Vehicle charging method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a vehicle charging method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that the current state of charge SOC, the current position and historical behavior data of a target vehicle to be charged, the current available electric energy and the current load of a power grid, and the position and the current charging unit price of at least one initial charging station connected to the power grid are obtained; determining a target charging station from the at least one initial charging station based on the current SOC, the current position, the position of the initial charging station and the historical behavior data; on the basis of the current SOC, the historical behavior data, the current available electric energy, the current load and the current charging unit price, acquiring the required electric energy of the target to-be-charged vehicle; and controlling the target charging station to charge the target to-be-charged vehicle based on the required electric energy. According to the method provided by the invention, the charging requirement of the vehicle can be met, and the safety of the power grid can be ensured.

Description

Technical Field

[0001] The present application relates to the field of vehicle-grid interaction technology, and in particular to a vehicle charging method, device, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] Distributed vehicle-grid interaction technology refers to connecting electric vehicles to the power grid through an intelligent network to achieve two-way energy flow and information interaction between electric vehicles and the power grid. This technology not only allows electric vehicles to charge from the power grid, but also allows electric vehicles to discharge to the power grid at appropriate times to provide power support for the power grid. With the rapid growth of electric vehicle ownership and the construction of new power systems, distributed vehicle-grid interaction technology is gradually becoming an important development direction in the energy field. However, in the existing distributed vehicle-grid interaction technology, the amount of electricity that electric vehicles need to charge from the power grid is only predicted based on the current state of charge SOC of the electric vehicle. The electric vehicle charges from the power grid according to the predicted amount, which cannot guarantee the safety of the power grid. Summary of the invention

[0003] Based on this, it is necessary to provide a vehicle charging method, device, computer equipment, computer-readable storage medium and computer program product that can ensure the safety of the power grid in response to the above technical problems.

[0004] In a first aspect, the present application provides a vehicle charging method, the method comprising:

[0005] Obtaining the current state of charge (SOC), current location, historical behavior data, current available power and current load of the power grid, the location of at least one initial charging station connected to the power grid, and the current charging unit price of the target vehicle to be charged;

[0006] determining a target charging station from the at least one initial charging station based on the current SOC, the current location, the location of the initial charging station, and the historical behavior data;

[0007] Based on the current SOC, the historical behavior data, the current available electric energy, the current load and the current charging unit price, obtaining the required electric energy of the target vehicle to be charged;

[0008] Based on the required electric energy, the target charging station is controlled to charge the target vehicle to be charged.

[0009] In one embodiment, the determining a target charging station from the at least one initial charging station based on the current SOC, the current location, the location of the initial charging station, and the historical behavior data comprises:

[0010] Based on the current position and the position of the initial charging station, determining at least one intermediate charging station within a preset range where the target vehicle to be charged is located;

[0011] For each intermediate charging station, determining at least one drivable route between the target vehicle to be charged and the intermediate charging station, and obtaining the current number and current motion status of traffic participants in the drivable route;

[0012] A target charging station is determined from the at least one intermediate charging station based on the current SOC, the historical behavior data, the current quantity, and the current motion state.

[0013] In one embodiment, determining a target charging station from the at least one intermediate charging station based on the current SOC, the historical behavior data, the current quantity, and the current motion state includes:

[0014] For each drivable route, based on the current number, the current motion state and the historical behavior data, obtaining the time required for the target vehicle to be charged to travel from the current position to the position of the intermediate charging station along the drivable route;

[0015] Determine the drivable route with the shortest corresponding duration as the optimal driving route corresponding to the intermediate charging station;

[0016] A target charging station is determined from the at least one intermediate charging station based on the current SOC and the corresponding duration of the optimal driving route.

[0017] In one embodiment, determining a target charging station from the at least one intermediate charging station based on the current SOC and the corresponding duration of the optimal driving route includes:

[0018] Acquire the current charging state of the vehicle currently being charged in the intermediate charging station and the current SOC of other vehicles to be charged corresponding to the intermediate charging station;

[0019] Based on the current charging state, the current SOC of the target vehicle to be charged and the current SOC of the other vehicles to be charged, obtaining a charging time point when the target vehicle to be charged starts charging in the intermediate charging station;

[0020] Based on the corresponding duration of the optimal driving route and the charging time, a target charging station is determined from the at least one intermediate charging station.

[0021] In one embodiment, the process of determining the target vehicle to be charged includes:

[0022] For each vehicle, obtaining the current SOC of the vehicle in real time;

[0023] In a case where the current SOC of the vehicle is less than a first SOC threshold, the vehicle is determined as the target vehicle to be charged.

[0024] In one embodiment, the method further comprises:

[0025] In the case where the current load is greater than the preset load, for each vehicle, if the current SOC of the vehicle is greater than a second SOC threshold, determining the current distributable electric energy of the vehicle based on the SOC difference between the current SOC of the vehicle and the second SOC threshold;

[0026] A current discharge unit price is obtained, and the current distributable electric energy and the current discharge unit price are sent to the vehicle.

[0027] In a second aspect, the present application further provides a vehicle charging device, the device comprising:

[0028] The first acquisition module is used to obtain the current state of charge SOC, current location, historical behavior data, current available power and current load of the power grid, the location of at least one initial charging station connected to the power grid, and the current charging unit price of the target vehicle to be charged;

[0029] a determination module, configured to determine a target charging station from the at least one initial charging station based on the current SOC, the current location, the location of the initial charging station, and the historical behavior data;

[0030] A second acquisition module is used to acquire the required electric energy of the target vehicle to be charged based on the current SOC, the historical behavior data, the current available electric energy, the current load and the current charging unit price;

[0031] A control module is used to control the target charging station to charge the target vehicle to be charged based on the required electric energy.

[0032] In a third aspect, the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in any one of the above embodiments when executing the computer program.

[0033] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.

[0034] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.

[0035] The above-mentioned vehicle charging method, device, computer equipment, computer-readable storage medium and computer program product obtain the current state of charge SOC, current position, historical behavior data, current available power and current load of the power grid, the position of at least one initial charging station connected to the power grid and the current charging unit price of the target vehicle to be charged; based on the current SOC, current position, the position of the initial charging station and historical behavior data, determine a target charging station from at least one initial charging station; based on the current SOC, historical behavior data, current available power, current load and current charging unit price, obtain the required power of the target vehicle to be charged; based on the required power, control the target charging station to charge the target vehicle to be charged. The method provided by the present application can not only meet the charging needs of the vehicle, but also ensure the safety of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A schematic flow chart of a vehicle charging method in one embodiment;

[0038] Figure 2 is a flow chart of a method for determining a target charging station in one embodiment;

[0039] Figure 3 A schematic flow chart of a vehicle charging method according to another embodiment;

[0040] Figure 4 A schematic diagram of the overall design of charging load prediction in another embodiment;

[0041] Figure 5 A schematic diagram of the overall architecture of large-scale electric vehicles participating in power grid dispatching in another embodiment;

[0042] Figure 6 is a structural block diagram of a vehicle charging device in one embodiment;

[0043] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] In one embodiment, Figure 1 As shown, a vehicle charging method is provided. This embodiment takes the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0046] S102, obtaining the current state of charge SOC, current location, historical behavior data, current available power and current load of the power grid, the location of at least one initial charging station connected to the power grid, and the current charging unit price of the target vehicle to be charged.

[0047] Among them, SOC (State of Charge) refers to the ratio between the current remaining power of the vehicle battery and the total battery capacity; historical behavior data refers to the various behaviors and status information of the vehicle in different driving scenarios within the historical time period; the current available power refers to the power that the current power grid can provide to vehicles and other charging equipment; the current load refers to the total power consumed by all power-consuming devices in the current power grid; the charging unit price refers to the fee paid for each unit of power during the charging process of the vehicle at the charging station.

[0048] S104: Determine a target charging station from at least one initial charging station based on the current SOC, the current location, the location of the initial charging station, and the historical behavior data.

[0049] Optionally, based on the current position and the position of the initial charging station, at least one intermediate charging station within a preset range where the target vehicle to be charged is located is determined; for each intermediate charging station, at least one drivable route between the target vehicle to be charged and the intermediate charging station is determined, and the current number and current motion status of traffic participants in the drivable route are obtained; based on the current SOC, historical behavior data, current number and current motion status, a target charging station is determined from at least one intermediate charging station.

[0050] S106. Obtain the required power of the target vehicle to be charged based on the current SOC, historical behavior data, current available power, current load and current charging unit price.

[0051] Optionally, by analyzing the current SOC and historical behavior data of the target vehicle to be charged, the maximum driving time that the target vehicle to be charged can be predicted; by combining the peak-shaving and valley-filling electricity consumption strategy, the current available power and current load of the power grid are analyzed, and the optimal charging time point for the target vehicle to be charged is predicted from the load perspective of the power grid; the optimal charging time point is adjusted based on the corresponding maximum driving time of the target vehicle to be charged; based on the current charging unit price, the initial required power of the target vehicle to be charged is determined from the perspective of minimizing the charging cost; the initial required power is adjusted based on the adjusted optimal charging time point to obtain the required power of the target vehicle to be charged.

[0052] S108: Based on the required electric energy, control the target charging station to charge the target vehicle to be charged.

[0053] Optionally, the location of the target charging station is sent to the target vehicle to be charged so that the target vehicle to be charged drives to the target charging station; when the target vehicle to be charged drives to the target charging station, the target charging station is controlled to charge the target vehicle to be charged according to the required power.

[0054] In the above vehicle charging method, the current state of charge SOC, current position, historical behavior data, current available power and current load of the power grid, the position of at least one initial charging station connected to the power grid and the current charging unit price of the target vehicle to be charged are obtained; based on the current SOC, current position, the position of the initial charging station and historical behavior data, a target charging station is determined from at least one initial charging station; based on the current SOC, historical behavior data, current available power, current load and current charging unit price, the required power of the target vehicle to be charged is obtained; based on the required power, the target charging station is controlled to charge the target vehicle to be charged. The method provided by the present application can not only meet the charging needs of the vehicle, but also ensure the safety of the power grid.

[0055] In some embodiments, Figure 2 As shown, based on the current SOC, the current location, the location of the initial charging station and the historical behavior data, a target charging station is determined from at least one initial charging station, including:

[0056] S202: Based on the current position and the position of the initial charging station, determine at least one intermediate charging station within a preset range where the target vehicle to be charged is located.

[0057] S204: for each intermediate charging station, determine at least one drivable route between the target vehicle to be charged and the intermediate charging station, and obtain the current number and current movement status of traffic participants in the drivable route.

[0058] S206 : Determine a target charging station from at least one intermediate charging station based on the current SOC, the historical behavior data, the current quantity, and the current motion state.

[0059] Optionally, the preset range may be a circular range with the current position of the target vehicle to be charged as the center and a preset length as the radius. In other embodiments, the preset range may also be other ranges, which is not specifically limited in comparison with the embodiments of the present application.

[0060] In this embodiment, based on the current position and the position of the initial charging station, at least one intermediate charging station within a preset range where the target vehicle to be charged is located is determined, so that the determined intermediate charging station is more accurate.

[0061] In some embodiments, based on the current SOC, historical behavior data, current number and current motion state, a target charging station is determined from at least one intermediate charging station, including: for each drivable route, based on the current number, current motion state and historical behavior data, obtaining the time required for the target vehicle to be charged to travel from the current position to the position of the intermediate charging station along the drivable route; determining the drivable route with the shortest corresponding time as the corresponding optimal driving route of the intermediate charging station; based on the current SOC and the corresponding time of the optimal driving route, determining a target charging station from at least one intermediate charging station.

[0062] Optionally, for each drivable route, by analyzing the current number and current motion status of traffic participants in the drivable route, the current road condition of the target vehicle to be charged when it is currently traveling on the drivable route can be determined; historical behavior data corresponding to the target road condition similar to the current road condition in the historical behavior data is obtained; and by analyzing the historical behavior data corresponding to the target road condition, the corresponding duration of the target vehicle to be charged traveling on the drivable route is predicted.

[0063] In this embodiment, for each drivable route, based on the current number, current motion state and historical behavior data, the time required for the target vehicle to be charged to travel from the current position to the position of the intermediate charging station along the drivable route is obtained; the drivable route with the shortest corresponding time is determined as the optimal driving route corresponding to the intermediate charging station. The optimal driving route determined in this way is more accurate, so that the target charging station determined based on the optimal driving route is more accurate.

[0064] In some embodiments, based on the current SOC and the corresponding duration of the optimal driving route, a target charging station is determined from at least one intermediate charging station, including: obtaining the current charging state of the vehicle currently charging in the intermediate charging station, and the current SOC of other vehicles to be charged corresponding to the intermediate charging station; based on the current charging state, the current SOC of the target vehicle to be charged, and the current SOC of other vehicles to be charged, obtaining the charging time point when the target vehicle to be charged starts charging in the intermediate charging station; based on the corresponding duration and charging time point of the optimal driving route, a target charging station is determined from at least one intermediate charging station.

[0065] Optionally, by analyzing the current charging status of the vehicle currently charging in the intermediate charging station, the charging end time of the vehicle can be determined; by analyzing the current SOC of other vehicles to be charged at the intermediate charging station and the current SOC of the target vehicle to be charged, the urgency of the charging demand of each vehicle to be charged at the intermediate charging station can be determined, and based on the corresponding charging end time of the vehicle currently charging in the intermediate charging station, the charging time for each vehicle to be charged at the intermediate charging station to start charging is determined according to the urgency of the charging demand of the vehicle to be charged; by analyzing the charging time of the target vehicle to be charged at each intermediate charging station and the corresponding duration of the best driving route at each intermediate charging station, a target charging station is determined from at least one intermediate charging station.

[0066] In this embodiment, based on the corresponding duration and charging time point of the optimal driving route, a target charging station is determined from at least one intermediate charging station, so that the target vehicle to be charged is charged in the target charging station, which can not only ensure the charging needs of the target vehicle to be charged, but also ensure the safety of the power grid.

[0067] In some embodiments, the process of determining the target vehicle to be charged includes: for each vehicle, acquiring the current SOC of the vehicle in real time; and determining the vehicle as the target vehicle to be charged when the current SOC of the vehicle is less than a first SOC threshold.

[0068] Optionally, the current SOC of the vehicle is less than the first SOC threshold, indicating that the current SOC of the vehicle is low and needs to be charged.

[0069] In this embodiment, when the current SOC of the vehicle is less than the first SOC threshold, the vehicle is determined as a target vehicle to be charged, which can ensure the safety of the vehicle during driving.

[0070] In some embodiments, the method also includes: when the current load is greater than the preset load, for each vehicle, if the current SOC of the vehicle is greater than a second SOC threshold, determining the current distributable electrical energy of the vehicle based on the SOC difference between the current SOC of the vehicle and the second SOC threshold; obtaining the current discharge unit price, and sending the current distributable electrical energy and the current discharge unit price to the vehicle.

[0071] Among them, the discharge unit price refers to the fee that the vehicle can obtain per unit of electricity when discharging to the power grid at a charging station.

[0072] Optionally, the current load of the power grid is greater than the preset load, indicating that the current load of the power grid is high. Based on the peak shaving and valley filling power consumption strategy, a charging request can be sent to vehicles with a higher current SOC, so that the owner of the vehicle can comprehensively consider whether to discharge to the power grid based on the current SOC of the vehicle and the current discharge unit price.

[0073] In this embodiment, the current distributable electric energy and the current discharge unit price are sent to the vehicle so that the owner can consider whether to discharge to the power grid, which can ensure both the driving safety of the vehicle and the safety of the power grid.

[0074] In one embodiment, Figure 3 As shown, another vehicle charging method is provided, the method comprising the following contents:

[0075] (1) Electric vehicle charging load prediction

[0076] The electric vehicle charging load prediction adopts a technical solution based on graph convolutional networks to cope with the problems of load growth and power quality degradation caused by the rapid development of electric vehicles. The solution combines traffic flow and queuing theory, and captures the temporal and spatial dynamic characteristics of traffic data by establishing a spatiotemporal graph convolutional network. The convolution operation is improved to optimize the residual module to achieve accurate prediction of the electric vehicle arrival rate. Based on the traffic flow prediction results, the electric vehicle arrival rate is converted into charging load, and the charging station service capacity and user behavior are considered. The charging process is simulated through the queuing theory model, and a load prediction model with customers is established to improve the accuracy of charging load prediction. Figure 4 Schematic diagram of the overall design scheme for charging load prediction.

[0077] (2) Assessment of electric vehicle charging potential

[0078] The design of the electric vehicle charging potential assessment task includes two aspects: vehicle-grid interaction potential assessment and real-time scheduling. First, by establishing an analysis model based on the electric vehicle status and battery SOC, the discharge scheduling potential of electric vehicles in the idle state of the grid is evaluated. Considering the charging and discharging needs of electric vehicles and the grid regulation capacity, a charging scheduling potential assessment model is established to determine whether the vehicle has the ability to participate in grid interaction. At the same time, a real-time scheduling scheme is proposed, which applies centralized, distributed or hierarchical control methods for scheduling according to the real-time load of the grid, vehicle status and charging demand. The scheme classifies the status of electric vehicles, calculates the real-time SOC and scheduling priority, and realizes efficient scheduling of vehicle-grid interaction, thereby improving the safety and stability of the grid.

[0079] (3) Research on market mechanism and planning and scheduling methods considering flexible interaction of electric vehicles

[0080] The research design of the market mechanism and planning and dispatching method considering the flexible interaction of electric vehicles aims to enhance the support capacity and market participation of electric vehicles for the power grid. The allocation and assessment mechanism of carbon emissions from electric vehicle electricity consumption is studied, and the charging time and power structure are recorded through smart meters and real-time monitoring systems to accurately calculate carbon emissions and promote environmental protection behavior; at the same time, the operation optimization and bidding strategy of electric vehicle aggregators in the day-ahead market are explored, and the demand-side bidding mechanism is used to encourage electric vehicles to participate in the power market and enhance the regulation capacity of the power grid; the operation mode of the day-ahead energy market and the ancillary service market is studied to clarify how electric vehicles optimize resource allocation in the two markets, participate in market transactions and provide ancillary services, and promote the stability and economic operation of the power system.

[0081] (4) Combined optimization of the day-ahead market and the real-time market for electric vehicles

[0082] A joint optimization model of the day-ahead market and the real-time market for electric vehicles is established to operate at the lowest electricity sales cost. The electricity purchase and sales plans are optimized through an improved particle swarm optimization algorithm, and the economics of the optimization cost and the traditional separate market are compared. Electric vehicles can not only serve as loads but also supply power to the grid through V2G technology, reducing energy loss and improving the response speed of spare capacity. The model also analyzes the charging characteristics and periodic use of electric vehicles, optimizes the day-ahead charging and discharging plans of electric vehicle aggregators, considers operational optimization and market clearing through a two-layer model, and finally transforms it into a mixed integer linear programming model for solution to maximize the benefits of electricity purchase and social welfare.

[0083] (5) Development of intelligent control algorithms for vehicle-grid interaction

[0084] The development of intelligent control algorithm for vehicle-grid interaction realizes the interaction between large-scale electric vehicles and power grids by optimizing the dispatching strategy, promotes the consumption of new energy and reduces the impact of its uncertainty on the power grid. First, define the control potential of electric vehicle clusters, formulate time-of-use electricity prices for different clusters, and encourage high-potential clusters to respond to power grid dispatch to achieve peak shaving and valley filling. At the same time, use electric vehicle load aggregators (EVLA) as a bridge between the power grid and users to improve resource allocation efficiency. Consider the optimization model of wind power output uncertainty and electric vehicle charging load constraints to minimize the peak-to-valley difference of the power grid and reduce user charging costs. At the same time, a strategy based on deep reinforcement learning is adopted to optimize the dispatching of large-scale electric vehicle charging and swapping loads in real time to ensure friendly interaction with the power system, improve the satisfaction of electric vehicle users, and ensure the safe and economic operation of the power grid. Figure 5 Schematic diagram of the overall architecture for large-scale electric vehicles participating in grid dispatching.

[0085] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0086] Based on the same inventive concept, the embodiment of the present application also provides a vehicle charging device for implementing the vehicle charging method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more vehicle charging device embodiments provided below can refer to the limitations on the vehicle charging method above, and will not be repeated here.

[0087] In an exemplary embodiment, Figure 6 As shown, a vehicle charging device 600 is provided, comprising: a first acquisition module 601, a determination module 602, a second acquisition module 603 and a control module 604, wherein:

[0088] The first acquisition module 601 is used to obtain the current state of charge SOC, current location, historical behavior data, current available power and current load of the power grid, the location of at least one initial charging station connected to the power grid, and the current charging unit price of the target vehicle to be charged.

[0089] The determination module 602 is configured to determine a target charging station from the at least one initial charging station based on the current SOC, the current location, the location of the initial charging station, and the historical behavior data.

[0090] The second acquisition module 603 is used to acquire the required electric energy of the target vehicle to be charged based on the current SOC, the historical behavior data, the current available electric energy, the current load and the current charging unit price.

[0091] The control module 604 is used to control the target charging station to charge the target vehicle to be charged based on the required electric energy.

[0092] In some embodiments, the determination module 602 includes:

[0093] The first determination submodule is used to determine at least one intermediate charging station within a preset range where the target vehicle to be charged is located based on the current position and the position of the initial charging station.

[0094] A second determination submodule is used to determine, for each intermediate charging station, at least one drivable route between the target vehicle to be charged and the intermediate charging station, and obtain the current number and current motion status of traffic participants in the drivable route;

[0095] The third determination submodule is configured to determine a target charging station from the at least one intermediate charging station based on the current SOC, the historical behavior data, the current number, and the current motion state.

[0096] In some embodiments, the third determining submodule includes:

[0097] The acquisition unit is used to acquire, for each drivable route, the time required for the target vehicle to be charged to travel from the current position to the position of the intermediate charging station along the drivable route based on the current number, the current motion state and the historical behavior data.

[0098] The first determining unit is used to determine a drivable route with the shortest corresponding duration as an optimal driving route corresponding to the intermediate charging station.

[0099] The second determining unit is configured to determine a target charging station from the at least one intermediate charging station based on the current SOC and a corresponding duration of the optimal driving route.

[0100] In some embodiments, the second determination unit is also used to obtain the current charging state of the vehicle currently being charged in the intermediate charging station and the current SOC of other vehicles to be charged at the intermediate charging station; based on the current charging state, the current SOC of the target vehicle to be charged and the current SOC of the other vehicles to be charged, obtain the charging time point when the target vehicle to be charged starts charging in the intermediate charging station; based on the corresponding duration of the optimal driving route and the charging time point, determine a target charging station from the at least one intermediate charging station.

[0101] In some embodiments, the vehicle charging device 600 is specifically used to obtain the current SOC of each vehicle in real time; when the current SOC of the vehicle is less than a first SOC threshold, the vehicle is determined as the target vehicle to be charged.

[0102] In some embodiments, the vehicle charging device 600 is also used to, when the current load is greater than a preset load, for each vehicle, if the current SOC of the vehicle is greater than a second SOC threshold, determine the current distributable electric energy of the vehicle based on the SOC difference between the current SOC of the vehicle and the second SOC threshold; obtain the current discharge unit price, and send the current distributable electric energy and the current discharge unit price to the vehicle.

[0103] Each module in the above-mentioned vehicle charging device can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0104] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, a near field communication (NFC) or other technologies. When the computer program is executed by the processor, a vehicle charging method is implemented.

[0105] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0106] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0107] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0108] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0109] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0110] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0111] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0112] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A vehicle charging method, characterized in that: The method comprises: Obtaining the current state of charge (SOC), current location, historical behavior data, current available power and current load of the power grid, the location of at least one initial charging station connected to the power grid, and the current charging unit price of the target vehicle to be charged; determining a target charging station from the at least one initial charging station based on the current SOC, the current location, the location of the initial charging station, and the historical behavior data; Based on the current SOC, the historical behavior data, the current available electric energy, the current load and the current charging unit price, obtaining the required electric energy of the target vehicle to be charged; Based on the required electric energy, the target charging station is controlled to charge the target vehicle to be charged.

2. The method according to claim 1, characterized in that: The determining a target charging station from the at least one initial charging station based on the current SOC, the current position, the position of the initial charging station, and the historical behavior data comprises: Based on the current position and the position of the initial charging station, determining at least one intermediate charging station within a preset range where the target vehicle to be charged is located; For each intermediate charging station, determining at least one drivable route between the target vehicle to be charged and the intermediate charging station, and obtaining the current number and current motion status of traffic participants in the drivable route; A target charging station is determined from the at least one intermediate charging station based on the current SOC, the historical behavior data, the current quantity, and the current motion state.

3. The method according to claim 2, characterized in that The step of determining a target charging station from the at least one intermediate charging station based on the current SOC, the historical behavior data, the current quantity, and the current motion state comprises: For each drivable route, based on the current number, the current motion state and the historical behavior data, obtaining the time required for the target vehicle to be charged to travel from the current position to the position of the intermediate charging station along the drivable route; Determine the drivable route with the shortest corresponding duration as the optimal driving route corresponding to the intermediate charging station; A target charging station is determined from the at least one intermediate charging station based on the current SOC and the corresponding duration of the optimal driving route.

4. The method according to claim 3, characterized in that The determining a target charging station from the at least one intermediate charging station based on the current SOC and the corresponding duration of the optimal driving route includes: Acquire the current charging state of the vehicle currently being charged in the intermediate charging station and the current SOC of other vehicles to be charged corresponding to the intermediate charging station; Based on the current charging state, the current SOC of the target vehicle to be charged and the current SOC of the other vehicles to be charged, obtaining a charging time point when the target vehicle to be charged starts charging in the intermediate charging station; Based on the corresponding duration of the optimal driving route and the charging time, a target charging station is determined from the at least one intermediate charging station.

5. The method according to claim 1, characterized in that: The process of determining the target vehicle to be charged includes: For each vehicle, obtaining the current SOC of the vehicle in real time; In a case where the current SOC of the vehicle is less than a first SOC threshold, the vehicle is determined as the target vehicle to be charged.

6. The method according to claim 1, characterized in that The method further comprises: In the case where the current load is greater than the preset load, for each vehicle, if the current SOC of the vehicle is greater than a second SOC threshold, determining the current distributable electric energy of the vehicle based on the SOC difference between the current SOC of the vehicle and the second SOC threshold; A current discharge unit price is obtained, and the current distributable electric energy and the current discharge unit price are sent to the vehicle.

7. A vehicle charging device, characterized in that: The device comprises: The first acquisition module is used to obtain the current state of charge SOC, current location, historical behavior data, current available power and current load of the power grid, the location of at least one initial charging station connected to the power grid, and the current charging unit price of the target vehicle to be charged; a determination module, configured to determine a target charging station from the at least one initial charging station based on the current SOC, the current location, the location of the initial charging station, and the historical behavior data; A second acquisition module is used to acquire the required electric energy of the target vehicle to be charged based on the current SOC, the historical behavior data, the current available electric energy, the current load and the current charging unit price; A control module is used to control the target charging station to charge the target vehicle to be charged based on the required electric energy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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