination confirmation window does not need to pop up.

[0130] Step S55: Determine the final destination in response to the user's selection.

[0131] In some embodiments, users can select the final destination by operating the screen or by issuing voice commands.

[0132] If the user does not select a destination in the confirmation window, then proceed to step S54.

[0133] Step S56: Determine the final path from the current location to the final destination based on the final destination.

[0134] In this application, the final path is also referred to as the first path.

[0135] The final route may include: the route from the current location to the final destination in the vehicle's historical journey; and the route from the current location to the final destination recommended by the navigation system.

[0136] Step S6: Determine the energy consumption per unit distance of the known journey based on the final path.

[0137] like Figure 2C As shown, step S6 may include the following sub-steps S61-S63: Step S61: Determine the credibility of the final destination based on the probability of going to each route from each intersection and the probability of going to the final destination from the current location.

[0138] by Figure 4 Taking the scenario shown as an example, three predicted destinations (destination A, destination B, and destination C) are confirmed through step S53. If the user does not respond to the destination confirmation window, the three predicted destinations will be taken as the final destinations, namely destination A, destination B, and destination C.

[0139] Schematic, the path from the current location to destination A passes through intersections 2 and 3. There are multiple paths from the current location to destination A. In this embodiment, the three paths with the highest probability are selected: path 1 (i.e., starting point - intersection 2 - destination A), path 2 (i.e., starting point - intersection 3 - destination A), and path 3 (i.e., starting point - intersection 2 - intersection 3 - destination A). There is only one path from the current location to destination B. In this embodiment, this path is selected (i.e., path 4). There is only one path from the current location to destination C. In this embodiment, this path is selected (i.e., path 5).

[0140] The sum of the probabilities of the three paths from the current location to destination A is:

[0141] in, Let the sum of the probabilities of the three paths from the current location to destination A be the sum of the probabilities. The probability of going from the current position to intersection 2; This represents the probability of traveling from the current location to intersection 3. Let be the probability of going from intersection 2 to intersection 3; Let be the probability of traveling from intersection 2 to destination A; Let be the probability of traveling from intersection 3 to destination A; that is... , , , as well as This represents the probability of going to each route from each intersection.

[0142] The probabilities of a user choosing one of the three paths to destination A are:

[0143] in, Let the sum of the probabilities of the three paths from the current location to destination A be the sum of the probabilities. Let A be the probability that a user travels to destination A. The probability of a user choosing one of the three paths to destination A.

[0144] It should be noted that when there are more than three paths from the current location to the final destination (e.g., destination A), you can choose the three paths with the highest probability to calculate their sum of probabilities, or you can choose the two or N paths with the highest probability to calculate their sum of probabilities.

[0145] The sum of probabilities of the path from the current location to destination B is:

[0146] in, Let S be the sum of probabilities of the path from the current location to destination B; Let be the probability of traveling from the current location to destination B.

[0147] The probability that a user chooses the path to destination B is:

[0148] in, Let S be the sum of probabilities of the path from the current location to destination B; The probability that a user will travel from their current location to destination B; The probability of selecting the path to destination B for the user.

[0149] The sum of probabilities of the path from the current location to destination C is: 6 in, Let C be the sum of probabilities of the path from the current location to destination C; Let C be the probability of traveling from the current location to the destination C.

[0150] The probability that the user chooses the path to destination C is:

[0151] in, Let C be the sum of probabilities of the path from the current location to destination C; The probability that a user will travel from their current location to destination C; The probability of selecting a path to destination C for the user.

[0152] Credibility of the final destination (also referred to in this application as credibility of the first destination) for:

[0153] in, Let the sum of the probabilities of the three paths from the current location to destination A be the sum of the probabilities. The probability that a user will travel to destination A; The probability of a user choosing one of the three paths to destination A; Let S be the sum of probabilities of the path from the current location to destination B; The probability that a user will travel from their current location to destination B; The probability of selecting the path to destination B for the user; Let C be the sum of probabilities of the path from the current location to destination C; The probability that a user will travel from their current location to destination C; The probability of selecting a path to destination C for the user.

[0154] Step S62: Determine the equivalent path length based on the length of the final path and the probability of going to each route at each intersection.

[0155] When there are multiple final destinations and / or multiple final paths to the final destination, the equivalent path length needs to be determined based on the multiple final paths.

[0156] Still with Figure 4 Taking the three final destinations A, B, and C as an example, there are multiple paths for a vehicle to travel from its current location to destination A. Therefore, the equivalent path length from the current location to destination A is:

[0157] in, It is the equivalent path length for the vehicle to travel from its current location to its destination A; It is the length of path 1 from the current location to destination A; It is the length of path 2 from the current location to destination A; It is the length of path 3 from the current location to destination A; The probability of going from the current position to intersection 2; This represents the probability of traveling from the current location to intersection 3. Let be the probability of going from intersection 2 to intersection 3; Let be the probability of traveling from intersection 2 to destination A; Let be the probability of going from intersection 3 to destination A, i.e. , , , as well as This represents the probability of going to each route from each intersection.

[0158] The equivalent path length for the vehicle to travel from its current location to destination B:

[0159] It is the equivalent path length for the vehicle to travel from its current location to its destination B; It is the length of the path 4 from the current location to the destination B; Let be the probability of traveling from the current location to destination B.

[0160] The equivalent path length for the vehicle to travel from its current location to destination C:

[0161] It is the equivalent path length for the vehicle to travel from its current location to its destination C; It is the length of the path 5 from the current location to the destination C; Let C be the probability of traveling from the current location to the destination C.

[0162] The equivalent path length for a vehicle from its current location to its final destination:

[0163] in, It is the equivalent path length for a vehicle to travel from its current location to its final destination; It represents the probability that a user chooses one of the three paths to destination A; It represents the probability that the user chooses the path to destination B; It represents the probability that the user chooses the path to destination C; It is the equivalent path length for the vehicle to travel from its current location to its destination A. It is the equivalent path length of the vehicle from its current location to its destination B. It is the equivalent path length for the vehicle to travel from its current location to its destination C.

[0164] Step S63: Determine the energy consumption per unit distance of the known journey based on the length of the final path, the energy consumption required for the final path, the probability of going to each route at each intersection, and the equivalent path length.

[0165] Still with Figure 4 Taking the three final destinations A, B, and C as examples, the average energy consumption per unit distance for a vehicle to travel from its current location to destination A is:

[0166] in, It is the average energy consumption per unit distance for a vehicle to travel from its current location to its destination A. It is the length of path 1 from the current location to destination A; It is the length of path 2 from the current location to destination A. It is the length of path 3 from the current location to destination A. Let the probability of going from the current position to intersection 2 be _____. Let the probability of going to intersection 3 from the current position be _____. Let be the probability of traveling from intersection 2 to intersection 3. Let be the probability of traveling from intersection 2 to destination A. Let be the probability of going from intersection 3 to destination A, i.e. , , , as well as Let each intersection represent the probability of reaching each route. It is the average energy consumption per unit distance for the vehicle to travel from its current location to its destination A along path 1. It is the average energy consumption per unit distance for the vehicle to travel from its current location to its destination A via path 2. It is the average energy consumption per unit distance for the vehicle to travel from its current location to its destination A along path 3.

[0167] The average energy consumption per unit distance for a vehicle to travel from its current location to destination B is:

[0168] in, It is the average energy consumption per unit distance for a vehicle to travel from its current location to its destination B. It is the average energy consumption per unit distance for the vehicle to travel from its current location to its destination B via path 5. It is the length of path 4, from the current location to destination B. Let be the probability of traveling from the current location to destination B.

[0169] The average energy consumption per unit distance for a vehicle to travel from its current location to its destination C is:

[0170] in, It is the average energy consumption per unit distance for a vehicle to travel from its current location to its destination C. It is the average energy consumption per unit distance for the vehicle to travel from its current location to its destination C along path 5. It is the length of the path 5 from the current location to the destination C. Let C be the probability of traveling from the current location to the destination C.

[0171] Energy consumption per unit distance for a vehicle traveling from its current location to its final destination:

[0172] in, It is the average energy consumption per unit of the known journey to the final destination; It represents the probability that a user chooses one of the three paths to destination A; It is the average energy consumption per unit distance for a vehicle to travel from its current location to its destination A. It is the equivalent path length for the vehicle to travel from its current location to its destination A; It represents the probability that the user chooses the path to destination B; It is the average energy consumption per unit distance for the vehicle to travel from its current location to its destination B; It is the equivalent path length for the vehicle to travel from its current location to its destination B; It represents the probability that the user chooses the path to destination C; It is the average energy consumption per unit distance for the vehicle to travel from its current location to its destination C; It is the equivalent path length for the vehicle to travel from its current location to its destination C; It is the equivalent path length for a vehicle to travel from its current location to its final destination.

[0173] like Figure 2A As shown, step S7: Determine the final energy consumption per unit distance based on the known energy consumption per unit distance, the historical energy consumption per unit distance, and the weight.

[0174] like Figure 2D As shown, step S7 may include the following sub-steps S71-S75: Step S71: Determine the historical energy consumption per unit distance based on the historical cumulative travel distance and historical total energy consumption.

[0175] Among them, the historical energy consumption per unit distance can be determined by the quotient of the historical total energy consumption and the historical cumulative distance traveled.

[0176] Step S72: Determine a rough estimate of the remaining mileage based on the remaining available power and historical energy consumption per unit distance.

[0177] The remaining mileage can be roughly estimated based on the ratio of the remaining available electricity to the historical energy consumption per unit distance.

[0178] Step S73: Determine the energy consumption prediction accuracy based on the equivalent path length and the correspondence between the equivalent path length and the energy consumption prediction accuracy.

[0179] When a user uses navigation, traffic information may include real-time traffic flow information for various road segments provided by the Ministry of Transport, the location and speed information of the user currently using navigation, and historical traffic information for various roads. When the navigation path is too short, the randomness of the energy consumption prediction model can affect the accuracy of energy consumption prediction; when the navigation path is too long, the accuracy of traffic prediction will also decrease. In some embodiments, the relationship between the finally fitted equivalent path length (referred to as the navigation path length when using navigation) and the accuracy of energy consumption prediction is as follows: Figure 5 As shown.

[0180] Step S74: Determine the weights based on the equivalent path length, the rough estimate of the remaining mileage, the accuracy of energy consumption prediction, and the credibility of the final destination.

[0181] In some embodiments, the weights can be calculated using the following formula:

[0182] As weight; This is the equivalent path length; To roughly estimate the remaining mileage; To improve the accuracy of energy consumption prediction; The credibility of the final destination has been determined in step S61. It should be noted that when a user starts navigation, the reliability of the final destination... The value is 1.

[0183] Step S75: Determine the final energy consumption per unit distance based on historical energy consumption per unit distance, known energy consumption per unit distance for travel, and weights.

[0184] In some embodiments, the final energy consumption per unit distance can be calculated using the following formula:

[0185] in, This represents the final energy consumption per unit distance. Historical energy consumption per unit distance; Energy consumption per unit distance of known travel; As weight.

[0186] like Figure 2A As shown, step S8: Determine the driving range based on the remaining available power and the final energy consumption per unit distance.

[0187] The driving range can be determined based on the quotient of remaining available battery power and the final energy consumption per unit distance. The determined driving range can then be displayed on the central control screen or in the vehicle's instrument panel, and / or announced to the user via voice.

[0188] In some embodiments, the location information, destination information, road condition information, weather information, energy consumption, and time data obtained by the vehicle during a trip can be stored in the road network database for use in step S9 to train and update the road network model. Figure 8This is a schematic diagram of the driving range determination device 5000 provided in this application embodiment, including: a transceiver module 1000, used to acquire a first path from the vehicle's current location to a first destination; a first determination module 2000, used to determine a first unit distance energy consumption based on the first path; a second determination module 3000, used to determine a second unit distance energy consumption based on the first unit distance energy consumption and historical unit distance energy consumption; and a third determination module 4000, used to determine the vehicle's driving range based on the vehicle's remaining available battery power and the second unit distance energy consumption.

[0189] In some embodiments, the transceiver module 1000 is specifically used to: acquire the vehicle's current location information and environmental information; and determine a first destination and a first path based on the location information and environmental information.

[0190] In some embodiments, the transceiver module 1000 is specifically configured to: determine the probability of a second destination and a second destination corresponding to each other based on location information and environmental information; when the maximum value of the probability is less than a first preset value, output at least one of the second destinations based on the probability; and determine a first destination from the at least one output second destination in response to the user's selection.

[0191] In some embodiments, the transceiver module 1000 is specifically configured to: determine the probability of a second destination and a second destination corresponding to a second destination based on location information and environmental information; when the remaining available battery power of the vehicle is less than a second preset value, output at least one of the second destinations according to the magnitude of the probability; and determine a first destination from the at least one output second destination in response to the user's selection.

[0192] In some embodiments, the transceiver module 1000 is specifically used to: input location information and environmental information into a first model to obtain the probability of a second destination and the probability of the second destination, wherein the first model is trained based on the path information of the historical trip and the environmental information.

[0193] In some embodiments, the first model includes a random forest model.

[0194] In some embodiments, the first determining module 2000 is specifically configured to: obtain the average energy consumption per unit distance of the first path; determine the average energy consumption per unit distance to the first destination based on the average energy consumption per unit distance of the first path to the first destination; and determine the first unit distance energy consumption based on the average energy consumption per unit distance to the first destination.

[0195] In some embodiments, the second determining module 3000 is specifically used to: determine a weight based on historical unit distance energy consumption, remaining available power, and the first path; and determine a second unit distance energy consumption based on the first unit distance energy consumption, historical unit distance energy consumption, and the weight.

[0196] In some embodiments, the second determining module 3000 is specifically used to: determine a rough estimated driving range based on historical energy consumption per unit distance and remaining available power; determine the energy consumption prediction accuracy based on a first path and the correspondence between the first path and a preset energy consumption prediction accuracy; determine the credibility of a first destination; and determine a weight based on the ratio of the first path to the rough estimated driving range, the credibility of the first destination, and the predicted energy consumption accuracy.

[0197] For the technical effects and detailed description of the driving range determination device provided in this application embodiment, please refer to the driving range determination method provided in this application embodiment. For the sake of brevity, it will not be repeated here.

[0198] In this embodiment, the driving range determination device is presented in the form of a module. Here, "module" can refer to an application-specific integrated circuit (ASIC), a processor and memory executing one or more software or firmware programs, integrated logic circuits, and / or other devices that can provide the above-mentioned functions. Furthermore, the above-mentioned driving range determination device can be... Figure 9 The processor 1510 of the vehicle-mounted device shown is used for implementation.

[0199] Figure 9 This is a schematic structural diagram of a computing device 1500 provided in an embodiment of this application. The computing device 1500 includes a processor 1510 and a memory 1520.

[0200] The processor 1510 can be connected to the memory 1520. The memory 1520 can be used to store the program code and data. Therefore, the memory 1520 can be a storage unit inside the processor 1510, an external storage unit independent of the processor 1510, or a component that includes both the storage unit inside the processor 1510 and the external storage unit independent of the processor 1510.

[0201] Optionally, the computing device 1500 may also include a bus. The memory 1520 and communication interface can be connected to the processor 1510 via the bus. The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc.

[0202] It should be understood that in the embodiments of this application, the processor 1510 may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), ASICs, field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. Alternatively, the processor 1510 may employ one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0203] The memory 1520 may include read-only memory and random access memory, and provides instructions and data to the processor 1510. A portion of the processor 1510 may also include non-volatile random access memory. For example, the processor 1510 may also store device type information.

[0204] When the computing device 1500 is running, the processor 1510 executes the computer execution instructions in the memory 1520 to perform the operation steps of the above method.

[0205] It should be understood that the computing device 1500 according to the embodiments of this application can correspond to the corresponding subject in executing the methods according to the various embodiments of this application, and the above and other operations and / or functions of each module in the computing device 1500 are respectively for implementing the corresponding processes of the methods of this embodiment. For the sake of brevity, they will not be described in detail here.

[0206] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0207] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0208] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of 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 system, or some features may be ignored or not executed. Furthermore, the 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.

[0209] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0210] In addition, 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.

[0211] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in 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, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0212] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs a method for determining driving range, the method including at least one of the solutions described in the above embodiments.

[0213] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is 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, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a 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.

[0214] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0215] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wire, optical fiber, radio frequency (RF), or any suitable combination thereof.

[0216] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0217] The terms "first," "second," and similar expressions used in the specification and claims are used only to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that, where permissible, a specific order or sequence may be interchanged so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0218] The term "comprising" as used in the specification and claims should not be construed as limiting itself to what follows; it does not exclude other elements or steps. Therefore, it should be interpreted as specifying the presence of the mentioned feature, integral, step, or component, but does not exclude the presence or addition of one or more other features, integrals, steps, or components, or groups thereof. Thus, the statement "device comprising means A and B" should not be limited to a device consisting solely of components A and B.

[0219] The terms "an embodiment" or "an embodiment" as used in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in at least one embodiment of this application. Therefore, the terms "in one embodiment" or "in an embodiment" appearing throughout this specification do not necessarily refer to the same embodiment, but may refer to the same embodiment. Furthermore, in one or more embodiments, the particular features, structures, or characteristics can be combined in any suitable manner, as will be apparent to those skilled in the art from this disclosure.

[0220] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the 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 determining driving range, characterized in that, include: Obtain the first path from the vehicle's current location to the first destination; Based on the first path, determine the energy consumption per unit distance of the first path; The second unit distance energy consumption is determined based on the first unit distance energy consumption and the historical unit distance energy consumption. as well as The vehicle's remaining available battery power and the second unit distance energy consumption are used to determine the vehicle's driving range. Specifically, the second unit distance energy consumption is determined based on the first unit distance energy consumption and historical unit distance energy consumption, including: The weights are determined based on historical energy consumption per unit distance, the remaining available power, and the first path; and The second unit distance energy consumption is determined based on the first unit distance energy consumption, the historical unit distance energy consumption, and the weight.

2. The method according to claim 1, characterized in that, The step of obtaining the first path from the vehicle's current location to the first destination includes: Obtain the vehicle's current location and environmental information; and The first destination and the first route are determined based on the location information and the environmental information.

3. The method according to claim 2, characterized in that, Determining the first destination includes: Based on the location information and the environmental information, determine the probability of the second destination and the corresponding second destination. When the maximum value of the probabilities is less than a first preset value, at least one of the second destinations is output according to the probabilities; and The first destination is determined from at least one of the output second destinations in response to the user's selection.

4. The method according to claim 2, characterized in that, Determining the first destination specifically includes: Determine the second destination and the probability corresponding to the second destination based on the location information and the environmental information; When the remaining available battery power of the vehicle is less than a second preset value, at least one of the second destinations is output according to the probability; and The first destination is determined from at least one of the output second destinations in response to the user's selection.

5. The method according to claim 3 or 4, characterized in that, Based on the location information and the environmental information, determining the probability of the second destination and its corresponding location includes: The location information and the environmental information are input into the first model to obtain the second destination and the probability corresponding to the second destination. The first model is trained based on the path information and environmental information of the historical itinerary.

6. The method according to claim 5, characterized in that, The first model includes the random forest model.

7. The method according to any one of claims 1-4, characterized in that, Based on the first path, the energy consumption per unit distance of the first path is determined, specifically including: Obtain the average energy consumption per unit distance of the first path; For the first destination, the average energy consumption per unit distance to the first destination is determined based on the average energy consumption per unit distance of the first path to the first destination; and The energy consumption per unit distance is determined based on the average energy consumption per unit distance to the first destination.

8. The method according to any one of claims 1-4, characterized in that, The weights are determined based on historical energy consumption per unit distance, the remaining available power, and the first path, specifically including: A rough estimate of the driving range is determined based on the historical energy consumption per unit distance and the remaining available power. The energy consumption prediction accuracy is determined based on the first path and the correspondence between the first path and the preset energy consumption prediction accuracy. Determine the credibility of the first destination; and The weights are determined based on the ratio of the first route to the estimated driving range, the credibility of the first destination, and the accuracy of the predicted energy consumption.

9. A device for determining driving range, characterized in that, include: The transceiver module is used to obtain the first path from the vehicle's current location to the first destination; The first determining module is used to determine the energy consumption per unit distance of the first path based on the first path. The second determining module is used to determine the second unit distance energy consumption based on the first unit distance energy consumption and the historical unit distance energy consumption. as well as The third determining module is used to determine the vehicle's remaining driving range based on the vehicle's remaining available battery power and the second unit distance energy consumption. Specifically, the second determining module is used for: The weights are determined based on historical energy consumption per unit distance, the remaining available power, and the first path; and The second unit distance energy consumption is determined based on the first unit distance energy consumption, the historical unit distance energy consumption, and the weight.

10. The apparatus according to claim 9, characterized in that, The transceiver module is specifically used for: Obtain the vehicle's current location and environmental information; and The first destination and the first route are determined based on the location information and the environmental information.

11. The apparatus according to claim 10, characterized in that, The transceiver module is specifically used for: Based on the location information and the environmental information, determine the probability of the second destination and the corresponding second destination. When the maximum value of the probabilities is less than a first preset value, at least one of the second destinations is output according to the probabilities; and The first destination is determined from at least one of the output second destinations in response to the user's selection.

12. The apparatus according to claim 10, characterized in that, The transceiver module is specifically used for: Determine the second destination and the probability corresponding to the second destination based on the location information and the environmental information; When the remaining available battery power of the vehicle is less than a second preset value, at least one of the second destinations is output according to the probability. as well as The first destination is determined from at least one of the output second destinations in response to the user's selection.

13. The apparatus according to claim 11 or 12, characterized in that, The transceiver module is specifically used for: The location information and the environmental information are input into the first model to obtain the second destination and the probability corresponding to the second destination. The first model is trained based on the path information and environmental information of the historical itinerary.

14. The apparatus according to claim 13, characterized in that, The first model includes the random forest model.

15. The apparatus according to any one of claims 9-12, characterized in that, The first determining module is specifically used for: Obtain the average energy consumption per unit distance of the first path; For the first destination, the average energy consumption per unit distance to the first destination is determined based on the average energy consumption per unit distance of the first path to the first destination. as well as The energy consumption per unit distance is determined based on the average energy consumption per unit distance to the first destination.

16. The apparatus according to any one of claims 9-12, characterized in that, The second determining module is specifically used for: A rough estimate of the driving range is determined based on the historical energy consumption per unit distance and the remaining available power. The energy consumption prediction accuracy is determined based on the first path and the correspondence between the first path and the preset energy consumption prediction accuracy. Determine the credibility of the first destination; as well as The weights are determined based on the ratio of the first route to the estimated driving range, the credibility of the first destination, and the accuracy of the predicted energy consumption.

17. A computer-readable storage medium, characterized in that, The device stores a method for determining the driving range as described in any one of claims 1-8.

18. A computing device, comprising a processor and a memory, characterized in that, The memory contains a program that is executed by a processor to perform the driving range determination method according to any one of claims 1-8.

19. A computer program product, characterized in that, When the computer program is run on a computer, it causes the computer to perform the method as described in any one of claims 1 to 8.

20. A vehicle, characterized in that, Includes the range determination device as described in any one of claims 9-16.

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