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

Abstract

The application relates to a vehicle charging method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: obtaining the current state of charge (SOC), the current position, the historical behavior data, the current available power and the 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; determining one 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; obtaining the required power of the target vehicle to be charged based on the current SOC, the historical behavior data, the current available power, the current load and the current charging unit price; and controlling the target charging station to charge the target vehicle to be charged based on the required power. The method provided by the application can meet the charging demand of the vehicle and ensure the safety of the power grid.

Description

Technical Field

[0001] This application relates to the field of vehicle-to-everything (V2X) technology, and in particular to a vehicle charging method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] Distributed vehicle-to-grid (V2G) technology refers to connecting electric vehicles (EVs) to the power grid via a smart network, enabling bidirectional energy flow and information exchange between the two systems. This technology not only allows EVs to charge from the grid but also allows them to discharge back into the grid when necessary, providing power support. With the rapid growth of EV ownership and the construction of new power systems, distributed V2G technology is gradually becoming an important development direction in the energy sector. However, existing distributed V2G technologies only predict the amount of electricity an EV needs to charge from the grid based on its current state of charge (SOC). Relying on this predicted charging amount does not guarantee grid security. Summary of the Invention

[0003] Therefore, 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-mentioned technical problems.

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

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

[0006] 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 the at least one initial charging station;

[0007] Based on the current SOC, the historical behavior data, the current available electrical energy, the current load, and the current charging unit price, the electrical energy demand of the target vehicle to be charged is obtained;

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

[0009] In one embodiment, 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 includes:

[0010] Based on the current location and the location 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;

[0011] For each intermediate charging station, at least one drivable route is determined between the target vehicle to be charged and the intermediate charging station, and the current number and current movement status of traffic participants in the drivable route are obtained.

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

[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 quantity, the current motion state, and the historical behavior data, the time required for the target vehicle to be charged to travel along the drivable route from the current location to the location of the intermediate charging station is obtained;

[0015] The shortest possible driving route is determined as the optimal driving route for the intermediate charging station.

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

[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] Obtain the current charging status of the vehicle currently charging at the intermediate charging station, and the current SOC of other vehicles waiting to be charged at the intermediate charging station;

[0019] Based on the current charging status, the current SOC of the target vehicle to be charged, and the current SOC of the other vehicles to be charged, the charging time point when the target vehicle to be charged starts charging at the intermediate charging station is obtained.

[0020] Based on the duration of the optimal driving route and the charging time point, 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, the current SOC of the vehicle is obtained in real time;

[0023] If the current SOC of the vehicle is less than the first SOC threshold, the vehicle is identified as the target vehicle to be charged.

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

[0025] When the current load is greater than the preset load, for each vehicle, if the current SOC of the vehicle is greater than the second SOC threshold, the current available electrical energy of the vehicle is determined based on the SOC difference between the current SOC of the vehicle and the second SOC threshold.

[0026] Obtain the current discharge unit price and send the current allocable electrical energy and the current discharge unit price to the vehicle.

[0027] Secondly, this application also provides a vehicle charging device, the device comprising:

[0028] The first acquisition module is used to acquire the target vehicle's 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.

[0029] The determination module is used 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] The second acquisition module is used to acquire the energy demand of the target vehicle to be charged based on the current SOC, the historical behavior data, the current available energy, the current load, and the current charging unit price.

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

[0032] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the methods in any of the above embodiments.

[0033] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0034] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0035] The aforementioned vehicle charging method, apparatus, computer equipment, computer-readable storage medium, and computer program product acquire the current state of charge (SOC), current location, historical behavior data, current available electrical energy and current load of the power grid, and the location and current charging unit price of at least one initial charging station connected to the power grid for the target vehicle to be charged; based on the current SOC, current location, location 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 electrical energy, current load, and current charging unit price, obtain the electrical energy demand of the target vehicle to be charged; and based on the electrical energy demand, control the target charging station to charge the target vehicle to be charged. The method provided in this application can both meet the charging needs of vehicles and ensure the security of the power grid. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating a vehicle charging method in one embodiment;

[0038] Figure 2 This is a flowchart illustrating a method for determining a target charging station in one embodiment;

[0039] Figure 3 A flowchart illustrating a vehicle charging method in another embodiment;

[0040] Figure 4 This is a schematic diagram of the overall design scheme for charging load prediction in another embodiment;

[0041] Figure 5 This is a schematic diagram of the overall architecture for large-scale electric vehicles participating in grid dispatch, as shown in another embodiment.

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

[0043] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

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

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

[0047] Among them, SOC (State of Charge) refers to the ratio between the current remaining charge of the vehicle's battery and its total battery capacity; historical behavior data refers to the various behaviors and status information of the vehicle under different driving scenarios within a historical period; currently available electrical energy refers to the electrical energy that the current power grid can provide to the vehicle and other charging devices; current load refers to the total electrical energy consumed by all electrical devices in the current power grid; and charging unit price refers to the cost required to charge each unit of electricity during the charging process at a charging station.

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

[0049] Optionally, based on the current location and the location 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 movement status of traffic participants in the drivable route are obtained; based on the current SOC, historical behavior data, current number and current movement status, a target charging station is determined from the at least one intermediate charging station.

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

[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 can still drive can be predicted; by combining peak shaving and valley filling power consumption strategies, the current available power and current load of the power grid are analyzed, and the optimal charging time of the target vehicle to be charged is predicted from the perspective of power grid load; the optimal charging time 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 power demand of the target vehicle to be charged is determined from the perspective of minimizing charging costs; the initial power demand is adjusted based on the adjusted optimal charging time to obtain the power demand of the target vehicle to be charged.

[0052] S108. Based on the demand for electricity, 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 can drive to the target charging station; when the target vehicle to be charged has driven 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] The aforementioned vehicle charging method involves acquiring the target vehicle's current state of charge (SOC), current location, historical behavior data, current available electrical energy and current load of the power grid, and the location and current charging price of at least one initial charging station connected to the grid. Based on the current SOC, current location, location 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 electrical energy, current load, and current charging price, the energy demand of the target vehicle is obtained. Based on the energy demand, the target charging station is controlled to charge the target vehicle. The method provided in this application can both meet the vehicle's charging needs and ensure the safety of the power grid.

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

[0056] S202. Based on the current location and the location 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. Based on the current SOC, historical behavior data, current quantity, and current motion state, determine a target charging station from at least one intermediate charging station.

[0059] Optionally, the preset range can 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 can also be other ranges. The comparison of the embodiments in this application does not make specific limitations.

[0060] In this embodiment, based on the current location and the location 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, making the determined intermediate charging station more accurate.

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

[0062] Optionally, for each drivable route, by analyzing the current number and current movement status of traffic participants in the drivable route, the current road conditions when the target vehicle to be charged is currently driving in the drivable route can be determined; historical behavior data corresponding to the target road conditions that are similar to the current road conditions can be obtained from historical behavior data; and by analyzing the historical behavior data corresponding to the target road conditions, the corresponding duration when the target vehicle to be charged is driving in the drivable route can be predicted.

[0063] In this embodiment, for each drivable route, based on the current number, current motion status, and historical behavior data, the time required for the target vehicle to travel from its current location to the intermediate charging station along the drivable route is obtained. The drivable route with the shortest corresponding time is determined as the optimal drivable route for the intermediate charging station. This method of determining the optimal drivable route is more accurate, thus making the target charging station determined based on the optimal drivable route more accurate.

[0064] In some embodiments, determining a target charging station from at least one intermediate charging station based on the current State of Charge (SOC) and the duration corresponding to the optimal driving route includes: obtaining the current charging state of a vehicle currently charging at an intermediate charging station and the current SOC of other vehicles waiting to be charged at the intermediate charging station; obtaining the charging time point when the target vehicle starts charging at the intermediate charging station based on the current charging state, the current SOC of the target vehicle, and the current SOC of the other vehicles; and determining a target charging station from at least one intermediate charging station based on the duration corresponding to the optimal driving route and the charging time point.

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

[0066] In this embodiment, based on the corresponding duration of the optimal driving route and the charging time point, a target charging station is determined from at least one intermediate charging station, so that the target vehicle to be charged can be charged in the target charging station, which can both ensure the charging needs of the target vehicle to be charged and ensure the safety of the power grid.

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

[0068] Optionally, if the vehicle's current SOC is less than the first SOC threshold, it indicates that the vehicle's current SOC is low and needs to be charged.

[0069] In this embodiment, if the vehicle's current SOC is less than the first SOC threshold, the vehicle is identified as the target vehicle to be charged, thus ensuring the safety of the vehicle during operation.

[0070] In some embodiments, the method further includes: 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, determining the current allocable 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 allocable electrical energy and the current discharge unit price to the vehicle.

[0071] The unit price for discharging refers to the fee that a vehicle can receive for each unit of electricity it discharges into the grid at a charging station.

[0072] Optionally, if the current load of the power grid is greater than the preset load, it indicates 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 high current SOC, so that the vehicle owner can consider whether to discharge to the power grid based on the vehicle's current SOC and the current discharge unit price.

[0073] In this embodiment, the currently available electrical energy and the current discharge unit price are sent to the vehicle so that the vehicle owner can consider whether to discharge to the power grid. This ensures both the driving safety of the vehicle and the security of the power grid.

[0074] In one embodiment, such as Figure 3 As shown, another vehicle charging method is provided, which includes the following:

[0075] (1) Electric vehicle charging load forecast

[0076] Electric vehicle charging load forecasting employs a graph convolutional network-based approach to address the load growth and power quality degradation issues arising from the rapid development of electric vehicles. The solution combines traffic flow and queuing theory, establishing a spatiotemporal graph convolutional network to capture the temporal and spatial dynamic characteristics of traffic data. Improved convolutional operations optimize the residual module, enabling accurate prediction of electric vehicle arrival rates. Based on traffic flow forecasting results, the electric vehicle arrival rate is converted into charging load. Considering charging station service capacity and user behavior, a queuing theory model simulates the charging process, establishing a load forecasting model with customer data to enhance the accuracy of charging load forecasting. Figure 4 Schematic diagram of the overall design scheme for charging load prediction.

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

[0078] The electric vehicle (EV) charging potential assessment task design includes two aspects: vehicle-to-grid (V2G) interaction potential assessment and real-time scheduling. First, an analytical model based on EV status and battery SOC is established to assess the discharge scheduling potential of EVs in their idle state after grid connection. Considering the charging and discharging demands of EVs and the grid's regulation capacity, a charging scheduling potential assessment model is established to determine whether vehicles have the capability to participate in grid interaction. Simultaneously, a real-time scheduling scheme is proposed, applying centralized, distributed, or hierarchical control methods based on real-time grid load, vehicle status, and charging demand. The scheme achieves efficient V2G interaction scheduling by classifying EV status, calculating real-time SOC, and determining scheduling priorities, thereby improving grid security and stability.

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

[0080] This research and design, considering the flexible interaction of electric vehicles (EVs) in the market mechanism and planning and scheduling methods, aims to enhance EVs' support capacity for the power grid and their market participation. It studies the allocation and assessment mechanism of EV electricity carbon emissions, using smart meters and real-time monitoring systems to record charging time and power supply structure to accurately calculate carbon emissions and promote environmentally friendly behavior. Simultaneously, it explores the operational optimization and bidding strategies of EV aggregators in the day-ahead market, utilizing demand-side bidding mechanisms to incentivize EV participation in the electricity market and improve the grid's regulation capacity. Furthermore, it investigates the operational models of the day-ahead energy market and ancillary services market, clarifying how EVs can optimize resource allocation, participate in market transactions, and provide ancillary services in both markets to promote the stable and economical operation of the power system.

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

[0082] A joint optimization model for the day-ahead and real-time markets of electric vehicles (EVs) is established to operate at the lowest electricity sales cost. An improved particle swarm optimization algorithm is used to optimize the electricity purchase and sales plans, comparing the optimized cost with the economics of the traditional separate market. EVs can not only serve as loads but also supply power to the grid via V2G technology, reducing energy loss and improving reserve capacity response speed. The model also analyzes the charging characteristics and periodic usage of EVs, optimizing the day-ahead charging and discharging plans of EV aggregators. A two-layer model is used to consider operational optimization and market clearing, ultimately transforming the solution into a mixed-integer linear programming model to maximize electricity purchase efficiency and social welfare.

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

[0084] The development of a vehicle-grid interactive intelligent control algorithm aims to achieve large-scale interaction between electric vehicles and the power grid through optimized scheduling strategies, promoting the consumption of new energy sources and reducing the impact of their uncertainties on the power grid. First, the control potential of electric vehicle clusters is defined, and time-of-use pricing for different clusters is established to incentivize high-potential clusters to respond to grid scheduling, achieving peak shaving and valley filling. Simultaneously, an Electric Vehicle Load Aggregator (EVLA) is used as a bridge between the power grid and users to improve resource allocation efficiency. An optimization model considering the uncertainty of wind power output and the constraints of electric vehicle charging load is employed to minimize the peak-valley difference in the power grid and reduce user charging costs. Furthermore, a deep reinforcement learning-based strategy is used to perform real-time optimized scheduling of large-scale electric vehicle charging and swapping loads, ensuring friendly interaction with the power system, improving electric vehicle user satisfaction, and guaranteeing the safe and economical operation of the power grid. Figure 5 A schematic diagram of the overall architecture for large-scale electric vehicles to participate in power grid dispatch.

[0085] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0086] Based on the same inventive concept, this application also provides a vehicle charging device for implementing the vehicle charging method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more vehicle charging device embodiments provided below can be found in the limitations of the vehicle charging method described above, and will not be repeated here.

[0087] In one exemplary embodiment, such as Figure 6 As shown, a vehicle charging device 600 is provided, including: 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 acquire the target vehicle's 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.

[0089] The determination module 602 is used 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 energy demand of the target vehicle to be charged based on the current SOC, the historical behavior data, the current available 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 electrical energy.

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

[0093] The first determining 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 location and the location of the initial charging station.

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

[0095] The third determining submodule is used 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 quantity, 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 along the drivable route from its current location to the location of the intermediate charging station, based on the current quantity, the current motion state, and the historical behavior data.

[0098] The first determining unit is used to determine the shortest drivable route as the optimal drivable route for the intermediate charging station.

[0099] The second determining unit is used to determine 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.

[0100] In some embodiments, the second determining unit is further configured to obtain the current charging status of a vehicle currently charging at the intermediate charging station and the current SOC of other vehicles waiting to be charged at the intermediate charging station; based on the current charging status, the current SOC of the target vehicle waiting to be charged, and the current SOC of the other vehicles waiting to be charged, to obtain the charging time point when the target vehicle starts charging at the intermediate charging station; and based on the duration of the optimal driving route and the charging time point, to determine a target charging station from the at least one intermediate charging station.

[0101] In some embodiments, the vehicle charging device 600 is specifically configured to acquire the current SOC of each vehicle in real time; and if the current SOC of a vehicle is less than a first SOC threshold, identify the vehicle as the target vehicle to be charged.

[0102] In some embodiments, the vehicle charging device 600 is further configured 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 allocable electrical 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 allocable electrical energy and the current discharge unit price to the vehicle.

[0103] Each module in the aforementioned vehicle charging device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0104] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a vehicle charging method.

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

[0106] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0107] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

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

[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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0110] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0111] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A vehicle charging method, characterized in that, The method includes: Acquire the target vehicle's current state of charge (SOC), current location, historical behavior data, current available power and current load of the power grid, location of at least one initial charging station connected to the power grid, and current charging unit price; Based on the current state of charge (SOC), the current location, the location of the initial charging station, and the historical behavior data, a target charging station is determined from the at least one initial charging station. Based on the current state of charge (SOC), the historical behavior data, the currently available electrical energy, the current load, and the current charging unit price, the electrical energy demand of the target vehicle to be charged is obtained; The process of obtaining the energy demand of the target vehicle to be charged based on the current state of charge (SOC), historical behavior data, currently available electrical energy, current load, and current charging unit price includes: analyzing the current SOC and historical behavior data of the target vehicle to predict its maximum remaining driving time; analyzing the current available electrical energy and current load of the power grid using peak shaving and valley filling strategies to predict the optimal charging time for the target vehicle from the perspective of the power grid load; adjusting the optimal charging time based on the corresponding maximum driving time of the target vehicle; determining the initial energy demand of the target vehicle based on the current charging unit price from the perspective of minimizing charging costs; and adjusting the initial energy demand based on the adjusted optimal charging time to obtain the energy demand of the target vehicle. Based on the required electrical 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 step of determining a target charging station from the at least one initial charging station based on the current state of charge (SOC), the current location, the location of the initial charging station, and the historical behavior data includes: Based on the current location and the location 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 is determined between the target vehicle to be charged and the intermediate charging station, and the current number and current movement status of traffic participants in the drivable route are obtained. Based on the current state of charge (SOC), the historical behavior data, the current quantity, and the current motion state, a target charging station is determined from the at least one intermediate charging station.

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 state of charge (SOC), the historical behavior data, the current quantity, and the current motion state includes: For each drivable route, based on the current quantity, the current motion state, and the historical behavior data, the time required for the target vehicle to be charged to travel along the drivable route from the current location to the location of the intermediate charging station is obtained; The shortest possible driving route is determined as the optimal driving route for the intermediate charging station. Based on the current state of charge (SOC) and the corresponding duration of the optimal driving route, a target charging station is determined from the at least one intermediate charging station.

4. The method according to claim 3, characterized in that, The step of determining a target charging station from the at least one intermediate charging station based on the current state of charge (SOC) and the corresponding duration of the optimal driving route includes: Obtain the current charging status of the vehicle currently charging in the intermediate charging station, and the current state of charge (SOC) of other vehicles waiting to be charged at the intermediate charging station. Based on the current charging status, the current state of charge (SOC) of the target vehicle to be charged, and the current state of charge (SOC) of the other vehicles to be charged, the charging time point when the target vehicle to be charged starts charging at the intermediate charging station is obtained. Based on the duration of the optimal driving route and the charging time point, 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, the current state of charge (SOC) of the vehicle is acquired in real time. If the current state of charge (SOC) of the vehicle is less than a first SOC threshold, the vehicle is identified as the target vehicle to be charged.

6. The method according to claim 1, characterized in that, The method further includes: When the current load is greater than the preset load, for each vehicle, if the current state of charge (SOC) of the vehicle is greater than the second SOC threshold, the current available electrical energy of the vehicle is determined based on the SOC difference between the current state of charge (SOC) of the vehicle and the second SOC threshold. Obtain the current discharge unit price and send the current allocable electrical energy and the current discharge unit price to the vehicle.

7. A vehicle charging device, characterized in that, The device includes: The first acquisition module is used to acquire the target vehicle's 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. The determination module is used to determine a target charging station from the at least one initial charging station based on the current state of charge (SOC), the current location, the location of the initial charging station, and the historical behavior data. The second acquisition module is used to acquire the energy demand of the target vehicle to be charged based on the current state of charge (SOC), the historical behavior data, the current available energy, the current load, and the current charging unit price. The second acquisition module is further configured to: analyze the current state of charge (SOC) and historical behavior data of the target vehicle to be charged to predict the maximum drivable time that the target vehicle can still travel; analyze the current available power and current load of the power grid by combining peak shaving and valley filling power consumption strategies, and predict the optimal charging time for the target vehicle from the perspective of the power grid load; adjust the optimal charging time based on the corresponding maximum drivable time of the target vehicle; determine the initial power demand of the target vehicle from the perspective of minimizing charging costs based on the current charging unit price; and adjust the initial power demand based on the adjusted optimal charging time to obtain the power demand of the target vehicle. The control module is used to control the target charging station to charge the target vehicle to be charged based on the required electrical energy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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

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

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