Energy supplement control method of vehicle and vehicle

Abstract

A method for energy supplement control of a vehicle and the vehicle are disclosed, and relate to the technical field of vehicle endurance. In the method, a knowledge graph of the vehicle and a plurality of energy supplement devices is obtained, wherein the knowledge graph is used to represent the relationship between the vehicle and the plurality of energy supplement devices. Then, based on the knowledge graph, a target energy supplement device meeting a requirement is determined from the plurality of energy supplement devices. Subsequently, the vehicle is supplemented with energy by the target energy supplement device. The present application converts the information between the vehicle and the plurality of energy supplement devices into an association relationship by using the knowledge graph, and determines the target energy supplement device meeting the requirement from the plurality of energy supplement devices based on the knowledge graph. The association relationship between the vehicle and the plurality of energy supplement devices in the knowledge graph can be referred to, so that the accuracy of matching the vehicle and the energy supplement device can be improved.

Description

Technical Field

[0001] This application relates to the field of vehicle range technology, and in particular to a vehicle energy replenishment control method and vehicle. Background Technology

[0002] With the rapid development of new energy vehicle technology, users' demand for vehicle charging is increasing day by day. Generally speaking, vehicle charging mainly relies on users manually selecting charging piles or battery swapping stations, or on a single navigation recommendation.

[0003] However, relying on user or navigation recommendations can easily lead to inaccurate assessments of charging equipment. This not only results in low charging efficiency but may also cause safety hazards due to the mismatch between the vehicle and the charging equipment. Summary of the Invention

[0004] In view of this, embodiments of this application propose a vehicle energy replenishment control method and a vehicle to improve the above-mentioned problems.

[0005] According to a first aspect of the embodiments of this application, a vehicle refueling control method is provided, the method comprising: acquiring a knowledge graph of the vehicle and multiple refueling devices; the knowledge graph being used to characterize the relationship between the vehicle and the multiple refueling devices; determining a target refueling device that meets the requirements from the multiple refueling devices based on the knowledge graph; and refueling the vehicle through the target refueling device.

[0006] In one possible implementation, the above-mentioned acquisition of the knowledge graph of the vehicle and multiple charging devices includes: acquiring the vehicle's status data and the information of multiple charging devices; extracting the relationship between the vehicle and the charging devices based on the vehicle's status data and the information of multiple charging devices, the relationship between the vehicle and the charging devices including charging time, charging cost and / or battery health degradation; and constructing a knowledge graph based on the relationship between the vehicle and the charging devices.

[0007] In one possible implementation, the above-mentioned determination of target charging devices that meet the requirements from multiple charging devices based on knowledge graphs includes: inputting the knowledge graph into the detection model, using charging time, charging cost and / or battery health deterioration as detection indicators, and determining target charging devices that meet the detection requirements from multiple charging devices.

[0008] In one possible implementation, the vehicle status data includes one or more of the following: vehicle location, remaining battery power, battery health deterioration, battery temperature, driving mode, and navigation route.

[0009] In one possible implementation, the information of the aforementioned power replenishment equipment includes one or more of the following: the identifier of the power replenishment equipment, the location of the power replenishment equipment, the number of idle power replenishment equipment, the estimated queuing time, the rated power, the output voltage range, the service fee, and the historical failure rate.

[0010] In one possible implementation, before acquiring a knowledge graph of the vehicle and multiple charging devices, the method further includes: identifying multiple charging devices based on the vehicle's location and remaining battery power.

[0011] In one possible implementation, the aforementioned energy replenishment equipment includes charging equipment and / or battery swapping equipment.

[0012] In one possible implementation, before replenishing the vehicle's power via the target replenishment device, the method further includes: if the target replenishment device includes a target charging device, adjusting the vehicle's battery temperature to a temperature that meets the charging efficiency threshold requirements of the target charging device, based on the rated power and output voltage range of the target charging device.

[0013] In one possible implementation, before replenishing the vehicle's energy via the target energy replenishment device, the method further includes: if the target energy replenishment device includes a target charging device, determining a target charging power that satisfies the vehicle's charging current constraints based on the relationship between the vehicle and the target charging device in a knowledge graph, wherein the target charging power is used to guide the target charging device to charge the vehicle.

[0014] According to a second aspect of the embodiments of this application, a vehicle is provided, the vehicle including a power replenishment controller, the power replenishment controller being configured to perform the method described in any one of the first aspects.

[0015] The beneficial effects of the technical solution provided in this application include at least the following: By using a knowledge graph of vehicles and multiple charging stations, information between vehicles and charging stations can be transformed into relationships between them. Therefore, by using the knowledge graph to identify suitable target charging stations from among multiple charging stations, it is possible to filter target charging stations based on the relationships between vehicles and multiple charging stations, thereby improving the accuracy of vehicle-charging station matching.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit the embodiments of this application. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0018] Figure 1 This is a schematic diagram of a vehicle refueling scenario provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a vehicle energy replenishment control system provided in an embodiment of this application; Figure 3 This is a schematic flowchart of a vehicle energy replenishment control method provided in an embodiment of this application; Figure 4 This is a knowledge graph diagram of a vehicle and multiple energy replenishment devices provided in an embodiment of this application; Figure 5 This is a flowchart illustrating a vehicle energy replenishment control method provided in an embodiment of this application. Figure 6 This is a flowchart illustrating another vehicle energy replenishment control method provided in this application embodiment; Figure 7 This is a schematic diagram of the structure of a vehicle energy replenishment control device provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a power replenishment controller provided in an embodiment of this application. Detailed Implementation

[0019] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0020] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0021] Please see Figure 1 , Figure 1 This illustration shows a schematic diagram of a vehicle refueling scenario provided in an embodiment of this application. Figure 1 As shown, this application scenario includes a vehicle 101, a control device 102, multiple charging devices 103, and multiple battery swapping devices 104. The control device 102 is used to acquire the status data of the vehicle 101, and also to acquire information from the multiple charging devices 103 and multiple battery swapping devices 104. The control device 102 is further used to construct a knowledge graph based on the status data of the vehicle 101 and the information from the multiple charging devices 103 and multiple battery swapping devices 104. The control device 102 is also used to determine a target energy replenishment device that meets the requirements from the multiple charging devices 103 and multiple battery swapping devices 104 based on the constructed knowledge graph. The control device 102 recommends the target energy replenishment device to the vehicle 101 to control the vehicle to replenish its energy through the target energy replenishment device. Optionally, the application scenario also includes a client 105, to which the control device 102 recommends the target energy replenishment device to the client 105 to control the vehicle to replenish its energy through the target energy replenishment device.

[0022] Optionally, vehicle 101 arrives at the location of the target charging station via autonomous driving and then charges the station. Alternatively, the user determines the target charging station via client 105, drives vehicle 101 to the location of the target charging station, and charges vehicle 101 via the target charging station.

[0023] It should be understood that the target energy replenishment device may be a charging device 103, a battery swapping device 104, or both. For example, if the battery in the battery swapping device 104 has insufficient charge to support vehicle 101 to reach its next destination, vehicle 101 can replace the battery and then use the charging device 103 to recharge it, ensuring that the battery in vehicle 101 has sufficient charge to support vehicle 101 to reach its next destination. Furthermore, the number of target energy replenishment devices may be multiple candidate devices, from which the user ultimately selects the energy replenishment device to replenish vehicle 101.

[0024] Optionally, the charging device 103 includes a charging pile with a charging head that can be directly plugged into the vehicle. When the vehicle 101 needs charging, the charging pile can charge the vehicle. The battery swapping device 104 includes a battery compartment and a battery swapping device. The battery compartment stores several vehicle batteries, which are charged by the charging device. The battery swapping device is used to replace the vehicle batteries in the battery compartment with those in the vehicle 101.

[0025] In one possible implementation, vehicle 101 and control device 102 communicate via a network, multiple charging devices 103 and multiple battery swapping devices 104 also communicate with control device 102 via a network, and client 105 also communicates with control device 102 via a network.

[0026] In one embodiment, the control device 102 may be configured as a cloud server or cloud server cluster that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. The cloud server cluster is deployed in several cloud data centers.

[0027] Among them, the client 105 includes, but is not limited to, terminal devices such as mobile phones, desktop computers, laptops, and displays in vehicles 101.

[0028] Optionally, vehicle 100 can be a means of transportation (such as commercial vehicles, passenger cars, motorcycles, flying cars, trains, etc.), industrial vehicles (such as forklifts, trailers, tractors, etc.), engineering vehicles (such as excavators, bulldozers, cranes, etc.), agricultural equipment (such as lawnmowers, harvesters, etc.), amusement equipment, toy vehicles, etc.; the embodiments of this application do not specifically limit the type of vehicle 100.

[0029] It should be noted that the specific types, quantities and combinations of the vehicle 101, control device 102, multiple charging devices 103, multiple battery swapping devices 104 and client 105 can be adjusted according to the actual needs of the application scenario, and this application embodiment does not limit this.

[0030] Please see Figure 2 , Figure 2 This application provides a schematic diagram of the structure of a vehicle refueling control system according to an embodiment of the present application. Figure 2 As shown, the system includes a vehicle, a control unit, and a power supply terminal. The vehicle includes a multimodal perception module and a cooperative execution module. The control unit includes a construction module and a detection model. The power supply terminal includes an identity broadcasting & situational awareness module, as well as power supply equipment.

[0031] Continue reading Figure 2 The system consists of a multimodal perception module that transmits vehicle status data to the control unit, and an identity broadcasting & situational awareness module that transmits information about the power replenishment equipment to the control unit. A construction module builds a knowledge graph between the vehicle and multiple power replenishment devices based on the vehicle's status data and the equipment information. A detection model acquires the knowledge graph and, based on it, identifies target power replenishment devices that meet the requirements from among the multiple devices. The control unit formulates a power replenishment strategy based on the target power replenishment devices identified by the detection model. The vehicle's cooperative execution module receives the power replenishment strategy and controls the vehicle to replenish power through the target devices according to the strategy.

[0032] Optionally, the energy replenishment equipment includes charging equipment and / or battery swapping equipment. Energy replenishment strategies include charging the vehicle using only the target charging equipment, swapping the vehicle's battery using only the target battery swapping equipment, swapping the vehicle's battery using the battery swapping equipment first, and then charging the vehicle using the target charging equipment, etc.

[0033] In one possible implementation, see [link / reference]. Figure 2 The vehicle also includes a battery pre-conditioning unit. When the target charging equipment includes a charging device, and before charging the vehicle, the control device determines the battery pre-conditioning temperature based on the rated power and output voltage range of the target charging device. The battery pre-conditioning unit adjusts the vehicle's battery temperature to a temperature that meets the charging efficiency threshold requirements of the target charging device, thereby improving charging efficiency, extending battery life, and reducing thermal management energy consumption.

[0034] Optionally, when the target energy replenishment device includes a target charging device, and before replenishing the vehicle's energy, the control device determines a target charging power that satisfies the vehicle's charging current constraints based on the relationship between the vehicle and the target charging device in a knowledge graph, in order to protect the battery from high current surges and reduce hazards. The target charging device is used to charge the vehicle according to the target charging power.

[0035] In one possible implementation, the vehicle's cooperative execution module controls the vehicle to automatically park in the charging area based on the location of the target charging device. If the target charging device includes a charging station, the vehicle's cooperative execution module controls the physical connection between the vehicle and the target charging device, for example, controlling the charging pantograph / gun to automatically align with the charging port, thereby charging the vehicle's battery.

[0036] Optionally, the vehicle status data may include one or more of the following: vehicle location, remaining battery power, battery health degradation, battery temperature, driving mode, and navigation route.

[0037] In one possible implementation, the information of the power replenishment equipment includes one or more of the following: the identifier of the power replenishment equipment, the location of the power replenishment equipment, the number of idle power replenishment equipment, the estimated queuing time, the rated power, the output voltage range, the service fee, and the historical failure rate.

[0038] Figure 3 A schematic flowchart of a vehicle refueling control method according to an embodiment of this application is shown. Figure 3 As shown, the vehicle's energy replenishment control method includes steps S301 to S303.

[0039] In step S301, a knowledge graph of the vehicle and multiple energy replenishment devices is obtained. The knowledge graph is used to characterize the relationship between the vehicle and multiple energy replenishment devices.

[0040] For example, vehicles and multiple charging devices are treated as entities in a knowledge graph. Relevant data about vehicles and multiple charging devices is collected, and relationships between vehicles and multiple charging devices are extracted based on this data. Vehicle entities and multiple charging device entities are used as nodes in the knowledge graph, and the extracted relationships are used as edges in the knowledge graph, thereby constructing a knowledge graph of vehicles and multiple charging devices.

[0041] In one possible implementation, multiple charging devices are identified based on the vehicle's location and remaining battery power before a knowledge graph of the vehicle and multiple charging devices is constructed.

[0042] For example, the system determines whether the vehicle's remaining battery power is below a safety threshold. If it is, it searches for nearby charging stations based on the vehicle's location. For instance, the vehicle can use its navigation route and the remaining driving range supported by its remaining battery power to find multiple nearby charging stations. The safety threshold can be calibrated based on human experience or set according to the vehicle's current driving scenario. For example, the safety threshold can be calibrated based on human experience. During the R&D phase, a physical baseline is determined through battery limit testing, and a safe redundancy mileage (e.g., 30-50 kilometers) is reserved to prevent the vehicle from breaking down, combined with a large amount of road test data and statistical methods, ultimately resulting in a relatively reliable safety threshold. Another example is setting the safety threshold based on the vehicle's current driving scenario. When the vehicle is detected to be traveling at high speed or in extremely cold winter environments, the safety threshold is increased to reserve more buffer power to prevent a sudden drop in battery power leading to a breakdown. In urban areas with low-speed congestion and a high density of charging stations, the threshold is appropriately lowered to maximize battery utilization.

[0043] In this implementation, multiple charging devices are selected based on the vehicle's location and remaining battery power. This provides users with a choice of charging devices while ensuring the vehicle's range is safe and reachable. This not only effectively reduces the risk of running out of power due to queuing or detours, but also saves the vehicle's charging time.

[0044] In one possible implementation, a knowledge graph of the vehicle and multiple charging devices is obtained, including: acquiring vehicle status data and information about the multiple charging devices; extracting relationships between the vehicle and the charging devices based on the vehicle status data and the information about the multiple charging devices, including charging time, charging cost, and / or battery health degradation; and constructing a knowledge graph based on the relationships between the vehicle and the charging devices.

[0045] Optionally, after acquiring vehicle status data and information from multiple charging devices, entities and relationships are automatically identified from the vehicle status data and charging device information using methods such as Large Language Model (LLM) and Hidden Markov Model (HMM), generating triples. Entities include the vehicle and multiple charging devices, and relationships between the vehicle and multiple charging devices include charging time, charging cost, and / or battery health degradation. The constructed triple data is then stored in a graph database.

[0046] For example, refer to Figure 4 Multiple charging devices are designated as Charging Device 1, Charging Device 2, Charging Device 3, and Charging Device 4. Vehicle status data and information from the four charging devices are collected. Relationships extracted from the vehicle status data and the four charging device information include charging time, charging cost, and battery health deterioration. A knowledge graph is constructed using the vehicle and the four charging devices as entity nodes, and charging time, charging cost, and battery health deterioration as edges between the vehicle and the four charging devices. Optionally, the knowledge graph is constructed in the form of (Entity A) --[Relationship]-- (Entity B) | Relationship attribute: {parameter value}. Taking vehicle A and charging device 1 as an example, the constructed knowledge graph is: (Vehicle A) --[Charging Time] --(Charging Device 1) | {3.5 minutes}, (Vehicle A) --[Charging Cost] --(Charging Device 1) | {60 yuan}, (Vehicle A) --[Battery Health Deterioration] --(Charging Device 1) | {0.02%}.

[0047] Optionally, vehicle status data can be collected through in-vehicle sensors and a battery management system (BMS). For example, the vehicle's location information can be collected via a Global Positioning System (GPS). Data such as battery temperature and remaining charge can be collected through the BMS. Driving information can be collected through the chassis controller or body control module. Optionally, after collecting the vehicle status data, it can be uploaded to the cloud for processing. For example, vehicle status data can be continuously acquired from the in-vehicle network via an in-vehicle terminal and uploaded to a cloud server in real-time or periodically via a communication network.

[0048] Additionally, information about multiple power replenishment devices can be obtained through data integration and batch synchronization via cloud-based Application Programming Interface (API). For example, power replenishment devices can be registered as digital assets in the cloud, forming a basic data table. The power replenishment devices themselves have internet connectivity and maintain a persistent connection with the cloud via a communication network, automatically pushing information about multiple power replenishment devices from the data table to the cloud server.

[0049] It should be understood that charging time refers to the time required for the vehicle to travel from its current location to the target charging station and complete the charging process. Charging cost refers to the cost incurred in using the charging equipment to complete the charging process. Battery health degradation refers to the specific amount or degree of depletion or degradation of the battery's State of Health (SOH) caused by completing a single charging process.

[0050] In this implementation, by constructing a knowledge graph of the vehicle and multiple charging devices, the information between the vehicle and the charging devices can be transformed into an associated semantic network. Furthermore, by constructing a multi-dimensional relationship between the knowledge graph of the vehicle and multiple charging devices, including charging time, charging cost, and / or battery health degradation, comprehensive reference factors can be provided for the selection of charging devices. This ensures that the matching of charging devices focuses on the dynamic adaptability between the actual operating conditions of the vehicle and the capabilities of the charging devices, which not only improves charging efficiency and reduces charging costs but also reduces safety hazards caused by mismatch between the vehicle and the charging devices.

[0051] In one possible implementation, the vehicle's status data includes, but is not limited to, one or more of the following: vehicle location, remaining battery power, battery health deterioration, battery temperature, driving mode, and navigation route.

[0052] Optionally, the information of the power replenishment equipment includes, but is not limited to, one or more of the following: the power replenishment equipment's identifier, location, number of available power replenishment equipment, estimated queuing time, rated power, output voltage range, service fee, and historical failure rate. The power replenishment equipment's identifier is a unique identifier that identifies a single power replenishment equipment.

[0053] In one possible implementation, the vehicle's recharging time is extracted based on the vehicle's location and remaining battery power from its status data, and the location, estimated queuing time, rated power, and output voltage range of the recharging equipment from its information. This recharging time is then used as an edge between the vehicle node and the recharging equipment node. For example, the time it takes for the vehicle to arrive at the recharging equipment is calculated based on the vehicle's location and the recharging equipment's location. The charging time is calculated based on the remaining battery power, the recharging equipment's rated power, and the recharging equipment's output voltage range. The vehicle's recharging time is obtained by adding the vehicle's arrival time, the charging time, and the estimated queuing time. It's easy to understand that if the recharging equipment is a battery swapping station, the vehicle's recharging time is obtained by adding the vehicle's arrival time, the battery swapping time, and the estimated queuing time.

[0054] Optionally, the vehicle's charging cost can be extracted based on the remaining battery power in the vehicle's status data and the service fee in the charging equipment information. This charging cost can then be used as an edge between the vehicle node and the charging equipment node. For example, the vehicle's charging cost can be calculated using the following formula: Charging Cost = Total Battery Capacity × (Target Battery Percentage - Current Remaining Battery Percentage) × Comprehensive Unit Price (Electricity Cost + Service Fee). It's easy to understand that if the charging equipment is a battery swapping station, the vehicle's charging cost can be calculated using the following formula: Charging Cost = (Target Battery Power - Current Remaining Battery Power) × Comprehensive Unit Price (Electricity Cost + Service Fee).

[0055] In one possible implementation, the vehicle's battery health deterioration level is extracted based on the vehicle's state data (battery health deterioration level and battery temperature) and the charging equipment's information (rated power and output voltage range). This battery health deterioration level is then used as an edge between the vehicle node and the charging equipment node. For example, the vehicle's battery health deterioration level can be estimated using the following logical framework: Health Deterioration Level ≈ Base Cycle Loss Factor × Power Penalty Factor × Temperature Penalty Factor × Aging Multiplier. The base cycle loss factor is a baseline value determined by the battery chemistry (e.g., ternary lithium or lithium iron phosphate) and the output voltage range. The power penalty factor is positively correlated with the charging equipment's rated power; for example, when using a 100 kW (kilowatt, kW) fast charger, the power penalty factor is approximately 1.5, while when using a 7 kW slow charger, the power penalty factor is approximately 1.0. The temperature penalty factor is calculated based on an electrothermal coupling model. When the battery is in its optimal temperature range (e.g., 20-30℃), the temperature penalty factor is 1.0; when the temperature is >40℃ or <-10℃, the temperature penalty factor increases exponentially. The aging multiplier can be calculated based on the current battery health degradation level; the lower the battery health degradation level, the larger the aging multiplier.

[0056] In step S302, a target power supply device that meets the requirements is determined from multiple power supply devices based on the knowledge graph.

[0057] It's easy to understand that the knowledge graph of vehicles and multiple refueling devices reflects the relationships between them. Based on these relationships, the target refueling device that meets the requirements is identified from among the multiple devices.

[0058] For example, the adjacency list of vehicle entities in the knowledge graph is read through a Graph Engine Service (GES), and the edges pointing from vehicle entities to each power replenishment device entity are traversed. During the traversal, conditional calculations are performed on the attributes of the edges, and only edges that meet the conditions are retained. The power replenishment device entities pointed to by the edges that meet the conditions are extracted, and aggregated, sorted, or selected based on business requirements (Top-K), ultimately determining the target power replenishment devices that meet the requirements.

[0059] In one possible implementation, based on a knowledge graph, a target power supply device that meets the requirements is determined from multiple power supply devices. This includes: inputting the knowledge graph into a detection model, using power supply time, power supply cost, and / or battery health deterioration as detection indicators, to determine the target power supply device that meets the detection requirements from multiple power supply devices.

[0060] Optionally, the charging time, charging cost, and battery health deterioration are used as detection indicators. Taking a charging device as an example, the edges between the vehicle node and the charging device node are instantiated as triples, that is, the charging time, charging cost, and battery health deterioration are instantiated as triples. A combination to be detected is constructed using charging time, charging cost, and battery health deterioration as relation types. The combination to be detected is input into the detection model, and the detection model outputs scores corresponding to the three relation types respectively. After obtaining the three scores, a comprehensive detection of the charging device is performed according to business requirements, such as using a weighted summation or averaging method to process the three scores, obtaining the comprehensive detection result for the charging device. Similarly, the same method is used to perform comprehensive detection on other charging devices, obtaining the comprehensive detection result for each charging device. Based on the comprehensive detection results of multiple charging devices, a screening process is performed, and finally, the target charging device that meets the detection requirements is selected.

[0061] For example, continue to refer to Figure 4Taking a vehicle and charging device 1 as an example, there are three edges between the vehicle node and the charging device 1 node, representing charging time, charging cost, and battery health degradation. The edges between the vehicle node and the charging device 1 node are instantiated as triples, such as (vehicle, charging time, charging device 1), (vehicle, charging cost, charging device 1), and (vehicle, battery health degradation, charging device 1). These three triples are input into the detection model, which will output scores for each of the three dimensions. For example, the score for charging time is... Score 1. The score corresponding to the energy replenishment fee is Score 2. The score for battery health degradation is: Score 3. Assign weights to the three edges based on their importance in the current business scenario. For example, assign weight α to recharging time, weight β to recharging cost, and weight γ to battery health degradation. Then, sum the scores corresponding to the three edges using weighted averages to calculate the comprehensive score: Final _ Score 1 = α × Score 1+β× Score 2+γ× Score 3. Similarly, the same method was used to conduct comprehensive testing on other energy replenishment devices, and comprehensive scores were obtained for each device. For example, the comprehensive score for energy replenishment device 2 was... Final _ Score 2. The comprehensive score corresponding to energy replenishment equipment 3 is: Final _ Score 3. The comprehensive score corresponding to energy replenishment equipment 4 is: Final _ Score 4. By comparing the comprehensive scores of these four energy replenishment devices, select the target energy replenishment device that meets the scoring requirements, or select the energy replenishment device with the highest comprehensive score as the target energy replenishment device.

[0062] Optionally, a detection model can be trained. For example, multiple edges between vehicles and charging devices (such as charging costs and battery health degradation) can be extracted from a knowledge graph as training samples. Each edge is assigned a quality label (e.g., a 0 / 1 classification label, or a confidence score of 1-5) based on business rules or manual annotation, serving as the standard answer for model learning. Subsequently, the topological structure of the edges (e.g., node degree, path information) and semantic features (e.g., textual descriptions of relation types) are input into the model. A graph convolutional network (GCN) is used to aggregate neighbor information, and an attention mechanism is combined to fuse features of the multiple edges between vehicles and charging devices, generating a comprehensive representation vector. A loss function is chosen, such as contrastive loss or ranking loss, to widen the score distance between the "best charging device" and the "second-best charging device" in the vector space. Then, labeled samples are input into the model in batches, and the error between the predicted results and the true labels is calculated. The network parameters (including the weights of the feature extractor and the classification / regression head) are updated through the backpropagation algorithm until the model's metrics (such as accuracy and F1 score) on the validation set converge, thus obtaining a well-trained detection model.

[0063] In this implementation, on the one hand, the multi-dimensional relationship between charging time, charging cost, and battery health deterioration in the knowledge graph is used as a detection index for selecting charging equipment. This provides a comprehensive reference factor for the selection of charging equipment, enabling the matching of charging equipment to focus on the dynamic adaptability between the actual operating conditions of the vehicle and the capabilities of the charging equipment. Focusing on the charging time dimension can improve charging efficiency, focusing on the charging cost dimension can reduce charging costs, and focusing on the battery health deterioration dimension can reduce safety hazards caused by mismatch between the vehicle and the charging equipment. On the other hand, by processing the knowledge graph through the detection model, dynamic reasoning based on the global context of the knowledge graph can be achieved. This allows for real-time perception of the state changes of vehicle entity nodes and charging equipment entity nodes, thereby improving the accuracy and response speed of charging equipment matching.

[0064] In one possible implementation, the detection model uses a preset detection function to determine the target charging device that meets the detection requirements from multiple charging devices, using charging time, charging cost, and / or battery health deterioration as detection indicators.

[0065] For example, extract the parameter values ​​corresponding to the edges between the vehicle node and multiple charging nodes in the knowledge graph of the vehicle and multiple charging devices, namely the parameter values ​​corresponding to charging time, charging cost, and battery health deterioration. Normalize the extracted parameter values, for example, using Min-Max linear normalization to map them uniformly to the dimensionless interval [0, 1]. Assign weights to each detection attribute according to business requirements, such as α for charging time, β for charging cost, and γ for battery health deterioration. Further, construct a three-dimensional detection function, such as: F = αT + βC + γH, where F represents the comprehensive detection score of the charging device, T represents the normalized charging time, C represents the normalized charging cost, and H represents the normalized battery health deterioration. Following this example, detect each charging device based on the three-dimensional detection function to obtain the comprehensive detection score for each charging device. Further, determine the target charging device that meets the detection requirements from among the multiple charging devices. For example, a comprehensive detection threshold of 0.7 can be set. If two power replenishment devices have a score greater than or equal to 0.7, these two devices are identified as target power replenishment devices. Alternatively, multiple power replenishment devices can be sorted according to their comprehensive detection scores, and the device with the highest comprehensive detection score is identified as the target power replenishment device.

[0066] In this implementation, a detection function is used to detect relationships in the knowledge graph. On the one hand, the computational overhead during the model inference stage is lower than running a full neural network. On the other hand, when the charging scenario changes, only the relationship score between the newly added charging device and the vehicle needs to be calculated, without retraining the model, which improves the generalization ability of the samples.

[0067] In one possible implementation, the energy replenishment equipment includes charging equipment and / or battery swapping equipment. For example, the charging equipment includes a charging station equipped with a charging head that can be directly plugged into a vehicle, which charges the vehicle when it needs charging. The battery swapping equipment includes a battery compartment and a battery swapping device. The battery compartment stores several vehicle batteries, which are charged by the charging device, and the battery swapping device is used to replace the vehicle batteries in the battery compartment with those in the vehicle.

[0068] Optionally, before controlling the vehicle to replenish energy through the target energy replenishment device, the vehicle energy replenishment control method further includes: if the target energy replenishment device includes a target charging device, adjusting the vehicle's battery temperature to a temperature that meets the charging efficiency threshold requirements of the target charging device based on the rated power and output voltage range of the target charging device.

[0069] For example, through intelligent collaboration and algorithms between the vehicle's Battery Management System (BMS) and the charging equipment, the maximum output power and output voltage range characteristics of the charging equipment, as well as the current state of charge (SOC) and battery temperature of the vehicle's battery, are evaluated to calculate the optimal charging start temperature (e.g., the optimal charging start temperature is set within the optimal window of 15℃-35℃). The algorithms include Kalman filtering (KF), extended Kalman filtering (EKF), or particle filtering algorithms. These algorithms, by fusing noise data such as voltage, current, and temperature, deduce parameters that cannot be directly measured inside the battery (such as battery SOC, battery health status, and battery power status). For high-power fast charging scenarios, if the battery temperature is below this threshold, an active preheating mechanism is activated in advance (e.g., through heaters, heat pumps, or liquid cooling circulation); if the battery temperature is too high, liquid cooling or external cooling interfaces are activated to lower the temperature. During charging, the coolant flow and charging current can be dynamically adjusted based on real-time cell temperature data to ensure that the battery is always in a highly active and safe temperature range, thereby maximizing charging efficiency while ensuring battery life.

[0070] In this implementation, adjusting the vehicle's battery temperature to meet the charging efficiency threshold requirements of the target charging equipment can improve charging efficiency, extend battery life, and reduce thermal management energy consumption.

[0071] In one possible implementation, before controlling the vehicle to recharge via the target recharge device, the vehicle recharge control method further includes: if the target recharge device includes a target charging device, determining a target charging power that satisfies the vehicle's charging current constraints based on the relationship between the vehicle and the target charging device in a knowledge graph. The target charging power is used to guide the target charging device to charge the vehicle according to the target charging power.

[0072] It should be understood that knowledge graphs are used to represent the relationships between vehicles and multiple charging devices. Once a target charging device is identified, the relationship between the vehicle and the target charging device can be determined based on the knowledge graph. Based on the vehicle state data and target charging device information involved in the relationship between the vehicle and the target charging device, the target charging power that satisfies the vehicle's charging current constraints can be determined.

[0073] For example, the vehicle sends data from a knowledge graph to the cloud, including battery health degradation, battery state of charge, battery temperature, maximum allowable charging current, and maximum allowable charging voltage. The charging equipment sends data from the knowledge graph to the cloud, including its maximum output power, voltage range, and current range. Based on the data sent by the vehicle and the data from the charging equipment, the cloud determines the target charging power and issues target current and voltage parameters.

[0074] For example, the initial charging power limit is calculated by comparing the hardware capabilities of the vehicle and the charging equipment. For instance, the upper limit of the charging power that the vehicle battery can withstand is calculated based on the maximum allowable charging current and maximum charging voltage reported by the vehicle. The upper limit of the charging power that the charging equipment can provide is calculated based on the maximum charging current and maximum voltage reported by the charging equipment. By comparing the upper limit of the charging power that the vehicle battery can withstand with the upper limit of the charging power that the charging equipment can provide, the initial charging power limit is obtained. Subsequently, based on the real-time battery status reported by the vehicle, a dynamic correction coefficient between 0 and 1 is calculated. This correction coefficient is determined by a three-dimensional algorithm model, such as a preset "state of charge-charging rate curve" corresponding to the vehicle battery model. As the state of charge increases, the state of charge coefficient K... soc It decreases non-linearly. For example, when SOC < 30%, K soc =1.0; when SOC reaches 80%, K soc The derating rate drops to 0.3. This is based on a temperature protection strategy matched to the battery temperature. If the battery temperature is below 0°C or above 45°C, derating is triggered, and the battery temperature coefficient K... temp <1.0; if within the optimal temperature range (e.g., 25℃-35℃), then K temp =1.0. The degree of increase in battery internal resistance is assessed based on the battery health degradation level. The lower the battery health degradation level, the worse the battery's ability to withstand high currents. The battery health degradation level coefficient K... soh The corresponding decrease is achieved by adjusting the state-of-charge coefficient K. soc Battery temperature coefficient K temp and battery health degradation coefficient K soh We perform a weighted summation to obtain the dynamic correction coefficient K. dynamic Subsequently, the dynamic correction coefficient K... dynamic Multiply by the initial charging power limit to obtain the final target charging power.

[0075] In this implementation, the target charging power that meets the vehicle charging current constraint can ensure that the current does not exceed the battery's maximum capacity throughout the charging process, thus extending battery life and matching the available power of the charging equipment, thereby improving energy replenishment efficiency.

[0076] In step S303, the vehicle is controlled to replenish its energy through the target energy replenishment device.

[0077] Once the target energy replenishment facility is identified, the vehicle replenishes its energy through that facility. Energy replenishment facilities include charging equipment and / or battery swapping equipment. The target energy replenishment facility may be a charging facility, a battery swapping facility, or both. In other words, the vehicle may only charge, only swap batteries, or swap batteries first and then charge.

[0078] Optionally, the charging power of the target charging device to charge the vehicle is the target charging power determined in the above embodiments to prevent damage to the battery.

[0079] In the vehicle refueling control method provided in this application, a knowledge graph of the vehicle and multiple refueling devices can be used to transform the information between the vehicle and the refueling devices into association relationships. Based on the knowledge graph, a target refueling device that meets the requirements can be determined from multiple refueling devices by referring to the association relationships between the vehicle and multiple refueling devices in the knowledge graph, thereby improving the accuracy of vehicle-refueling device matching.

[0080] Figure 5 This diagram illustrates a flowchart of a vehicle refueling control method according to an embodiment of this application. Figure 5 As shown, the vehicle's energy replenishment control method is applied to the vehicle's energy replenishment control system, which includes, but is not limited to, the vehicle controller, battery management module, thermal management module, target charging equipment, and cloud decision engine.

[0081] It should be understood that the cloud-based decision engine constructs a knowledge graph based on the vehicle's status information and information from multiple charging devices, and then determines a target charging device that meets the requirements from among the multiple charging devices based on the knowledge graph. The method for determining the target charging device that meets the requirements can be referred to the above embodiments, and will not be repeated here.

[0082] refer to Figure 5 After identifying the target charging device, the cloud-based decision engine sends a target charging power to the target charging device. This target charging power is determined by the cloud-based decision engine based on vehicle status data and the information of the target charging device, and it meets the vehicle's charging current constraints. The method for determining the target charging power can be found in the above embodiments, and will not be repeated here. The cloud-based decision engine also sends a battery pre-conditioning temperature to the vehicle controller. The battery pre-conditioning temperature refers to the temperature that meets the charging efficiency threshold requirements of the target charging device. The battery pre-conditioning temperature is determined by the cloud-based decision engine based on the rated power and output voltage range of the target charging device. The method for determining the battery pre-conditioning temperature can be found in the above embodiments, and will not be repeated here.

[0083] Then, the vehicle controller triggers a temperature pre-adjustment command based on the battery pre-adjustment temperature issued by the cloud-based decision engine. After receiving the pre-adjustment command, the battery management module instructs the thermal management module to activate the heating / cooling circuit, thereby adjusting the vehicle battery temperature. During the temperature adjustment process, the thermal management module provides real-time feedback on the battery temperature to the battery management module.

[0084] Next, the vehicle controller sends a wireless handshake protocol (such as protocol ISO 15118) request to the target charging device. After receiving the wireless handshake protocol request, the vehicle and the target charging device verify each other's identity, confirm the voltage platform of the target charging device (such as 800 volts (V)), and determine whether the peak power of the target charging device (such as 250kW) meets the target charging power requirements, in order to prevent voltage platform mismatch or excessive peak power, which could damage the vehicle battery.

[0085] Once the battery temperature has reached the pre-adjusted temperature, the battery management module reports the battery temperature to the vehicle controller, which then instructs the thermal management module to enter a heat preservation mode. When the vehicle arrives at the target charging station, the vehicle controller automatically parks the vehicle in the charging position and controls the robotic arm to insert the charging gun into the vehicle's charging port to begin charging. During charging, electrical energy is delivered to the vehicle according to the predetermined target charging power to prevent battery damage. The cloud-based decision engine monitors the vehicle's battery status and the target charging station's status in real time. If abnormal battery temperature or power fluctuations in the target charging station are detected, the charging current of the target charging station is dynamically adjusted. After charging is complete, the cloud-based decision engine generates a report on the impact of this charging on the vehicle's battery health and pushes it to the user's device.

[0086] Figure 6 This diagram illustrates a flowchart of another vehicle refueling control method provided in an embodiment of this application.

[0087] refer to Figure 6 When a vehicle receives a navigation activation or charging trigger signal while driving, it enters charging mode. Subsequently, it predicts whether the vehicle's remaining battery level is below a threshold. If not, it continues monitoring the remaining battery level without interfering with the vehicle's status. If so, it calculates the remaining driving distance based on the vehicle's location and remaining battery level, and then delineates the charging area based on this remaining distance. Next, it acquires information about the charging devices within the charging area. Multi-objective optimization detection is performed based on the charging device information and vehicle status data. For example, a knowledge graph is constructed based on the charging device information and vehicle status data, and a detection model is used to detect multiple charging devices in the knowledge graph. Detection indicators include charging time, charging cost, and battery health degradation.

[0088] Then, based on the test results, the optimal energy replenishment strategy is output, including charging only, battery swapping only, and battery swapping followed by charging. When the energy replenishment strategy includes charging, the vehicle battery temperature is pre-adjusted, wherein the pre-adjusted battery temperature is determined based on the rated power and output voltage range of the target charging equipment.

[0089] Next, the target charging power of the target charging device is pre-negotiated. This target charging power is determined based on the relationship between the vehicle and the target charging device in the knowledge graph, and it satisfies the vehicle's charging current constraints. Then, automatic charging of the vehicle is executed through coordinated control. For example, the vehicle is controlled to automatically park itself at the charging device's charging location, and the charging device is automatically physically linked to the vehicle's battery charging port.

[0090] Before charging the vehicle, the battery temperature has already reached the vehicle's pre-adjusted temperature. During charging, the battery is charged according to the target charging power, and the battery temperature and the status of the charging equipment are monitored. The charging current is dynamically adjusted based on changes in these factors. After charging is complete, the charging fee is settled seamlessly, and a health report for the vehicle battery is output. The health report includes the battery's health degradation level during this charge and its current health status. The charging process then ends.

[0091] The above embodiments describe in detail the vehicle refueling control method provided in this application. In other embodiments, this application also provides a vehicle refueling control device 700. Figure 7 This is a schematic diagram of the structure of a vehicle refueling control device provided in an embodiment of this application, as shown below. Figure 7 As shown, the vehicle's refueling control device 700 includes: The acquisition module 710 is used to acquire a knowledge graph of the vehicle and multiple refueling devices. The knowledge graph is used to represent the relationships between the vehicle and multiple refueling devices.

[0092] The first determination module 720 is used to determine the target energy replenishment device that meets the requirements from multiple energy replenishment devices based on a knowledge graph.

[0093] The control module 730 is used to control the vehicle to be recharged by the target recharge device.

[0094] In one possible implementation, the acquisition module 710 is used to acquire vehicle status data and information from multiple charging devices; based on the vehicle status data and the information from multiple charging devices, it extracts the relationships between the vehicle and the charging devices, including charging time, charging cost, and / or battery health degradation. Based on the relationships between the vehicle and the charging devices, a knowledge graph is constructed.

[0095] In one possible implementation, the first determining module 720 is used to input the knowledge graph into the detection model, and use the charging time, charging cost and / or battery health deterioration as detection indicators to determine the target charging device that meets the detection requirements from multiple charging devices.

[0096] In one possible implementation, the vehicle's status data includes one or more of the following: vehicle location, remaining battery power, battery health deterioration, battery temperature, driving mode, and navigation route.

[0097] In one possible implementation, the information of the power replenishment equipment includes one or more of the following: the identifier of the power replenishment equipment, the location of the power replenishment equipment, the number of idle power replenishment equipment, the estimated queuing time, the rated power, the output voltage range, the service fee, and the historical failure rate.

[0098] In one possible implementation, the vehicle's power replenishment control device 700 further includes a second determining module for determining multiple power replenishment devices based on the vehicle's location and remaining battery power.

[0099] In one possible implementation, the power replenishment equipment includes charging equipment and / or battery swapping equipment.

[0100] In one possible implementation, the vehicle's energy replenishment control device 700 further includes an adjustment module, which, when the target energy replenishment device includes a target charging device, adjusts the vehicle's battery temperature to a temperature that meets the charging efficiency threshold requirements of the target charging device based on the rated power and output voltage range of the target charging device.

[0101] In one possible implementation, the vehicle's energy replenishment control device 700 further includes a third determining module, which is used to determine a target charging power that satisfies the vehicle's charging current constraint based on the relationship between the vehicle and the target charging device in a knowledge graph when the target energy replenishment device includes a target charging device. The target charging power is used to guide the target charging device to charge the vehicle.

[0102] It should be understood that the above Figure 7 The provided device, in implementing its functions, is only illustrated by the division of the above-described functional modules. In practical applications, the functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the device and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation processes are detailed in the method embodiments, and will not be repeated here.

[0103] According to one aspect of the embodiments of this application, a structural schematic diagram of a power replenishment controller 800 is also provided, as shown below. Figure 8As shown, the power replenishment controller 800 includes a processor 810, a memory 820, and a communication interface 830. The processor 810, memory 820, and communication interface 830 are interconnected via a bus. The memory 820 stores one or more program instructions, and the processor 810 reads the program instructions stored in the memory 820 to execute the aforementioned commands. Figure 3 The methods provided in [the document / platform].

[0104] Furthermore, the processor 810 may include one or more processing cores. The processor 810 runs or executes instructions, programs, code sets, or instruction sets stored in the memory 820, and retrieves data stored in the memory 820. Optionally, the processor 810 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 810 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 810 and may be implemented using a separate communication chip.

[0105] The memory 820 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM). The memory 820 is used to store related computer programs and data. The communication interface 830 is used to receive and transmit data.

[0106] According to one aspect of this application, a computer-readable storage medium is also provided. This computer-readable medium may be included in the power supply controller 800 described in the above embodiments; or it may exist independently and not assembled into the power supply controller 800. The aforementioned computer-readable storage medium carries computer-readable instructions that, when executed by a processor, implement the methods in any of the above embodiments.

[0107] According to one aspect of this application, this application also provides a vehicle including a power replenishment controller 800, which is used to perform the methods in any of the above embodiments.

[0108] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0109] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0110] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the object information and detailed information involved in this application were obtained with full authorization.

[0111] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0112] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for energy replenishment control of a vehicle, characterized in that, The method includes: A knowledge graph of the vehicle and multiple energy replenishment devices is obtained, wherein the knowledge graph is used to characterize the relationship between the vehicle and the multiple energy replenishment devices; Based on the knowledge graph, a target power supply device that meets the requirements is determined from the plurality of power supply devices; Control the vehicle to recharge through the target recharge device.

2. The method according to claim 1, characterized in that, The acquisition of the knowledge graph of vehicles and multiple refueling devices includes: Acquire the vehicle's status data and the information of the multiple energy replenishment devices; Based on the vehicle's status data and the information of the multiple charging devices, the relationship between the vehicle and the charging devices is extracted. The relationship between the vehicle and the charging devices includes charging time, charging cost, and / or battery health deterioration. The knowledge graph is constructed based on the relationship between the vehicle and the refueling equipment.

3. The method according to claim 1 or 2, characterized in that, The step of determining the target power supply device that meets the requirements from the plurality of power supply devices based on the knowledge graph includes: The knowledge graph is input into the detection model, and the charging time, charging cost and / or battery health deterioration are used as detection indicators to determine the target charging device that meets the requirements from the multiple charging devices.

4. The method according to claim 2, characterized in that, The vehicle status data includes one or more of the following: vehicle location, remaining battery power, battery health deterioration, battery temperature, driving mode, and navigation route.

5. The method according to claim 2, characterized in that, The information of the power replenishment equipment includes one or more of the following: the power replenishment equipment identifier, the location of the power replenishment equipment, the number of idle power replenishment equipment, the estimated queuing time, the rated power, the output voltage range, the service fee, and the historical failure rate.

6. The method according to any one of claims 1-5, characterized in that, Prior to acquiring the knowledge graph of the vehicle and multiple refueling devices, the method further includes: Based on the vehicle's location and remaining battery power, the plurality of power replenishment devices are determined.

7. The method according to any one of claims 1-6, characterized in that, The energy replenishment equipment includes charging equipment and / or battery swapping equipment.

8. The method according to claim 7, characterized in that, Before controlling the vehicle to be recharged by the target recharge device, the method further includes: When the target energy replenishment device includes a target charging device, the battery temperature of the vehicle is adjusted to a temperature that meets the charging efficiency threshold requirement of the target charging device, based on the rated power and output voltage range of the target charging device.

9. The method according to claim 7 or 8, characterized in that, Before controlling the vehicle to be recharged by the target recharge device, the method further includes: When the target energy replenishment device includes a target charging device, based on the relationship between the vehicle and the target charging device in the knowledge graph, a target charging power that satisfies the charging current constraint of the vehicle is determined, and the target charging power is used to guide the target charging device to charge the vehicle.

10. A vehicle, characterized in that, The vehicle includes a recharge controller for performing the method of any one of claims 1 to 9 to control the vehicle to recharge.

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