Automobile charging guiding method, device and equipment and medium
By combining charging pile status, vehicle queuing sequence, real-time energy consumption, and geographical data, and using machine learning algorithms for energy consumption prediction and dynamic queue management, the problem of insufficient accuracy in existing charging guidance schemes is solved, achieving efficient charging route selection and improved user experience.
Patent Information
- Application Number
- CN202511829289.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-05
- Publication Date
- 2026-02-17
AI Technical Summary
Existing charging guidance solutions fail to effectively combine multi-dimensional parameters for accurate prediction, resulting in a significant discrepancy between estimated range and actual range, causing severe range anxiety among users.
By acquiring charging pile status data and vehicle queuing sequences, combined with real-time energy consumption correlation data, power system parameters, and geographical data, machine learning algorithms are used to establish an energy consumption prediction model, dynamically manage vehicle queuing, and generate accurate navigation guidance information.
It achieves accurate charging queue management and energy consumption prediction, reduces user waiting time, improves the accuracy of range prediction, optimizes charging route selection, and enhances user experience.
Smart Images
Figure CN121540443A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of new energy vehicle charging technology, specifically to a vehicle charging guidance method, device, equipment, and medium. Background Technology
[0002] With the rapid development of the new energy vehicle industry, the issues of charging convenience and range reliability faced by pure electric vehicle users in long-distance travel scenarios are becoming increasingly prominent. Currently, existing charging guidance solutions on the market mainly rely on static location information of charging stations and simple remaining battery power estimates for service recommendations, which have significant technical limitations. For example, traditional energy consumption prediction methods often depend on theoretical calculations or limited historical data averages, failing to effectively integrate multi-dimensional parameters. This results in a significant discrepancy between the range predictions provided by the system and the actual situation, directly leading to range anxiety for users. Summary of the Invention
[0003] In view of the shortcomings of the prior art described above, this application provides a method, apparatus, device and medium for guiding automobile charging to solve the above technical problems.
[0004] This application provides a method for guiding vehicle charging, the method comprising: The system acquires charging pile status data and vehicle queuing sequences from multiple service areas, and performs dynamic queuing management for vehicles requesting charging based on vehicle identification information, so as to generate charging queuing sequence information containing estimated waiting time based on the results of dynamic management. Based on real-time energy consumption data of the vehicle, power system parameters, and geographical data, energy consumption is predicted for the vehicle's journey to the multiple service areas to generate an estimated energy consumption set. Based on the charging queue timing information and the estimated energy consumption set, and at least based on the vehicle's remaining driving range, a target service area is determined from the plurality of service areas to generate navigation guidance information; the navigation guidance information includes: target service area and driving suggestions.
[0005] In one embodiment of this application, the step of predicting energy consumption for a vehicle's journey to the multiple service areas based on real-time energy consumption correlation data, powertrain parameters, and geographical data includes: Based on the vehicle's real-time energy consumption correlation data and the trained basic energy consumption prediction model, a basic energy consumption prediction value is obtained; wherein, the basic energy consumption prediction model is obtained by using a machine learning algorithm, with historical energy consumption correlation data as input features and historical energy consumption values as training targets. The basic energy consumption prediction value is corrected by using power system parameters and geographical data to obtain the estimated energy consumption set.
[0006] In one embodiment of this application, the step of correcting the basic energy consumption prediction value using the power system parameters and the geographical data includes: Based on the power system parameters, the geographical data, and the trained uphill and downhill energy consumption prediction model, the predicted uphill and downhill energy consumption values are obtained; wherein, the uphill and downhill energy consumption prediction model is obtained by using a machine learning algorithm, taking historical power system parameters and historical geographical data as inputs, and using historical uphill and downhill energy consumption values as training targets. The base energy consumption correction value is superimposed with the uphill and downhill energy consumption prediction values to obtain the predicted energy consumption of the vehicle traveling to the multiple service areas.
[0007] In one embodiment of this application, determining the target service area from the plurality of service areas includes: Based on the first condition, the multiple service areas are filtered to obtain at least one candidate service area; wherein, the first condition is: the distance from the vehicle's current location to the service area is less than or equal to the vehicle's remaining driving range. The at least one candidate service area is screened based on a second condition to determine the target service area; wherein the second condition is: the candidate service area with the shortest estimated waiting time.
[0008] In one embodiment of this application, dynamic queuing management of vehicles requesting charging based on vehicle identification information includes: In response to a queuing request initiated by a vehicle requesting charging, the vehicle requesting charging is added to the vehicle queuing sequence corresponding to one or more service areas based on the vehicle identification information of the vehicle.
[0009] In one embodiment of this application, the basic energy consumption prediction model is represented by the following model: E=β0+β1v+β2a+β3r+β4t+
[0010] Where E represents the predicted basic energy consumption, v represents vehicle speed, a represents acceleration, r represents road condition information, and t represents ambient temperature; β0, β1, β2, β3, and β4 represent the model regression coefficients. This indicates the error term.
[0011] In one embodiment of this application, the driving recommendation includes at least one of the following: The average driving speed recommended based on the estimated energy consumption set to achieve the lowest energy consumption; The optimal driving route recommended based on charging queue timing information and estimated energy consumption from different candidate service areas; Predictive driving operation prompts generated based on altitude change information of the route.
[0012] This application provides a vehicle charging guidance device, the guidance device comprising: The queue management module is used to acquire charging pile status data and vehicle queue sequences from multiple service areas, and to dynamically manage the vehicle queue sequences based on vehicle identification information, so as to generate charging queue timing information containing estimated waiting time based on the results of dynamic management. The energy consumption prediction module is used to predict the energy consumption of the vehicle's journey to the multiple service areas based on the vehicle's real-time energy consumption correlation data, power system parameters and geographical data, so as to generate a set of predicted energy consumption. The guidance information generation module is used to determine the target service area from the plurality of service areas based on the charging queuing time sequence information and the estimated energy consumption set, and at least based on the vehicle's remaining driving range, so as to generate navigation guidance information; the navigation guidance information includes: target service area and driving suggestions.
[0013] This application provides a vehicle charging guidance device, the vehicle charging guidance device comprising: One or more processors; A memory for storing one or more programs that, when executed by one or more processors, cause the charging and discharging device to perform the steps of the above-described vehicle charging guidance method.
[0014] This application provides a computer-readable storage medium storing a computer program that, when executed by a computer's processor, causes the computer to perform the steps of the above-described vehicle charging guidance method.
[0015] The beneficial effects of this application are as follows: A vehicle charging guidance method of this application includes: acquiring charging pile status data and vehicle queuing sequences from multiple service areas, and dynamically managing the queuing of vehicles requesting charging based on vehicle identification information, to generate charging queuing sequence information including estimated waiting time based on the results of dynamic management; predicting energy consumption for the vehicle's journey to the multiple service areas based on real-time energy consumption correlation data, power system parameters, and geographical data, to generate an estimated energy consumption set; and determining a target service area from the multiple service areas based on the charging queuing sequence information and the estimated energy consumption set, and at least based on the vehicle's remaining driving range, to generate navigation guidance information; the navigation guidance information includes: the target service area and driving suggestions. This application achieves precise queuing management by dynamically integrating charging pile status and vehicle queuing sequences, combining real-time energy consumption correlation data, power system characteristics, and geographical data for multi-dimensional energy consumption prediction, and coordinating queuing sequence and energy consumption data to optimize target service area selection, effectively reducing user charging queuing waiting time, improving energy consumption prediction accuracy, optimizing charging route selection, and enhancing user experience.
[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 This is a flowchart illustrating an exemplary vehicle charging guidance method of this application; Figure 2 This is an exemplary flowchart for predicting energy consumption during a vehicle's journey to multiple service areas, as described in this application. Figure 3 This is an exemplary flowchart for correcting a base energy consumption prediction value according to this application; Figure 4 This is a block diagram of an exemplary vehicle charging guidance device of this application; Figure 5 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation
[0018] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.
[0019] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.
[0020] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.
[0021] Please see Figure 1 , Figure 1 This is a flowchart illustrating an exemplary vehicle charging guidance method according to this application. The vehicle charging guidance method includes at least steps S110 to S130, which are described in detail below: Step S110: Obtain charging pile status data and vehicle queuing sequence from multiple service areas, and perform dynamic queuing management for vehicles requesting charging based on vehicle identification information, so as to generate charging queuing sequence information containing estimated waiting time based on the results of dynamic management. The charging pile status data can include the occupancy status and number of available charging piles; the vehicle queue sequence can be understood as the number of vehicles currently in the queue and the queuing time. Specifically, the charging pile status data and vehicle queue information can be collected in real time through sensors and IoT devices installed on the charging piles in the service area, with a collection frequency of once every 10 seconds. After collection, the charging pile status data and vehicle queue sequence are uploaded to the cloud platform via a high-speed network. Users can view the charging pile usage and the number of vehicles in the queue in each service area in real time through the in-vehicle navigation system or mobile application, and then make a queue reservation to initiate a charging request by entering vehicle identification information (such as license plate number or VIN code). After receiving the charging request, the cloud platform allocates the vehicle to the queue sequence of one or more service areas based on the vehicle identification information (such as license plate number or VIN code), and dynamically sorts them according to the first-in-first-out principle or priority rules, updating the queuing status of the charging piles in each service area in real time to dynamically manage the queue of vehicles requesting charging, thereby generating charging queue sequence information including estimated waiting time.
[0022] The formula for calculating the estimated waiting time is: Estimated waiting time = Current number of vehicles in the queue × Average charging time + Sum of waiting times for vehicles already in the queue. Here, "currently in the queue" refers to vehicles that are currently queuing but not yet charging, "vehicles already in the queue" refers to vehicles that are currently charging, and "waiting time for vehicles already in the queue" refers to the remaining charging time for vehicles already charging.
[0023] Step S120: Based on the vehicle's real-time energy consumption correlation data, power system parameters, and geographical data, predict the energy consumption of the vehicle's journey to multiple service areas to generate an estimated energy consumption set. The real-time energy consumption data for vehicles includes vehicle speed (v), acceleration (a), road condition information (r), and ambient temperature (t). Vehicle speed (v), acceleration (a), and ambient temperature (t) can be directly collected by onboard sensors, while road condition information can include the road congestion level (smooth, congested, slow-moving). The real-time energy consumption data can be collected every 2 seconds.
[0024] Powertrain parameters are data reflecting the operating status of a vehicle's powertrain system, and may include motor output power, battery status, etc. Data is collected every second to ensure real-time performance and accuracy. Geographic data is information related to road topographic features, such as slope angle, slope length, and altitude, which can be obtained by integrating high-precision digital maps.
[0025] Specifically, real-time energy consumption data of the vehicle is input into a trained basic energy consumption prediction model to generate basic energy consumption prediction values, thereby predicting the vehicle's basic energy consumption. Then, using powertrain parameters and geographical data such as slope and slope length extracted from high-precision maps, an uphill / downhill energy consumption prediction model quantifies the actual impact of complex terrain on energy consumption. This prediction and correction process is repeated to generate an estimated energy consumption set for each candidate service area. This estimated energy consumption set includes sets containing different average speeds and their corresponding total estimated energy consumption.
[0026] Step S130: Based on the charging queue timing information and the estimated energy consumption set, and at least based on the vehicle's remaining driving range, determine the target service area from multiple service areas to generate navigation guidance information; the navigation guidance information includes: the target service area and driving suggestions.
[0027] Specifically, by collaboratively analyzing charging queue timing information, estimated energy consumption sets, and remaining driving range, the optimal target service area can be determined.
[0028] The determination of the target service area includes multiple constraints: 1. Select all candidate service areas that meet the condition that "the driving distance from the vehicle's current location to the service area does not exceed the vehicle's current remaining driving range"; 2. Select the service area with the shortest estimated waiting time from the candidate service areas as the target service area to recommend the target service area to the user.
[0029] Driving recommendations can be understood as the average driving speed recommended based on the estimated energy consumption set to achieve the lowest energy consumption; and the optimal driving route recommended based on charging queue time information and estimated energy consumption in different candidate service areas. Simultaneously, it can also incorporate predictive driving operation prompts generated based on altitude changes along the route, such as advance warning of long uphill and downhill sections and suggesting corresponding speed adjustment strategies, helping users further reduce energy consumption by optimizing driving behavior. Specific driving recommendations can be found in Table 1.
[0030] Table 1
[0031] Highway service area section coding: The highway numbering system mainly uses a combination of "G + number", where k represents the number of a certain service area, i represents the number of the next service area, and i and k are adjacent service areas. Highway service area section coding is only for the uniqueness of adjacent service area segments under a specific highway.
[0032] The final recommended driving advice must meet the following conditions, if and only if: The target service area i is the service area with the shortest estimated waiting time Tw under the condition that the distance Di from the current location to service area i is less than or equal to the vehicle's driving range R. The vehicle's remaining energy consumption E (remaining energy consumption) is greater than Emax (E1, E2, E3, E4). The vehicle's speed is the minimum speed Vmin (V1, V2, V3, V4) corresponding to Emin (E1, E2, E3, E4).
[0033] Please see Figure 2 , Figure 2 This is an exemplary flowchart for predicting energy consumption during a vehicle's journey to multiple service areas, as described in this application. Figure 2 The energy consumption prediction process includes at least steps S210 to S220, which are detailed below: Step S210: Based on the vehicle's real-time energy consumption correlation data and the trained basic energy consumption prediction model, a basic energy consumption prediction value is obtained; wherein, the basic energy consumption prediction model is obtained by using a machine learning algorithm, with historical energy consumption correlation data as input features and historical energy consumption values as training targets. The basic energy consumption prediction model can be implemented using machine learning algorithms, such as support vector machines, random forests, linear regression, and neural networks. The training process of this model can use historical energy consumption data as input features and fit the model to the historical energy consumption value as the target variable, thereby generating a basic energy consumption function that reflects the vehicle's energy consumption patterns. Input features include, but are not limited to: vehicle speed v, acceleration a, road condition information r, and ambient temperature t.
[0034] This application utilizes machine learning technology to conduct in-depth analysis of collected energy consumption-related data and establish a basic energy consumption prediction model. This model considers multiple factors, such as vehicle speed, road conditions, and ambient temperature, and can accurately predict vehicle energy consumption under different conditions. Furthermore, by continuously optimizing the model parameters, the accuracy of the prediction can be improved, providing users with more reliable energy consumption estimates. The model optimization algorithm can be the Adam algorithm with a learning rate of 0.001.
[0035] Step S220: Correct the basic energy consumption prediction value using power system parameters and geographical data to obtain the estimated energy consumption set.
[0036] Specifically, the basic energy consumption prediction is corrected by using powertrain parameters and geographical data, organically integrating the vehicle's dynamic characteristics with geographical data to dynamically quantify the actual impact of complex road conditions such as uphill and downhill sections on energy consumption. Ultimately, the predicted energy consumption for the vehicle to reach multiple service areas is obtained, ensuring that navigation guidance information is generated based on accurate energy consumption assessments, enabling the vehicle to reliably select accessible and efficient charging destinations.
[0037] Please see Figure 3 , Figure 3 This is an exemplary flowchart for correcting a base energy consumption prediction value according to this application. Figure 3 The process of correcting the basic energy consumption forecast includes at least steps S310 to S320, which are detailed below: Step S310: Based on the power system parameters, geographical data, and the trained uphill and downhill energy consumption prediction model, obtain the predicted uphill and downhill energy consumption values; wherein, the uphill and downhill energy consumption prediction model is obtained by using a machine learning algorithm, taking historical power system parameters and historical geographical data as input, and using historical uphill and downhill energy consumption values as training targets. Among them, the uphill and downhill energy consumption correction model is a mathematical model generated based on historical data fitting. It can use machine learning algorithms (such as support vector machine, random forest or neural network) to capture the nonlinear relationship between terrain slope and vehicle energy consumption, thereby providing a more accurate correction basis for energy consumption prediction.
[0038] Step S320: The basic energy consumption correction value is superimposed with the predicted energy consumption values for uphill and downhill sections to obtain the predicted energy consumption of the vehicle traveling to multiple service areas.
[0039] Specifically, by combining powertrain parameters and geographical data, a trained uphill / downhill energy consumption correction model is used to generate predicted uphill / downhill energy consumption values. These predicted values accurately reflect the additional energy consumption requirements under actual driving conditions. By overlaying the base energy consumption prediction values with the uphill / downhill energy consumption prediction values, the additional energy consumption caused by terrain undulations is integrated, making the prediction results closer to real driving scenarios.
[0040] In one embodiment, determining a target service area from a plurality of service areas includes: Based on the first condition, multiple service areas are filtered to obtain at least one candidate service area; wherein, the first condition is: the distance Di from the vehicle's current location to service area i is less than or equal to the vehicle's remaining driving range R. At least one candidate service area is screened based on a second condition to determine the target service area; wherein the second condition is: the candidate service area with the shortest estimated waiting time Tw.
[0041] The method for determining candidate service areas is as follows: the distance Di between the vehicle's current location and each service area is calculated in real time, and the distance Di is dynamically compared with the vehicle's remaining driving range R. Service areas that meet the condition that the distance Di is less than or equal to the vehicle's remaining driving range R are included in the candidate service areas.
[0042] Specifically, multiple service areas are screened based on the first condition to select candidate service areas, and then further optimized and selected based on the second condition. This not only ensures basic battery life requirements but also improves the user's charging experience.
[0043] In one embodiment, dynamic queuing management of vehicles requesting charging based on vehicle identification information includes: In response to a queuing request initiated by a vehicle requesting charging, the vehicle requesting charging is added to the vehicle queuing sequence corresponding to one or more service areas based on the vehicle identification information.
[0044] Specifically, users initiate queuing requests to the cloud via a mobile application or the human-machine interface of the in-vehicle intelligent system. Initiation methods include, but are not limited to: scanning a specific QR code for the service area, selecting the target service area on the navigation interface, and confirming charging needs. Upon receiving the queuing request, the cloud platform verifies the vehicle's VIN code and adds the verified VIN code to the end of the service area's queuing sequence. When the status of a charging station changes in any service area, the cloud platform immediately updates the corresponding queuing sequence for that service area. Simultaneously, the cloud platform continuously evaluates the queuing situation in different service areas, and when it detects a significant reduction in waiting time in a particular service area, it can proactively push suggestions to users to adjust the queuing strategy.
[0045] In one embodiment, the basic energy consumption function is represented by the following model: E=β0+β1v+β2a+β3r+β4t+
[0046] Where E represents the predicted base energy consumption, v represents vehicle speed, a represents acceleration, r represents road condition information, and t represents temperature; β0, β1, β2, β3, and β4 represent the model regression coefficients. This indicates the error term.
[0047] Specifically, the base energy consumption prediction value E refers to the expected energy consumption of a vehicle under specific conditions (such as on a flat road). The model regression coefficients β0, β1, β2, β3, and β4 are obtained by fitting historical energy consumption data. Error term This indicates random fluctuations, avoiding oversimplification of the actual energy consumption process in the model, thus making the prediction results closer to the actual observations.
[0048] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0049] Figure 4 This is a block diagram illustrating an exemplary embodiment of the present application of a vehicle charging guidance device. Figure 4 As shown, this application provides an automotive charging guidance device, which includes: The queue management module 410 is used to acquire charging pile status data and vehicle queue sequence from multiple service areas, and to dynamically manage the vehicle queue sequence based on vehicle identification information, so as to generate charging queue timing information containing estimated waiting time based on the results of dynamic management. The energy consumption prediction module 420 is used to predict the energy consumption of the vehicle's journey to the multiple service areas based on the vehicle's real-time energy consumption correlation data, power system parameters and geographical data, so as to generate a set of predicted energy consumption. The guidance information generation module 430 is used to determine the target service area from multiple service areas based on the charging queue timing information and the estimated energy consumption set, and at least based on the vehicle's remaining driving range, so as to generate navigation guidance information; the navigation guidance information includes: target service area and driving suggestions.
[0050] It should be noted that the car charging guidance device and the car charging guidance method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the car charging guidance device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.
[0051] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the car charging guidance method provided in the above embodiments.
[0052] Figure 5 A schematic diagram of a computer system suitable for implementing the memory of embodiments of this application is shown. It should be noted that... Figure 5The computer system with the memory shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.
[0053] like Figure 5 As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage into Random Access Memory (RAM) 503. The RAM also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.
[0054] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.
[0055] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the aforementioned vehicle charging guidance method. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, it performs various functions defined in the system of this application.
[0056] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM) 503, read-only memory (ROM) 502, erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.
[0057] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block or combination of blocks in a block diagram or flowchart may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0058] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.
[0059] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a computer's processor, causes the computer to perform the aforementioned vehicle charging guidance method. This computer-readable storage medium may be included in the memory described in the above embodiments, or it may exist independently and not incorporated into that memory.
[0060] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the vehicle charging guidance method provided in the various embodiments described above.
[0061] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.
Claims
1. A method for guiding automobile charging, characterized in that, The guiding method includes: The system acquires charging pile status data and vehicle queuing sequences from multiple service areas, and performs dynamic queuing management for vehicles requesting charging based on vehicle identification information, so as to generate charging queuing sequence information containing estimated waiting time based on the results of dynamic management. Based on the vehicle's real-time energy consumption data, power system parameters, and geographical data, energy consumption is predicted for the vehicle's journey to the multiple service areas to obtain an estimated energy consumption set. Based on the charging queue timing information and the estimated energy consumption set, and at least based on the vehicle's remaining driving range, a target service area is determined from the plurality of service areas to generate navigation guidance information; the navigation guidance information includes: target service area and driving suggestions.
2. The vehicle charging guidance method according to claim 1, characterized in that, The step of predicting energy consumption for a vehicle's journey to the multiple service areas based on real-time energy consumption data, vehicle powertrain parameters, and geographical data includes: Based on the vehicle's real-time energy consumption correlation data and the trained basic energy consumption prediction model, a basic energy consumption prediction value is obtained; wherein, the basic energy consumption prediction model is obtained by using a machine learning algorithm, with historical energy consumption correlation data as input features and historical energy consumption values as training targets. The basic energy consumption prediction value is corrected by using the power system parameters and the geographical data to obtain the estimated energy consumption set.
3. The vehicle charging guidance method according to claim 2, characterized in that, The step of correcting the basic energy consumption prediction value using the power system parameters and the geographical data includes: Based on the power system parameters, the geographical data, and the trained uphill and downhill energy consumption prediction model, the predicted uphill and downhill energy consumption values are obtained; wherein, the uphill and downhill energy consumption prediction model is obtained by using a machine learning algorithm, taking historical power system parameters and historical geographical data as inputs, and using historical uphill and downhill energy consumption values as training targets. The base energy consumption correction value is superimposed with the uphill and downhill energy consumption prediction values to obtain the predicted energy consumption of the vehicle traveling to the multiple service areas.
4. The vehicle charging guidance method according to claim 1, characterized in that, Determining the target service area from the plurality of service areas includes: Based on a first condition, the multiple service areas are filtered to obtain at least one candidate service area; wherein, the first condition is: the distance from the vehicle's current location to the service area is less than or equal to the vehicle's remaining driving range. The at least one candidate service area is screened based on a second condition to determine the target service area; wherein the second condition is: the candidate service area with the shortest estimated waiting time.
5. The vehicle charging guidance method according to claim 1, characterized in that, The dynamic queuing management of vehicles requesting charging based on vehicle identification information includes: In response to a queuing request initiated by a vehicle requesting charging, the vehicle requesting charging is added to the vehicle queuing sequence corresponding to one or more service areas based on the vehicle identification information of the vehicle.
6. The vehicle charging guidance method according to claim 2, characterized in that, The basic energy consumption prediction model is represented by the following model: E=β0+β1v+β2a+β3r+β4t+ Where E represents the predicted basic energy consumption, v represents vehicle speed, a represents acceleration, r represents road condition information, and t represents ambient temperature; β0, β1, β2, β3, and β4 represent the model regression coefficients. This indicates the error term.
7. The vehicle charging guidance method according to claim 4, characterized in that, The driving recommendations include at least one of the following: The average driving speed recommended based on the estimated energy consumption set to achieve the lowest energy consumption; The optimal driving route recommended based on charging queue timing information and estimated energy consumption from different candidate service areas; Predictive driving operation prompts generated based on altitude change information of the route.
8. A car charging guidance device, characterized in that, The guiding device includes: The queue management module is used to acquire charging pile status data and vehicle queue sequences from multiple service areas, and to dynamically manage the vehicle queue sequences based on vehicle identification information, so as to generate charging queue timing information containing estimated waiting time based on the results of dynamic management. The energy consumption prediction module is used to predict the energy consumption of the vehicle's journey to the multiple service areas based on the vehicle's real-time energy consumption correlation data, power system parameters and geographical data, so as to generate a set of predicted energy consumption. The guidance information generation module is used to determine the target service area from the plurality of service areas based on the charging queuing time sequence information and the estimated energy consumption set, and at least based on the vehicle's remaining driving range, so as to generate navigation guidance information; the navigation guidance information includes: target service area and driving suggestions.
9. A car charging guidance device, characterized in that, include: One or more processors; A memory for storing one or more programs that, when executed by the one or more processors, cause the vehicle charging guidance device to perform the steps of the vehicle charging guidance method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the steps of the vehicle charging guidance method according to any one of claims 1 to 7.