A charging method and system for new energy vehicles

Abstract

A charging method and system for new energy vehicles, relating to the field of vehicle charging. This method is applied to a charging pile control system and includes: acquiring battery data and historical driving data of the target vehicle; the battery data including battery type, remaining capacity, calibrated electrical parameters, and actual electrical parameters; formulating a first charging curve based on the battery data; generating a vehicle driving characteristic map of the target vehicle based on the historical driving data; the vehicle driving characteristic map including the correspondence between driving paths, path driving frequencies, and driving time periods; determining the required charging time for the target vehicle based on the vehicle driving characteristic map; and adjusting the first charging curve according to the required charging time to obtain a second charging curve, so that the charging pile charges the target vehicle according to the second charging curve. Implementing the technical solution provided in this application solves the problem of charging curves being difficult to accurately match user needs.

Description

Technical Field

[0001] This application relates to the technical field of vehicle charging, specifically to a charging method and system for new energy vehicles. Background Technology

[0002] With the popularization of new energy vehicles, the charging problem has received increasing attention, and different types of batteries have very different electrochemical characteristics.

[0003] Currently, in order to meet the charging efficiency requirements of various types of batteries, some charging stations have the function of setting charging curves for different battery types. Taking ternary lithium batteries as an example, at the beginning of charging, because their nominal voltage is relatively high, the charging station will output a higher voltage to match. As the charging process progresses, when the battery voltage gradually rises and approaches full charge, the charging voltage needs to be slowly reduced to prevent overcharging. The whole process ensures both charging efficiency and charging safety.

[0004] However, the above charging methods only consider the current state of the battery and lack consideration for the user's charging needs, which makes it difficult for the charging strategy to accurately match the user's needs and thus reduces the user experience. Summary of the Invention

[0005] To address the problem that charging strategies are difficult to accurately match user needs, this application provides a charging method and system for new energy vehicles.

[0006] Firstly, this application provides a charging method for new energy vehicles, applied in a charging pile control system, the method comprising:

[0007] Obtain the battery data and historical driving data of the target vehicle. The battery data includes battery type, remaining power, calibrated electrical parameters, and actual electrical parameters.

[0008] Based on the battery data, a first charging curve is determined;

[0009] Based on the historical driving data, a vehicle driving feature map of the target vehicle is generated. The vehicle driving feature map includes the correspondence between driving path, driving frequency of the path, and driving time period.

[0010] Based on the vehicle driving characteristic map, determine the required charging time for the target vehicle;

[0011] Based on the required charging time, the first charging curve is adjusted to obtain a second charging curve, so that the charging pile charges the target vehicle according to the second charging curve.

[0012] Optionally, the step of formulating a first charging curve based on the battery data specifically involves:

[0013] Based on the battery type and the calibrated electrical parameters, a target charging model is obtained by matching from a preset charging model library;

[0014] Based on the actual electrical parameters, the target charging model is calibrated to obtain a calibrated charging model;

[0015] Based on the remaining power, the calibration charging model is run to simulate charging and obtain the first charging curve. The first charging curve consists of multiple power points, and each power point stores verification conditions and verification methods.

[0016] Optionally, the verification conditions include ideal ranges for various electrical parameter types, and the verification method is as follows:

[0017] Determine the charging stage of the first charge point, wherein the charging stage is either the early charging stage or the middle and late charging stage, and the first charge point is any one of the multiple charge points in the first charging curve.

[0018] Based on the charging stage-core monitoring electrical parameter mapping table, determine multiple core monitoring electrical parameters for the first charge point;

[0019] Based on the charging stage-verification time mapping table, determine the required verification time for multiple core monitoring electrical parameters at the first charge point;

[0020] If at least two of the multiple core monitoring electrical parameters deviate from their respective ideal ranges within the required verification time, then the first electrical energy point is determined to be an abnormal electrical energy point.

[0021] Optionally, the step of formulating the first charging curve based on the battery data further includes:

[0022] Obtain information on the battery pack of the target vehicle, the battery pack information including battery data of various batteries, wherein the battery data also includes rated charging power and SOH;

[0023] Based on the battery data of the various batteries, a reference charging curve for the various batteries is generated;

[0024] The maximum permissible charging power of the various batteries is determined based on their State of Harm (SOH) and rated charging power.

[0025] The maximum allowable charging power of various batteries is normalized to obtain the fusion coefficient of various batteries;

[0026] Based on the fusion coefficients of the various batteries, the reference charging curves of the various batteries are weighted and fused to obtain the first charging curve.

[0027] Optionally, determining the required charging time for the target vehicle based on the vehicle driving feature map specifically involves:

[0028] Obtain the vehicle location and current time of the target vehicle;

[0029] Based on the vehicle location and the current time, multiple target driving paths are obtained from the vehicle driving feature map;

[0030] Based on the path lengths of the multiple target driving paths, the charging time corresponding to the multiple target driving paths is calculated;

[0031] The charging time weights of the multiple target driving routes are determined based on the route driving frequency of the multiple target driving routes.

[0032] The required charging time for the target vehicle is obtained by calculating a weighted average of the charging time and the charging time weights for multiple target driving paths.

[0033] Optionally, adjusting the first charging curve according to the required charging time to obtain the second charging curve further includes:

[0034] The adjustment coefficient is determined based on the required charging time of the target vehicle and the required charging time of the first charging curve.

[0035] Based on the adjustment coefficient, the charging time of the first charging curve is adjusted to obtain the second charging curve.

[0036] Secondly, this application provides a charging system for new energy vehicles, wherein the system is a charging pile control system, and the system includes an acquisition module, a processing module, and a charging module, wherein:

[0037] The acquisition module is used to acquire the battery data and historical driving data of the target vehicle. The battery data includes battery type, remaining power, calibrated electrical parameters and actual electrical parameters.

[0038] The processing module is used to formulate a first charging curve based on the battery data; generate a vehicle driving feature map of the target vehicle based on the historical driving data, the vehicle driving feature map including the correspondence between driving path, path driving frequency and driving time period; and determine the required charging time of the target vehicle based on the vehicle driving feature map.

[0039] The charging module (3) is used to adjust the first charging curve according to the required charging time to obtain a second charging curve, so that the charging pile charges the target vehicle according to the second charging curve.

[0040] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0041] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0042] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0043] Because current charging curves based on battery parameters lack consideration for user charging needs, they are difficult to accurately match user requirements. Therefore, this application first generates a first charging curve based on the vehicle's current battery data. This first charging curve can meet the battery's charging needs. Then, a vehicle driving characteristic map is generated using the vehicle's historical driving data. The vehicle driving characteristic map reflects the user's driving habits and patterns. Therefore, the required charging time for the vehicle can be calculated based on the user's current driving information (vehicle location and current time). Finally, the first charging curve is adjusted according to the required charging time to meet both the battery's charging needs and the user's charging needs, thereby improving the user experience. Attached Figure Description

[0044] Figure 1 This is a schematic flowchart of a charging method for a new energy vehicle provided in an embodiment of this application.

[0045] Figure 2 This is a schematic diagram of the structure of a charging system for a new energy vehicle provided in an embodiment of this application.

[0046] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0047] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 2. Processing module; 3. Charging module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0049] Unlike gasoline-powered vehicles, new energy vehicles, powered by electricity, cannot be recharged at any time and require charging time. Therefore, the charging speed has always been a crucial research topic in the development of new energy vehicles. With the rapid development of charging technology, various fast-charging and super-charging interfaces have become increasingly common, significantly improving charging speed. Furthermore, various types of batteries, such as ternary lithium batteries and lithium iron phosphate batteries, have been developed for different applications of new energy vehicles.

[0050] To meet the charging efficiency requirements of different battery types, charging stations typically store corresponding charging curves for different battery types. When a new energy vehicle needs to be charged, the complete charging curve is determined by querying the battery type. Then, based on the remaining power and calibrated electrical parameters, the corresponding charging start point is found in the charging curve. Finally, the curve after the charging start point is extracted as the current charging curve for the vehicle.

[0051] However, the above charging methods only consider the current battery parameters and lack consideration for the user's charging needs, resulting in a reduced user experience. For example, if the user is in a hurry and still charges according to the preset charging curve, it may result in insufficient charging time, causing the user to need to recharge while driving, thus reducing the user's driving experience.

[0052] Although some new energy vehicles are equipped with multiple charging modes, allowing users to choose the charging speed according to their own needs, most of the charging modes available to users are pre-set at the factory, which still cannot meet the complex and diverse needs of users.

[0053] Therefore, in order to solve the above problems, this application provides a charging method for new energy vehicles, which is applied in a charging pile control system, such as... Figure 1 As shown, the method includes steps S101 to S105, which are as follows:

[0054] S101. Obtain the battery data and historical driving data of the target vehicle. The battery data includes battery type, remaining power, calibrated electrical parameters, and actual electrical parameters.

[0055] In the above steps, once the target vehicle's charging port is connected to the charging pile, the charging pile control system accesses the vehicle's battery management system and then reads the battery data and historical driving data stored in the system. The battery data includes battery type, remaining capacity, calibrated electrical parameters, and actual electrical parameters. The calibrated parameters consist of the battery's maximum allowable current, internal resistance, and rated voltage, while the actual electrical parameters consist of the actual current, actual internal resistance, and actual voltage. Historical driving data includes the roads the user has traveled within the past six months, the time of each trip, and the duration of each trip. In essence, the battery data reflects the battery's performance status, while the historical driving data reflects the user's driving habits and patterns.

[0056] S102. Based on the battery data, formulate the first charging curve.

[0057] In the above steps, after the charging pile control system reads the battery data, it first matches multiple charging models of the same type of battery from a preset charging model library based on the battery type of the target vehicle. Then, it selects the target charging model that matches the battery type of the target vehicle based on the calibrated electrical parameters. The target charging model can be understood as an equivalent circuit model, which simplifies the charging process by replacing complex circuits or actual devices with simple combinations of circuit components. However, the parameters of various circuit components are preset standard parameters, so it is necessary to readjust the parameters of various circuit components in the equivalent circuit model according to the actual situation to make it more suitable for the current charging scenario. In this application, the target charging model is calibrated based on the actual electrical parameters to obtain a standard charging model. The calibration method adopts error feedback calibration, specifically:

[0058] The circuit structure representing the actual electrical parameters in the target charging model is run. The running results are then compared with the actual electrical parameters to calculate the error. Based on the magnitude of the error, an optimization algorithm is used to adjust the parameters in the model to minimize the error between the running results and the actual electrical parameters. For example, in the equivalent circuit model, if there is a deviation between the battery voltage obtained by the model and the actual measured voltage, the resistance and capacitance parameters are adjusted to make the model's predicted voltage closer to the actual voltage.

[0059] Finally, the charge level of the calibration charging model is configured to the remaining charge level, and the calibration charging model is run to simulate charging, resulting in the first charging curve, which is a two-dimensional curve of charge level versus time. In addition, to prevent safety issues such as overheating and over-discharge during charging, verification conditions are stored at each charge level. These verification conditions include ideal ranges for various electrical parameters, such as voltage and current parameters. During charging, if a certain electrical parameter deviates from its corresponding ideal range, the charging and discharging power is adjusted to bring it back to the normal range.

[0060] In actual charging scenarios, the charging risks faced at different charging stages are different. In the early stage of charging, the core risks are connection abnormalities, instantaneous impacts, and abnormal initial state of the battery. Therefore, it is necessary to focus on electrical parameters such as initial voltage, starting current, and circuit continuity. In the middle and late stages of charging, as the battery enters a stable charging state, the core risks are gradual abnormal parameter changes, thermal runaway, and chemical side reactions. Therefore, it is necessary to focus on electrical parameters such as battery body temperature, voltage change rate, and current stability.

[0061] Furthermore, since the charging risks differ at different charging stages, the verification methods for these risks also differ. Specifically, the risks in the early stages of charging are mainly instantaneous, while the risks in the middle and later stages of charging are mainly gradual. Therefore, this application, in addition to setting verification conditions, sets verification methods for each charge level. Specifically, it determines the charging stage of the first charge level, which can be either the early or middle / late stages of charging, and the first charge level is any one of multiple charge levels in the first charging curve. Then, based on the charging stage-core monitoring electrical parameter mapping table, it determines multiple core monitoring electrical parameters for the first charge level. Based on the charging stage-verification time mapping table, it determines the required verification time for the multiple core monitoring electrical parameters for the first charge level. In the charging stage-verification time mapping table, the required verification time increases as the charging stage progresses. Finally, if at least two of the multiple core monitoring electrical parameters deviate from the ideal range within the required verification time, the first charge level is determined to be an abnormal charge level. In this case, the charging and discharging power is adjusted to bring the first charge level back to the normal range. Otherwise, the first charge level is determined to be a normal charge level, and no action is taken.

[0062] In one possible implementation, as the service life of new energy vehicles increases, battery performance deteriorates significantly. Most users address this issue by replacing the battery. However, because many new energy vehicles were sold in the same batch, the number of vehicles requiring battery replacement increases as their batteries reach their replacement age. This often leads to long queues for battery replacements. To ensure normal vehicle use, users often choose to combine different battery types. However, different battery types have varying charging performance, and current charging stations still use charging curves based on the standard battery type, resulting in curves that only meet the charging needs of a portion of the batteries. Therefore, when designing the initial charging curve, the situation of combined batteries must be considered to avoid the problem of a single battery dominating the charging curve. Specifically:

[0063] The process involves acquiring battery pack information for the target vehicle, including data on various batteries. This data includes battery type, remaining capacity, calibrated electrical parameters, actual electrical parameters, rated charging power, and State of Health (SOH). Based on the battery types, a baseline charging curve is generated for each battery type. This baseline charging curve represents the charging curve of the battery under standard healthy conditions. The SOH of each battery is then multiplied by its corresponding rated charging power to obtain the maximum allowable charging power. This maximum allowable charging power is then normalized to obtain a fusion coefficient. Finally, the baseline charging curves are weighted and fused based on this fusion coefficient to obtain a first charging curve. Specifically, if the battery pack includes a first battery and a second battery, the capacity values ​​of the first and second batteries at the same time point are extracted. The capacity value of the first battery at that time point is multiplied by its fusion coefficient to obtain a first capacity value. The capacity value of the second battery at that time point is multiplied by its fusion coefficient to obtain a second capacity value. The first and second capacity values ​​are then averaged to obtain the capacity value of the first charging curve at that time point.

[0064] S103. Based on historical driving data, generate a vehicle driving feature map of the target vehicle. The vehicle driving feature map includes the correspondence between driving path, driving frequency of the path, and driving time period.

[0065] In the above steps, by statistically analyzing historical driving data, including the roads the user has traveled within the past six months, the time of each trip, and the duration of each trip, multiple driving events are generated. For example, a driving event could be "departing from xx at 7:00 AM on Monday and arriving at xx at 8:00 AM." These multiple driving events are then categorized and integrated to generate a vehicle driving feature map of the target vehicle. This vehicle driving feature map includes the correspondence between driving routes, route frequency, and driving time periods. For example, the correspondence could be "from xx (starting point) to xx (destination), 20 trips, Monday morning 7:00-8:00 AM." By analyzing the vehicle driving feature map, the user's frequently used routes, as well as their driving habits and patterns such as travel time and speed, can be determined, thus providing reliable data support for adjusting the first charging curve.

[0066] In one possible implementation, due to the massive volume of historical driving data and the significant differences in historical driving data for each vehicle, the charging pile control system requires a considerable amount of time to statistically analyze the historical driving data. Therefore, to reduce the statistical analysis time, this application first extracts features from the historical driving data to generate multiple event sets, where each event set consists of driving path and driving time. Then, cluster analysis is performed on multiple event sets to obtain multiple event clusters. This application employs the DBSCAN clustering algorithm, a density-based spatial clustering method. This algorithm can identify clusters by calculating the density of local data without loading all data into memory, greatly reducing memory requirements and making it highly suitable for massive and complex datasets like historical driving data. Specifically:

[0067] First, the event set is quantized, where the travel path can be quantified as geographical coordinates and the travel time can be quantified as travel time intervals. Then, the quantized event set is constructed as vector points in a coordinate system. At this point, based on the preset neighborhood radius and minimum number of points, core points among multiple vector points are determined. Then, multiple core points that are directly and indirectly reachable by density are considered as an event cluster. It can be understood that the number of core points in an event cluster is the path travel frequency, the coordinates of the core points are the travel path, and the average travel time interval of multiple core points is the travel time interval.

[0068] Finally, based on multiple clustered event clusters, a vehicle driving feature map of the target vehicle is constructed.

[0069] S104. Determine the required charging time for the target vehicle based on the vehicle driving characteristic map.

[0070] In the above steps, the vehicle location and current time of the target vehicle are first obtained. Then, the vehicle location is matched with the driving feature map to obtain multiple driving paths. The starting points of the multiple driving paths are all coordinates near the vehicle location of the target vehicle. Then, the current time is matched with the starting time of the multiple driving paths. If the current time is earlier than the starting time of a certain driving path, and the starting date is the same as the starting date of the driving path, then the driving path is the target driving path. For example, if the driving time of a certain driving path is from 16:00 to 18:00 on Monday, then the starting time is 8:00 and the starting date is Monday. If the current time is 7:00 on Monday, then the driving path can be determined as the target driving path.

[0071] Then, the path lengths of multiple target driving routes are calculated to determine the required power consumption for each route. Based on the energy consumption and battery capacity of the new energy vehicle, the required power consumption can be converted into target power values ​​for each route. The charging time for each route can then be obtained from the first charging curve based on the remaining power of the vehicle. For example, if the remaining power is 20 and the target power value for the route is 80, and the time point corresponding to the power value of 20 is 7:00 and the time point corresponding to the power value of 80 is 10:00 on the first charging curve, then the charging time for the route is 3 hours.

[0072] Then, based on the route frequency of multiple target driving routes, the charging time weight of multiple target driving routes is determined. It can be understood that if the route frequency of a certain target driving route is high, it means that the user is likely to take that target driving route. Therefore, it is necessary to focus on ensuring that the charging time of that target driving route is met, and its charging time weight will be higher. Specifically, the method for determining the charging time weight of multiple target driving routes is as follows: the ratio of the route frequency of multiple target driving routes is normalized to obtain the charging time weight of multiple target driving routes.

[0073] Finally, the charging time of multiple target driving routes is calculated as a weighted average of the charging time weights to obtain the required charging time for the target vehicle. This process takes into account the user's current travel plans and historical driving habits, so that the final required charging time is more in line with the user's real charging needs in different scenarios, thereby improving the user experience.

[0074] S105. Adjust the first charging curve according to the required charging time to obtain the second charging curve, so that the charging pile charges the target vehicle according to the second charging curve.

[0075] In the above steps, a first adjustment coefficient is determined by calculating the ratio between the required charging time of the target vehicle and the required charging time of the first charging curve. Then, the time axis of the first charging curve is scaled according to the first adjustment coefficient to obtain a second charging curve. Furthermore, the first adjustment coefficient is used to scale multiple electrical parameters corresponding to each charge point in the first charging curve. Specifically, each electrical parameter is multiplied by the first adjustment coefficient to obtain adjustment values ​​for multiple electrical parameters. Then, it is determined whether the adjustment values ​​of multiple electrical parameters are within their respective allowable ranges. For electrical parameters within the allowable range, their adjustment values ​​are used as target values; for electrical parameters outside the allowable range, the upper limit of their allowable range is used as the target value. This ensures that the required charging time of the second charging curve meets the required charging time of the target vehicle while avoiding parameter mismatch issues. Finally, the charging pile control system controls the charging pile to charge the target vehicle according to the second charging curve, thereby meeting the user's travel needs and improving the user's charging experience.

[0076] Reference Figure 2 This application also provides a charging system for new energy vehicles. The system is a charging pile control system, which includes an acquisition module 1, a processing module 2, and a charging module 3, wherein:

[0077] Module 1 is used to acquire the battery data and historical driving data of the target vehicle. The battery data includes battery type, remaining power, calibrated electrical parameters and actual electrical parameters.

[0078] Processing module 2 is used to formulate a first charging curve based on battery data; generate a vehicle driving feature map of the target vehicle based on historical driving data, the vehicle driving feature map including the correspondence between driving path, driving frequency of path and driving time period; and determine the required charging time of the target vehicle based on the vehicle driving feature map.

[0079] The charging module 3 is used to adjust the first charging curve according to the required charging time to obtain the second charging curve, so that the charging pile charges the target vehicle according to the second charging curve.

[0080] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0081] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0082] The communication bus 302 is used to enable communication between these components.

[0083] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0084] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0085] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0086] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a charging method for new energy vehicles.

[0087] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a charging method of a new energy vehicle. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0088] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0089] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0090] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0092] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0093] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0094] 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 described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A charging method for a new energy vehicle, characterized in that, The method, applied in a charging pile control system, includes: Obtain the battery data and historical driving data of the target vehicle. The battery data includes battery type, remaining power, calibrated electrical parameters, and actual electrical parameters. Based on the battery data, a first charging curve is determined, which is as follows: Based on the battery type and the calibrated electrical parameters, a target charging model is obtained by matching from a preset charging model library; Based on the actual electrical parameters, the target charging model is calibrated to obtain a calibrated charging model; Based on the remaining power, the calibration charging model is run to simulate charging and the first charging curve is obtained. The first charging curve is composed of multiple power points, and each power point stores verification conditions and verification methods. Based on the historical driving data, a vehicle driving feature map of the target vehicle is generated. The vehicle driving feature map includes the correspondence between driving path, driving frequency of the path, and driving time period. Based on the vehicle driving characteristic map, the required charging time for the target vehicle is determined, specifically as follows: Obtain the vehicle location and current time of the target vehicle; Based on the vehicle location and the current time, multiple target driving paths are obtained from the vehicle driving feature map; Based on the path lengths of the multiple target driving paths, the charging time corresponding to the multiple target driving paths is calculated; The charging time weights of the multiple target driving routes are determined based on the route driving frequency of the multiple target driving routes. The required charging time for the target vehicle is obtained by calculating the weighted average of the charging time and the charging time weights for multiple target driving paths. Based on the required charging time, the first charging curve is adjusted to obtain a second charging curve, so that the charging pile charges the target vehicle according to the second charging curve.

2. The method according to claim 1, characterized in that, The verification conditions include ideal ranges for various electrical parameter types, and the verification method is as follows: Determine the charging stage of the first charge point, wherein the charging stage is either the early charging stage or the middle and late charging stage, and the first charge point is any one of the multiple charge points in the first charging curve. Based on the charging stage-core monitoring electrical parameter mapping table, determine multiple core monitoring electrical parameters for the first charge point; Based on the charging stage-verification time mapping table, determine the required verification time for multiple core monitoring electrical parameters at the first charge point; If at least two of the core monitoring electrical parameters deviate from their respective ideal ranges within the required verification time, then the first electrical energy point is determined to be an abnormal electrical energy point.

3. The method according to claim 1, characterized in that, The step of formulating the first charging curve based on the battery data further includes: Obtain information on the battery pack of the target vehicle, the battery pack information including battery data of various batteries, wherein the battery data also includes rated charging power and SOH; Based on the battery data of the various batteries, a reference charging curve for the various batteries is generated; The maximum permissible charging power of the various batteries is determined based on their State of Harm (SOH) and rated charging power. The maximum allowable charging power of the various batteries is normalized to obtain the fusion coefficient of the various batteries; Based on the fusion coefficients of the various batteries, the reference charging curves of the various batteries are weighted and fused to obtain the first charging curve.

4. The method according to claim 1, characterized in that, The step of adjusting the first charging curve according to the required charging time to obtain the second charging curve further includes: The adjustment coefficient is determined based on the required charging time of the target vehicle and the required charging time of the first charging curve. Based on the adjustment coefficient, the charging time of the first charging curve is adjusted to obtain the second charging curve.

5. A charging system for a new energy vehicle, the system being used to execute a charging method for a new energy vehicle as described in any one of claims 1 to 4, characterized in that, The system is a charging pile control system, which includes an acquisition module (1), a processing module (2), and a charging module (3), wherein: The acquisition module (1) is used to acquire the battery data and historical driving data of the target vehicle. The battery data includes battery type, remaining power, calibrated electrical parameters and actual electrical parameters. The processing module (2) is used to formulate a first charging curve based on the battery data; generate a vehicle driving feature map of the target vehicle based on the historical driving data, the vehicle driving feature map including the correspondence between driving path, driving frequency of path and driving time period; and determine the required charging time of the target vehicle based on the vehicle driving feature map. The charging module (3) is used to adjust the first charging curve according to the required charging time to obtain a second charging curve, so that the charging pile charges the target vehicle according to the second charging curve.

6. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 4.

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