Vehicle charging control method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle charging control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a planned charging time period of an online vehicle at a target charging station, and determining the charging pile occupation degree of the target charging station within a specified time period based on the planned charging time period; generating charging scheduling data according to the charging pile occupation degree; wherein the charging scheduling data comprises charging control suggestions corresponding to different remaining electric quantities of the vehicle; and in response to a received charging request of a target vehicle, sending a charging control suggestion in the charging scheduling data to the target vehicle according to the remaining electric quantity of the target vehicle. According to the invention, the target vehicle can be controlled to be charged according to the charging control suggestion corresponding to the remaining electric quantity, the matching relationship between the charging pile and the vehicle is optimized, the utilization rate of the charging pile is improved, and the charging time cost of the vehicle is shortened.

Description

Technical Field

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

[0002] With the rapid development of the new energy vehicle industry, especially the large-scale application of new energy freight vehicles, the construction and efficient utilization of charging infrastructure have become key factors affecting the industry's development. Currently, new energy autonomous freight vehicles often experience a situation where charging piles are idle while vehicles are queuing, resulting in low resource utilization. Existing solutions cannot meet the needs of the new energy autonomous freight sector for efficient charging and precise dispatching. Summary of the Invention

[0003] This application provides a vehicle charging control method, device, electronic device, and storage medium to solve one or more of the above-mentioned technical problems.

[0004] In a first aspect, embodiments of this application provide a vehicle charging control method, comprising: acquiring a planned charging period for a vehicle on the network at a target charging station; determining, based on the planned charging period, the occupancy level of charging piles at the target charging station within a specified time period; generating charging scheduling data according to the occupancy level of the charging piles; wherein the charging scheduling data includes charging control suggestions corresponding to different remaining battery levels of the vehicle; and, in response to receiving a charging request from a target vehicle, sending the charging control suggestions from the charging scheduling data to the target vehicle according to the remaining battery level of the target vehicle.

[0005] Secondly, embodiments of this application provide a vehicle charging control device, comprising: a data processing module, configured to acquire the planned charging time period of a vehicle on the network at a target charging station, and determine the occupancy level of the charging piles at the target charging station within a specified time period based on the planned charging time period; a matching and scheduling module, configured to generate charging scheduling data according to the occupancy level of the charging piles; wherein the charging scheduling data includes charging control suggestions corresponding to different remaining battery levels of the vehicle; and a charging control module, configured to, in response to receiving a charging request from a target vehicle, send the charging control suggestions from the charging scheduling data to the target vehicle according to the remaining battery level of the target vehicle.

[0006] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor, when executing the computer program, implements the method described in any of the above-mentioned embodiments.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in any of the above-mentioned embodiments.

[0008] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a computer program that, when executed by a processor, implements the above-described method.

[0009] Compared with related technologies, this application has the following advantages:

[0010] This application provides a vehicle charging control method, device, electronic device, and storage medium. The method obtains the planned charging time period of a vehicle connected to the network at a target charging station; based on the planned charging time period, determines the occupancy level of charging piles at the target charging station within the specified time period; generates charging scheduling data based on the charging pile occupancy level; wherein the charging scheduling data includes charging control suggestions corresponding to different remaining battery levels of the vehicle; in response to receiving a charging request from a target vehicle, the method sends the charging control suggestions from the charging scheduling data to the target vehicle based on its remaining battery level. According to the embodiments of this application, the occupancy level of charging piles at a target charging station can be obtained in real time based on the planned charging time period of a vehicle connected to the network at the target charging station, thereby generating charging scheduling data. Based on the remaining battery level of the target vehicle, the method controls the target vehicle to charge according to the corresponding charging control suggestions, optimizing the matching relationship between charging piles and vehicles, improving the utilization rate of charging piles, and reducing the charging time cost of vehicles.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0012] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments according to this application and should not be construed as limiting the scope of this application.

[0013] Figure 1 A flowchart of a vehicle charging control method provided in an embodiment of this application is shown;

[0014] Figure 2 The diagram shows a structural block of a vehicle charging control device provided in an embodiment of this application. Figure 1 ;

[0015] Figure 3 The diagram shows a structural block of a vehicle charging control device provided in an embodiment of this application. Figure 2 ;

[0016] Figure 4 A block diagram of an electronic device used to implement embodiments of this application is shown. Detailed Implementation

[0017] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the concept or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0018] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and all of them fall within the protection scope of the embodiments of this application.

[0019] Existing solutions often focus on monitoring individual charging stations, while vehicle management systems primarily monitor vehicle status, failing to meet the demands of efficient charging and precise dispatching in the new energy autonomous freight sector. Furthermore, existing solutions struggle to obtain accurate real-time status information for charging stations (e.g., idle, occupied, faulty), leading to aimless station searches and wasted time. The lack of a matching mechanism between charging stations and vehicles often results in idle charging stations alongside waiting vehicles, leading to low resource utilization. Autonomous vehicles cannot plan their charging behavior in advance based on their battery status and transportation tasks, potentially impacting transportation efficiency due to insufficient power. Finally, dispatching management lacks effective monitoring of the overall charging station operation and the ability to predict vehicle charging needs, hindering proactive dispatching and resource optimization.

[0020] Therefore, there is an urgent need for a system and method that can realize real-time interaction, intelligent matching, and dynamic scheduling of charging pile and vehicle information.

[0021] Based on this, this application provides a vehicle charging control method, device, electronic device, and storage medium. This application addresses the problems of information silos, low matching efficiency, and inaccurate scheduling in the charging scheduling of new energy autonomous freight vehicles. The method integrates static and real-time status information of charging piles with the battery status and location information of new energy autonomous freight vehicles, utilizing intelligent algorithms to achieve efficient matching and dynamic scheduling between charging piles and vehicles. This method can provide personalized charging suggestions for new energy autonomous freight vehicles, shorten charging time costs, improve charging pile utilization, and assist dispatchers in making scientific decisions.

[0022] The technical solution of this application and how it solves the aforementioned technical problems are described in detail below with specific embodiments. The listed specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0023] This application provides a vehicle charging control method. This method can be applied to a cloud server deployed on a cloud computing platform. The cloud computing platform establishes a communication connection with platforms requiring data interaction, such as, but not limited to, vehicle data platforms, through API interfaces. Figure 1 The diagram shown is a flowchart of a vehicle charging control method according to an embodiment of this application. The method may include:

[0024] Step S101: Obtain the planned charging time period of the vehicles on the network at the target charging station, and determine the occupancy rate of the charging piles at the target charging station during the specified time period based on the planned charging time period.

[0025] In this embodiment, the vehicles on the network can refer to electric vehicles or autonomous vehicles that are connected to the vehicle management platform and have charging requests and plans. The planned charging time information of the vehicles can be proactively reported to the vehicle management platform by the onboard terminal, mobile application, or charging reservation system. The target charging station is the charging station that the vehicles on the network plan to visit for charging.

[0026] Charging station occupancy level describes the utilization rate of charging stations within the target charging station category. For example, occupancy levels may include, but are not limited to, idle, moderate, Level 1 busy, Level 2 busy, and overflow. The specified time period is a future period, such as the next two hours or the next half day.

[0027] This step allows for advance prediction of the charging station's resource availability at different times, enabling refined management and optimized resource allocation of charging stations. This reduces the coexistence of idle resources and congestion, helps optimize the allocation of charging station selection and scheduled charging time for vehicles waiting to be charged, and reduces unnecessary waiting time.

[0028] Step S102: Generate charging scheduling data based on the occupancy level of the charging piles; wherein, the charging scheduling data includes charging control suggestions corresponding to different battery charge balances of the vehicles.

[0029] In this embodiment, charging scheduling data is used to configure the charging time periods for vehicles waiting to be charged, such as autonomous vehicles, in order to integrate charging pile and vehicle data in real time and improve the matching efficiency between idle charging piles and vehicles waiting to be charged. The charging scheduling data includes charging control suggestions corresponding to different vehicle battery levels, enabling the data to charge vehicles with lower battery levels promptly and coordinate off-peak charging for vehicles with higher battery levels. This allows for global resource allocation of vehicles and charging piles at target charging stations, reducing queuing time during peak charging periods and minimizing the time cost for autonomous vehicles to find charging piles, thereby improving charging pile utilization and charging efficiency.

[0030] In this embodiment, the status of charging piles at the target charging station is dynamically monitored based on dynamically updated planned charging periods, reducing the idle rate of charging piles. Additionally, personalized charging suggestions based on the autonomous vehicle's own battery level and the status of the charging piles can be provided.

[0031] In one possible implementation, the charging scheduling data is described in conjunction with the information shown in Table 1. The charging scheduling data includes scenario-defined data such as idle or moderate occupancy, based on the charging pile's occupancy level. For each scenario-defined data set, a set of charging scheduling data is determined.

[0032]

[0033] For example, in the idle state, the charging scheduling data is as follows: vehicles with less than 100% battery that are charging will be fully charged before leaving; vehicles with more than 50% battery that are not charging will come to charge if they have no business, and will continue to execute tasks if they have tasks; vehicles with 30%-50% battery that are not charging will charge after completing their tasks; vehicles with less than 30% battery that are not charging will immediately return to the depot to charge.

[0034] It should be noted that the charging control suggestions corresponding to different remaining battery levels in the charging scheduling data can be set according to actual needs, and this application embodiment does not impose specific limitations on this. For example, for the overflow state, charging control suggestions can be set based on whether the vehicle has a task or not, using the charging scheduling data.

[0035] Step S103: In response to receiving a charging request from the target vehicle, a charging control suggestion from the charging scheduling data is sent to the target vehicle based on the remaining battery power of the target vehicle.

[0036] In this embodiment, the target vehicle is one or more vehicles in the network. The charging request includes vehicle information, such as remaining battery power, battery status, and required voltage / current, and is a signal or instruction to request the start or conduct of charging. Based on the correspondence between remaining battery power and charging control recommendations in the charging scheduling data, a charging control recommendation corresponding to the target vehicle is determined based on the remaining battery power of the target vehicle, and the charging control recommendation is sent to the target vehicle.

[0037] This application provides a vehicle charging control method, device, electronic device, and storage medium. The method obtains the planned charging time period of a vehicle connected to the network at a target charging station; based on the planned charging time period, determines the occupancy level of charging piles at the target charging station within the specified time period; generates charging scheduling data based on the charging pile occupancy level; wherein the charging scheduling data includes charging control suggestions corresponding to different remaining battery levels of the vehicle; in response to receiving a charging request from a target vehicle, the method sends the charging control suggestions from the charging scheduling data to the target vehicle based on its remaining battery level. According to the embodiments of this application, the occupancy level of charging piles at a target charging station can be obtained in real time based on the planned charging time period of a vehicle connected to the network at the target charging station, thereby generating charging scheduling data. Based on the remaining battery level of the target vehicle, the method controls the target vehicle to charge according to the corresponding charging control suggestions, optimizing the matching relationship between charging piles and vehicles, improving the utilization rate of charging piles, and reducing the charging time cost of vehicles.

[0038] In one possible implementation, obtaining the planned charging time of a vehicle in the network at a target charging station can be performed according to the following steps: obtaining feature data and environmental data corresponding to the vehicle in the network; wherein, the feature data includes task data and vehicle data; calculating the remaining battery power of the vehicle in the network after completing the current task based on the feature data and the environmental data; determining that the remaining battery power is greater than a preset battery power threshold, and determining the planned charging time of the vehicle in the network at the target charging station.

[0039] In this embodiment, the networked vehicles can be used to perform transportation tasks; for example, the networked vehicles can be autonomous trucks. Task data is used to characterize the details of the transportation task to be performed by the vehicle, including, but not limited to, the weight of the transported goods, the origin of the transportation, the destination of the transportation, the timeliness of the transportation, and the transportation route. Vehicle data is used to describe detailed information about the vehicles used to perform the transportation task, including, but not limited to, vehicle type and vehicle load capacity. Environmental data refers to the environmental state in which the vehicle is performing the transportation task, including but not limited to weather conditions and road congestion levels.

[0040] After obtaining feature data and environmental data, the amount of electricity required for the networked vehicle to complete the current task is calculated. The remaining electricity after completing the current task is obtained by subtracting the required electricity from the current electricity. A preset electricity threshold can be configured according to actual needs, and this application embodiment does not specifically limit this. For example, in one possible implementation, the preset electricity threshold should be sufficient to support the vehicle in completing the current task and traveling to the target charging station. If the remaining electricity is determined to be greater than the preset electricity threshold, based on the collected energy consumption prediction results of the networked vehicles, including but not limited to: the current task completion time, the time taken to travel to the charging pile after the service is completed, and the estimated charging time, the planned charging period for the networked vehicle at the target charging station is determined.

[0041] In this embodiment, the appropriate charging action for an autonomous vehicle in the current charging environment can be determined based on different vehicle models and the expected target cargo to be transported.

[0042] In one possible implementation, the remaining battery power of the connected vehicle after completing the current task is calculated based on the feature data and the environmental data, which can be performed according to the following steps:

[0043] Based on historical characteristic energy consumption data, regression processing is performed on the characteristic data to obtain the energy consumption value corresponding to the characteristic data; according to the environmental data and the preset environmental energy consumption correspondence, an energy consumption fluctuation coefficient is determined, and the energy consumption value is corrected using the energy consumption fluctuation coefficient; based on the corrected energy consumption value, the remaining power of the vehicle on the network after completing the current task is calculated.

[0044] In this embodiment, historical energy consumption data is pre-stored data, including the items included in the feature data and their corresponding historical energy consumption values. For example, both include business type data, vehicle type data, load data, and route data. Based on the historical energy consumption values ​​corresponding to the historical feature energy consumption data, such as the average energy consumption per unit mileage, a regression algorithm is used to perform regression processing to obtain the energy consumption value corresponding to the feature data. In one possible implementation, the regression algorithm can be the KNN (K-Nearest Neighbors) regression algorithm.

[0045] The preset environmental energy consumption correspondence includes the relationship between environmental data and energy consumption fluctuation coefficients. By classifying environmental data, different energy consumption fluctuation coefficients can be set. The energy consumption fluctuation coefficient characterizes the degree of influence of environmental data on energy consumption values, and its value is greater than or equal to 0. The energy consumption value is corrected using the energy consumption fluctuation coefficient, making it closer to the actual value required by the vehicle. Based on the corrected energy consumption value, the remaining battery power of the vehicle on the network after completing the current task is calculated, thereby improving the accuracy of the remaining battery power calculation.

[0046] In this embodiment of the application, by accumulating and analyzing historical data, the matching algorithm and scheduling strategy are continuously optimized, laying the foundation for integrated vehicle-road-cloud scheduling.

[0047] In one possible implementation, the environmental data includes congestion data and / or weather data; the energy consumption fluctuation coefficient is determined based on the environmental data and a preset environmental energy consumption correspondence, which can be performed according to the following steps: determining a congestion coefficient based on the congestion data and the preset environmental energy consumption correspondence; determining a weather coefficient based on the weather data and the preset environmental energy consumption correspondence; and using the congestion coefficient and / or the weather coefficient as the energy consumption fluctuation coefficient.

[0048] In this embodiment, the environmental data includes congestion data and / or weather data along the current task route, obtained in real time through connection to the vehicle-road-cloud road network. It should be noted that in this embodiment, the environmental data can be updated in real time to correct the prediction results; for example, current road condition data can be retrieved every 30 seconds via the vehicle-road-cloud interface.

[0049] Referring to Table 2, which displays the preset environmental energy consumption correspondence, specifically including: congestion data, such as smooth traffic, light congestion, moderate congestion, and heavy congestion, as well as congestion levels such as 0 to 3. Table 2 also shows the values ​​of the congestion coefficient σ, with different congestion coefficients having different impacts on energy consumption values; see the impact explanation in Table 2 for details. When the vehicle's environmental data is light congestion, according to Table 2, the congestion coefficient is 0.15. The method for determining the weather coefficient can refer to the steps for determining the congestion coefficient, and will not be repeated here.

[0050]

[0051] Table 2

[0052] In this embodiment, when calculating the dynamic power requirement C for the current service, the subsequent route mileage D for completing the current task is first obtained. Based on the energy consumption value E0, congestion coefficient σ, and weather coefficient W corresponding to the feature data, the calculation is performed according to the following formula: C = (D × E0 × (1 + σ) × (1 + W). Then, the current remaining power S of the vehicle is subtracted from the dynamic power requirement C for the current service to obtain the remaining power of the vehicle after completing the current task. The dynamic remaining power value can be used to determine whether a task can be assigned to the autonomous vehicle. If the dynamic power after the service ends is less than 20% of the minimum requirement, the vehicle should not be assigned a task.

[0053] In one possible implementation, the specified time period includes multiple consecutive sub-time periods; determining the charging pile occupancy level of the target charging station within the specified time period based on the planned charging time period can be performed according to the following steps: calculating the charging pile utilization rate corresponding to the sub-time period based on the planned charging time period; determining the charging pile occupancy level of the target charging station within the specified time period based on multiple charging pile utilization rates.

[0054] In this embodiment of the application, for example, the specified time period is 10:00-11:15. The multiple future time periods shown in Table 3 are multiple consecutive sub-time periods. The duration of the sub-time periods can be set according to actual needs and the duration of the specified time period. For example, each sub-time period can be set to 15 minutes. For the sub-time period 10:00-10:15, the number of vehicles requiring charging in this sub-time period can be obtained based on the planned charging time periods of each vehicle collected. Then, combined with the total charging pile capacity of the target charging station, the charging pile utilization rate corresponding to the sub-time period can be calculated. After obtaining the charging pile utilization rate of each sub-time period within the specified time period, the charging pile occupancy level of the target charging station within the specified time period is obtained.

[0055]

[0056] Table 3

[0057] In one possible implementation, the method may further perform the following steps: determining a target time period within the specified time period based on the occupancy level of the charging pile; and sending the target time period to the interaction module to control vehicle charging before the target time period.

[0058] In this embodiment, the target time period is a period of significant interest within a specified time frame. For example, the charging pile utilization rate during the target time period is higher than that during other times within the specified time frame. Table 3 shows three sub-time periods, with the highest utilization rate occurring between 10:15 and 10:30. This sub-time period can then be designated as the target time period. The target time period is sent to the interaction module to control vehicle charging before the target time period, thereby predicting peak charging pile usage times in advance, avoiding vehicle waiting at charging stations, and reducing resource waste. It should be noted that the interaction module can interact with the autonomous vehicle, receiving control commands from the autonomous vehicle and controlling vehicle charging before the target time period based on these commands.

[0059] Based on the embodiments of this application, charging pile operation statistics and charging demand forecast results can be provided to dispatchers, supporting proactive scheduling and advance resource allocation to alleviate peak charging pressure.

[0060] The vehicle charging control method provided in this application will be described below with a specific embodiment. This application can be implemented based on a smart scheduling system for charging new energy autonomous freight vehicles using vehicle-cloud data interaction. This system includes: a data acquisition module, a data processing and storage module, a charging pile management module, a vehicle monitoring module, an intelligent scheduling decision-making module, an information interaction module, and a data analysis and prediction module. The functions of each module are described below.

[0061] The data acquisition module includes: a charging pile data acquisition unit: This unit connects to the charging pile data cloud platform to periodically (e.g., daily) acquire static data about the charging piles, including charging gun name, charging gun code, brand and model, charging power, and charging gun type; it also receives real-time dynamic status data of the charging piles pushed by the charging pile data cloud platform (push frequency e.g., every 15 seconds), including charging equipment interface status (idle, occupied, faulty, etc.), start charging time, remaining battery charge (SOC), and charged amount; after charging is completed, it receives historical charging order data pushed by the charging pile data cloud platform, including charging order number, charging pile name, charging duration, start and end times, start and end SOC, total electricity cost, and reason for charging termination. A vehicle data acquisition unit connects to the vehicle data open platform to acquire real-time vehicle data, including vehicle battery status (SOC), vehicle location information (latitude and longitude), vehicle status (online, parked online, offline), and last location time.

[0062] The data processing and storage module is used to: standardize and store the collected static data to ensure data consistency; perform real-time analysis and updates of dynamic data, such as calculating the charging time based on "current time - start charging time"; and store historical data in a structured manner and set the data retention period (such as the last 40 days).

[0063] The charging pile management module includes: a static information maintenance unit, which displays and regularly updates static information such as the terminal name, code, brand, model, power, and type of the charging pile, and supports synchronous updates from the charging pile data cloud platform; a dynamic status monitoring unit, which displays dynamic information such as the charging status (idle, occupied, faulty, etc.), charging duration, remaining battery power, and amount of charge in real time, and refreshes in real time based on data pushed from the cloud; and a historical data recording unit, which records and displays historical data such as the charging order number, charging duration, start and end time, electricity cost, and reason for termination of the charging pile, and supports querying and analysis.

[0064] The vehicle monitoring module includes: a status monitoring unit that acquires and displays the vehicle's SOC (power octane percentage), vehicle status (online / offline, etc.), and last location time in real time; a location tracking unit that displays the vehicle's real-time location on a map based on the acquired latitude and longitude information; and a charging demand triggering unit that automatically triggers a charging demand prompt when the vehicle's power octane reaches a preset SOC threshold (e.g., 20% by default).

[0065] The intelligent scheduling and decision-making module includes: a data fusion unit: fusing static and dynamic status information of charging piles with vehicle battery level and location information to establish a real-time database; a matching and recommendation unit: matching charging piles and vehicles based on the fused data using intelligent algorithms. For vehicles initiating charging requests (including those initiated by platform dispatchers and those automatically initiated when vehicle battery level reaches a threshold), a personalized charging recommendation list is generated by comprehensively considering factors such as the charging pile's idle status, charging power, distance to the vehicle (a reserved interface for future implementation), and vehicle battery demand; (scheduling is purely rule-based, but will be based on regression models in the future). A scheduling strategy unit: formulating different scheduling strategies based on the charging pile's busyness (e.g., the percentage of charging piles in use within the facility) and the vehicle's battery level. For example: When the charging pile utilization rate is less than 50% (idle), vehicles with less than 30% battery power are prompted "Please go to charge immediately," and vehicles with 30%-50% battery power are prompted "It is recommended to go to charge in advance during off-peak hours." When the charging pile utilization rate is ≥80% (busy / overflowing), vehicles with >80% battery power that are charging are prompted "Charging peak, please leave as soon as possible," and vehicles with 30%-50% battery power that are not charging are prompted "It is recommended to go to charge later during off-peak hours." Peak period management unit: Identify charging peak periods (such as a period of utilization rate ≥80% and lasting ≥1 hour), prompt the dispatcher 2 hours before the peak arrives to "Control the allocation of waybills to allow vehicles to charge as early as possible," and during the peak period, prompt "Coordinate the departure of vehicles with sufficient battery power, and suggest that vehicles with sufficient battery power charge later."

[0066] It should be noted that the vehicle charging control method of this application embodiment can be implemented based on the intelligent scheduling decision module.

[0067] The information interaction module includes: an instruction issuing unit, which pushes charging suggestions and dispatch instructions (such as recommended charging pile lists and travel time suggestions) generated by the intelligent dispatch decision module to the corresponding vehicles via a communication application; and a feedback receiving unit, which receives vehicle confirmation information or abnormal feedback (such as charging pile malfunctions) and synchronizes it to the dispatch platform. Dispatchers can access the dispatch platform via a browser, while vehicles receive information and instructions via the communication application.

[0068] The data analysis and prediction module includes: a statistical analysis unit: calculating and displaying charging pile utilization (occupancy time / total time), failure rate (failure time / total time), vehicle battery distribution, average battery change, and other data, supporting data export (e.g., Excel format); and a demand prediction unit: based on historical and real-time data, performing short-term (next 3 hours) and long-term (combining historical data and business conditions) charging demand predictions, forecasting the number of available charging piles, and assisting in advance resource allocation. For example, if a queue is predicted to form, it prompts some vehicles to charge in advance; for queued scenarios, it prompts vehicles that have not yet arrived to come later, and prompts vehicles that are charging and have sufficient battery power to leave as soon as possible.

[0069] This application provides a vehicle charging control method, device, electronic device, and storage medium. The method is applicable to the intelligent matching and scheduling management of charging piles for new energy autonomous freight vehicles. Based on this method, real-time and comprehensive static and dynamic status information of charging piles, as well as key data such as vehicle battery level and location, can be obtained. Based on the above data, intelligent matching of charging piles and vehicles can be achieved, providing personalized charging suggestions for autonomous vehicles. Through data analysis and prediction, it assists dispatchers in optimizing the allocation and proactive scheduling of charging pile resources. Through historical data mining and trend prediction, it can proactively address charging peaks, reduce vehicle queuing time, and improve the utilization rate of charging piles.

[0070] Corresponding to the application scenarios and methods provided in the embodiments of this application, the embodiments of this application also provide a vehicle charging control device. For example... Figure 2 The diagram shown is a structural block of a vehicle charging control device according to an embodiment of this application. Figure 1 The device may include:

[0071] The data processing module 201 is used to acquire the planned charging time period of the vehicles on the network at the target charging station, and determine the occupancy level of the charging piles at the target charging station within the specified time period based on the planned charging time period; the matching and scheduling module 202 is used to generate charging scheduling data according to the occupancy level of the charging piles; wherein, the charging scheduling data includes charging control suggestions corresponding to different battery levels of the vehicles; the charging control module 203 is used to respond to receiving a charging request from the target vehicle, and send the charging control suggestions in the charging scheduling data to the target vehicle according to the battery level of the target vehicle.

[0072] In one possible implementation, obtaining the planned charging time of a vehicle connected to the network at a target charging station includes: obtaining characteristic data and environmental data corresponding to the vehicle connected to the network; wherein the characteristic data includes task data and vehicle data; calculating the remaining battery power of the vehicle connected to the network after completing the current task based on the characteristic data and the environmental data; determining that the remaining battery power is greater than a preset battery power threshold, and determining the planned charging time of the vehicle connected to the network at the target charging station.

[0073] In one possible implementation, the remaining battery power of the connected vehicle after completing the current task is calculated based on the feature data and the environmental data, including: performing regression processing on the feature data based on historical feature energy consumption data to obtain the energy consumption value corresponding to the feature data; determining an energy consumption fluctuation coefficient based on the environmental data and a preset environmental energy consumption correspondence, and correcting the energy consumption value using the energy consumption fluctuation coefficient; and calculating the remaining battery power of the connected vehicle after completing the current task based on the corrected energy consumption value.

[0074] In one possible implementation, the environmental data includes congestion data and / or weather data; determining an energy consumption fluctuation coefficient based on the environmental data and a preset environmental energy consumption correspondence includes: determining a congestion coefficient based on the congestion data and the preset environmental energy consumption correspondence; determining a weather coefficient based on the weather data and the preset environmental energy consumption correspondence; and using the congestion coefficient and / or the weather coefficient as the energy consumption fluctuation coefficient.

[0075] In one possible implementation, the specified time period includes multiple consecutive sub-time periods; determining the charging pile occupancy level of the target charging station within the specified time period based on the planned charging time period includes: calculating the charging pile utilization rate corresponding to the sub-time period based on the planned charging time period; and determining the charging pile occupancy level of the target charging station within the specified time period based on multiple charging pile utilization rates.

[0076] See Figure 3 The structural block diagram of a vehicle charging control device according to an embodiment of this application is shown. Figure 2 In one possible implementation, the device further includes a peak prediction module 301, configured to: determine a target time period within the specified time period based on the occupancy level of the charging pile; and send the target time period to the interaction module to control vehicle charging before the target time period.

[0077] The functions of each module in each device in the embodiments of this application can be found in the corresponding description in the above method, and they have corresponding beneficial effects, which will not be repeated here.

[0078] Figure 4 This is a block diagram of an electronic device used to implement embodiments of this application. For example... Figure 4 As shown, the electronic device includes a memory 401 and a processor 402. The memory 401 stores a computer program that can run on the processor 402. When the processor 402 executes the computer program, it implements the method described in the above embodiments. The number of memories 401 and processors 402 can be one or more.

[0079] The electronic device also includes:

[0080] Communication interface 403 is used to communicate with external devices and perform data exchange and transmission.

[0081] If the memory 401, processor 402, and communication interface 403 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0082] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0083] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method provided in this application.

[0084] This application provides a computer program product, wherein the computer program product includes a computer program, which, when executed by a processor, implements the method provided in this application embodiment.

[0085] This application also provides a chip including a processor for calling and executing instructions stored in a memory, causing a communication device with the chip installed to perform the method provided in this application.

[0086] This application also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the method provided in the application embodiment.

[0087] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0088] Further, optionally, the aforementioned memory may include read-only memory and random access memory. The memory may be volatile memory or non-volatile memory, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0089] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0090] It should be noted that the information (including but not limited to equipment information, vehicle information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0091] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0092] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0093] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0094] The logic and / or steps described in the flowchart or otherwise herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0095] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0096] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0097] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope described in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle charging control method, comprising: Obtain the planned charging time period of vehicles on the network at the target charging station, and determine the occupancy rate of the charging piles at the target charging station during the specified time period based on the planned charging time period; Charging scheduling data is generated based on the occupancy level of the charging piles; wherein, the charging scheduling data includes charging control suggestions corresponding to different battery charge levels of the vehicles; In response to receiving a charging request from a target vehicle, a charging control suggestion from the charging scheduling data is sent to the target vehicle based on the target vehicle's remaining battery power.

2. The method according to claim 1, wherein, Obtain the planned charging time slots for vehicles currently connected to the network at the target charging station, including: Acquire feature data and environmental data corresponding to vehicles on the network; wherein, the feature data includes task data and vehicle data; Based on the feature data and the environmental data, calculate the remaining battery power of the vehicle in the network after completing the current task; If the remaining battery power is determined to be greater than a preset battery power threshold, the planned charging time for the vehicle in the network at the target charging station is determined.

3. The method according to claim 2, wherein, Based on the feature data and the environmental data, calculate the remaining battery power of the connected vehicle after completing the current task, including: Based on historical characteristic energy consumption data, regression processing is performed on the characteristic data to obtain the energy consumption value corresponding to the characteristic data. Based on the environmental data and the preset environmental energy consumption correspondence, an energy consumption fluctuation coefficient is determined, and the energy consumption value is corrected using the energy consumption fluctuation coefficient. Based on the corrected energy consumption value, calculate the remaining battery power of the vehicle on the network after completing the current task.

4. The method according to claim 3, wherein, The environmental data includes traffic congestion data and / or weather data; based on the environmental data and a preset environmental energy consumption correspondence, an energy consumption fluctuation coefficient is determined, including: The congestion coefficient is determined based on the congestion data and the preset relationship between environmental energy consumption. The weather coefficient is determined based on the weather data and the preset environmental energy consumption correspondence. The congestion coefficient and / or the weather coefficient are used as the energy consumption fluctuation coefficient.

5. The method according to claim 1, wherein, The specified time period includes multiple consecutive sub-time periods; Based on the planned charging period, determine the occupancy rate of the charging piles at the target charging station within the specified time period, including: Based on the planned charging period, calculate the charging pile utilization rate corresponding to the sub-period; Based on the utilization rates of multiple charging piles, the occupancy rate of the charging piles at the target charging station within a specified time period is determined.

6. The method according to any one of claims 1-5, wherein, The method further includes: Based on the occupancy level of the charging piles, a target time period is determined within the specified time period; The target time period is sent to the interaction module to control the charging of the vehicle before the target time period.

7. A vehicle charging control device, comprising: The data processing module is used to obtain the planned charging time period of vehicles on the network at the target charging station, and based on the planned charging time period, determine the occupancy rate of the charging piles at the target charging station during the specified time period. The matching and scheduling module is used to generate charging scheduling data based on the occupancy level of the charging piles; wherein, the charging scheduling data includes charging control suggestions corresponding to different battery charge levels of the vehicle. The charging control module is used to respond to a charging request received from a target vehicle and, based on the remaining battery power of the target vehicle, send charging control suggestions from the charging scheduling data to the target vehicle.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor, when executing the computer program, implements the method of any one of claims 1-6.

9. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

10. A computer program product, wherein, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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