Method, apparatus, and storage medium for determining an energy replenishment method

Abstract

Embodiments of this disclosure disclose a method, apparatus, and storage medium for determining an energy replenishment method, and belong to the computer technology field. In this invention, an in-vehicle terminal transmits status information of multiple in-vehicle electrical devices and the number of passengers to a big data platform. Based on this information, the big data platform calculates the amount of charge required for the target vehicle to reach its destination (i.e., the target charge amount), and further determines multiple energy replenishment methods based on the battery level and charge amount, and marks the corresponding charging pile combinations and charging times. In this way, by automatically determining the energy replenishment method, the user can understand the energy replenishment method for different routes without having to plan the energy replenishment method themselves, and furthermore, the user can replenish the vehicle with the corresponding energy replenishment method based on the selected route and arrive at the destination, which is relatively convenient.

Description

[Technical Field]

[0001] This disclosure relates to the technology of new energy vehicles, and more particularly to methods, equipment, and storage media for determining an energy replenishment method. [Background technology]

[0002] With the development of new energy vehicle technology, new energy vehicles are gaining increasing market approval and their market share is gradually growing. For electric vehicles, the issue of driving range is always a concern for users. Generally, users can determine charging time and location themselves based on the battery level displayed on the meter or in-car terminal screen, and the location of charging stations on an electronic map.

[0003] However, when traveling long distances, it is difficult for users to plan a complete charging schedule, and the vehicle may break down on the road due to insufficient battery power, causing considerable inconvenience to the user. [Overview of the project]

[0004] This disclosure provides a method, apparatus, and storage medium for determining an energy replenishment method that can solve related technical problems. The proposed technical solution is as follows:

[0005] In the first embodiment, a method for determining an energy replenishment method, The in-vehicle terminal, after detecting the user's input operation for a destination, sends a route acquisition request to the map platform, which includes the identifier information of the target vehicle. The above-mentioned in-vehicle terminal acquires status information of multiple in-vehicle electrical devices, whether they are running or not, and the number of passengers, and transmits an energy replenishment method recommendation request to the big data platform, which includes the identifier information of the target vehicle and the status information of the multiple in-vehicle electrical devices. The above map platform determines multiple routes between the location of the target vehicle and the destination, and transmits them to the above big data platform and the above in-vehicle terminal. The big data platform determines that at least one of the status information of the multiple in-vehicle electrical devices is in the activated state, identifies the in-vehicle electrical device whose status information is in the activated state as the target in-vehicle electrical device, and determines the rated power of the target in-vehicle electrical device based on the correspondence between the vehicle identifier information and the rated power of the in-vehicle electrical device, and the identifier information of the target vehicle. The big data platform determines the total weight of the target vehicle based on the number of passengers and the identifier information of the target vehicle, adds the rated power of the target vehicle's in-vehicle electrical equipment, obtains the total power value of the target vehicle's in-vehicle electrical equipment, and determines the driving range of the target vehicle with the remaining battery power based on the battery level obtained from the vehicle network platform, the total weight, the total power value, and the estimated speed of the target vehicle obtained from the vehicle network platform. The big data platform described above determines the target driving distance for each route based on the drivable distance and the corresponding route length. The big data platform determines the first energy consumed by the target in-vehicle electrical equipment corresponding to each route based on the target driving distance, the expected speed of the target vehicle, and the total power value corresponding to each route; determines the second energy consumed by the target vehicle's operation corresponding to each route based on the target driving distance, the number of passengers, and the identification information of the target vehicle corresponding to each route; adds the first and second energies together to obtain the target charge amount required for the target vehicle corresponding to each route. The big data platform inputs the battery level, the target charge amount corresponding to each route, the charging power of multiple charging piles in each route, the distance between multiple charging piles in each route, and the rated charge amount of the target vehicle (the amount of charge when the target vehicle is fully charged) into the energy replenishment recommendation algorithm to determine the combination of charging piles corresponding to each route and the charging time corresponding to each charging pile in the combination of charging piles. The big data platform transmits to the in-vehicle terminal the combination of charging piles corresponding to each of the above routes and the charging time corresponding to each charging pile in the above combination of charging piles. The above-mentioned in-vehicle terminal provides a method that includes displaying the above-mentioned multiple routes, marking the combination of charging piles corresponding to each of the above-mentioned routes, and the charging time corresponding to each charging pile in the above-mentioned combination of charging piles.

[0006] In one possible embodiment, the in-vehicle terminal acquires status information of multiple in-vehicle electrical devices and the number of passengers. The above-mentioned in-vehicle terminal includes acquiring status information of multiple in-vehicle electrical devices and the number of passengers entered by the user.

[0007] In one possible embodiment, the in-vehicle terminal acquires status information of multiple in-vehicle electrical devices. The above-mentioned in-vehicle terminal includes detecting status information of multiple in-vehicle electrical devices at specified intervals.

[0008] In one possible embodiment, the big data platform determines the total weight of the target vehicle based on the number of passengers and the identifier information of the target vehicle, adds the rated power of the target vehicle's in-vehicle electrical equipment, obtains the total power value of the target vehicle's in-vehicle electrical equipment, and determines the driving distance of the target vehicle with the remaining battery capacity based on the remaining battery capacity, the total weight, the total power value, and the expected speed of the target vehicle. This includes determining the above drivable distance based on the following formula, TIFF2026511418000002.tif3174 Here, G is the total weight of the target vehicle, n is the number of passengers, m1 is the standard weight, m2 is the mass of the target vehicle, P is the total power value of the target in-vehicle electrical equipment, P n is the power value of any target in-vehicle electrical equipment, S is the driving range mentioned above, W is the battery charge mentioned above, k is the friction coefficient, and v is the expected speed of the target vehicle mentioned above.

[0009] In one possible embodiment, the big data platform determines the target driving distance corresponding to each route based on the drivable distance and the route length corresponding to each route. This includes determining the target driving distance for each route by subtracting the distance obtained by adding a predetermined reserve distance to the drivable distance from the route length corresponding to each of the above routes.

[0010] In one possible embodiment, the big data platform determines, based on the target mileage, the expected speed of the target vehicle, and the total power value corresponding to each route, the first energy consumed by the target in-vehicle electrical equipment whose status information in the plurality of in-vehicle equipment is activated, and determines, based on the target mileage, the number of passengers, and the identifier information of the target vehicle corresponding to each route, the second energy consumed by the target vehicle's travel corresponding to each route, and adds the first energy and the second energy to obtain the target charge amount required for the target vehicle corresponding to each route. This includes determining the target charge amount required for the target vehicle corresponding to each of the above routes, based on the following formula: TIFF2026511418000003.tif2753 Here, W1 is the first energy, P is the total power value of the target in-vehicle electrical equipment, S1 is the target driving distance, v is the expected speed of the target vehicle, W2 is the second energy, G is the total weight of the target vehicle, k is the friction coefficient, and W3 is the target charge amount.

[0011] In one possible embodiment, the big data platform inputs the remaining battery charge, the target charge amount corresponding to each route, the charging power of multiple charging piles in each route, the distance between multiple charging piles in each route, and the rated power of the target vehicle into an energy replenishment recommendation algorithm, and determines the combination of charging piles corresponding to each route and the charging time corresponding to each charging pile in the combination of charging piles. The process involves arranging and combining multiple charging piles in each of the above paths to obtain a combination of multiple base charging piles corresponding to each of the above paths, Based on the charging power of the charging piles in each combination of basic charging piles and the target charging amount corresponding to each of the above paths, the charging time corresponding to each charging pile in each combination of basic charging piles is determined. Based on the above battery level, the charging power of the charging piles in each combination of basic charging piles, the charging time corresponding to each charging pile in each combination of basic charging piles, the distance between multiple charging piles in each combination of basic charging piles, and the rated power of the target vehicle, multiple combinations of basic charging piles corresponding to each path are screened to obtain a feasible combination of charging piles corresponding to each path. This includes determining the combination of charging piles with the shortest charging time among the feasible combinations of charging piles corresponding to each of the above paths as the combination of charging piles corresponding to each of the above paths.

[0012] In one possible embodiment, the above method is The big data platform determines whether the target vehicle supports a power exchange function based on the identifier information of the target vehicle. When the target vehicle supports the power exchange function, the total weight of the target vehicle is determined based on the number of passengers and the identifier information of the target vehicle, the rated power of the in-vehicle devices is added, the total power value of the in-vehicle electrical devices of the in-vehicle devices whose status information is startup is obtained, and based on the remaining battery capacity, the total weight, the total power value, and the predicted speed of the target vehicle, the travelable distance of the target vehicle with the remaining battery capacity is determined. The big data platform transmits a plurality of power exchange stations within the travelable distance in each of the routes determined to the in-vehicle terminal. The in-vehicle terminal further includes displaying the plurality of routes and marking a plurality of power exchange stations within the travelable distance in each of the routes.

[0013] In a second aspect, there is provided a computer device including a memory for storing computer instructions and a processor for executing the computer instructions stored in the memory for the computer device to execute the methods of the first aspect and its possible embodiments.

[0014] In a third aspect, there is provided a computer-readable storage medium storing computer program code, and in response to the computer program code being executed by a computer device, the computer device executes the methods of the first aspect and its possible embodiments.

[0015] In a fourth aspect, there is provided a computer program product including computer program code, and in response to the computer program code being executed by a computer device, the computer device executes the methods of the first aspect and its possible embodiments.

[0016] The technical solutions provided by the embodiments of the present disclosure can include the following beneficial effects:

[0017] According to the technical solution provided by the embodiments of the present disclosure, the in-vehicle terminal transmits the status information of a plurality of in-vehicle electrical devices and the number of passengers to the big data platform. Based on this information, the big data platform calculates the charging amount required for the target vehicle to reach the destination (i.e., the target charging amount). Furthermore, based on the remaining battery level and the charging amount, a plurality of energy replenishment methods can be determined, and the corresponding combination of charging piles and charging time can be marked. In this way, by automatically determining the energy replenishment method, the user can understand the energy replenishment methods on different routes without planning the energy replenishment method by himself / herself. Furthermore, the user can perform energy replenishment on the vehicle according to the selected energy replenishment method based on the selected route and reach the destination, which is relatively convenient.

Brief Description of the Drawings

[0018] To more clearly explain the technical solution in the embodiments of the present disclosure, the drawings used in the following embodiments are briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative labor. [Figure 1] It is a block diagram of an energy replenishment method recommendation system provided by the embodiments of the present disclosure. [Figure 2] It is a block diagram of a terminal provided by the embodiments of the present disclosure. [Figure 3] It is a block diagram of a server provided by the embodiments of the present disclosure. [Figure 4] It is a flowchart of a method for determining an energy replenishment method provided by the embodiments of the present disclosure. [Figure 5] It is a schematic diagram for determining the target driving distance provided by the embodiments of the present disclosure. [Figure 6] It is a schematic diagram for determining the target driving distance provided by the embodiments of the present disclosure. [Figure 7] It is a block diagram of an electronic device provided by the embodiments of the present disclosure. [Modes for carrying out the invention]

[0019] Embodiments of this disclosure provide a method for determining an energy replenishment method, which is used to recommend an energy replenishment method corresponding to the user's travel route while the user is driving. This method can be implemented by an energy replenishment method recommendation system. The energy replenishment method recommendation system may include an in-vehicle terminal, a big data platform, a vehicle network platform, a map platform, etc., as shown in Figure 1. The big data platform, vehicle network platform, and map platform may also be background servers for related applications. Here, the big data platform is used to acquire relevant data from the vehicle network platform and the map platform and to determine an energy replenishment method corresponding to the vehicle. The vehicle network platform is used to record relevant vehicle driving data such as speed and battery level. The map platform is used to determine the vehicle's location and to provide the vehicle with multiple routes to reach its destination, etc.

[0020] From a hardware configuration perspective, the structure of the in-vehicle terminal may include a processor 210, memory 220, display component 230, and communication component 240, as shown in Figure 2.

[0021] The processor 210 may be a CPU (central processing unit) or a SoC (system on chip), and the processor 110 may be used to execute various instructions related to the said method.

[0022] The memory 220 may include various volatile or non-volatile memories, such as SSDs (solid-state disks) and DRAM (dynamic random access memory). The memory 220 may be used to store pre-storage data, intermediate data, and result data in the process of determining the energy replenishment method.

[0023] The display component 230 may be a separate screen, a screen integrated with the terminal body, a projector, etc. The screen may be a touchscreen or a non-touchscreen. The display component is used as an operation interface, such as a target route display interface.

[0024] The communication component 240 may be a wired network connector, a WiFi (Wireless Fidelity) module, a Bluetooth® module, a honeycomb network communication module, or the like. The communication component 240 may also be used to transmit data with other devices, such as a server or other terminal.

[0025] In addition to the processor and memory, the in-vehicle terminal may also include a voice acquisition component, a voice output component, and the like.

[0026] The audio collection component may be a microphone and is used to collect the user's voice. The audio output component may be a speaker box, headset, etc., and is used to play back the audio.

[0027] From a hardware configuration perspective, the server structure may include a processor 310, memory 320, and communication components 330, as shown in Figure 3.

[0028] The processor 310 may be a CPU or an SoC, and the processor 310 may be used to execute various instructions related to the said method.

[0029] The memory 320 may include various volatile or non-volatile memories, such as SSDs and DRAM memory. The memory 320 may be used to store pre-storage data, intermediate data, and result data in the process of determining the energy replenishment method, such as multiple correspondences.

[0030] The communication component 330 may be a wired network connector, a WiFi module, a Bluetooth® module, a honeycomb network communication module, or the like. The communication component 330 may also be used to transmit data with other devices, such as a server or other terminal.

[0031] In the field of new energy vehicle technology, particularly electric vehicles, range is always a concern for users. Therefore, when people choose to drive themselves, they can enter the name of their destination into an in-car terminal, select a route to that destination, and determine whether there are energy refueling stations such as power exchange stations and charging stations along the selected route.

[0032] Embodiments of this disclosure provide a method for determining an energy replenishment method for the above application scenario. The processing flow of this method may include the following processing steps, as shown in Figure 4.

[0033] 401. After detecting the user's input of a destination, the in-vehicle terminal sends a route acquisition request to the map platform.

[0034] Here, the route acquisition request includes identifier information for the target vehicle and identifier information for the destination. The identifier information for the target vehicle may include the license plate number, frame number, and vehicle type of the target vehicle. The identifier information for the destination may be the name of the destination.

[0035] In practice, if a user needs to drive to a certain destination, they can enter the name of the destination into the map application. In this case, the in-vehicle terminal detects the user's input of the destination name and sends a route acquisition request to the map platform.

[0036] 402. The in-vehicle terminal acquires status information of multiple in-vehicle electrical devices and the number of passengers, and sends an energy replenishment method recommendation request to the big data platform.

[0037] Here, multiple in-vehicle electrical devices may include air conditioners, sound systems, etc. The status information is either running or not running. The recommended energy replenishment method request may include the target vehicle identifier information and the status information of multiple in-vehicle electrical devices, etc.

[0038] In implementation, users can select status information for each in-vehicle electrical device based on their usage habits, using the application recommended by the energy replenishment method. Users will also need to input the number of passengers into the in-vehicle terminal.

[0039] In other possible embodiments, the in-vehicle terminal can detect status information for multiple in-vehicle electrical devices at specified intervals and detect the number of occupants using pressure sensors located below the seats of the target vehicle. After detecting that the user has entered a destination, the in-vehicle terminal can add the status information for multiple in-vehicle electrical devices and the number of occupants acquired in the most recent interval to an energy replenishment method recommendation request and transmit it to a big data platform.

[0040] 403. The map platform determines multiple routes between the target vehicle's location and its destination, and transmits them to the big data platform and the in-vehicle terminal.

[0041] In implementation, the map platform, after receiving a route acquisition request, can determine the location of the target vehicle based on the target vehicle identifier information carried with the route acquisition request, determine the location of the destination based on the destination identifier information carried with the route acquisition request, and further plan multiple routes based on the location of the target vehicle and the location of the destination, and transmit them to the big data platform and the in-vehicle terminal.

[0042] 404. The big data platform determines that the status information of at least one of the multiple in-vehicle electrical devices is in the activated state, and identifies the in-vehicle electrical device whose status information is in the activated state as the target in-vehicle electrical device. Based on the correspondence between the vehicle identifier information and the rated power of the in-vehicle electrical device, and the identifier information of the target vehicle, it determines the rated power of the target in-vehicle electrical device.

[0043] In implementation, after receiving an energy replenishment method recommendation request, the big data platform retrieves status information for multiple in-vehicle electrical devices from the energy replenishment method recommendation request. If the status information for all of the multiple in-vehicle electrical devices indicates an inactive state, the subsequent processing in this step may be omitted. If the status information for at least one of the multiple in-vehicle electrical devices indicates an active state, the in-vehicle electrical device with the active status information can be determined as the target in-vehicle electrical device.

[0044] The big data platform may store the correspondence between vehicle identifier information and the rated power of in-vehicle electrical equipment, and this correspondence may be shown in Table 1. In this way, the big data platform can determine, based on the identifier information of the target in-vehicle electrical equipment, multiple in-vehicle electrical equipment corresponding to the target vehicle and the rated power corresponding to the multiple in-vehicle electrical equipment in the correspondence table, and further determine the rated power of the target in-vehicle electrical equipment in the correspondence table based on the identifier information of the target in-vehicle electrical equipment.

[0045] [Table 1]

[0046] For example, if the vehicle identifier is "Type A vehicle," then Table 1 above can be used to determine the multiple in-vehicle electrical devices corresponding to the Type A vehicle and the rated power of those devices. If the target in-vehicle electrical device is "air conditioner," then the rated power of the air conditioner in the Type A vehicle can be determined to be 3kW.

[0047] 405. The big data platform determines the total weight of the target vehicle based on the number of passengers and the identifier information of the target vehicle, adds the rated power of the target vehicle's in-vehicle electrical equipment to obtain the total power value of the target vehicle's in-vehicle electrical equipment, and determines the target vehicle's driving range based on the remaining battery level, total weight, total power value, and the target vehicle's estimated speed.

[0048] Here, the battery level and the target vehicle's predicted speed may be obtained by the big data platform from the vehicle network platform. The target vehicle can transmit its own status parameters, such as driving speed and battery level, to the vehicle network platform at a specified frequency. The vehicle network platform can statistically analyze the received driving speeds to obtain the target vehicle's average speed and update the average speed at a preset interval. In this way, the big data platform can obtain the target vehicle's average speed from the vehicle network platform and use it as the target vehicle's predicted speed.

[0049] In implementation, the big data platform may store the correspondence between vehicle identifier information and the vehicle's mass (i.e., net weight). In this way, the big data platform can determine the target vehicle's mass based on the correspondence between the vehicle identifier information and the vehicle's mass, using the target vehicle's identifier information as a basis. Furthermore, the big data platform can determine the target vehicle's total weight based on equation (1) below. Simultaneously, the big data platform can obtain the total power value of the target vehicle's in-vehicle electrical equipment by adding the rated power of the target vehicle's in-vehicle electrical equipment based on equation (2). Furthermore, the big data platform can determine the target vehicle's remaining driving range based on equation (3).

[0050] The calculation formula is as follows: TIFF2026511418000005.tif3174 Here, G is the total weight of the target vehicle, n is the number of passengers, m1 is the standard weight, m2 is the mass of the target vehicle, P is the total power value of the target in-vehicle electrical equipment, P n P is the power value of any target in-vehicle electrical equipment, S is the remaining driving range, W is the battery charge, k is the friction coefficient, and v is the expected speed of the target vehicle. It should be understood that in equation (1), n ​​refers to the number of passengers, and in equation (2), P is... n In equation (1), n ​​refers to the total number of target in-vehicle electrical devices, and therefore, n in equation (1) is different from n in equation (2).

[0051] In another possible embodiment, the in-vehicle terminal can calculate the weight of each occupant based on the pressure detected by a pressure sensor under the seat of the target vehicle and transmit this information to a big data platform. In this way, the big data platform can calculate the total weight of the target vehicle based on the actual weight of the occupants, which is more accurate.

[0052] 406. The big data platform determines the target driving distance for each route based on the drivable distance and the corresponding route length.

[0053] In implementation, the big data platform can calculate the drivable distance and then compare it with the route length of each route. If the route length of each route is less than or equal to the drivable distance, it indicates that the target vehicle has sufficient power and can reach its destination without refueling, in which case no further processing is required. If the route length of at least one route is greater than the drivable distance, the big data platform can subtract the drivable distance from the route length of each route, as shown in Figure 5, to obtain the target drivable distance for each route. Alternatively, as shown in Figure 6, the big data platform can subtract the distance obtained by adding a pre-set reserve distance to the drivable distance from the route length of each route, to obtain the target drivable distance for each route. For example, the pre-set reserve distance may be 10 kilometers. By setting a pre-set reserve distance in this way, the target vehicle can still have some margin after arriving at its destination, or even if the target vehicle consumes a significant amount of power, it can still reach its destination, making it safer.

[0054] 407. The big data platform determines the first energy consumed by the target in-vehicle electrical equipment for each route based on the target mileage, target vehicle speed, and total power value for each route. It then determines the second energy consumed by the target vehicle's operation for each route based on the target mileage, number of passengers, and target vehicle identifier information for each route. The first and second energies are added together to obtain the target charge amount required for the target vehicle for each route.

[0055] In implementation, the big data platform can calculate the first energy consumed by the target in-vehicle electrical equipment when the target vehicle travels the target distance based on equation (4), and the second energy consumed by the target vehicle when it travels the target distance based on equation (5). Finally, the big data platform adds the first and second energies to obtain the target charge amount. In this way, the target charge amount required for the target vehicle corresponding to each route can be calculated and obtained.

[0056] The calculation formula is as follows: TIFF2026511418000006.tif2753 Here, W1 is the first energy source, S1 is the target driving distance, W2 is the second energy source, and W3 is the target charge amount.

[0057] 408. The big data platform inputs battery level, target charge amount for each route, charging power of multiple charging piles in each route, distance between multiple charging piles in each route, and target vehicle rated power into the energy replenishment recommendation algorithm to determine the combination of charging piles for each route and the charging time for each charging pile in that combination.

[0058] Here, the rated electric charge is the amount of electric charge when the vehicle is fully charged.

[0059] The specific steps for determining the combination of charging piles corresponding to each path and the charging time corresponding to each charging pile in that combination may be as follows.

[0060] In step 1, multiple charging piles in each path are arranged and combined to obtain a combination of multiple base charging piles corresponding to each path.

[0061] In implementation, for each path, the big data platform can identify all the charging piles on the path and determine the usage status, health status, etc. of all the charging piles. Next, after excluding the charging piles with the usage status of being in use or the health status of being unhealthy, the remaining charging piles are arranged and combined. For example, in a certain path, there are five charging piles: charging pile A, charging pile B, charging pile C, charging pile D, charging pile E, and charging pile F. Here, the usage status of charging pile F is in use. After excluding it, after arranging and combining the remaining five charging piles, multiple combinations of basic charging piles such as (A), (B), (C)……(A,B), (A,C)……(A,B,C)……(A,B,C,D)……(A,B,C,D,E) can be obtained.

[0062] In step 2, based on the charging power of the charging piles in each combination of basic charging piles and the target charging amount corresponding to each path, the charging time corresponding to each charging pile in each combination of basic charging piles is determined.

[0063] In implementation, for each combination of basic charging piles, the big data platform uses the formula W3 = P a t a +……+P n t n to determine multiple numerical solutions for the charging time of each charging pile, and further, one numerical solution can be randomly selected from these multiple numerical solutions as the combination of the charging times of the combination of basic charging piles, that is, the charging time corresponding to each charging pile in the combination of basic charging piles is determined. Here, n represents the symbol of the charging pile, P n represents the charging power of the charging pile, and t n represents the charging time of the charging pile.

[0064] In step 3, based on the remaining battery charge, the charging power of each charging pile in each combination of basic charging piles, the charging time corresponding to each charging pile in each combination of basic charging piles, the distance between multiple charging piles in each combination of basic charging piles, and the rated energy of the target vehicle, multiple combinations of basic charging piles corresponding to each route are screened to obtain a feasible combination of charging piles corresponding to each route.

[0065] In implementation, for each combination of base charging piles, the big data platform can calculate the product of the charging power and charging time of each charging pile, and this product can be used as the charge amount of the charging pile. Next, the big data platform can determine the distance between each pair of charging piles, and further calculate the amount of electricity required from one charging pile to the other based on equations (4) to (6), and determine this amount of electricity as the power consumption corresponding to the charging pile located further back among the two charging piles.

[0066] During screening, the battery level and the charge level and power consumption of each charging pile can be processed: Firstly, for the first charging pile in each combination of basic charging piles, the distance from the target vehicle to the first charging pile is determined, and based on equations (4) to (6) above, the amount of electricity required from the target vehicle to the first charging pile is obtained, and this amount of electricity can be determined as the amount of electricity consumed corresponding to the first charging pile. Furthermore, it is determined whether the remaining battery capacity is equal to or greater than the amount of electricity consumed corresponding to the first charging pile. If not, the combination of basic charging piles can be excluded. If so, screening is performed according to the second rule.

[0067] Secondly, for the m-th charging pile (where m is a positive integer greater than 1) in each combination of basic charging piles, the charge amount of the first m charging piles is added, the remaining battery charge is added, and then the added value of the power consumption of the first m charging piles is subtracted to obtain the power amount at the m-th charging pile of the target vehicle. Furthermore, it is determined whether this power amount is greater than the rated power of the target vehicle, and if so, the combination of basic charging piles can be excluded. Otherwise, if m is equal to the number of charging piles in the combination of basic charging piles, the combination of basic charging piles is determined as a viable combination of charging piles corresponding to the corresponding path.

[0068] In step 4, the combination of charging piles with the shortest charging time among the feasible combinations of charging piles corresponding to each path is determined as the charging pile combination corresponding to each path.

[0069] In implementation, the above process can determine multiple feasible charging pile combinations corresponding to each route. The big data platform adds up the charging times of the charging piles in each feasible charging pile combination to obtain the charging time corresponding to each feasible charging pile combination. Furthermore, the feasible charging pile combinations are sorted from smallest to largest charging time, and the first feasible charging pile combination is determined as the charging pile combination corresponding to the corresponding route. This allows the charging pile combination corresponding to each route to be determined.

[0070] In other possible embodiments, the energy replenishment recommendation algorithm may be a machine learning algorithm.

[0071] 409. The big data platform transmits to the in-vehicle terminal the combination of charging piles corresponding to each route and the charging time corresponding to each charging pile in that combination.

[0072] 410. The in-vehicle terminal displays multiple routes and marks the combination of charging piles corresponding to each route and the charging time corresponding to each charging pile in the combination of charging piles.

[0073] In implementation, when the in-vehicle terminal receives multiple routes transmitted from the map platform, it displays the multiple routes and, upon receiving the combination of charging piles corresponding to each route and the corresponding charging time for each charging pile in that combination, marks the charging piles and corresponding charging times in the determined charging pile combination on the corresponding route. The specific marking method may be to highlight the charging piles in the charging pile combination, for example, by enlarging the icon of the charging pile in the charging pile combination and marking it with a special symbol. At the same time, the symbol for the charging pile combination and the corresponding charging time for each charging pile can also be displayed in the lower right corner of the related display interface.

[0074] Since some new energy vehicles support power swapping, energy replenishment via power swapping is relatively more convenient than energy replenishment via charging. Accordingly, embodiments of this disclosure further provide a method for determining a power swapping station, and the specific processing steps may be as follows.

[0075] In Step 1, the big data platform determines whether the target vehicle supports power swapping based on the target vehicle's identifier information. If the target vehicle supports power swapping, it determines the total weight of the target vehicle based on the number of passengers and the target vehicle's identifier information. It then adds the rated power of the target vehicle's in-vehicle electrical equipment whose status information indicates it is running, obtains the total power value of the target vehicle's in-vehicle electrical equipment whose status information indicates it is running, and determines the target vehicle's driving range based on the remaining battery capacity, total weight, total power value, and the target vehicle's estimated speed.

[0076] In implementation, the big data platform may store the correspondence between vehicle identifier information and energy replenishment methods. In this way, the big data platform can obtain the target vehicle identifier information from the energy replenishment method acquisition request and, based on the target vehicle identifier information, determine whether the target vehicle supports the power swapping function in the correspondence between the vehicle identifier information and the energy replenishment method. If so, the big data platform can calculate the target vehicle's driving range based on the remaining battery charge. The specific calculation method may be the same as in step 405, and its explanation is omitted here. Otherwise, the process in step 406 can be performed after determining the driving range.

[0077] In step 2, the big data platform transmits to the in-vehicle terminal multiple power exchange stations within the drivable distance for each determined route.

[0078] In implementation, the big data platform identifies all power switching stations within the drivable distance along multiple routes provided by the map platform and transmits this information to the in-vehicle terminal.

[0079] In step 3, the in-vehicle terminal displays multiple routes and marks multiple power exchange stations within the drivable distance for each route.

[0080] In implementation, the in-vehicle terminal receives multiple routes, displays them, and after receiving multiple power exchange stations within the drivable distance for each route, marks the power exchange stations. The specific marking method may be to highlight the multiple power exchange stations within the drivable distance, for example, by enlarging the icons of the multiple power exchange stations within the drivable distance and marking them with a special symbol.

[0081] In the method provided by the embodiments of this disclosure, an in-vehicle terminal transmits status information of multiple in-vehicle electrical devices and the number of passengers to a big data platform. Based on this information, the big data platform calculates the amount of charge required for the target vehicle to reach its destination (i.e., the target charge amount), and further determines multiple energy replenishment methods based on the battery level and charge amount, and marks the corresponding charging pile combinations and charging times. This method of automatically determining the energy replenishment method allows the user to understand the energy replenishment method for different routes without having to plan it themselves. Furthermore, the user can replenish the vehicle using the corresponding energy replenishment method based on the selected route and arrive at their destination, which is relatively convenient.

[0082] Figure 7 shows a configuration diagram of the electronic device 700 provided by an embodiment of the present disclosure. The electronic device may be any of the terminals in the above embodiment. The electronic device 700 may be a smartphone, tablet, MP3 (moving picture experts group audio layer III) player, MP4 (moving picture experts group audio layer IV) player, laptop computer, or desktop computer portable mobile terminal. The electronic device 700 may also be referred to by other names such as user device, portable terminal, laptop terminal, or desktop terminal.

[0083] Typically, the electronic device 700 includes a processor 701 and memory 702.

[0084] The processor 701 may include one or more processing cores, such as a 4-core processor or an 8-core processor. The processor 701 can be implemented in at least one hardware form from among DSP (digital signal processing), FPGA (field-programmable gate array), and PLA (programmable logic array). The processor 701 may include a main processor and a sub-processor. The main processor is a processor used to process data in the awake state and is also called a CPU. The sub-processor is a low-power processor used to process data in the standby state. In some embodiments, the processor 701 may integrate a GPU (graphics processing unit) responsible for rendering and drawing content displayed on a screen. In some embodiments, the processor 701 may further include an AI (artificial intelligence) processor for processing computational operations related to machine learning.

[0085] The memory 702 may include one or more computer-readable storage media, which may be non-temporary. The memory 702 may include one or more high-speed random-access memories and non-volatile memories, such as magnetic disk storage devices and flash memory devices. In some embodiments, the non-temporary computer-readable storage media in the memory 702 are used to store at least one instruction to be executed by the processor 701 to implement the method provided by embodiments of the present disclosure.

[0086] In some embodiments, the electronic device 700 further optionally includes a peripheral device interface 703 and at least one peripheral device. The processor 701, memory 702, and peripheral device interface 703 may be connected via a bus or signal lines. Each peripheral device may be connected to the peripheral device interface 703 via a bus, signal lines, or circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707, a positioning assembly 708, and a power supply 709.

[0087] The peripheral interface 703 may be used to connect at least one I / O (input / output) related peripheral device to the processor 701 and the memory 702. In some embodiments, the processor 701, memory 702, and peripheral interface 703 are integrated on the same chip or circuit board. In some other embodiments, one or two of the processor 701, memory 702, and peripheral interface 703 may be implemented on separate chips or circuit boards, and this embodiment is not limited thereto.

[0088] The radio frequency circuit 704 is used for receiving and transmitting RF (radio frequency) signals, also known as electromagnetic signals. The radio frequency circuit 704 communicates with communication networks and other communication equipment using electromagnetic signals. The radio frequency circuit 704 converts electrical signals into electromagnetic signals and transmits them, or converts received electromagnetic signals into electrical signals. Selectively, the radio frequency circuit 704 includes an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user ID module card, etc. The radio frequency circuit 704 can communicate with other terminals by at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, the World Wide Web, metropolitan area networks, intranets, each generation of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (wireless fidelity) networks. In some embodiments, the radio frequency circuit 704 may further include circuits related to NFC (near-field communication), and the disclosure is not limited thereto.

[0089] The display screen 705 is used to display a UI (user interface). The UI may include graphics, text, icons, videos, and any combination thereof. If the display screen 705 is a touchscreen display, the display screen 705 also has the ability to collect touch signals on or above the surface of the display screen 705. These touch signals may be input to the processor 701 for processing as control signals. In this case, the display screen 705 may further be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, there may be one display screen 705, which is mounted on the front panel of the electronic device 700. In some other embodiments, there may be at least two display screens 705, which are mounted on different surfaces of the electronic device 700, or are designed to fold. In some other embodiments, the display screen 705 may be a flexible display screen mounted on a curved or folding surface of the electronic device 700. Furthermore, the display screen 705 may be mounted on an irregular shape other than a rectangle, i.e., a non-rectangular screen. The display screen 705 may be manufactured using materials such as LCD (liquid crystal display) or OLED (organic light-emitting diode).

[0090] The camera assembly 706 is used to collect images or videos. Selectively, the camera assembly 706 includes a front camera and a rear camera. Typically, the front camera is mounted on the front panel of the terminal, and the rear camera is mounted on the back of the terminal. In some embodiments, there are at least two rear cameras, each being one of a main camera, a depth-of-field camera, a wide-angle camera, or a telephoto camera, thereby enabling a background blur function by fusing the main camera and the depth-of-field camera, a panoramic shooting and VR (virtual reality) shooting function or other fused shooting function by fusing the main camera and the wide-angle camera. In some embodiments, the camera assembly 706 may further include a flash lamp. The flash lamp may be a monochromatic temperature flash lamp or a dichromatic temperature flash lamp. A dichromatic temperature flash lamp refers to a combination of a warm-light flash lamp and a cold-light flash lamp, which may be used for ray compensation at different color temperatures.

[0091] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, convert the sound waves into electrical signals, and input them to the processor 701 for processing, or to input them to the radio frequency circuit 704 to realize voice communication. There may be multiple microphones for the purpose of stereo sound collection or noise reduction, and each may be installed at a different location on the electronic device 700. The microphone may be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker may be a conventional thin-film speaker or a piezoelectric ceramic speaker. If the speaker is a piezoelectric ceramic speaker, it can convert electrical signals into sound waves that are not audible to humans, and can be used for applications such as distance measurement. In some embodiments, the audio circuit 707 may further include an earphone jack.

[0092] The positioning assembly 708 is used to determine the current geographical location of the electronic device 700 in order to implement navigation or LBS (location-based service). The positioning assembly 708 may be a positioning assembly based on GPS (global positioning system) or the Beidou system.

[0093] The power supply 709 is used to supply power to each assembly within the electronic device 700. The power supply 709 may be AC ​​power, DC power, a disposable battery, or a rechargeable battery. If the power supply 709 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery may further be used to support fast charging technology.

[0094] In some embodiments, the electronic device 700 may further include one or more sensors 710. These one or more sensors 710 include, but are not limited to, an accelerometer 711, a gyroscope 712, a pressure sensor 713, a fingerprint sensor 714, an optical sensor 715, and a proximity sensor 716.

[0095] The accelerometer 711 can detect the magnitude of acceleration in three coordinate axes of a coordinate system established by the electronic device 700. For example, the accelerometer 711 may be used to detect the components of gravitational acceleration in three coordinate axes. Based on the gravitational acceleration signal collected by the accelerometer 711, the processor 701 can control the display screen 705 to display the user interface in a horizontal or vertical view. The accelerometer 711 may further be used to collect game or user motion data.

[0096] The gyro sensor 712 can detect the orientation and rotation angle of the electronic device 700, and can work in cooperation with the accelerometer 711 to collect 3D motion data of the user on the electronic device 700. Based on the data collected by the gyro sensor 712, the processor 701 can implement motion sensing (e.g., UI changes due to user tilt operations), image stabilization during shooting, game control, and inertial navigation functions.

[0097] The pressure sensor 713 may be installed on the side frame of the electronic device 700 and / or beneath the display screen 705. When the pressure sensor 713 is installed on the side frame of the electronic device 700, it can detect the user's gripping signal to the electronic device 700, and the processor 701 performs left / right hand recognition or quick operation based on the gripping signal collected by the pressure sensor 713. When the pressure sensor 713 is installed beneath the display screen 705, the processor 701 implements control over operable controls in the UI interface based on the user's pressure operation on the display screen 705. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0098] The fingerprint sensor 714 is used to collect the user's fingerprint. The processor 701 recognizes the user's identity based on the fingerprint collected by the fingerprint sensor 714, or the fingerprint sensor 714 recognizes the user's identity based on the collected fingerprint. If the user's identity is recognized as a trusted identity, the processor 701 allows the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 714 may be installed on the front, back, or side of the electronic device 700. If the electronic device 700 has physical buttons or a manufacturer's logo, the fingerprint sensor 714 may be integrated with the physical buttons or manufacturer's logo.

[0099] The optical sensor 715 is used to collect ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 based on the ambient light intensity collected by the optical sensor 715. Specifically, the display brightness of the display screen 705 is increased when the ambient light intensity is relatively high, and decreased when the ambient light intensity is relatively low. In another embodiment, the processor 701 may dynamically adjust the shooting parameters of the camera assembly 706 based on the ambient light intensity collected by the optical sensor 715.

[0100] The proximity sensor 716, also known as a distance sensor, is typically installed on the front panel of the electronic device 700. The proximity sensor 716 is used to collect the distance between the user and the front of the electronic device 700. In one embodiment, if the proximity sensor 716 detects that the distance between the user and the front of the electronic device 700 is gradually decreasing, the processor 701 controls the display screen 705 to switch from a screen-on state to a screen-off state. If the proximity sensor 716 detects that the distance between the user and the front of the electronic device 700 is gradually increasing, the processor 701 controls the display screen 705 to switch from a screen-off state to a screen-on state.

[0101] As will be understood by those skilled in the art, the structure shown in Figure 7 is not limited to the electronic device 700 and may include more or fewer assemblies than those shown, or may be arranged using a combination of several assemblies or different assemblies.

[0102] In embodiments of the present disclosure, a computer-readable storage medium, such as memory containing instructions, may be further provided, the instructions being executed by a processor in a terminal to complete the method of performing interaction operations in the embodiments. The computer-readable storage medium may be non-temporary. For example, the computer-readable storage medium may be ROM (read-only memory), RAM (random access memory), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0103] It should be explained that any information relating to this disclosure (including, but not limited to, user device information and user personal information), data (including, but not limited to, data used for analysis, stored data, and displayed data), and signals (including, but not limited to, signals transmitted between the user terminal and other devices) must be obtained with the user's permission or with the full permission of each party involved, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant country or region.

[0104] Those skilled in the art will understand that all or part of the steps for carrying out the above embodiments can be performed by hardware or by a program that instructs the relevant hardware, and that the program can be stored in a computer-readable storage medium such as read-only memory, magnetic disk, or optical disk.

[0105] The foregoing description is only a part of the possible embodiments of this disclosure and does not limit it. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of the claims of this disclosure.

[0106] This disclosure claims priority to the Chinese patent application filed on 26 June 2023, application number 202310771113.0, with the title "Method, apparatus and memory medium for determining an energy replenishment method," the entire contents of which are incorporated herein by reference.

Claims

1. A method for determining the energy replenishment method, The in-vehicle terminal, after detecting the user's input operation for a destination, sends a route acquisition request to the map platform, which includes the identifier information of the target vehicle. The in-vehicle terminal acquires status information of multiple in-vehicle electrical devices, whether they are running or not, and the number of passengers, and transmits an energy replenishment method recommendation request to the big data platform, which includes the identifier information of the target vehicle and the status information of the multiple in-vehicle electrical devices. The map platform determines multiple routes between the location of the target vehicle and the destination and transmits them to the big data platform and the in-vehicle terminal. The big data platform determines that the status information of at least one of the multiple in-vehicle electrical devices is in an activated state, identifies the in-vehicle electrical device whose status information is in an activated state as the target in-vehicle electrical device, and determines the rated power of the target in-vehicle electrical device based on the correspondence between the vehicle identifier information and the rated power of the in-vehicle electrical device, and the identifier information of the target vehicle. The big data platform determines the total weight of the target vehicle based on the number of passengers and the identifier information of the target vehicle, adds the rated power of the target in-vehicle electrical equipment, obtains the total power value of the target in-vehicle electrical equipment, and determines the driving range of the target vehicle with the remaining battery power based on the battery level, total weight, total power value, and the estimated speed of the target vehicle obtained from the vehicle network platform by the big data platform. The big data platform determines the target driving distance for each route based on the drivable distance and the route length corresponding to each route, The big data platform determines the first energy consumed by the target in-vehicle electrical equipment corresponding to each route based on the target mileage, the expected speed of the target vehicle, and the total power value corresponding to each route; determines the second energy consumed by the target vehicle's operation corresponding to each route based on the target mileage, the number of passengers, and the identifier information of the target vehicle corresponding to each route; adds the first energy and the second energy to obtain the target charge amount required for the target vehicle corresponding to each route. The big data platform inputs the remaining battery charge, the target charge amount corresponding to each route, the charging power of the multiple charging piles in each route, the distance between the multiple charging piles in each route, and the rated charge of the target vehicle, which is the amount of charge when the target vehicle is fully charged, into an energy replenishment recommendation algorithm to determine the combination of charging piles corresponding to each route and the charging time corresponding to each charging pile in the combination of charging piles. The big data platform transmits to the in-vehicle terminal the combination of charging piles corresponding to each route and the charging time corresponding to each charging pile in the combination of charging piles. The in-vehicle terminal includes displaying the plurality of routes, marking the combination of charging piles corresponding to each route, and marking the charging time corresponding to each charging pile in the combination of charging piles, A method characterized by the following:

2. The in-vehicle terminal acquires status information of multiple in-vehicle electrical devices and the number of passengers. The in-vehicle terminal includes acquiring status information of multiple in-vehicle electrical devices and the number of passengers entered by the user. The method according to feature 1.

3. The in-vehicle terminal acquires status information for multiple in-vehicle electrical devices. The in-vehicle terminal includes detecting status information of multiple in-vehicle electrical devices at a specified interval, The method according to feature 1.

4. The big data platform determines the total weight of the target vehicle based on the number of passengers and the identifier information of the target vehicle, adds the rated power of the target vehicle's in-vehicle electrical equipment, obtains the total power value of the target vehicle's in-vehicle electrical equipment, and determines the driving distance of the target vehicle with the remaining battery capacity based on the remaining battery capacity, the total weight, the total power value, and the estimated speed of the target vehicle. This includes determining the drivable distance based on the following formula, Here, G is the total weight of the target vehicle, n is the number of passengers, and m １ This is standard weight, m ２ is the mass of the target vehicle, P is the total power value of the target in-vehicle electrical equipment, P ｎ is the power value of any target in-vehicle electrical equipment, S is the driving range, W is the battery charge, k is the friction coefficient, and v is the expected speed of the target vehicle. The method according to feature 1.

5. The big data platform determines the target driving distance for each route based on the drivable distance and the route length corresponding to each route, This includes determining the target distance for each route by subtracting the distance obtained by adding a predetermined reserve distance to the drivable distance from the route length corresponding to each route, The method according to feature 1.

6. The big data platform determines, based on the target mileage, the expected speed of the target vehicle, and the total power value corresponding to each route, the first energy consumed by the target in-vehicle electrical equipment whose status information in the multiple in-vehicle equipment is activated, and determines, based on the target mileage, the number of passengers, and the identifier information of the target vehicle corresponding to each route, the second energy consumed by the target vehicle's operation corresponding to each route, and adds the first and second energies to obtain the target charge amount required for the target vehicle corresponding to each route. This includes determining the target charge amount required for the target vehicle corresponding to each of the aforementioned routes based on the following formula, Here, W １ is the first energy, P is the total power value of the target in-vehicle electrical equipment, S １ v is the target distance traveled, v is the expected speed of the target vehicle, and W is the target distance traveled. ２ is the second energy, G is the total weight of the target vehicle, k is the friction coefficient, W is ３ This is the target charge amount. The method according to feature 1.

7. The big data platform inputs the remaining battery charge, the target charge amount corresponding to each route, the charging power of multiple charging piles in each route, the distance between multiple charging piles in each route, and the rated power of the target vehicle into an energy replenishment recommendation algorithm, and determines the combination of charging piles corresponding to each route and the charging time corresponding to each charging pile in the combination of charging piles. To arrange and combine multiple charging piles in each of the aforementioned paths, and to obtain a combination of multiple base charging piles corresponding to each of the aforementioned paths, Based on the charging power of the charging piles in each combination of basic charging piles and the target charging amount corresponding to each path, the charging time corresponding to each charging pile in each combination of basic charging piles is determined. Based on the remaining battery charge, the charging power of the charging piles in each combination of basic charging piles, the charging time corresponding to each charging pile in each combination of basic charging piles, the distance between multiple charging piles in each combination of basic charging piles, and the rated power of the target vehicle, the combination of multiple basic charging piles corresponding to each route is screened, and a feasible combination of charging piles corresponding to each route is obtained. This includes determining the combination of charging piles with the shortest charging time among the feasible combinations of charging piles corresponding to each of the aforementioned paths as the combination of charging piles corresponding to each of the aforementioned paths. The method according to feature 1.

8. The aforementioned method, The big data platform determines, based on the identifier information of the target vehicle, whether the target vehicle supports a power swapping function; if the target vehicle supports a power swapping function, it determines the total weight of the target vehicle based on the number of passengers and the identifier information of the target vehicle, adds the rated power of the target vehicle's in-vehicle electrical equipment, obtains the total power value of the target vehicle's in-vehicle electrical equipment whose status information is in the activated state, and determines the driving distance of the target vehicle with the remaining battery capacity based on the remaining battery capacity, the total weight, the total power value, and the estimated speed of the target vehicle. The big data platform determines that multiple power exchange stations within the drivable distance along each route are transmitted to the in-vehicle terminal, The in-vehicle terminal further includes displaying the plurality of routes and marking the plurality of power exchange stations within the drivable distance along each of the routes, The method according to feature 1.

9. Computer equipment, Memory for storing computer instructions, The computer device includes a processor that executes computer instructions stored in the memory in order to perform the method according to any one of claims 1 to 8, Computer equipment characterized by the following features.

10. A computer-readable storage medium, A computer program code is stored, and in response to the computer program code being executed by a computer device, the computer device performs the method according to any one of claims 1 to 8. A computer-readable storage medium characterized by the following features.

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