Charging method, device, vehicle, storage medium and program product

Abstract

The present disclosure provides a charging method, device, vehicle, storage medium and program product, relating to the technical field of charging. The method comprises: in response to the vehicle connecting a charging pile, determining the user's willingness to wait for a charging duration, and determining the shortest estimated charging duration for the vehicle to complete charging; in response to the shortest estimated charging duration being less than the willingness to wait for the charging duration, determining a target charging path from a pre-constructed charging strategy database; generating a charging rate control strategy for this charging according to the target charging path; and controlling the vehicle to perform charging based on the charging rate control strategy. The method can filter out a target charging path from a strategy library when the shortest estimated charging duration of the vehicle is less than the user's willingness to wait for the duration, and generate a charging rate control strategy based on the target charging path, so that the charging rate can be dynamically optimized under the satisfaction of the user's waiting constraint, taking into account charging efficiency, energy consumption and battery life, and improving charging experience and adaptability of the charging strategy.

Description

Technical Field

[0001] This disclosure relates to the field of charging technology, and in particular to a charging method, apparatus, vehicle, storage medium, and program product. Background Technology

[0002] With the development of computer technology and electric vehicle technology, the number of electric vehicles on the road continues to increase, and the demand for charging is growing daily. During vehicle charging, energy loss occurs due to battery internal resistance, polarization, and thermal management, accompanied by cell temperature rise and battery life degradation. Therefore, the rational formulation of charging rate control strategies is very important.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide a charging method, apparatus, vehicle, storage medium, and program product.

[0005] According to a first aspect of the present disclosure, a charging method is provided, comprising: in response to a vehicle connecting to a charging pile, determining a user's desired waiting time for charging, and determining a minimum estimated charging time for the vehicle to complete charging; in response to the minimum estimated charging time being less than the desired waiting time for charging, determining a target charging path from a pre-built charging strategy database; generating a charging rate control strategy for the current charging based on the target charging path; and controlling the vehicle to perform charging based on the charging rate control strategy.

[0006] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: This disclosure can select a target charging path from a strategy library when the shortest estimated charging time of the vehicle is less than the user's desired waiting time, and generate a charging rate control strategy based on the target charging path. This allows for dynamic optimization of the charging rate while meeting the user's waiting constraints, achieving intelligent matching between the charging process and the user's time needs, taking into account charging efficiency, energy consumption, and battery life, and improving the charging experience and the adaptability of the charging strategy.

[0007] In some implementations, the charging strategy database is a directed graph database, which contains state nodes and directed charging paths between state nodes; wherein each state node corresponds to a binary pair consisting of the state of charge and the cell temperature; the directed charging path connects state nodes adjacent to each other in the direction of increasing state of charge, and each directed charging path is labeled with the corresponding charging rate, charging time and charging energy loss.

[0008] In the above embodiments, a binary state node database containing state of charge and cell temperature, along with a directed graph database, can be used to discretize the complex electrochemical and thermal characteristics of the battery. This structure of the charging strategy database facilitates the precise quantification of energy consumption and duration at different charging rates, providing a structured and computationally accurate data foundation for subsequent refined path search and rate control.

[0009] In some implementations, determining the target charging path from a pre-built charging strategy database includes: obtaining the current state of the vehicle battery and a preset target state of charge; filtering candidate charging paths from the charging strategy database based on the current state, the target state of charge, and the desired waiting time for charging; determining the charging cost of each candidate charging path; and determining the target charging path from the candidate charging paths based on the charging cost.

[0010] In the above implementation, candidate paths can be selected from the database based on the current state of the battery and the target state of charge, combined with the user's desired waiting time. Then, the target path is selected based on the charging cost of each candidate path. This achieves intelligent optimization of multiple paths under the condition of meeting time constraints, taking into account the minimization of charging efficiency and overall cost. The final path not only meets the user's waiting requirements, but also balances speed, energy consumption and lifespan on demand, improving the personalization and adaptability of the charging strategy.

[0011] In some implementations, the current state includes the current state of charge and the current cell temperature; wherein, the step of selecting candidate charging paths in the charging strategy database based on the current state, the target state of charge, and the desired waiting time for charging includes: in the charging strategy database, determining a corresponding starting state node based on the current state, and determining a corresponding ending state node based on the target state of charge; among the reachable paths from the starting state node to the ending state node, selecting candidate charging paths whose total cumulative charging time does not exceed the desired waiting time for charging; wherein each candidate charging path is composed of at least one directed charging path connected in series.

[0012] In the above implementation, by matching the start and end state nodes, candidate paths with a cumulative duration not exceeding the desired waiting time can be filtered from the reachable paths, realizing efficient path search based on graph structure. The method of using charged state discrete stepping to extend the serial path ensures the rationality and continuity of state transition, ensuring that the candidate path is physically reachable and temporally controllable.

[0013] In some implementations, determining the charging cost of each candidate charging path includes: determining the charging cost of each candidate charging path based on constraint factors and corresponding factor weights; the constraint factors include at least one of the following: charging energy loss, charging time, and battery life degradation; the factor weights are determined based on the user's historical charging behavior data and / or preset by the user.

[0014] In the above implementation, constraints such as energy loss, charging time, and battery life degradation can be introduced, along with weights based on user history behavior, to determine the charging cost. This enables personalized multi-objective optimization of the charging strategy, allowing different users to balance efficiency and battery protection according to their own preferences, thereby improving the adaptability of the strategy and user satisfaction.

[0015] In some implementations, generating a charging rate control strategy for the current charging based on the target charging path includes: extracting the state nodes and charging rates sequentially traversed in the target charging path to form a charging rate sequence; and generating a two-dimensional charging rate mapping table with state of charge as the first dimension and cell temperature as the second dimension based on the charging rate sequence, as the charging rate control strategy.

[0016] In the above implementation, the state nodes and charging rates in the target path can be extracted as sequences and a two-dimensional mapping table can be generated. This realizes the transformation from discrete paths to control rules that can be looked up in real time. This makes it easier for the battery management system to map and call appropriate rates in two dimensions: state of charge and cell temperature. This reduces the online calculation burden and improves the real-time performance and execution accuracy of charging rate control.

[0017] In some implementations, the charging strategy database is constructed as follows: the state of charge (SBC) and cell temperature of the vehicle battery are discretized to construct multiple discrete state nodes; based on the electrical and thermal characteristic simulation models of the vehicle battery, two state nodes with adjacent SBC discrete step sizes and a directed charging path are identified, and the charging rate, charging duration, and charging energy loss between the two state nodes are determined as annotation information for the corresponding directed charging path; the state nodes, the directed charging path, and the annotation information of the directed charging path are summarized to form the charging strategy database.

[0018] In the above implementation, the state of charge and cell temperature can be discretized, and the paths and labeling information between nodes can be determined based on the simulation models of electrical and thermal characteristics. This enables the scientific and reasonable construction of the charging strategy database, ensures the physical accuracy and reliability of the path data in the database, provides a reliable data foundation for online path optimization, and enables the generated charging strategy to have simulation verification support.

[0019] In some embodiments, the method further includes: in response to detecting that the cell temperature deviates from the temperature range corresponding to the target charging path, determining a new current state of the vehicle battery, and determining a new target charging path from the charging strategy database based on the new current state; generating a new charging rate control strategy based on the new target charging path to update the charging control of the vehicle.

[0020] In the above implementation, the state and target path can be dynamically updated when the cell temperature deviates, realizing real-time adaptive adjustment during the charging process. This can cope with model deviations caused by temperature changes during the charging process, improve the safety and environmental adaptability of the charging process, reduce the risk of temperature rise runaway, and make the charging process more stable and reliable.

[0021] In some implementations, determining the user's desired charging waiting time includes: inputting the acquired current charging scenario data into a pre-trained large model, and obtaining the desired charging waiting time through the output of the pre-trained large model; wherein the pre-trained large model is trained based on the user's historical charging behavior data; the current charging scenario data includes at least one of the following: current state of charge, current time information, current charging pile information, and vehicle driving mode information; and / or, determining the desired charging waiting time based on the duration data input by the user.

[0022] In the above implementation, the willingness waiting time can be predicted by combining pre-trained large models with multi-source scenario data, or determined by combining user input, thereby realizing the intelligent and personalized acquisition of willingness time and providing accurate time constraints for path selection.

[0023] In some implementations, determining the shortest estimated charging time for the vehicle to complete charging includes: calculating the shortest estimated charging time based on the vehicle's current battery state parameters and the charging capacity of the charging pile, according to the maximum allowable charging rate of the vehicle battery; wherein the current battery state parameters include at least one of the following: the vehicle battery's current initial state of charge, a preset target state of charge, the current cell temperature, and the vehicle's air conditioning operating status.

[0024] In the above implementation, the shortest estimated charging time can be calculated based on the current battery state parameters and the charging pile capacity, according to the maximum allowable rate, as a criterion for determining whether to perform path optimization.

[0025] In some embodiments, the method further includes: controlling the vehicle to perform charging in response to the shortest estimated charging time being greater than or equal to the desired waiting time for charging, using a maximum charging rate control strategy allowed by the vehicle battery.

[0026] In the above implementation, when the shortest estimated charging time is greater than or equal to the desired waiting time, the charging can be controlled directly at the maximum charging rate allowed by the vehicle battery. This ensures that the user's energy replenishment speed needs can be prioritized in scenarios where the user's time is tight, minimizing charging time and ensuring travel efficiency. This demonstrates the system's ability to flexibly switch between different constraints.

[0027] According to a second aspect of the present disclosure, a charging device is provided, comprising: a duration determination unit, configured to, in response to a vehicle connecting to a charging pile, determine a user's desired waiting time for charging and determine a minimum estimated charging time for the vehicle to complete charging; a path optimization unit, configured to, in response to the minimum estimated charging time being less than the desired waiting time for charging, determine a target charging path from a pre-built charging strategy database; a strategy generation unit, configured to, based on the target charging path, generate a charging rate control strategy for the current charging operation; and a control unit, configured to, based on the charging rate control strategy, control the vehicle to perform charging.

[0028] In some implementations, the charging strategy database is a directed graph database, which contains state nodes and directed charging paths between state nodes; wherein each state node corresponds to a binary pair consisting of the state of charge and the cell temperature; the directed charging path connects state nodes adjacent to each other in the direction of increasing state of charge, and each directed charging path is labeled with the corresponding charging rate, charging time and charging energy loss.

[0029] In some embodiments, the path optimization unit is further configured to: obtain the current state of the vehicle battery and a preset target state of charge; filter candidate charging paths in the charging strategy database based on the current state, the target state of charge and the desired waiting time for charging; determine the charging cost of each candidate charging path; and determine the target charging path from the candidate charging paths based on the charging cost.

[0030] In some implementations, the current state includes the current state of charge and the current cell temperature; wherein, the path optimization unit is further configured to: determine a corresponding starting state node based on the current state in the charging strategy database, and determine a corresponding ending state node based on the target state of charge; among the reachable paths from the starting state node to the ending state node, select candidate charging paths whose total cumulative charging time does not exceed the desired waiting charging time; wherein each candidate charging path is composed of at least one directed charging path connected in series.

[0031] In some implementations, the path optimization unit is further configured to: determine the charging cost of each candidate charging path based on constraint factors and corresponding factor weights; the constraint factors include at least one of the following: charging energy loss, charging time, and battery life degradation; the factor weights are determined based on the user's historical charging behavior data and / or preset by the user.

[0032] In some implementations, the strategy generation unit is further configured to: extract the state nodes and charging rates sequentially traversed in the target charging path to form a charging rate sequence; and generate a two-dimensional charging rate mapping table with state of charge as the first dimension and cell temperature as the second dimension based on the charging rate sequence, as the charging rate control strategy.

[0033] In some embodiments, the device further includes a construction unit for: discretizing the state of charge (SBC) and cell temperature of the vehicle battery to construct multiple discrete state nodes; determining two state nodes with adjacent SBC discrete step sizes and a directed charging path based on the electrical and thermal characteristic simulation models of the vehicle battery, and determining the charging rate, charging duration, and charging energy loss between the two state nodes as annotation information for the corresponding directed charging path; and summarizing the state nodes, the directed charging path, and the annotation information of the directed charging path to form the charging strategy database.

[0034] In some embodiments, the path optimization unit is further configured to: determine a new current state of the vehicle battery in response to detecting that the cell temperature deviates from the temperature range corresponding to the target charging path; determine a new target charging path from the charging strategy database based on the new current state; the strategy generation unit is further configured to: generate a new charging rate control strategy based on the new target charging path to update the charging control of the vehicle.

[0035] In some implementations, the duration determination unit is further configured to: input the acquired current charging scenario data into a pre-trained large model, and obtain the intended waiting time for charging through the output of the pre-trained large model; wherein the pre-trained large model is trained based on the user's historical charging behavior data; the current charging scenario data includes at least one of the following: current state of charge, current time information, current charging pile information, vehicle driving mode information; and / or, determine the intended waiting time for charging based on the duration data input by the user.

[0036] In some embodiments, the duration determination unit is further configured to: calculate the shortest estimated charging time based on the current battery state parameters of the vehicle and the charging capacity of the charging pile, according to the maximum allowable charging rate of the vehicle battery; wherein the current battery state parameters include at least one of the following: the current initial state of charge of the vehicle battery, the preset target state of charge, the current cell temperature, and the air conditioning operation status of the vehicle.

[0037] In some embodiments, the control unit is further configured to: control the vehicle to perform charging in response to the shortest estimated charging time being greater than or equal to the desired waiting time for charging, using a maximum charging rate control strategy allowed by the vehicle battery.

[0038] According to a third aspect of the present disclosure, a vehicle is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the charging method described above.

[0039] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the charging method described above.

[0040] According to a fifth aspect of the present disclosure, a computer-readable storage medium is provided, which, when instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to perform the charging method described above.

[0041] According to a sixth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the charging method described above.

[0042] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0044] Figure 1 This is a flowchart illustrating a charging method according to some embodiments of the present disclosure.

[0045] Figure 2 This is a schematic diagram of a charging strategy database in a charging method according to some embodiments of the present disclosure.

[0046] Figure 3 This is a flowchart illustrating a charging method for determining a target charging path from a charging strategy database, according to some embodiments of the present disclosure.

[0047] Figure 4 This is a flowchart illustrating the selection of candidate charging paths in a charging method according to some embodiments of the present disclosure.

[0048] Figure 5 This is a flowchart illustrating the construction of a charging strategy database in a charging method according to some embodiments of the present disclosure.

[0049] Figure 6 This is a schematic diagram illustrating a framework for implementing a charging method according to some embodiments of the present disclosure.

[0050] Figure 7 This is a schematic flowchart illustrating a charging method according to some embodiments of the present disclosure.

[0051] Figure 8 This is a block diagram illustrating a charging device according to some embodiments of the present disclosure.

[0052] Figure 9 This is a block diagram of a vehicle according to some embodiments of the present disclosure. Detailed Implementation

[0053] Some exemplary embodiments of this disclosure will be described in detail herein, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the flowcharts shown in the drawings are merely illustrative and do not necessarily include all contents and steps, nor do they necessarily have to be performed in the described order or in the order of step numbers. For example, some steps may be decomposed, while others may be combined or partially combined, and multiple steps may be interchanged or performed simultaneously; therefore, the actual order of execution may change depending on the actual situation. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0054] The embodiments described below, which are some of the embodiments of this disclosure, do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0055] The examples in this document may involve user data, data acquisition, and / or use. All of these aspects comply with relevant laws, regulations, and rules. In the examples, all data collection, acquisition, processing, manipulation, forwarding, and use are conducted with the user's knowledge and confirmation. Accordingly, when implementing each example, the type, scope of use, and usage scenarios of any data or information that may be involved should be communicated to the user and their authorization obtained through appropriate means, in accordance with relevant laws and regulations. The specific methods of notification and / or authorization can vary depending on the actual situation and application scenario; the scope of the solution is not limited in this regard.

[0056] In this manual and the sample solutions, any processing of personal information will be conducted only under legal grounds (such as obtaining the consent of the data subject or being necessary for the performance of a contract) and will only be carried out within the scope stipulated or agreed upon. A user's refusal to process personal information beyond what is necessary for basic functions will not affect the user's use of basic functions.

[0057] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0058] Figure 1 This is a flowchart illustrating a charging method according to some embodiments of the present disclosure, such as... Figure 1 As shown, the charging method can be applied to vehicles, as well as to electronic devices that communicate with vehicles and / or charging piles. These electronic devices include, but are not limited to, in-vehicle terminals, smartphones, smart tablets, desktop computers, laptops, battery management systems, and charging pile controllers. They may also include server-side components such as local servers and cloud servers, which can be deployed in a single computer or a cluster of multiple computers. The charging method may include the following steps.

[0059] Step S110: In response to the vehicle connecting to the charging station, determine the user's desired waiting time for charging, and determine the shortest estimated charging time for the vehicle to complete charging.

[0060] In this embodiment of the disclosure, after the vehicle connects to the charging pile and establishes a communication connection, the upper limit of the acceptable waiting time, either subjectively set by the user or inferred by the system, can be determined. For example, if the user inputs "I hope to complete charging within 30 minutes," then 30 minutes can be taken as the user's desired waiting time for charging; or the system can automatically obtain the necessary information to input into a trained model and output the user's desired waiting time for charging.

[0061] The system can also calculate the theoretical shortest charging time based on the vehicle battery's current battery state parameters (such as current battery state of charge, battery capacity, etc.) and the charging infrastructure capabilities (such as the rated power of the charging pile, etc.). For example, if the current battery level is 20%, the target battery level is 80%, and the charging pile power is 120kW, the shortest estimated charging time can be x minutes (e.g., 25 minutes). In the exemplary embodiment, the charging strategy corresponding to the shortest estimated charging time uses the maximum charging rate allowed by the vehicle battery.

[0062] This step can provide an input benchmark for subsequent strategy decisions.

[0063] In an exemplary embodiment, the vehicle connecting to a charging station signifies that a charging trigger event has occurred. This could be a physical connection between the vehicle and the charging station, such as when a user plugs in a charging gun, or a scenario involving wireless charging.

[0064] In an exemplary embodiment, the actions of determining the desired waiting time for charging and determining the shortest estimated charging time can be performed sequentially or in parallel.

[0065] Step S120: In response to the shortest estimated charging time being less than the desired waiting time for charging, a target charging path is determined from the pre-built charging strategy database.

[0066] In this embodiment, the shortest estimated charging time is compared with the user's desired waiting charging time. If the user's acceptable waiting charging time is longer than the shortest estimated charging time, it indicates that the vehicle can be charged within the user's acceptable waiting time, and there is a time margin. In this case, the path retrieval mechanism can be triggered. The charging time corresponding to the newly retrieved path can be longer than the shortest estimated charging time but shorter than or equal to the desired waiting charging time. In the charging strategy corresponding to the newly retrieved path, it is not necessary to always use the maximum charging rate.

[0067] An optimal target charging path can be matched and determined from a pre-built charging strategy database based on multi-dimensional conditions such as the current vehicle status and charging pile parameters. The target charging path can be a charging path selected from the pre-built charging strategy database that is suitable for the current charging conditions and meets the optimization objectives. It can also be the trajectory of charging parameters (such as charging rate, current, voltage, power, etc.) changing with SOC (State of Charge), time, temperature, etc.

[0068] For example, if the shortest estimated charging time is 25 minutes and the desired waiting time is 30 minutes, and 25 minutes < 30 minutes, the system can select a charging path from the database that can be completed within 30 minutes and is more beneficial to battery life.

[0069] In an exemplary embodiment, the charging strategy database may be pre-built and may be a database that stores charging strategies, charging paths, applicable conditions and corresponding control parameters, and may be searched and matched based on conditions such as vehicle status and temperature.

[0070] Step S130: Generate a charging rate control strategy for this charging based on the target charging path.

[0071] In this embodiment, the target charging path can describe the charging rate change curve and stage division during the charging process, and an executable charging rate control strategy can be derived based on this path. Here, the charging rate (C-rate) represents the ratio of the charging current to the battery's rated capacity. The charging rate control strategy can be a charging execution scheme with the charging rate as the core control variable, specifying the rate values, switching conditions, and durations for each stage of the charging process, thereby precisely controlling the charging power.

[0072] In an exemplary embodiment, the charging method provided in this disclosure can be implemented by a BMS (Battery Management System) determining a target charging path from a pre-built charging strategy database and generating a charging rate control strategy based on the target charging path. Alternatively, the vehicle can transmit relevant information (such as vehicle status, charging pile capacity parameters, and the user's desired charging time) to a cloud server, which will then determine the target charging path, generate a charging rate control strategy, and distribute the strategy to the vehicle.

[0073] Step S140: Control the vehicle to perform charging based on the charging rate control strategy.

[0074] In this embodiment of the disclosure, the vehicle can perform the charging process according to the charging rate control strategy. The vehicle battery management system (BMS) or the vehicle controller can send charging current, charging voltage or charging power requests to the charging pile, and monitor the battery voltage, temperature and SOC in real time. The charging rate is dynamically adjusted according to the charging rate control strategy until the battery state of charge reaches the target value or charging is completed.

[0075] As can be seen from the above steps, the charging method provided in this disclosure can search and filter the target charging path from the strategy library when the shortest estimated charging time of the vehicle is less than the user's desired waiting time, and generate a charging rate control strategy based on the target charging path. This allows for dynamic optimization of the charging rate while meeting the user's waiting constraints, achieving intelligent matching between the charging process and the user's time needs, taking into account charging efficiency, energy consumption and battery life, and improving the charging experience and the adaptability of the charging strategy.

[0076] In some embodiments of this disclosure, determining the user's desired charging waiting time includes: inputting the acquired current charging scenario data into a pre-trained large model, and obtaining the desired charging waiting time through the output of the pre-trained large model; wherein the pre-trained large model is trained based on the user's historical charging behavior data; the current charging scenario data includes at least one of the following: current state of charge, current time information, current charging pile information, vehicle driving mode information; and / or, determining the desired charging waiting time based on the duration data input by the user.

[0077] In this embodiment of the disclosure, after the vehicle connects to the charging pile, multi-dimensional information related to the current charging situation can be automatically collected, which may include one or more of the following: current state of charge, current time information, current charging pile information, and vehicle driving mode information. Then, the collected multi-dimensional scene data can be used as an input vector and fed into a pre-trained large model for inference calculation. Based on the mapping relationship between user behavior patterns and charging scenarios learned internally, the model can output a numerical value indicating the desired charging waiting time.

[0078] The pre-trained large model can be a deep learning model pre-trained based on large-scale historical data. It can predict the user's charging waiting time preference (i.e., the willingness to wait for charging) based on the user's historical charging behavior data, charging scenario data, etc. In an exemplary embodiment, the user's historical charging behavior data may include at least one of the following: the state of charge range of historical charging, the location of the charging station, the status of the charging pile, the actual charging time, the interval between the end of charging and the next trip, and the driving mode after charging is completed.

[0079] In this embodiment, the system can also directly receive the duration value input by the user through an in-vehicle interface, mobile application, or charging pile interaction terminal, and determine this value as the desired charging waiting time. For example, if the user selects "hope to charge within 30 minutes" on the in-vehicle screen, the system can use 30 minutes as the desired charging waiting time. This method provides users with a direct control approach and can be used in scenarios where users have clear time constraints.

[0080] Through the embodiments of this disclosure, the willingness waiting time can be predicted by combining a pre-trained large model with multi-source scenario data, or by combining user input to determine the willingness waiting time, thereby realizing the intelligent and personalized acquisition of willingness time and providing accurate time constraints for path selection.

[0081] In some embodiments of this disclosure, determining the shortest estimated charging time for the vehicle to complete charging includes: calculating the shortest estimated charging time based on the vehicle's current battery state parameters and the charging capacity of the charging pile, according to the maximum allowable charging rate of the vehicle battery; wherein, the current battery state parameters include at least one of the following: the vehicle battery's current initial state of charge, a preset target state of charge, the current cell temperature, and the vehicle's air conditioning operating status.

[0082] In this embodiment of the disclosure, after the vehicle is connected to the charging pile, multiple current battery state parameters of the vehicle's power battery can be read in real time. At the same time, the charging capacity information of the currently connected charging pile can be obtained. Then, based on the obtained battery state parameters and the charging capacity of the charging pile, combined with the upper limit of the maximum allowed charging rate of the vehicle battery, the theoretical shortest charging time required for the vehicle to reach the target charging state from the current charging state can be derived and calculated.

[0083] The current battery state parameters can be a set of parameters describing the physical and chemical state of the vehicle's power battery at the start of charging, which can be used to support the accurate calculation of charging time and the determination of safety constraints. The charging capacity of the charging pile can be the charging power, current, voltage, communication control capabilities, and continuous output capabilities that the charging pile can provide, as well as other charging-related performance parameters.

[0084] In the current battery state parameters, the initial state of charge (SBC) can be the percentage of the battery's remaining charge relative to its rated total capacity at the moment the vehicle begins charging. The target SBC can be a user-defined setting (such as 85%, 90%, etc.) or a default value (such as 95%, etc.), which can be used as a reference threshold for terminating charging.

[0085] The current cell temperature can be the temperature of the vehicle's battery cell at the start of charging, which can affect the battery's maximum allowable charging rate, charging efficiency, and charging safety boundaries. The vehicle's air conditioning operating status can be whether the vehicle's air conditioning system is currently running, its operating mode, power, or settings, which can affect the vehicle's energy consumption, battery thermal management, or available charging power, and can generate heat load, thereby indirectly affecting charging time and battery temperature changes.

[0086] In an exemplary embodiment, the logic for calculating the theoretical shortest charging time can be as follows: the target charging capacity is obtained by multiplying the difference between the initial state of charge and the target state of charge by the total battery capacity, and the maximum allowable charging power, which does not exceed the charging capacity limit of the charging pile, is calculated by converting the maximum charging rate and the rated battery capacity. Then, the target charging capacity is divided by the maximum allowable charging power to obtain the shortest estimated charging time.

[0087] Through the embodiments of this disclosure, the shortest estimated charging time can be calculated based on the current battery state parameters and the charging pile capacity, according to the maximum allowable charging rate, and used as a criterion for determining whether to perform path optimization.

[0088] In some embodiments of this disclosure, the method further includes: controlling the vehicle to perform charging in response to the shortest estimated charging time being greater than or equal to the desired waiting time for charging, using a maximum charging rate control strategy allowed by the vehicle battery.

[0089] In this embodiment of the disclosure, the shortest estimated charging time is compared with the desired waiting time for charging. If the shortest estimated charging time is greater than or equal to the desired waiting time for charging, it means that the theoretical shortest time has exceeded or equaled the user's acceptable waiting limit. At this time, it is no longer necessary to retrieve the target charging path from the charging strategy database. Instead, a control strategy with the maximum charging rate as the core is directly adopted to drive charging, so as to charge as much power as possible within the user's acceptable time.

[0090] In an exemplary embodiment, the maximum allowable charging rate control strategy for the vehicle battery can correspond to the maximum charging capacity of the vehicle battery. The maximum charging capacity of the vehicle battery can be achieved by calling a fixed charging map corresponding to the maximum allowable charging rate of the battery based on the current battery state of charge and battery temperature. The fixed charging map can be a pre-calibrated two-dimensional lookup table, also known as a maximum charging rate map. The fixed charging map can take the current battery state of charge and current cell temperature as input dimensions and the maximum allowable charging rate value of the battery under the corresponding operating conditions as the output value.

[0091] Through the embodiments disclosed herein, when the shortest estimated charging time is greater than or equal to the desired waiting time, charging can be directly controlled at the maximum charging rate allowed by the vehicle battery. This ensures that the user's energy replenishment speed needs can be prioritized in scenarios where the user's time is tight, minimizing charging time and ensuring travel efficiency. This demonstrates the system's ability to flexibly switch between different constraints.

[0092] In some embodiments of this disclosure, the charging strategy database is a directed graph database, which includes state nodes and directed charging paths between state nodes; wherein, each state node corresponds to a binary pair consisting of the state of charge and the cell temperature; the directed charging path connects state nodes adjacent to each other in the direction of increasing state of charge, and each directed charging path is labeled with the corresponding charging rate, charging time and charging energy loss.

[0093] In this embodiment of the disclosure, the charging strategy database can be an offline pre-built database, and its data structure type can be a directed graph database. The directed graph database organizes data with nodes and directed edges, where the edges have directionality and can be used to represent the transition relationships between charging states. The charging strategy database can use a directed graph structure to store the charging transition relationships from low-charge state nodes to high-charge state nodes.

[0094] The components of a directed graph database can include state nodes and directed charging paths between them. A state node represents a charging state, and each state node consists of two components: the state of charge and the cell temperature, uniquely represented as a binary tuple. For example, a state node can be represented as (50%, 26℃), where 50% is the state of charge and 26℃ is the cell temperature.

[0095] A directed charging path represents a charging transfer relationship from one state node to another. A directed charging path connects state nodes along the direction of increasing state of charge (SOC), and the two connected state nodes differ in SOC by a distance step size. This distance step size can be set to values ​​such as 5% or 10%, and can be used to define the granularity of the state nodes. For example, assuming the distance step size is set to 5%, a state node with a SOC of 30% can point to a state node with a SOC of 35%. Connecting state nodes with adjacent step sizes with directed edges along the direction of increasing SOC forms a directed charging path.

[0096] Each directed charging path can be marked with key parameters such as the required charging rate, the required charging time, and the charging energy loss generated by the path. The charging rate marked on the directed charging path is less than or equal to the maximum charging rate allowed by the vehicle battery at the corresponding state node. The information marked on the directed charging path describes the charging process characteristics corresponding to the path and can be used as a basis for selecting the target charging path. In an exemplary embodiment, the charging energy loss may include thermal management energy loss (such as the additional energy loss consumed by the thermal management system to maintain the battery operating temperature) and internal resistance heat loss (such as the energy loss caused by the heating of the battery's internal resistance during charging).

[0097] In an exemplary embodiment, charging energy loss can be a comprehensive energy cost, which may include thermal management energy loss (such as the additional energy loss consumed by the thermal management system to maintain the battery operating temperature) and internal resistance heat loss (such as the energy loss caused by the heating of the battery's internal resistance during charging). When building the charging strategy database offline, the charging energy loss of each directed charging path can be calculated through simulation using the battery electrical model and the battery thermal model.

[0098] Taking a state of charge distance step length set to 10% as an example, the directed path from node (SOC=30%, T=25℃) to node (SOC=40%, T=27℃) can be labeled as: charging rate 1.2C, charging time 10 minutes, energy loss 0.8kWh; the directed path from node (SOC=40%, T=27℃) to node (SOC=50%, T=29℃) can be labeled as: charging rate 1.0C, charging time 12 minutes, energy loss 0.9kWh.

[0099] In an exemplary embodiment, there is no direct directed charging path between some state nodes, meaning they are not reachable from each other. For example, when the cell temperature exceeds the battery safety threshold, the directed edge from the low SOC node to the high SOC node will be removed. State nodes within this temperature range cannot migrate to the high SOC node through the normal charging path. The system will automatically determine that such nodes are unreachable to avoid generating charging paths that do not meet battery safety constraints at extreme temperatures.

[0100] Through the embodiments of this disclosure, complex battery electrochemical and thermal characteristics can be discretized using binary state nodes containing state of charge and cell temperature, as well as a directed graph database. This structure of the charging strategy database facilitates the precise quantification of energy consumption and duration at different charging rates, providing a structured and computationally calculable data foundation for subsequent refined path search and rate control.

[0101] Figure 2 This is a schematic diagram of a charging strategy database in a charging method according to some embodiments of the present disclosure.

[0102] like Figure 2 As shown, the charging strategy database includes multiple state nodes, such as state nodes 201 to 210. Each state node is characterized by a binary pair consisting of the state of charge and the cell temperature.

[0103] Among them, the binary pair consisting of the state of charge and the cell temperature corresponding to state node 201 can be (10%, 20℃), and the binary pair consisting of the state of charge and the cell temperature corresponding to state node 210 can be (10%, 25℃). State node 210 and state node 201 have the same state of charge but different cell temperatures.

[0104] State node 201 can have directed charging paths 211, 212, and 213 with state nodes 202, 203, and 204, respectively. The states of charge in the binary pairs corresponding to state nodes 202, 203, and 204 can be the same, but the cell temperatures can be different; state nodes 202, 203, and 204 can be (15%, 25℃), (15%, 30℃), and (15%, 35℃), respectively. Directed charging paths 211, 212, and 213 can correspond to different combinations of charging rates, charging durations, and charging energy losses. Similarly, state node 210 can also have directed charging paths with state nodes 202, 203, and 204. Under the charging strategies represented by different directed charging paths, the charging rate, charging duration, and charging energy loss can be different, thus providing a basis for selecting the optimal charging path.

[0105] Similarly, state nodes 202, 203, and 204 are also represented by binary pairs consisting of state of charge and cell temperature, and can also serve as starting nodes to connect to subsequent state nodes with higher states of charge, forming a continuous directed charging path network, which will not be elaborated here.

[0106] Figure 2 Other aspects of the embodiments can be found in the other embodiments described above.

[0107] Figure 3 This is a flowchart illustrating a charging method for determining a target charging path from a charging strategy database, according to some embodiments of the present disclosure.

[0108] like Figure 3 As shown, the process of determining the target charging path from the pre-built charging strategy database may include the following steps.

[0109] Step S310: Obtain the current state of the vehicle battery and the preset target state of charge.

[0110] In this embodiment of the disclosure, before performing path retrieval, the current operating status of the power battery can be read through the battery management system, and the target state of charge threshold for this charge can be obtained by the user or the system in advance.

[0111] In some embodiments of this disclosure, the current state includes the current state of charge and the current cell temperature.

[0112] For example, the current state of the battery can be collected as SOC=30% and the cell temperature is 28℃, and the preset target state of charge is obtained as 80%. This step can provide boundary constraints for the start and end nodes of the subsequent path search and filtering.

[0113] Step S320: Based on the current state, the target state of charge, and the desired waiting time for charging, candidate charging paths are selected from the charging strategy database.

[0114] In this embodiment of the disclosure, the current state corresponding to the obtained state node can be used as the starting point and the target state of charge corresponding to the state node can be used as the ending point. Combined with the user's willingness to wait for charging time as a time constraint, all complete reachable paths that satisfy the start and end point constraints and whose total charging time does not exceed the willingness to wait for charging time can be traversed in the directed graph structure charging strategy database, and these paths are marked as candidate charging paths.

[0115] For example, if the starting point is a state node locked based on (SOC=30%, T=28℃), and the destination is multiple state nodes constrained by "SOC=80%", with a willingness to wait for 40 minutes, the system can filter out multiple candidate paths, such as filtering out three paths with total durations of 32 minutes, 35 minutes, and 38 minutes as candidate sets.

[0116] Step S330: Determine the charging cost of each candidate charging path.

[0117] In this embodiment of the disclosure, for each candidate charging path, the charging cost used to evaluate the merits of the path can be calculated based on parameters such as the charging rate, charging duration, and charging energy loss indicated on its labeled parameters. For example, the charging duration, charging energy loss, and battery aging impact factor of all directed edges on the path can be weighted and summed, or a comprehensive evaluation after weighting can be performed, to obtain the comprehensive charging cost of the path.

[0118] For example, by combining preset weight parameters, a comprehensive cost evaluation can be performed on at least one of the following factors in the candidate path set: thermal management energy consumption, internal resistance heat loss, charging time, and battery life degradation, and the path with the lowest comprehensive cost can be selected as the target charging path.

[0119] Step S340: Determine the target charging path from the candidate charging paths based on the charging cost.

[0120] In this embodiment of the disclosure, a lower cost value indicates better overall performance of the path. The overall charging cost value of all candidate charging paths can be sorted, and the path with the lowest cost value can be selected as the final target charging path.

[0121] Through the embodiments of this disclosure, candidate paths can be filtered from the database based on the current state of the battery and the target state of charge, combined with the user's desired waiting time. Then, the target path is selected based on the charging cost of each candidate path. This achieves intelligent multi-path optimization under time constraints, taking into account the minimization of charging efficiency and overall cost. The final path not only meets the user's waiting requirements, but also balances speed, energy consumption and lifespan on demand, improving the personalization and adaptability of the charging strategy.

[0122] Figure 4 This is a flowchart illustrating the selection of candidate charging paths in a charging method according to some embodiments of the present disclosure.

[0123] like Figure 4 As shown, the process of filtering candidate charging paths in the charging strategy database based on the current state, the target state of charge, and the desired waiting time for charging may include the following steps.

[0124] Step S410: In the charging strategy database, determine the corresponding starting state node based on the current state matching, and determine the corresponding ending state node based on the target state of charge matching.

[0125] In this embodiment, the current state of charge (SOC) and the current cell temperature are used as the search key to perform precise matching in a directed graph-structured charging strategy database, locating a unique starting state node. Simultaneously, a set of all ending state nodes satisfying the pre-defined SOC threshold is obtained by matching a pre-set target SOC as the search condition. This step clarifies the boundary range between the starting and ending points for subsequent path traversal.

[0126] For example, assuming the database state of charge distance step size is set to 5% and the cell temperature distance step size is set to 2℃, if the current state is (SOC=30%, T=28℃), it can directly match the unique starting node in the database; if the target state of charge is 80%, it can match the set of all ending nodes with SOC=80% and cell temperature within the safe range.

[0127] In an exemplary embodiment, the starting state node can be one or more matched based on the current state of charge and the current cell temperature. When the collected values ​​of the current state of charge and the current cell temperature exactly correspond to or significantly approach a binary pair of a certain state node in the database, one starting state node can be uniquely matched; when the collected current state of charge and / or the current cell temperature fall within the corresponding discrete step size, multiple surrounding starting state nodes that meet the boundary conditions can be matched simultaneously.

[0128] For example, assuming the database state of charge distance step size is set to 5% and the cell temperature distance step size is set to 2℃, when the current state collected is SOC=32% and cell temperature 29℃, this value does not completely overlap with any predefined node. It can simultaneously match 4 surrounding starting state nodes: (SOC=30%, T=28℃), (SOC=30%, T=30℃), (SOC=35%, T=28℃), and (SOC=35%, T=30℃). Subsequently, candidate charging paths can be searched in parallel based on these 4 nodes.

[0129] Step S420: Among the reachable paths from the starting state node to the ending state node, candidate charging paths whose total cumulative charging time does not exceed the desired waiting charging time are selected; wherein, each candidate charging path is composed of at least one directed charging path connected in series.

[0130] In this embodiment of the disclosure, based on the topological reachability rules of the directed graph, all complete paths starting from the starting node and proceeding along the direction of increasing charge state to the ending node can be traversed. Invalid paths with unreachable nodes or broken paths can be excluded. Then, the charging time of all directed edges of the remaining reachable paths can be summed to filter out paths whose total time is less than or equal to the user's desired waiting time for charging, thus forming a set of candidate charging paths.

[0131] The total cumulative charging time can be the sum of the labeled charging times of all directed charging paths included in a single complete charging path, which can represent the theoretical total time taken for the path to migrate from the starting point to the end point.

[0132] For example, if the willingness to wait is 40 minutes, there are 7 reachable paths from the starting node to the ending node, and the total durations of 3 of these paths are 32 minutes, 35 minutes, and 38 minutes respectively, all of which do not exceed the willingness to wait for 40 minutes, then these 3 paths can be marked as candidate charging paths.

[0133] In an exemplary embodiment, the smallest constituent unit of a candidate charging path can be a predefined directed charging path in the database. A single candidate path can be formed by connecting one or more directed edges in series in the direction of increasing charge state. The connection point of two adjacent directed edges is the same state node, ensuring the continuity of the path and the legality of the state transition.

[0134] Through the embodiments of this disclosure, candidate paths with a cumulative duration not exceeding the desired waiting time can be filtered from reachable paths by matching start and end state nodes, thus realizing efficient path search based on graph structure. The method of using charged state discrete step serial path generation ensures the rationality and continuity of state transition, ensuring that candidate paths are physically reachable and temporally controllable.

[0135] In some embodiments of this disclosure, determining the charging cost of each candidate charging path includes: determining the charging cost of each candidate charging path based on constraint factors and corresponding factor weights; the constraint factors include at least one of the following: charging energy loss, charging time, and battery life degradation; the factor weights are determined based on the user's historical charging behavior data and / or preset by the user.

[0136] In this embodiment of the disclosure, for each candidate charging path, the charging energy loss of all directed charging paths marked on the path can be extracted and summed, the charging time of all directed charging paths marked on the path can be summed, and the battery life decay can be calculated based on the charging rate of each segment on the path and the SOC interval of the corresponding state node.

[0137] Among them, battery life degradation can be the amount or index of degradation caused by the charging process of the corresponding candidate charging path to the battery life, which may include capacity degradation, internal resistance increase, cycle life loss, etc., and can be estimated by empirical models or data models based on parameters such as charging rate, charging depth, and temperature.

[0138] In an exemplary embodiment, the constraint factors used for cost calculation may be one or more, and one or more of them may be selected as the basis for calculation according to the actual application scenario or user needs.

[0139] In this embodiment, charging preference patterns can be mined from users' historical charging behavior data to determine factor weights. For example, this can be obtained through inference from a trained large model; it can also be preset by the user, or a combination of both. For instance, if historical charging behavior data shows that users typically focus more on charging duration, the factor weight corresponding to charging duration can be higher; if the user presets an "energy consumption priority" mode, the factor weight corresponding to charging energy loss can be higher. Alternatively, initial weights can be generated based on historical data and then corrected according to user presets.

[0140] The comprehensive charging cost of each candidate charging path can be obtained by weighted summation of the values ​​of each constraint factor and the corresponding factor weights, or by normalization followed by weighted summation.

[0141] For example, if candidate path A has a charging energy loss of 3.5 kWh, a charging time of 30 minutes, and a battery life degradation of 0.02%, with corresponding factor weights of 0.5, 0.3, and 0.2 respectively, then the charging cost can be directly obtained by weighted summation as 3.5 × 0.5 + 30 × 0.3 + 0.02 × 0.2 = 10.754. Alternatively, each constraint factor can be normalized first, that is, 3.5 kWh, 30 minutes, and 0.02% can be mapped to the [0,1] interval respectively before being multiplied by the weights and summed. This can eliminate the weight bias caused by the difference in the dimensions and numerical magnitudes of each factor, and make the comprehensive cost value more evenly reflect the performance of each dimension.

[0142] Through the embodiments disclosed herein, constraint factors including energy loss, charging time, and battery life degradation, as well as weights based on user historical behavior, can be introduced to determine the charging cost, thereby achieving personalized multi-objective optimization of the charging strategy. This allows different users to balance efficiency and battery protection according to their own preferences, improving the adaptability of the strategy and user satisfaction.

[0143] In some embodiments of this disclosure, generating a charging rate control strategy for this charging based on the target charging path includes: extracting the state nodes and charging rates sequentially traversed in the target charging path to form a charging rate sequence; and generating a two-dimensional charging rate mapping table with state of charge as the first dimension and cell temperature as the second dimension based on the charging rate sequence, as the charging rate control strategy.

[0144] In this embodiment of the disclosure, the determined target charging path can be sequentially traversed. According to the serial order of the directed charging paths on the path, the charging rate value marked on each directed charging path is extracted in turn. At the same time, the charge state and cell temperature information of the two state nodes connected by the directed edge are recorded, and all charging rates are arranged into an ordered sequence according to the path migration order.

[0145] For example, if the target charging path passes through nodes (SOC=30%, T=28℃) → (SOC=40%, T=30℃) → (SOC=50%, T=32℃) → (SOC=60%, T=34℃) in sequence, the corresponding charging rates marked on the directed edges are 1.2C, 1.1C, 1.0C, and 0.9C, which can form the charging rate sequence [1.2C, 1.1C, 1.0C, 0.9C].

[0146] Each rate value in the extracted charging rate sequence can be backfilled into the coordinate position of the state of charge and cell temperature determined by the corresponding state node tuple, and a two-dimensional mapping table with the state of charge as the horizontal axis and the cell temperature as the vertical axis can be constructed. Each coordinate cell in the table can store the charging rate value that should be executed in that state. This mapping table is the final control strategy output for this charging.

[0147] For example, you can enter 1.2C at the coordinate (SOC=30%, T=28℃) in the mapping table, 1.1C at the coordinate (SOC=40%, T=30℃), 1.0C at the coordinate (SOC=50%, T=32℃), and 0.9C at the coordinate (SOC=60%, T=34℃). The battery management system can read the current SOC and cell temperature in real time during charging and look up the appropriate charging rate in the table.

[0148] In the exemplary embodiment, the two-dimensional charging rate mapping table is a dedicated control table generated to adapt to the initial conditions of this charging operation. It includes effective rate values ​​corresponding to the expected state of charge (SOC) ranges and cell temperature ranges to be experienced during the charging process. Coordinate cells corresponding to SOC ranges and / or cell temperature ranges not covered can be set to invalid or default rate values. Furthermore, the target charging rate value at each position in the table does not exceed the maximum allowable charging rate of the vehicle battery and the maximum output capacity of the charging pile, ensuring the safety and compatibility of the charging process.

[0149] Through the embodiments of this disclosure, the state nodes and charging rates in the target path can be extracted into a sequence and a two-dimensional mapping table can be generated. This realizes the transformation from discrete paths to control rules that can be looked up in real time, which makes it easier for the battery management system to map and call the appropriate charging rate in two dimensions: state of charge and cell temperature. This reduces the online calculation burden and improves the real-time performance and execution accuracy of charging rate control.

[0150] Figure 5 This is a flowchart illustrating the construction of a charging strategy database in a charging method according to some embodiments of the present disclosure.

[0151] like Figure 5 As shown, the process of constructing and obtaining the charging strategy database may include the following steps.

[0152] Step S510: Discretize the state of charge and cell temperature of the vehicle battery to construct multiple discrete state nodes.

[0153] In this embodiment of the present disclosure, the continuous interval of the state of charge and the continuous interval of the cell temperature can be divided into several discrete values ​​according to the preset discrete step length values. Each pair of discrete state of charge values ​​and cell temperature values ​​is combined into a binary tuple to form a state node. The set of all binary tuples can constitute the node basis of the charging strategy database.

[0154] For example, if the step size of the state of charge is set to 10% and the step size of the cell temperature is set to 5℃, then the state of charge can be divided into 11 discrete values: {0%, 10%, 20%, ..., 100%}. The cell temperature can be divided into 13 discrete values: {-20℃, -15℃, -10℃, ..., 40℃} within the preset safe range of -20℃ to 40℃. The two can be combined in pairs to generate 143 discrete state nodes.

[0155] In an exemplary embodiment, the state of charge deviation distance or the cell temperature deviation distance can be a single value or different values ​​set based on a range of values. For example, for a state of charge of less than or equal to 10%, the state of charge deviation distance can be 2%; for a state of charge of greater than 10% and less than or equal to 20%, the state of charge deviation distance can be 5%; and for a state of charge of greater than 20%, the state of charge deviation distance can be 10%.

[0156] Step S520: Based on the electrical characteristic simulation model and thermal characteristic simulation model of the vehicle battery, determine two state nodes with adjacent state-of-charge distances and a directed charging path, and determine the charging rate, charging time and charging energy loss between the two state nodes as labeling information for the corresponding directed charging path.

[0157] In this embodiment, for each pair of adjacent state nodes that differ by one discrete step size in the state of charge dimension, the electrical characteristic simulation model and the thermal characteristic simulation model can be invoked. Based on the determined changes in state of charge and temperature between the two nodes, the charging rate that satisfies electrical feasibility and thermal response characteristics can be jointly solved, and the charging time and charging energy loss can be calculated accordingly. Then, it is verified whether the charging rate does not exceed the maximum allowable charging rate of the battery corresponding to the initial state node. If it does not exceed the maximum allowable charging rate of the battery, it can be considered that there is a legal directed charging path between the two nodes, and the simulated charging rate, charging time, and charging energy loss can be attached to the directed edge as annotation information. If the charging rate exceeds the maximum allowable charging rate of the battery corresponding to the corresponding state node, it can be determined that there is no directed charging path between the two state nodes.

[0158] For example, for state nodes (SOC=30%, T=25℃) and (SOC=40%, T=27℃) that are adjacent in terms of state of charge distance, if electrical and thermal characteristic simulations show that the charging process can be performed at a rate of 1.2C, take 10 minutes, and have an energy loss of 0.8kWh, and confirm that the 1.2C rate does not exceed the maximum allowable charging rate of the battery corresponding to the state node (SOC=30%, T=25℃), then it can be determined that there is a directed charging path between the two nodes, and the above three parameters can be labeled.

[0159] Step S530: Summarize the status nodes, the directed charging paths, and the annotation information of the directed charging paths to form the charging strategy database.

[0160] In this embodiment of the disclosure, all generated discrete state nodes, all determined legal directed charging paths and their labeled charging rates, charging durations, and charging energy loss information can be structurally integrated and stored in the form of a directed graph to form a complete charging strategy database that can be retrieved and called upon in subsequent paths.

[0161] Through the embodiments of this disclosure, the state of charge and cell temperature can be discretized, and the paths and labeling information between nodes can be determined based on the simulation models of electrical and thermal characteristics. This enables the scientific and reasonable construction of the charging strategy database, ensures the physical accuracy and reliability of the path data in the database, provides a reliable data foundation for online path optimization, and enables the generated charging strategy to have simulation verification support.

[0162] In some embodiments of this disclosure, the method further includes: in response to detecting that the cell temperature deviates from the temperature range corresponding to the target charging path, determining a new current state of the vehicle battery, and determining a new target charging path from the charging strategy database based on the new current state; generating a new charging rate control strategy based on the new target charging path to update the charging control of the vehicle.

[0163] In this embodiment, the temperature range corresponding to the target charging path can be the cell temperature value marked by the state node of the current stage on the target charging path and its preset allowable fluctuation range, which can be used to define the legal fluctuation boundary of the cell temperature during the charging process of that stage. During the charging process, cell temperature data can be continuously collected, and the real-time value of the cell temperature can be compared with the temperature range corresponding to the current stage on the target charging path. If the measured temperature exceeds the upper and lower temperature limits set by the path, it can be determined that a temperature deviation has occurred. Then, the current state of charge and the measured cell temperature can be read and combined into a new current state node.

[0164] For example, if the temperature range corresponding to the current stage of the target charging path is 32℃~36℃, and the cell temperature is monitored to rise to 38℃ in real time, a deviation judgment is triggered. The new current state can be determined by combining the current state of charge SOC=45% and the measured temperature of 38℃ (SOC=45%, T=38℃).

[0165] The new current state can be used as the starting node to re-filter the charging strategy database, determine the new target charging path, extract the state node and charging rate according to the new target charging path, generate a new charging rate control strategy, and replace the original charging rate control strategy with the new strategy to update the control of the vehicle charging process.

[0166] Through the embodiments of this disclosure, the state and target path can be dynamically updated when the cell temperature deviates, realizing real-time adaptive adjustment during the charging process. This can cope with model deviations caused by temperature changes during the charging process, improve the safety and environmental adaptability of the charging process, reduce the risk of temperature rise runaway, and make the charging process more stable and reliable.

[0167] Figure 6 This is a schematic diagram illustrating a framework for implementing a charging method according to some embodiments of the present disclosure.

[0168] like Figure 6 As shown, it includes a large model of user's historical charging behavior 601 and a path search module 602.

[0169] The user historical charging behavior big data model 601 can output user charging behavior habits and user's willingness to wait for charging time based on the input current charging scenario data. The user charging behavior habits can include the factor weights of the above-mentioned constraint factors (including at least one of the following: charging energy loss, charging time, and battery life degradation).

[0170] The current charging scenario data may include at least one of the following: off-vehicle charging time, charging air conditioning status, ambient temperature, initial charging SOC, charging end SOC, charging time (year, month, day), specific charging time period, waiting time after fully charging, historical single charging time, charging pile power, charging pile voltage, charging pile current, charging pile idle / busy status, charging station price information, charging station geographical location and number of charging piles, initial charging battery temperature, charging end battery temperature, single charging electricity cost, single driving distance after charging end, destination after charging end, driving interval time after charging end, and user driving mode.

[0171] The system can acquire basic path search data, and input the basic path search data, user charging behavior habits, and user's willingness to wait for charging time into the path search module 602, which is based on the battery electrothermal model.

[0172] The basic data for path search may include at least one of the following: charging station location, charging pile power, charging pile voltage, charging pile current, charging period, current initial SOC, end-of-charge SOC, battery temperature, air conditioning set temperature, air conditioning set fan speed, ambient temperature, occupants in the passenger compartment, and current temperature in the passenger compartment.

[0173] The path search module 602 can first calculate the shortest estimated charging time based on the current battery state parameters and the charging capacity of the charging pile in the path search basic data. Then, it compares the shortest estimated charging time with the desired waiting charging time. If the shortest estimated charging time is less than the desired waiting charging time, it can execute the search for the target charging path and output a charging rate control strategy generated based on the target charging path for this charging, which serves as the globally optimal charging strategy to control the vehicle to perform charging. If the shortest estimated charging time is greater than or equal to the desired waiting charging time, it can output the maximum allowed charging rate control strategy for the vehicle battery (this strategy corresponds to the shortest estimated charging time), which serves as the globally optimal charging strategy to control the vehicle to perform charging.

[0174] Figure 6 Other aspects of the embodiments can be found in the other embodiments described above.

[0175] Figure 7 This is a schematic flowchart illustrating a charging method according to some embodiments of the present disclosure.

[0176] like Figure 7 As shown, the charging method may include the following steps.

[0177] Step S701: Determine the user's desired waiting time for charging T2.

[0178] Among them, based on the user's historical charging behavior, such as the SOC range of each charge, the selected station location, the status of the charging pile, the charging time, the driving interval after charging, and the driving mode after charging, a large model can be used to learn and summarize certain patterns. When the user arrives at a certain station to charge again, the user's willingness to wait for charging this time can be estimated based on the historical charging behavior.

[0179] Step S703: Determine the shortest estimated charging time T1 for the vehicle to complete charging.

[0180] Specifically, based on the battery cell temperature, initial and final SOC (i.e., the preset target state of charge), charging air conditioning status, and current charging pile capacity during the user's current charging, the shortest estimated charging time T1 can be calculated based on the maximum capacity of the battery map (i.e., the maximum charging rate control strategy allowed by the vehicle battery).

[0181] Step S705: Determine whether the shortest estimated charging time T1 is less than the desired waiting charging time T2. If not, proceed to step S707; if yes, proceed to step S709.

[0182] Step S707: Prioritize charging time and control the vehicle to perform charging using the maximum charging rate allowed by the vehicle battery, until the target state of charge is reached and charging is completed.

[0183] If T1≥T2, it means that the user's need to use the vehicle is urgent after the current charging is completed. In this case, charging time should be given priority. Therefore, according to the current maximum battery capacity, a charging map (i.e., the maximum charging rate control strategy allowed by the vehicle battery) is output until the charging is completed.

[0184] Step S709: Perform target charging path search.

[0185] Step S711: Output the charging rate control strategy for this charging based on the target charging path.

[0186] If T1 < T2, it means that the user's need to use the vehicle is not urgent after this charging is completed. At this time, the charging speed can be appropriately reduced. Within the user's intended charging time T2, the charging cost is calculated using the charging strategy database built based on the battery electrothermal model, and a dynamic charging map (i.e. the charging rate control strategy used for this charging) is output to achieve a balance between charging energy consumption and charging time. In this way, the charging energy loss is minimized without affecting the user's vehicle use, taking into account charging efficiency, energy consumption and battery life.

[0187] Step S713: Determine if a charging temperature deviation has occurred. If yes, proceed to step S715; otherwise, proceed to step S717.

[0188] Step S715: Determine the new current state of the vehicle battery. Then, based on the new current state, re-execute the target charging path search to generate a new charging rate control strategy to update the vehicle charging control.

[0189] Step S717: Perform charging according to the current charging rate control strategy until the target state of charge is reached, and the charging is completed.

[0190] Figure 7 Other aspects of the embodiments can be found in the other embodiments described above.

[0191] It should be noted that the above figures are merely illustrative representations of the processes included in methods according to some embodiments of this disclosure, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0192] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0193] Figure 8 This is a block diagram illustrating a charging device according to some embodiments of the present disclosure. (Refer to...) Figure 8 The device includes: a duration determination unit 801, a path optimization unit 802, a strategy generation unit 803, a control unit 804, and a construction unit 805.

[0194] The duration determination unit 801 is used to determine the user's desired waiting time for charging and the shortest estimated charging time for the vehicle to complete charging in response to the vehicle connecting to the charging pile; the path optimization unit 802 is used to determine a target charging path from a pre-built charging strategy database in response to the shortest estimated charging time being less than the desired waiting time for charging; the strategy generation unit 803 is used to generate a charging rate control strategy for this charging based on the target charging path; and the control unit 804 is used to control the vehicle to perform charging based on the charging rate control strategy.

[0195] In some embodiments of this disclosure, the charging strategy database is a directed graph database, which includes state nodes and directed charging paths between state nodes; wherein, each state node corresponds to a binary pair consisting of the state of charge and the cell temperature; the directed charging path connects state nodes adjacent to each other in the direction of increasing state of charge, and each directed charging path is labeled with the corresponding charging rate, charging time and charging energy loss.

[0196] In some embodiments of this disclosure, the path optimization unit 802 is further configured to: obtain the current state of the vehicle battery and a preset target state of charge; filter candidate charging paths in the charging strategy database based on the current state, the target state of charge and the desired waiting time for charging; determine the charging cost of each candidate charging path; and determine the target charging path from the candidate charging paths based on the charging cost.

[0197] In some embodiments of this disclosure, the current state includes the current state of charge and the current cell temperature; wherein, the path optimization unit 802 is further configured to: determine a corresponding starting state node based on the current state in the charging strategy database, and determine a corresponding ending state node based on the target state of charge; among the reachable paths from the starting state node to the ending state node, select candidate charging paths whose total cumulative charging time does not exceed the desired waiting charging time; wherein each candidate charging path is composed of at least one directed charging path connected in series.

[0198] In some embodiments of this disclosure, the path optimization unit 802 is further configured to: determine the charging cost of each candidate charging path based on constraint factors and corresponding factor weights; the constraint factors include at least one of the following: charging energy loss, charging time, and battery life degradation; the factor weights are determined based on the user's historical charging behavior data and / or preset by the user.

[0199] In some embodiments of this disclosure, the strategy generation unit 803 is further configured to: extract the state nodes and charging rates sequentially passed through the target charging path to form a charging rate sequence; and generate a two-dimensional charging rate mapping table with state of charge as the first dimension and cell temperature as the second dimension based on the charging rate sequence, as the charging rate control strategy.

[0200] In some embodiments of this disclosure, the construction unit 805 is used to: discretize the state of charge and cell temperature of the vehicle battery to construct multiple discrete state nodes; based on the electrical characteristic simulation model and thermal characteristic simulation model of the vehicle battery, determine two state nodes with adjacent state of charge discrete step lengths and a directed charging path, and determine the charging rate, charging time and charging energy loss between the two state nodes as annotation information of the corresponding directed charging path; and summarize the state nodes, the directed charging path and the annotation information of the directed charging path to form the charging strategy database.

[0201] In some embodiments of this disclosure, the path optimization unit 802 is further configured to: determine a new current state of the vehicle battery in response to detecting that the cell temperature deviates from the temperature range corresponding to the target charging path; determine a new target charging path from the charging strategy database based on the new current state; the strategy generation unit 803 is further configured to: generate a new charging rate control strategy based on the new target charging path to update the charging control of the vehicle.

[0202] In some embodiments of this disclosure, the duration determination unit 801 is further configured to: input the acquired current charging scenario data into a pre-trained large model, and obtain the intended waiting charging duration through the output of the pre-trained large model; wherein the pre-trained large model is trained based on the user's historical charging behavior data; the current charging scenario data includes at least one of the following: current state of charge, current time information, current charging pile information, vehicle driving mode information; and / or, determine the intended waiting charging duration based on the duration data input by the user.

[0203] In some embodiments of this disclosure, the duration determination unit 801 is further configured to: calculate the shortest estimated charging time based on the current battery state parameters of the vehicle and the charging capacity of the charging pile, according to the maximum allowable charging rate of the vehicle battery; wherein the current battery state parameters include at least one of the following: the current initial state of charge of the vehicle battery, the preset target state of charge, the current cell temperature, and the air conditioning operation status of the vehicle.

[0204] In some embodiments of this disclosure, the control unit 804 is further configured to: control the vehicle to perform charging in response to the shortest estimated charging time being greater than or equal to the intended waiting time for charging, using a maximum charging rate control strategy allowed by the vehicle battery.

[0205] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0206] Figure 9 This is a block diagram illustrating a vehicle 900 according to some embodiments of the present disclosure. For example, vehicle 900 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicle. Vehicle 900 may be a driver-assisted vehicle, a semi-driver-assisted vehicle, or a driver-free vehicle.

[0207] Reference Figure 9 The vehicle 900 may include various subsystems, such as an infotainment system 910, a perception system 920, a decision control system 930, a drive system 940, and a computing platform 950. The vehicle 900 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and component of the vehicle 900 can be interconnected via wired or wireless means.

[0208] In some embodiments, the infotainment system 910 may include a communication system, an entertainment system, and a navigation system, etc.

[0209] The perception system 920 may include several sensors for sensing information about the environment surrounding the vehicle 900. For example, the perception system 920 may include a global positioning system (which may be GPS, BeiDou, or other positioning systems), an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0210] The decision control system 930 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0211] The drive system 940 may include components that provide powered motion to the vehicle 900. In one embodiment, the drive system 940 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0212] Some or all of the functions of the vehicle 900 are controlled by a computing platform 950. The computing platform 950 may include at least one processor 951 and a memory 952, the processor 951 being able to execute instructions 953 stored in the memory 952.

[0213] The processor 951 can be any conventional processor, such as a commercially available CPU. The processor may also include, for example, a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), a System on Chip (SOC), an Application Specific Integrated Circuit (ASIC), or a combination thereof.

[0214] The memory 952 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0215] In addition to instruction 953, memory 952 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 952 can be used by computing platform 950.

[0216] In this embodiment of the present disclosure, the processor 951 may execute instructions 953 to complete all or part of the steps of the charging method described above.

[0217] A computer-readable storage medium, when the instructions in the storage medium are executed by the processor of a mobile terminal, enables the mobile terminal to perform the charging method described above.

[0218] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps and corresponding content of the charging method embodiments described above.

[0219] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0220] It should be understood that this disclosure is not limited to the precise structures 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 disclosure is limited only by the appended claims.

Claims

1. A charging method, characterized in that, include: In response to the vehicle connecting to the charging station, determine the user's desired waiting time for charging, and determine the shortest estimated charging time for the vehicle to complete charging; In response to the fact that the shortest estimated charging time is less than the desired waiting time for charging, a target charging path is determined from a pre-built charging strategy database; A charging rate control strategy for this charging operation is generated based on the target charging path. The vehicle is controlled to perform charging based on the charging rate control strategy.

2. The method according to claim 1, characterized in that, The charging strategy database is a directed graph database, which contains state nodes and directed charging paths between state nodes. Each state node corresponds to a binary pair consisting of the state of charge and the cell temperature. The directed charging path connects adjacent state nodes in the direction of increasing state of charge. Each directed charging path is labeled with the corresponding charging rate, charging time and charging energy loss.

3. The method according to claim 2, characterized in that, Determining the target charging path from a pre-built charging strategy database includes: Obtain the current state of the vehicle battery and the preset target state of charge; Based on the current state, the target state of charge, and the desired waiting time for charging, candidate charging paths are selected from the charging strategy database. Determine the charging cost of each candidate charging path; The target charging path is determined from the candidate charging paths based on the charging cost.

4. The method according to claim 3, characterized in that, The current state includes the current state of charge and the current cell temperature; wherein, the step of filtering candidate charging paths in the charging strategy database based on the current state, the target state of charge, and the desired charging waiting time includes: In the charging strategy database, the corresponding starting state node is determined based on the current state matching, and the corresponding ending state node is determined based on the target state of charge matching. Among the reachable paths from the starting state node to the ending state node, candidate charging paths with a total cumulative charging time not exceeding the desired waiting charging time are selected; wherein each candidate charging path is composed of at least one directed charging path connected in series.

5. The method according to claim 3, characterized in that, Determining the charging cost of each candidate charging path includes: The charging cost of each candidate charging path is determined based on the constraint factors and their corresponding weights. The constraint factors include at least one of the following: charging energy loss, charging time, and battery life degradation; the factor weights are determined based on the user's historical charging behavior data and / or are preset by the user.

6. The method according to claim 2, characterized in that, The step of generating a charging rate control strategy for this charging based on the target charging path includes: Extract the state nodes and charging rates that are sequentially passed through the target charging path to form a charging rate sequence; A two-dimensional charging rate mapping table is generated based on the charging rate sequence, with the state of charge as the first dimension and the cell temperature as the second dimension, as the charging rate control strategy.

7. The method according to claim 2, characterized in that, The charging strategy database is constructed in the following way: The state of charge and cell temperature of the vehicle battery are discretized to construct multiple discrete state nodes. Based on the electrical and thermal characteristic simulation models of the vehicle battery, two state nodes with adjacent state-of-charge distances and a directed charging path are identified, and the charging rate, charging duration, and charging energy loss between the two state nodes are determined as labeling information for the corresponding directed charging path. The charging strategy database is formed by summarizing the state nodes, the directed charging paths, and the annotation information of the directed charging paths.

8. The method according to claim 1, characterized in that, The method further includes: In response to detecting that the cell temperature deviates from the temperature range corresponding to the target charging path, a new current state of the vehicle battery is determined, and a new target charging path is determined from the charging strategy database based on the new current state; A new charging rate control strategy is generated based on the new target charging path to update the charging control of the vehicle.

9. The method according to claim 1, characterized in that, Determining the user's desired charging time includes: The acquired current charging scenario data is input into a pre-trained large model, and the intended charging waiting time is obtained through the output of the pre-trained large model; wherein, the pre-trained large model is trained based on the user's historical charging behavior data; the current charging scenario data includes at least one of the following: current state of charge, current time information, current charging pile information, vehicle driving mode information; and / or, The desired charging duration is determined based on the duration data input by the user.

10. The method according to claim 1, characterized in that, Determining the shortest estimated charging time for the vehicle to complete charging includes: Based on the vehicle's current battery state parameters and the charging capacity of the charging pile, the shortest estimated charging time is calculated according to the maximum allowable charging rate of the vehicle battery. The current battery state parameters include at least one of the following: the current initial state of charge of the vehicle battery, the preset target state of charge, the current cell temperature, and the air conditioning operation status of the vehicle.

11. The method according to claim 1, characterized in that, The method further includes: In response to the shortest estimated charging time being greater than or equal to the desired charging waiting time, the vehicle is controlled to perform charging using a control strategy based on the maximum charging rate allowed by the vehicle battery.

12. A charging device, characterized in that, include: The duration determination unit is used to determine the user's desired waiting time for charging and the shortest estimated charging time for the vehicle to complete charging in response to the vehicle connecting to the charging pile. The path optimization unit is used to determine the target charging path from a pre-built charging strategy database in response to the fact that the shortest estimated charging time is less than the desired waiting time for charging. The strategy generation unit is used to generate a charging rate control strategy for this charging based on the target charging path. A control unit is used to control the vehicle to perform charging based on the charging rate control strategy.

13. The apparatus according to claim 12, characterized in that, The charging strategy database is a directed graph database, which contains state nodes and directed charging paths between state nodes. Each state node corresponds to a binary pair consisting of the state of charge and the cell temperature. The directed charging path connects adjacent state nodes in the direction of increasing state of charge. Each directed charging path is labeled with the corresponding charging rate, charging time and charging energy loss.

14. The apparatus according to claim 13, characterized in that, The path optimization unit is also used for: Obtain the current state of the vehicle battery and the preset target state of charge; Based on the current state, the target state of charge, and the desired waiting time for charging, candidate charging paths are selected from the charging strategy database. Determine the charging cost of each candidate charging path; The target charging path is determined from the candidate charging paths based on the charging cost.

15. The apparatus according to claim 12, characterized in that, The control unit is also used for: In response to the shortest estimated charging time being greater than or equal to the desired charging waiting time, the vehicle is controlled to perform charging using a control strategy based on the maximum charging rate allowed by the vehicle battery.

16. A vehicle, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the steps of the method according to any one of claims 1-11.

17. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the steps of the method according to any one of claims 1-11.

18. A computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the steps of the method according to any one of claims 1-11.

19. 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 as described in any one of claims 1-11.

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