Charging method and device of vehicle, electronic equipment and storage medium

Abstract

The application discloses a charging method and device of a vehicle, an electronic device and a storage medium, wherein the method comprises: obtaining charging data and driving data of multiple users within a preset period; identifying an actual charging anxiety level of each user according to the charging data and the driving data of each user; generating a target anxiety reduction strategy of each user according to the actual charging anxiety level, and controlling the corresponding vehicle of each user to charge according to the target anxiety reduction strategy. Thus, the technical problem that in the related art, there is a lack of prediction research on the charging anxiety behavior of users, and only when the power is about to reach the limit, a charging warning is given and a charging strategy is provided, thereby shortening the service life of the battery, is solved.

Description

Technical Field

[0001] This application relates to the field of electric vehicle technology, and in particular to a vehicle charging method, device, electronic device, and storage medium. Background Technology

[0002] Electric vehicle users have significantly different daily charging habits. Even if they live in the same city or area and their daily driving habits are similar, the number of times they charge their electric vehicles varies greatly. The reason for this is that electric vehicle users all have different charging anxiety issues.

[0003] In related technologies, to alleviate users' charging anxiety, it is possible to predict the power consumption of electric vehicles, formulate corresponding charging strategies based on the power consumption status, and plan the best charging locations.

[0004] However, the relevant technologies lack predictive research on users' charging anxiety behavior. They only provide charging reminders and plan charging strategies when the battery is about to reach its limit. This can easily lead to the electric vehicle battery being fully charged and discharged, which not only makes it easier to cause irreversible damage to the battery pack in terms of battery mechanism, but also causes the charging speed to drop sharply and the charging time to be prolonged in the second half of the full charge stage, i.e. after 80% SOC. This is more likely to cause waste of charging pile resources and needs to be improved. Summary of the Invention

[0005] This application provides a vehicle charging method, device, electronic device, and storage medium to address the technical problem in related technologies that lack predictive research on users' charging anxiety behavior and only provide charging warnings and charging strategies when the battery level is about to reach its limit, thereby shortening battery life.

[0006] The first aspect of this application provides a vehicle charging method, comprising the following steps: acquiring charging data and driving data of multiple users within a preset time period; identifying the actual charging anxiety level of each user based on the charging data and driving data of each user; generating a target anxiety reduction strategy for each user based on the actual charging anxiety level, and controlling the vehicle corresponding to each user to charge according to the target anxiety reduction strategy.

[0007] Optionally, in one embodiment of this application, identifying the actual charging anxiety level of each user based on each user's charging data and driving data includes: calculating the data position of each user in a preset coordinate system based on the charging data and driving data; calculating the actual closest distance between the data position and the anxiety curve; and determining the actual charging anxiety level based on the actual closest distance, wherein the actual charging anxiety level includes a charging confidence level and a charging anxiety level.

[0008] Optionally, in one embodiment of this application, before obtaining the charging data and driving data of the multiple users within the preset time period, the method further includes: obtaining the identity identifier of each user; determining whether a corresponding charging anxiety level is stored based on the identity identifier of each user; and if a corresponding charging anxiety level is stored, then using the corresponding charging anxiety level as the actual charging anxiety level.

[0009] Optionally, in one embodiment of this application, the method further includes: receiving a setting instruction from any user; and modifying the actual charging anxiety level and / or the target anxiety reduction strategy corresponding to the any user according to the setting instruction.

[0010] Optionally, in one embodiment of this application, the charging data includes fast charging data, slow charging data, and charging type.

[0011] A second aspect of this application provides a vehicle charging device, comprising: a first acquisition module for acquiring charging data and driving data of multiple users within a preset time period; an identification module for identifying the actual charging anxiety level of each user based on the charging data and driving data of each user; and a control module for generating a target anxiety reduction strategy for each user based on the actual charging anxiety level, and controlling the vehicle corresponding to each user to charge according to the target anxiety reduction strategy.

[0012] Optionally, in one embodiment of this application, the identification module includes: a first calculation unit, configured to calculate the data position of each user in a preset coordinate system based on the charging data and the driving data; and a second calculation unit, configured to calculate the actual closest distance between the data position and the anxiety curve, and determine the actual charging anxiety level based on the actual closest distance, wherein the actual charging anxiety level includes a charging confidence level and a charging anxiety level.

[0013] Optionally, in one embodiment of this application, it further includes: a second acquisition module, used to acquire the identity identifier of each user; a judgment module, used to determine whether a corresponding charging anxiety level is stored based on the identity identifier of each user; and a matching module, used to use the corresponding charging anxiety level as the actual charging anxiety level when a corresponding charging anxiety level is stored.

[0014] Optionally, in one embodiment of this application, it further includes: a receiving module, configured to receive a setting instruction from any user; and a modification module, configured to modify the actual charging anxiety level and / or the target anxiety reduction strategy corresponding to the any user according to the setting instruction.

[0015] Optionally, in one embodiment of this application, the charging data includes fast charging data, slow charging data, and charging type.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle charging method as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle charging method described above.

[0018] This application's embodiments can categorize users' charging anxiety levels based on their daily charging and driving data. By classifying users according to their levels, refined strategy control can be provided to reduce overall user anxiety, thereby improving the user's daily driving experience, reducing the frequency of charging station usage, extending the lifespan of the battery and charging station, improving charging efficiency, and reducing energy waste. This solves the technical problem in related technologies that lack predictive research on user charging anxiety behavior, only providing charging warnings and strategies when the battery level is about to reach its limit, thus shortening battery life.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0021] Figure 1 This is a flowchart illustrating a vehicle charging method according to an embodiment of this application;

[0022] Figure 2 This is a data travel visualization diagram of a vehicle charging method according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of the anxiety curve of a data travel visualization image of a vehicle charging method according to an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of the region division of a data travel visualization image of a vehicle charging method according to an embodiment of this application;

[0025] Figure 5 This is a flowchart of a vehicle charging method according to an embodiment of this application;

[0026] Figure 6 This is a schematic diagram of the structure of a vehicle charging device according to an embodiment of this application;

[0027] Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0028] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0029] The following description, with reference to the accompanying drawings, describes a vehicle charging method, apparatus, electronic device, and storage medium according to embodiments of this application. Addressing the technical problem mentioned in the background section of the related art—a lack of predictive research on user charging anxiety behavior, and the provision of charging warnings and strategies only when the battery level is predicted to reach its limit, thus shortening battery life—this application provides a vehicle charging method. In this method, users are categorized into charging anxiety levels based on their daily charging and driving data. Through these user levels, refined strategy control can be provided to reduce the overall user anxiety level, thereby improving the user's daily driving experience, reducing the frequency of charging pile usage, extending the lifespan of the battery and charging pile, improving charging efficiency, and reducing energy waste. This solves the technical problem in the related art—a lack of predictive research on user charging anxiety behavior, and the provision of charging warnings and strategies only when the battery level is predicted to reach its limit, thus shortening battery life.

[0030] Specifically, Figure 1 This is a schematic flowchart illustrating a vehicle charging method provided in an embodiment of this application.

[0031] like Figure 1 As shown, the charging method for this vehicle includes the following steps:

[0032] In step S101, charging data and driving data of multiple users within a preset time period are obtained.

[0033] Understandably, according to the national standard GB32960, all electric vehicles are required to upload daily driving and charging data to the national platform and enterprise platforms. This application embodiment can obtain the daily driving and charging data of electric vehicles under the enterprise based on the enterprise platform, thereby obtaining the charging and driving data of multiple users within a preset time period. The data source of this application embodiment is simple and reliable, thereby reducing costs and facilitating promotion and application.

[0034] The driving data may include mileage, driving time, and other data.

[0035] Optionally, in one embodiment of this application, the charging data includes fast charging data, slow charging data, and charging type.

[0036] In some embodiments, charging data may include fast charging data, slow charging data, charging type, and other data, thereby completing the preparation of basic data for the embodiments of this application.

[0037] Optionally, in one embodiment of this application, before obtaining the charging data and driving data of multiple users within a preset time period, the method further includes: obtaining the identity identifier of each user; determining whether a corresponding charging anxiety level is stored based on the identity identifier of each user; and if a corresponding charging anxiety level is stored, then using the corresponding charging anxiety level as the actual charging anxiety level.

[0038] In actual implementation, this application embodiment can obtain the identity identifier of each user and determine whether a corresponding charging anxiety level is stored based on the identity identifier of each user. When a corresponding charging anxiety level is stored, this application embodiment can use the corresponding charging anxiety level as the actual charging anxiety level. When no stored record is found, this application embodiment can establish and store the user's actual anxiety level.

[0039] The method for generating each user's identity identifier can be as follows:

[0040] This application embodiment can obtain at least one user identifier of an electric vehicle user, such as the mobile phone number when purchasing the vehicle, the ID card number, the mobile phone number when registering the user app, the vehicle VIN, etc., to construct the basic content of a user tag. This application embodiment can also sort all electric vehicles, such as: 1, 2, 3, 4, 5, 6, 7, 8, ... i-2, i-1, i, and concatenate the sorted serial numbers into the user's tag to ensure a one-to-one correspondence.

[0041] First, this application embodiment can output vehicle cycle (such as daily, weekly, monthly, etc.) data within a preset time period. Taking weekly as an example, it outputs the user's average number of fast charging charges per week and the user's average weekly mileage. The calculation method can be as follows:

[0042] Average weekly mileage driven by the user X i : Mileage within the preset time period (mileage on the last day's odometer - mileage on the first day's odometer) / total number of natural weeks included in the preset time period;

[0043] Average number of fast charging sessions per week for users Y i : Total number of fast charging attempts within the preset time period / Total number of natural weeks within the preset time period.

[0044] Secondly, in the embodiments of this application, the number i (X) can be used as the vehicle (X) i ,Y i The numerical value is concatenated with the electric vehicle user identifier as a data tag, such as: vin-i-(Xi,Yi).

[0045] Finally, in this embodiment of the application, all vehicle data can be calculated according to the above process to realize the concatenation of electric vehicle user identification data tags in order to generate the identity identifier of each user.

[0046] In step S102, the actual charging anxiety level of each user is identified based on each user's charging data and driving data.

[0047] As one possible implementation method, this application embodiment can identify the actual charging anxiety level of each user based on the charging data and driving data of each user obtained above, and implement corresponding strategies for each user's different actual charging anxiety levels, thereby improving the user's daily driving experience, reducing the frequency of charging pile use, extending the life of the battery and charging pile, improving charging efficiency, and reducing energy waste.

[0048] Optionally, in one embodiment of this application, identifying the actual charging anxiety level of each user based on each user's charging data and driving data includes: calculating the data position of each user in a preset coordinate system based on the charging data and driving data; calculating the actual closest distance between the data position and the anxiety curve; and determining the actual charging anxiety level based on the actual closest distance. The actual charging anxiety level includes the charging confidence level and the charging anxiety level.

[0049] Specifically, in this application embodiment, before calculating the data position of each user in a preset coordinate system based on charging data and driving data, a trip visualization image can be pre-constructed and the actual charging anxiety level can be defined.

[0050] like Figure 2 As shown in the embodiments of this application, all data journeys can be visualized in an image.

[0051] In the figure, the X-axis represents the average weekly mileage of the vehicle, the Y-axis represents the average weekly number of fast charging charges, and the points in the coordinate system represent the data of the i-th vehicle (X... i Y i ).

[0052] like Figure 3As shown, this application embodiment can load the anxiety curve of the corresponding vehicle model. In this application embodiment, the anxiety curve of the corresponding vehicle model can be loaded according to the vehicle model category of the selected data. The curve is not limited to a straight line, but can also be a curved curve or other forms of curve.

[0053] like Figure 4 As shown, the regions are divided into two areas. In this embodiment, the region above the anxiety curve is defined as the charging anxiety region, and the region below the anxiety curve is defined as the charging confidence region.

[0054] This application embodiment can define the charging anxiety level of a vehicle, such as... Figure 4 As shown, this application embodiment can calculate the shortest distance from the point represented by all vehicle data to the anxiety curve, and the distance of the point that coincides with the anxiety curve is defined as zero.

[0055] Within the charging anxiety zone, the greater the distance, the more anxious the car owner is about charging, leading to more frequent charging and potentially wasting charging resources.

[0056] Within the charging confidence zone, the greater the distance, the more confident the car owner is in charging, and the more distance they will be able to travel on a single charge. However, this may also cause deep battery discharge, affecting battery health, or lead to extreme situations such as breakdowns.

[0057] In this embodiment, the distance values ​​of each vehicle can be sorted by size. The distance to vehicle locations in the charging anxiety zone is defined as a positive value, and the distance to vehicle locations in the charging confidence zone is defined as a negative value. The vehicle is then classified according to a classification unit Q, starting from 0, with no upper or lower limits set for the classification. For example, it can be as follows:

[0058] -n level: -nQ--(n-1)Q;

[0059] -(n-1) level: -(n-1)Q--(n-2)Q;

[0060] ...

[0061] Level -3: -3Q--2Q;

[0062] Level -2: -2Q--Q;

[0063] Level -1: Q-0;

[0064] Level 0: 0;

[0065] Level 1: 0-Q;

[0066] Level 2: Q-2Q;

[0067] Level 3: 2Q-3Q;

[0068] ...

[0069] (n-1) level: (n-2)Q-(n-1)Q;

[0070] n-level: (n-1)Q-nQ.

[0071] Furthermore, embodiments of this application can classify vehicles into charging anxiety tags: embodiments of this application can calculate the location (X, Y) of each user in a preset coordinate system based on charging data and driving data. i ,Y i The distance from the anxiety curve to the actual charging anxiety level is the anxiety level label of the electric vehicle, which corresponds to the anxiety level of the electric vehicle.

[0072] In step S103, a target anxiety reduction strategy is generated for each user based on the actual charging anxiety level, and the vehicle corresponding to each user is charged according to the target anxiety reduction strategy.

[0073] In actual implementation, this application embodiment can generate a target anxiety reduction strategy for each user based on their actual charging anxiety level. For example, for users with different actual charging anxiety levels, the user information of the corresponding level can be output to the customer operations department for daily promotion and explanation to users, guiding them to reduce charging anxiety and improve single charging efficiency. For users with a non-negative level, car owners can be guided to reasonably arrange their charging plans and experience a better car life. Alternatively, this application embodiment can also display corresponding battery icons or text information, such as a smiley face icon or the text "Battery is sufficient, please do not worry," on the battery display of the in-vehicle dashboard for users with different actual charging anxiety levels.

[0074] Furthermore, this application embodiment can also establish a forum for vehicles of the same brand and invite typical users with low charging anxiety levels to share their daily charging tips, thereby enabling interaction among users and providing targeted information to users with high charging anxiety levels. This also helps to create a positive communication atmosphere and enhance the brand's user appeal.

[0075] This application embodiment can recommend that each user perform a corresponding vehicle charging action according to the target anxiety reduction strategy.

[0076] Optionally, in one embodiment of this application, the method further includes: receiving a setting instruction from any user; and modifying the actual charging anxiety level and / or target anxiety reduction strategy corresponding to any user according to the setting instruction.

[0077] In some embodiments, users can modify the corresponding actual charging anxiety level and / or target anxiety reduction strategy, thereby making the embodiments of this application more in line with the user's charging habits.

[0078] Furthermore, the effectiveness of this application embodiment can also be determined by observing changes in user charging anxiety: if the clustering effect of the location towards the anxiety curve is obvious, it indicates that the charging anxiety relief strategy is effective and the user's charging habits have changed in a positive way.

[0079] Combination Figures 2 to 5 As shown, the working principle of the vehicle charging method of this application embodiment is explained in detail with reference to one embodiment.

[0080] like Figure 5 As shown, the embodiments of this application may include the following steps: Step S501: Establish user tags. The embodiments of this application can obtain at least one user identifier of the electric vehicle user, such as the mobile phone number at the time of purchase, ID card number, mobile phone number when registering the user app, vehicle VIN, etc., to construct the basic content of a user tag. The embodiments of this application can also sort all electric vehicles, such as: 1, 2, 3, 4, 5, 6, 7, 8, ... i-2, i-1, i, and concatenate the generated sequence numbers into the user's tag to ensure a one-to-one correspondence.

[0081] Step S502: Real-time data upload for electric vehicles. According to the national standard GB32960, all electric vehicles are required to upload daily driving and charging data to the national and enterprise platforms. This embodiment of the application can prepare basic data for user charging anxiety based on the driving mileage, fast charging data, slow charging data, charging type, and other data contained therein.

[0082] Step S503: Output electric vehicle cycle data. This embodiment of the application can output vehicle cycle data (such as daily, weekly, monthly, etc.) within a selected time period. Taking weekly as an example, it outputs the user's average number of fast charging charges per week and the user's average weekly mileage. The calculation method is as follows:

[0083] Average weekly mileage driven by the user X i : Mileage within the selected time period (mileage on the last day's odometer - mileage on the first day's odometer) / total number of natural weeks within the selected time period;

[0084] Average number of fast charging sessions per week for users Y i : Total number of fast charging attempts within the selected time period / Total number of natural weeks within the selected time period.

[0085] Step S504: Concatenate the data with the user tag to generate a user identity identifier. This embodiment of the application can use the vehicle number i (X) i ,Y iThe numerical value is concatenated with the electric vehicle user identifier as a data tag, such as vin-i-(Xi,Yi). Following the above process, all vehicle data is calculated to realize the concatenation of electric vehicle user identifier data tags, so as to generate the identity identifier of each user.

[0086] Step S505: Calculate the charging anxiety value of each electric vehicle and classify it into levels. Specifically, in this embodiment of the application, before calculating the data position of each user in a preset coordinate system based on charging data and driving data, a trip visualization image can be pre-constructed and the actual charging anxiety level can be defined.

[0087] like Figure 2 As shown in the embodiments of this application, all data journeys can be visualized in an image.

[0088] In the figure, the X-axis represents the average weekly mileage of the vehicle, the Y-axis represents the average weekly number of fast charging charges, and the points in the coordinate system represent the data of the i-th vehicle (X... i Y i ).

[0089] like Figure 3 As shown, this application embodiment can load the anxiety curve of the corresponding vehicle model. In this application embodiment, the anxiety curve of the corresponding vehicle model can be loaded according to the vehicle model category of the selected data. The curve is not limited to a straight line, but can also be a curved curve or other forms of curve.

[0090] like Figure 4 As shown, the regions are divided into two areas. In this embodiment, the region above the anxiety curve is defined as the charging anxiety region, and the region below the anxiety curve is defined as the charging confidence region.

[0091] This application embodiment can define the charging anxiety level of a vehicle, such as... Figure 4 As shown, this application embodiment can calculate the shortest distance from the point represented by all vehicle data to the anxiety curve, and the distance of the point that coincides with the anxiety curve is defined as zero.

[0092] Within the charging anxiety zone, the greater the distance, the more anxious the car owner is about charging, leading to more frequent charging and potentially wasting charging resources.

[0093] Within the charging confidence zone, the greater the distance, the more confident the car owner is in charging, and the more distance they will be able to travel on a single charge. However, this may also cause deep battery discharge, affecting battery health, or lead to extreme situations such as breakdowns.

[0094] In this embodiment, the distance values ​​of each vehicle can be sorted by size. The distance to vehicle locations in the charging anxiety zone is defined as a positive value, and the distance to vehicle locations in the charging confidence zone is defined as a negative value. The vehicle is then classified according to a classification unit Q, starting from 0, with no upper or lower limits set for the classification. For example, it can be as follows:

[0095] -n level: -nQ--(n-1)Q;

[0096] -(n-1) level: -(n-1)Q--(n-2)Q;

[0097] ...

[0098] Level -3: -3Q--2Q;

[0099] Level -2: -2Q--Q;

[0100] Level -1: Q-0;

[0101] Level 0: 0;

[0102] Level 1: 0-Q;

[0103] Level 2: Q-2Q;

[0104] Level 3: 2Q-3Q;

[0105] ...

[0106] (n-1) level: (n-2)Q-(n-1)Q;

[0107] n-level: (n-1)Q-nQ.

[0108] Furthermore, embodiments of this application can classify vehicles into charging anxiety tags: embodiments of this application can calculate the location (X, Y) of each user in a preset coordinate system based on charging data and driving data. i ,Y i The distance from the anxiety curve to the actual charging anxiety level is the anxiety level label of the electric vehicle, which corresponds to the anxiety level of the electric vehicle.

[0109] Step S506: Make business decisions and execute strategies based on charging anxiety levels. This embodiment can generate target anxiety reduction strategies for each user based on their actual charging anxiety level. For example, for users with different actual charging anxiety levels, the corresponding user information can be output to the customer operations department for daily promotion and explanation, guiding users to reduce charging anxiety and improve single-charge efficiency. For users with non-negative levels, car owners can be guided to rationally arrange their charging plans for a better automotive experience. Alternatively, this embodiment can also display corresponding battery icons or text information, such as a smiley face icon or the text "Battery is sufficient, please do not worry," on the battery display of the in-vehicle dashboard for users with different actual charging anxiety levels.

[0110] Furthermore, this application embodiment can also establish a forum for vehicles of the same brand and invite typical users with low charging anxiety levels to share their daily charging tips, thereby enabling interaction among users and providing targeted information to users with high charging anxiety levels. This also helps to create a positive communication atmosphere and enhance the brand's user appeal.

[0111] This application embodiment can recommend that each user perform a corresponding vehicle charging action according to the target anxiety reduction strategy.

[0112] Furthermore, the effectiveness of this application embodiment can also be determined by observing changes in user charging anxiety: if the clustering effect of the location on the anxiety curve is obvious, it indicates that the charging anxiety relief strategy is effective and the user's charging habits have changed in a positive way.

[0113] The vehicle charging method proposed in this application can classify users into charging anxiety levels based on their daily charging and driving data. By using these user levels, refined strategy control can be provided to reduce the overall user anxiety level, thereby improving the user's daily driving experience, reducing the frequency of charging pile use, extending the lifespan of the battery and charging pile, improving charging efficiency, and reducing energy waste. This solves the technical problem in related technologies that lack predictive research on user charging anxiety behavior and only provide charging warnings and strategies when the battery level is about to reach its limit, thus shortening battery life.

[0114] Next, a vehicle charging device according to an embodiment of this application is described with reference to the accompanying drawings.

[0115] Figure 6 This is a block diagram of a vehicle charging device according to an embodiment of this application.

[0116] like Figure 6 As shown, the charging device 10 of the vehicle includes: a first acquisition module 100, an identification module 200, and a control module 300.

[0117] Specifically, the first acquisition module 100 is used to acquire charging data and driving data of multiple users within a preset time period.

[0118] The identification module 200 is used to identify each user's actual charging anxiety level based on each user's charging data and driving data.

[0119] The control module 300 is used to generate a target anxiety reduction strategy for each user based on the actual charging anxiety level, and to control the charging of the vehicle corresponding to each user in accordance with the target anxiety reduction strategy.

[0120] Optionally, in one embodiment of this application, the identification module 200 includes: a first calculation unit and a second calculation unit.

[0121] The first calculation unit is used to calculate the data position of each user in a preset coordinate system based on charging data and driving data.

[0122] The second calculation unit is used to calculate the actual closest distance between the data location and the anxiety curve, and to determine the actual charging anxiety level based on the actual closest distance. The actual charging anxiety level includes the charging confidence level and the charging anxiety level.

[0123] Optionally, in one embodiment of this application, the vehicle charging device 10 further includes: a second acquisition module, a judgment module, and a matching module.

[0124] The second acquisition module is used to acquire the identity identifier of each user.

[0125] The judgment module is used to determine whether a corresponding charging anxiety level is stored based on each user's identity identifier.

[0126] The matching module is used to use the corresponding charging anxiety level as the actual charging anxiety level when a corresponding charging anxiety level is stored.

[0127] Optionally, in one embodiment of this application, the vehicle charging device 10 further includes a receiving module and a modification module.

[0128] The receiving module is used to receive setting instructions from any user.

[0129] The modification module is used to modify the actual charging anxiety level and / or target anxiety reduction strategy for any user according to the setting instructions.

[0130] Optionally, in one embodiment of this application, the charging data includes fast charging data, slow charging data, and charging type.

[0131] It should be noted that the foregoing explanation of the vehicle charging method embodiment also applies to the vehicle charging device of this embodiment, and will not be repeated here.

[0132] The vehicle charging device proposed in this application can classify users into charging anxiety levels based on their daily charging and driving data. By using these user levels, refined strategy control can be provided to reduce the overall user anxiety level, thereby improving the user's daily driving experience, reducing the frequency of charging pile use, extending the lifespan of the battery and charging pile, improving charging efficiency, and reducing energy waste. This solves the technical problem in related technologies that lack predictive research on user charging anxiety behavior and only provide charging warnings and strategies when the battery level is about to reach its limit, thus shortening battery life.

[0133] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0134] The memory 701, the processor 702, and the computer program stored on the memory 701 and capable of running on the processor 702.

[0135] When the processor 702 executes the program, it implements the vehicle charging method provided in the above embodiments.

[0136] Furthermore, electronic devices also include:

[0137] Communication interface 703 is used for communication between memory 701 and processor 702.

[0138] The memory 701 is used to store computer programs that can run on the processor 702.

[0139] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0140] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0141] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0142] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0143] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle charging method described above.

[0144] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0145] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0146] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0147] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0148] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0149] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0150] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0151] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for charging a vehicle, characterized in that, Includes the following steps: Acquire charging and driving data from multiple users within a preset time period; The actual charging anxiety level of each user is identified based on their charging and driving data. as well as Based on the actual charging anxiety level, a target anxiety reduction strategy is generated for each user, and the vehicle corresponding to each user is controlled to charge according to the target anxiety reduction strategy; The step of identifying the actual charging anxiety level of each user based on each user's charging data and driving data includes: pre-constructing a trip visualization image to define the actual charging anxiety level, wherein in the preset coordinate system of the trip visualization image, the X-axis is the vehicle's average weekly driving mileage, the Y-axis is the vehicle's average weekly fast charging frequency, and the points in the coordinate system represent the data (Xi, Yi) of the i-th vehicle. Based on the vehicle type of each user, load the anxiety curve corresponding to the vehicle type, calculate the data position of each user in the preset coordinate system based on the charging data and the driving data, calculate the actual closest distance between the data position and the anxiety curve, and determine the actual charging anxiety level based on the actual closest distance. The actual charging anxiety level includes charging confidence level and charging anxiety level.

2. The method according to claim 1, characterized in that, Before acquiring the charging and driving data of the multiple users within the preset time period, the process also includes: Obtain the identity identifier of each user; Based on the identity identifier of each user, determine whether a corresponding charging anxiety level is stored; If a corresponding charging anxiety level is stored, then the corresponding charging anxiety level is used as the actual charging anxiety level.

3. The method according to claim 1, characterized in that, Also includes: Receive configuration instructions from any user; Modify the actual charging anxiety level and / or the target anxiety reduction strategy for any user according to the setting instructions.

4. The method according to claim 1, characterized in that, The charging data includes fast charging data, slow charging data, and charging type.

5. A vehicle charging device, characterized in that, include: The first acquisition module is used to acquire charging data and driving data of multiple users within a preset time period; The identification module is used to identify the actual charging anxiety level of each user based on their charging and driving data. as well as The control module is used to generate a target anxiety reduction strategy for each user based on the actual charging anxiety level, and control the vehicle corresponding to each user to charge according to the target anxiety reduction strategy. The identification module is further configured to pre-construct a trip visualization image to define the actual charging anxiety level. In the preset coordinate system of the trip visualization image, the X-axis represents the vehicle's average weekly mileage, the Y-axis represents the vehicle's average weekly fast charging frequency, and the points in the coordinate system represent the data (Xi, Yi) of the i-th vehicle. Based on the vehicle type of each user, the corresponding anxiety curve is loaded. The identification module includes: a first calculation unit for calculating the data position of each user in the preset coordinate system based on the charging data and the driving data; and a second calculation unit for calculating the actual closest distance between the data position and the anxiety curve, and determining the actual charging anxiety level based on the actual closest distance. The actual charging anxiety level includes a charging confidence level and a charging anxiety level.

6. The apparatus according to claim 5, characterized in that, Also includes: The second acquisition module is used to acquire the identity identifier of each user; The judgment module is used to determine whether a corresponding charging anxiety level is stored based on the identity identifier of each user; The matching module is used to use the corresponding charging anxiety level as the actual charging anxiety level when a corresponding charging anxiety level is stored.

7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the charging method for a vehicle as described in any one of claims 1-4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the charging method for the vehicle as described in any one of claims 1-4.

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