A method and system for designing vehicle range

Abstract

The application discloses a kind of vehicle endurance mileage design method and system. Including: parking point type acquisition module, user type label calculation module;Parking point type acquisition module is used to obtain the parking point type of each vehicle according to the location information of mass vehicle parking point;User type label calculation module is used to calculate the driving behavior parameter and parking point type, obtain the user type label of each vehicle;According to the user type label and the driving mileage of user, determine the endurance mileage of vehicle;According to the data support of user vehicle parking point type distribution and trip frequency, driving mileage, driving duration, combined with reasonable algorithm model, the user can be quickly and accurately classified, and then the enterprise can focus on the charging behavior of this type of user, without market research or offline research, so that the research of user charging behavior of enterprise becomes more efficient, reliable and lower cost.

Description

Technical Field

[0001] This invention belongs to the field of vehicle range analysis technology, specifically relating to a vehicle range design method and system. Background Technology

[0002] Currently, the development of new energy vehicles is becoming increasingly rapid, with daily commuters making up the largest proportion of car buyers. Range is a crucial factor for these buyers, as longer range typically translates to larger battery capacity. However, larger battery capacity also means a heavier vehicle, which negatively impacts overall range and increases costs. Therefore, designing a reasonable range that meets the daily needs of this largest user group—commuters—while simultaneously controlling costs and maintaining a competitive price is of paramount importance for manufacturers.

[0003] Currently, the design of driving range involves not only technical factors such as battery technology, vehicle weight, and aerodynamic design, but also market research and user surveys. However, this approach to driving range design is inefficient, which affects the company's R&D progress and makes it difficult for the company to launch new models in a short period of time. Therefore, driving range design has become one of the key bottlenecks affecting the company's ability to launch new models. Summary of the Invention

[0004] To efficiently design the driving range for users, this invention proposes a method and system for designing vehicle driving range.

[0005] A method for designing vehicle driving range to achieve one of the objectives of this invention includes the following steps:

[0006] S1. Obtain the parking location type for each vehicle based on the location information of a large number of vehicle parking locations; the parking location types include: residential, office buildings, commercial districts, tourist attractions, and transportation hubs;

[0007] Obtain vehicle information and driving behavior parameters for each vehicle;

[0008] The vehicle information includes: vehicle model, purchase time, and transaction price;

[0009] The vehicle's driving behavior parameters include: usage frequency, mileage, time period, area of ​​use, and driving habits;

[0010] S2. Use agglomerative clustering algorithm to calculate the driving behavior parameters, vehicle information, and parking point type of each vehicle to obtain the user type label for each vehicle; the user type label includes: business people, leisure self-driving people, daily commuters, and ride-hailing vehicles; design the vehicle's range based on the user type label and the user's driving mileage.

[0011] In the above technical solution, the classification of user tag types largely depends on the areas that users frequently visit, and these areas can be reflected by the user's vehicle stopping points. Therefore, stopping points are mainly classified into several categories: residential, office buildings, business districts, scenic spots, and transportation hubs.

[0012] In the above technical solution, the massive vehicle parking location information and driving behavior parameters of each vehicle can be obtained from the Internet of Vehicles big data, and the present invention does not limit this.

[0013] In the above technical solution, the method for designing the vehicle's driving range in step S2 includes:

[0014] The percentage of users in different mileage segments is calculated based on the mileage driven within the set time period for each user type; the driving range of each user type is calculated based on the mileage segments of users with the highest percentage of users; and the driving range of the vehicle is designed based on the driving range time of each user type.

[0015] In another technical solution, the method for designing the vehicle's driving range in step S2 includes:

[0016] The system obtains the driving mileage of users with the highest percentage of user tag types within a set time period. Based on the driving mileage and the preset charging behavior of users of this type, the system obtains the driving range of users of this type. The system designs the driving range of the vehicle based on the driving range of users of this type. The set time period is related to the user's charging behavior.

[0017] Furthermore, the method for designing the driving range includes:

[0018] S41. Acquire the user type with the highest percentage of the target audience;

[0019] S42. Obtain the maximum driving mileage S1 within a set time period for the total or partial percentage of the number of people in the user type mentioned in step S41.

[0020] S43. Calculate the driving range S according to the following formula:

[0021] S = (S1 + S2) / P1

[0022] In the formula:

[0023] S2 is the set emergency driving range, which is based on emergency driving range considerations; S2 can also be the set range margin based on the mileage.

[0024] P1 is the set experience loss ratio; that is, the ratio of the actual driving range of a car to its designed driving range under non-ideal conditions.

[0025] In the above technical solution, the method for obtaining the vehicle's parking point type in step S1 includes:

[0026] S11. Construct and train a stopping point classification model based on support vector machine (SVM); the stopping point classification model is used to obtain the stopping point type of the vehicle based on the stopping point information of the vehicle.

[0027] The parking point information includes: Point of Information (POI) type, number of parking spaces, whether the location is indoors, distance to the nearest transportation hub, and parking duration;

[0028] S12. Input the stopping point information of each vehicle into the stopping point classification model based on support vector machine (SVM) to obtain the stopping point type of each vehicle.

[0029] In the above technical solution, the method for obtaining vehicle parking information in step S11 includes:

[0030] The location information of each vehicle's parking point is matched with the Point of Information database to obtain the parking information of each vehicle.

[0031] The information point database contains multiple pieces of information, each of which includes at least: name, category, latitude and longitude, and nearby businesses, and can be obtained from the Internet, open databases, and other channels.

[0032] The above technical solution also includes preprocessing the vehicle's parking location information, which includes removing abnormal values ​​of the location information caused by vehicle positioning drift and missed reporting.

[0033] A vehicle range design system for achieving the second objective of the present invention includes: a parking point type acquisition module and a user type tag calculation module;

[0034] The parking location type acquisition module is used to obtain the parking location type of each vehicle based on a large amount of vehicle parking location information; the parking location types include: residential, office building, commercial area, scenic spot, and transportation hub;

[0035] The user type tag calculation module is used to calculate the driving behavior parameters and stopping point types to obtain the user type tag for each vehicle; the vehicle's range is designed based on the user type tag and the user's driving mileage; the vehicle's driving behavior parameters include: usage frequency, usage mileage, usage time period, usage area, and driving habits.

[0036] The system also includes a first driving range calculation module, used to design the vehicle's driving range based on user type tags, the method of which includes:

[0037] The driving mileage of the user with the highest percentage of user tag types within a set time period is obtained. Based on the driving mileage and the preset charging behavior of the user type, the driving range of the user type is obtained. The driving range of the vehicle is designed based on the driving range of the user type.

[0038] The system also includes a second range calculation module, which is used to calculate the proportion of people in different mileage segments based on the driving mileage within a set time period for each type of user; calculate the range of each type of user according to the mileage segment of users with a high proportion of people; and design the range of the vehicle based on the range duration of each type of user.

[0039] A non-transitory computer-readable storage medium for achieving the third objective of the present invention, having stored thereon a computer program, wherein when executed by a processor, the computer program implements the steps of any of the vehicle range design methods described herein.

[0040] The beneficial effects of this invention include:

[0041] Based on data such as the distribution of user vehicle parking locations, travel frequency, mileage, and driving time, and combined with a reasonable algorithm model, users can be quickly and accurately classified. By analyzing the charging behavior of each user type, and then calculating the driving range for that user type based on the charging behavior of the company's target customer group and the driving mileage of that user type, companies can design vehicle driving range more efficiently, reliably, and at a lower cost, thereby launching models that better cater to the target consumer group more quickly. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the method described in this invention;

[0043] Figure 2 This is a schematic diagram of the user classification process described in this invention. Detailed Implementation

[0044] The following detailed embodiments are provided to explain the technical solutions of the claims of this invention, so that those skilled in the art can understand the claims. The scope of protection of this invention is not limited to the following specific embodiments. Any modifications made by those skilled in the art that incorporate the technical solutions of the claims but differ from the following detailed embodiments are also within the scope of protection of this invention.

[0045] like Figure 1 As shown in the figure, this application embodiment includes a method for designing vehicle driving range, specifically including the following steps:

[0046] S1. Obtain the parking location type for each vehicle based on the location information of a large number of vehicle parking locations; the parking location types include: residential, office buildings, commercial districts, tourist attractions, and transportation hubs;

[0047] Obtain vehicle information and driving behavior parameters for each vehicle;

[0048] The vehicle information includes: vehicle model, purchase time, and transaction price;

[0049] The role of vehicle type in determining user tags includes: different vehicle types may suit different types of car owners, for example, SUVs may suit business people, leisure driving enthusiasts, etc.

[0050] The role of purchase time in determining user tag types includes: purchase time may be related to the car owner's car purchase decision. For example, the vehicle may be purchased during a promotional period, which will attract a specific type of car owner.

[0051] The role of transaction price in determining user tag types includes: transaction price can reflect the car owner's car purchase budget and economic situation, and vehicles in different price ranges may attract different types of car owners.

[0052] User information (age, gender) plays a role in determining user segmentation, including: younger people may be more inclined to leisure driving or ride-hailing services, while older people may be more likely to be business professionals or daily commuters. Furthermore, some age groups may be more concerned with environmental protection and energy conservation, thus influencing their vehicle selection.

[0053] The vehicle's driving behavior parameters include: usage frequency, mileage, usage time period, usage area, and driving habits; as shown in Table 1 below;

[0054] Table 1 User Driving Behavior Parameters

[0055]

[0056] The role of usage frequency in determining user tag types includes: usage frequency can reflect the degree of demand for the vehicle and the frequency of use; vehicle owners who use their vehicles frequently may be more likely to be commuters or ride-hailing drivers, while vehicle owners who use their vehicles less frequently may be more suitable for leisure driving.

[0057] The role of mileage in determining user tag types includes: mileage can reveal the owner's driving needs and uses; long-distance, high-mileage owners may be more suitable for leisure driving, while short-distance, low-mileage owners may be more suitable for urban commuters.

[0058] The role of usage time in determining user tag types includes: usage time can help determine the daily travel patterns of car owners. For example, car owners who use the service frequently at night may be associated with ride-hailing services, while those who use it frequently during the day may be commuters.

[0059] The role of usage area in determining user tag type includes: providing information about the vehicle owner's usual residence and driving range. Based on different geographical locations and usage areas, it can be determined whether a vehicle owner is more suitable for a specific type.

[0060] Driving habits play a role in determining user tag types in several ways. Driving habits include driving style and speed habits, which reflect the owner's driving style and safety awareness. Driving habits may also affect vehicle wear and tear and maintenance needs, thus influencing the owner's classification.

[0061] S2. An agglomerative clustering algorithm is used to calculate the driving behavior parameters, vehicle information, and parking point type for each vehicle to obtain a user type label for each vehicle. The user type labels include: business professionals, leisure drivers, daily commuters, and ride-hailing drivers, such as... Figure 2 As shown; the vehicle's range is designed based on the user type label and the user's driving mileage.

[0062] In step S2, when inputting features for the agglomerative clustering algorithm, Z-score normalization is used to reduce interference from values ​​of different orders of magnitude. This helps ensure that the influence of different features on the clustering results is balanced.

[0063] In step S2, this embodiment uses the agglomerative clustering algorithm. Agglomerative clustering is a hierarchical clustering method whose core idea is to start with a single data point and gradually merge similar data points until a group of clusters with similar properties is formed. The process is as follows:

[0064] S2.1 In agglomerative clustering algorithms, the distance between different users is calculated to determine which users are more similar, thereby merging similar users. In this embodiment, Euclidean distance is used to calculate the distance between different users, which is suitable for continuous feature data in this invention. The calculation formula is as follows:

[0065] dist

[0066] In the formula:

[0067] x and y are feature vectors of two different users;

[0068] n is the dimension of the feature;

[0069] x i and y i These are the values ​​of the two users on the i-th feature, respectively.

[0070] S2.2. Based on the calculated distances between users, select the two users with the smallest distance values ​​and merge them into a new temporary cluster. This helps to gradually merge users with high similarity into different clusters, forming a hierarchical clustering structure. Assume we obtain the distances between four users: A, B, C, and D.

[0071] dist(A,B)=3.5

[0072] dist(A,C)=5.2

[0073] dist(A,D)=4.8

[0074] dist(B,C)=2.1

[0075] dist(B,D)=6.0

[0076] dist(C,D)=2.8

[0077] The closest user pair among these users is B and C, with a distance of 2.1. Therefore, we merge users B and C into a temporary cluster;

[0078] S2.3 After merging two users to form a new temporary cluster, it is necessary to recalculate the distance between the new temporary cluster and other users or temporary clusters. This will help us determine which clusters have the highest similarity. Then, we select the two clusters or two users, or one cluster and one user, corresponding to the smallest distance values ​​for merging. This iterative process will continue until the preset number of clusters is reached. This distance can be determined by different calculation methods, commonly including single linking, complete linking, and average linking. In this embodiment, the average linking method is used to calculate the distance between the new cluster and other clusters. The formula is as follows:

[0079]

[0080] In the formula:

[0081] C1 and C2 are two different clusters;

[0082] n1 and n2 are the number of data points corresponding to clusters C1 and C2, respectively;

[0083] x i1 and x j2 These are data points belonging to clusters C1 and C2, respectively.

[0084] After merging BC, the distances between all clusters are as follows:

[0085] dist(BC,A)=4.35,dist(BC,D)=4.4

[0086] dist(A,D)=4.8

[0087] The closest cluster pair is BC and A. Therefore, we merge cluster BC and user A into a new temporary cluster;

[0088] S2.4 Repeat step S2.3, continuously merging the clusters with the smallest distance until the set number of clusters is reached. Finally, for each cluster formed, assign a user type label, such as business people, leisure drivers, daily commuters, ride-hailing drivers, etc.

[0089] In this way, step S2 will assign a user type label to each vehicle, which will be used for subsequent range prediction.

[0090] In the above technical solution, the method for designing the vehicle's driving range in step S2 includes:

[0091] The percentage of users in different mileage segments is calculated based on the mileage driven within the set time period for each user type; the driving range of each user type is calculated based on the mileage segments of users with the highest percentage of users; and the driving range of the vehicle is designed based on the driving range time of each user type.

[0092] If the target consumer group for the vehicle is business professionals, and the vehicle is designed to be charged every two days, then the percentage of business professionals in different mileage segments (e.g., 0-100km, 100km-300km, etc.) is calculated based on their mileage over two days. The mileage segment with the highest percentage is selected, and the driving range can be calculated based on this mileage segment and the number of charging cycles. The calculation method includes:

[0093] S = (S1 + S2) / P1

[0094] In the formula:

[0095] S is designed for business people, offering extended battery life.

[0096] S1 represents the maximum mileage driven by all or a predetermined percentage of business professionals (e.g., 70% of business professionals) within a predetermined timeframe (e.g., two days).

[0097] S2 is the set emergency driving range;

[0098] P1 is the set experience loss ratio.

[0099] In another technical solution, the method for designing the vehicle's driving range in step S2 includes:

[0100] The system obtains the driving mileage of users with the highest percentage of user tag types within a specified time period. Based on the driving mileage and the preset charging behavior of these users, it calculates the vehicle's range. The vehicle's range is then designed based on this range, and the calculation method includes:

[0101] S41. Obtain the user type with the highest percentage of the population; in this embodiment, the user tag type with the highest percentage is commuter, so the user type here is commuter.

[0102] S42. Obtain the maximum driving mileage S1 of the user type group (all or part of the user type mentioned in step S41) within a set time period or set duration.

[0103] In this embodiment, the charging behavior of commuters is set as follows: charge once on Sunday evening to cover commuting for the week; charge once on Friday evening to cover weekend use. Based on the above charging behavior, the reasonable calculation method is to calculate the maximum mileage of commuters during the set time period from Monday to Friday; that is, analyze the percentage of people with a total mileage of 0-150km from Monday to Friday, the percentage of people with a total mileage of 150-300km, and so on, up to the percentage of people with a total mileage of more than 600km. Based on the assumption that 85% of the commuters charge twice a week, and that they must charge when the SOC drops to 50km;

[0104] S43. Calculate the driving range according to the following formula:

[0105] S = (S1 + S2) / P1

[0106] In the formula:

[0107] S: The driving range designed for commuters in this embodiment;

[0108] S1: In this embodiment, the maximum mileage for commuters is from Monday to Friday;

[0109] S2: The set emergency driving range, which is 50km in this embodiment;

[0110] P1: The set experience loss ratio, which is 80% in this embodiment.

[0111] In this embodiment, S1 represents the maximum driving range of 450km from Monday to Friday for all commuting users or 85% of commuting users; with charging twice a week, the driving range S = (450km + 50km) / 80% = 625km.

[0112] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0113] This application embodiment also includes a vehicle range design system, including: a parking point type acquisition module and a user type tag calculation module;

[0114] The parking location type acquisition module is used to obtain the parking location type of each vehicle based on a large amount of vehicle parking location information; the parking location types include: residential, office building, commercial area, scenic spot, and transportation hub;

[0115] The user type tag calculation module is used to calculate the driving behavior parameters and stopping point types to obtain the user type tag for each vehicle; the vehicle's range is designed based on the user type tag and the user's driving mileage; the vehicle's driving behavior parameters include: usage frequency, usage mileage, usage time period, usage area, and driving habits.

[0116] The system also includes a first driving range calculation module, used to design the vehicle's driving range based on user type tags, the method of which includes:

[0117] The driving mileage of the user with the highest percentage of user tag types within a set time period is obtained. Based on the driving mileage and the preset charging behavior of the user type, the driving range of the user type is obtained. The driving range of the vehicle is designed based on the driving range of the user type.

[0118] The system also includes a second range calculation module, which is used to calculate the proportion of people in different mileage segments based on the driving mileage within a set time period for each type of user; calculate the range of each type of user according to the mileage segment of users with a high proportion of people; and design the range of the vehicle based on the range duration of each type of user.

[0119] This application also includes a computer-readable storage medium storing a computer program, which includes program instructions that, when executed by a processor, implement the various steps of the method described in this invention, which will not be elaborated further here.

[0120] The computer-readable storage medium can be the data transmission apparatus or the internal storage unit of a computer device provided in any of the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium can also be the external storage device of the computer device, such as the plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device.

[0121] Furthermore, the computer-readable storage medium may include both internal storage units and external storage devices of the computer device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data that is to be output or has already been output.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A method for designing vehicle driving range, characterized in that, The steps include the following: S1. Obtain the parking location type for each vehicle based on the location information of a large number of vehicle parking locations; the parking location types include: residential, office buildings, commercial districts, tourist attractions, and transportation hubs; Obtain vehicle information and driving behavior parameters for each vehicle; The vehicle information includes: vehicle model, purchase time, and transaction price; The vehicle's driving behavior parameters include: usage frequency, mileage, time period, area of ​​use, and driving habits; S2. Use agglomerative clustering algorithm to calculate the driving behavior parameters, vehicle information, and parking point type of each vehicle to obtain the user type label for each vehicle; design the vehicle's range based on the user type label and the user's driving mileage. The method for designing the vehicle's driving range in step S2 includes: Get the driving mileage of the user with the highest proportion of user tag type within a set time period, obtain the driving range of the user with the preset charging behavior of the user with the driving mileage, and design the driving range of the vehicle based on the driving range of the user with the preset driving range. The method for designing the driving range includes: S41. Acquire the user type with the highest percentage of the target audience; S42. Obtain the maximum driving mileage S1 of the total or partial proportion of the number of people in the user type mentioned in step S41 within a set time period or set duration. S43. Calculate the driving range S according to the following formula: S=(S1+S2) / P1 In the formula: S2 is the set emergency driving range or the set range margin; P1 is the set experience loss ratio; Methods for obtaining the vehicle's parking point type include: S11. Construct and train a stopping point classification model based on support vector machine (SVM); the stopping point classification model is used to obtain the stopping point type of the vehicle based on the stopping point information of the vehicle. The parking location information includes: information location type, number of parking spaces, whether the location is indoors, distance to the nearest transportation hub, and parking duration; S12. Input the stopping point information of each vehicle into the stopping point classification model based on support vector machine (SVM) to obtain the stopping point type of each vehicle. The process of the agglomerative clustering algorithm is as follows: S2.1 The distance between different users is calculated using Euclidean distance. The calculation formula is as follows: dist ; In the formula: x and y are feature vectors of two different users; n is the dimension of the feature; x i and y i These are the values ​​of the two users on the i-th feature, respectively; S2.2 Based on the calculated distance between users, select the two users with the smallest distance values ​​and merge them into a new temporary cluster; S2.

3. Recalculate the distance between the new temporary cluster and other users or temporary clusters. Select the two clusters, two users, or one cluster and one user corresponding to the smallest distance and merge them. This iterative process will continue until the preset number of clusters is reached. The average linkage method is used to calculate the distance between the new cluster and other clusters, as shown in the following formula: ； In the formula: C1 and C2 are two different clusters; n1 and n2 are the number of data points corresponding to clusters C1 and C2, respectively; x i1 and x j2 These are data points belonging to clusters C1 and C2, respectively. S2.4 Repeat step S2.3 to continuously merge the clusters with the smallest distance until the set number of clusters is reached; finally, assign a user type label to each formed cluster.

2. The vehicle range design method as described in claim 1, characterized in that, The method for designing the vehicle's driving range in step S2 includes: The percentage of users in different mileage segments is calculated based on the mileage driven within the set time period for each user type; the driving range of each user type is calculated based on the mileage segments of users with the highest percentage of users; and the driving range of the vehicle is designed based on the driving range of each user type.

3. The vehicle range design method as described in claim 1, characterized in that, In step S11, the method for obtaining the vehicle's parking point information includes: The location information of each vehicle's parking spot is matched with the information point database to obtain the parking spot information of each vehicle.

4. The vehicle range design method as described in claim 3, characterized in that, It also includes preprocessing the vehicle's parking location, which includes removing outliers in the location information caused by vehicle positioning drift and / or missed reporting.

5. A system for designing vehicle range by performing the method of claim 1, characterized in that, This includes a stop point type acquisition module and a user type tag calculation module; The parking point type acquisition module is used to acquire the parking point type of each vehicle based on a large amount of vehicle parking point location information. The types of rest stops include: residential buildings, office buildings, commercial districts, tourist attractions, and transportation hubs; The user type tag calculation module is used to calculate the driving behavior parameters and stopping point types to obtain the user type tag for each vehicle; the vehicle's range is designed based on the user type tag and the user's driving mileage; the vehicle's driving behavior parameters include: usage frequency, usage mileage, usage time period, usage area, and driving habits.

6. The vehicle range design system as described in claim 5, characterized in that, It also includes a first range calculation module, used to design the vehicle's range based on user type tags, the method of which includes: The driving mileage of the user with the highest percentage of user tag types within a set time period is obtained. Based on the driving mileage and the preset charging behavior of the user type, the driving range of the user type is obtained. The driving range of the vehicle is designed based on the driving range of the user type.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle range design method as described in any one of claims 1 to 4.

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