Charging query method, device, system, storage medium and electronic equipment

Abstract

The present disclosure relates to a charging query method, device, system, storage medium and electronic equipment, and relates to the technical field of data processing. The method comprises: obtaining a charging station list meeting a query condition according to a query range parameter carried in a query request; determining the current available power of each charging station in the charging station list, and obtaining the historical charging demand information of a vehicle to be charged according to the vehicle information of the vehicle to be charged carried in the query request; based on the current SOC value of the vehicle to be charged contained in the vehicle information, and in combination with the current available power of each charging station and the historical charging demand information of the vehicle to be charged, performing matching analysis to obtain the estimated charging time corresponding to each charging station respectively for the vehicle to be charged; and then sending a query result to a client according to the charging station list and the estimated charging time corresponding to each charging station in the list respectively. The technical scheme of the present disclosure can effectively improve the service experience of users.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, specifically to a charging query method, apparatus, system, storage medium, and electronic device. Background Technology

[0002] With the rapid growth of the new energy vehicle market, the demand for charging infrastructure is also increasing. Currently, in related technologies, electric vehicle owners can search for charging stations in their vicinity based on their location. The returned search results will indicate the distance between the charging station and the electric vehicle owner's location, and the electric vehicle owner can then choose to go to a charging station to charge their electric vehicle based on this distance.

[0003] However, the types of charging equipment at different charging stations are not entirely the same, and these devices vary significantly in key indicators such as power output and charging speed. If electric vehicle owners choose charging stations solely based on distance, they may end up selecting stations with low charging efficiency, significantly extending charging time and negatively impacting their service experience, causing them to waste excessive time and energy during the charging process. Summary of the Invention

[0004] In view of this, the present disclosure provides a charging query method, device, system, storage medium and electronic device, the main purpose of which is to improve the technical problem in the current related technology where the charging query results only mark the distance to each charging station, but do not mark the estimated charging time. This makes it easy for users to select charging stations with low charging efficiency, which in turn leads to excessive charging time and affects the charging efficiency of the vehicle.

[0005] Firstly, this disclosure provides a charging query method, which can be applied to server-side execution, the method comprising:

[0006] Receive a query request sent by the client, the query request carrying the query range parameters of the charging station and the vehicle information of the vehicle to be charged;

[0007] A list of charging stations that meet the query conditions is obtained based on the query range parameters;

[0008] Determine the current available power of each charging station in the charging station list, and obtain the historical charging demand information of the vehicle to be charged based on the vehicle information;

[0009] Based on the current State of Charge (SOC) value of the vehicle to be charged contained in the vehicle information, and combined with the current available power of each charging station and the historical charging demand information of the vehicle to be charged, a matching analysis is performed to obtain the estimated charging time of the vehicle to be charged for each charging station.

[0010] The query result is determined based on the list of charging stations and the estimated charging time for each charging station, and the query result is sent to the client.

[0011] Secondly, this disclosure provides a charging query method that can be applied to the client side for execution, the method comprising:

[0012] A query request is sent to the server. The query request carries the query range parameters of charging stations and the vehicle information of the vehicle to be charged. The query range parameters are used to determine the list of charging stations that meet the query conditions. The vehicle information is used to determine the historical charging demand information of the vehicle to be charged. The current SOC value of the vehicle to be charged, which is included in the vehicle information, is used to match and analyze the current available power information of each charging station in the charging station list with the historical charging demand information of the vehicle to be charged to obtain the estimated charging time of the vehicle to be charged for each charging station.

[0013] The system receives and outputs the query results sent by the server, which are determined based on the list of charging stations and the estimated charging time for each charging station.

[0014] Thirdly, this disclosure provides a charging query device that can be applied to a server, the device comprising:

[0015] The receiving module is configured to receive a query request sent by a client, the query request carrying the query range parameters of the charging station and the vehicle information of the vehicle to be charged.

[0016] The acquisition module is configured to acquire a list of charging stations that meet the query conditions based on the query range parameters;

[0017] The determination module is configured to determine the current available power of each charging station in the charging station list, and to obtain the historical charging demand information of the vehicle to be charged based on the vehicle information.

[0018] The analysis module is configured to perform a matching analysis based on the current SOC value of the vehicle to be charged contained in the vehicle information, and in combination with the current available power of each charging station and the historical charging demand information of the vehicle to be charged, to obtain the estimated charging time of the vehicle to be charged for each charging station.

[0019] The sending module is configured to determine the query result based on the list of charging stations and the estimated charging time corresponding to each charging station, and send the query result to the client.

[0020] Fourthly, this disclosure provides a charging query device that can be applied to a client, the device comprising:

[0021] The sending module is configured to send a query request to the server. The query request carries a query range parameter for charging stations and vehicle information of the vehicle to be charged. The query range parameter is used to determine a list of charging stations that meet the query conditions. The vehicle information is used to determine the historical charging demand information of the vehicle to be charged. The current SOC value of the vehicle to be charged, which is included in the vehicle information, is used to perform matching analysis with the currently available power information of each charging station in the charging station list and the historical charging demand information of the vehicle to be charged to obtain the estimated charging time of the vehicle to be charged for each charging station.

[0022] The receiving module is configured to receive and output the query results sent by the server, the query results being determined based on the list of charging stations and the estimated charging time corresponding to each charging station.

[0023] Fifthly, this disclosure provides a charging query system, comprising: a server and a client, wherein the server is configured to implement the method described in the first aspect, and the client is configured to implement the method described in the second aspect.

[0024] In a sixth aspect, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the charging query method described in the first or second aspect.

[0025] In a seventh aspect, this disclosure provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the charging query method described in the first or second aspect.

[0026] Eighthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the charging query method described in the first or second aspect.

[0027] By employing the above technical solution, this disclosure provides a charging query method, device, system, storage medium, and electronic device. Compared with existing related technologies, this disclosure can determine the charging query result based on the estimated charging time corresponding to each charging station. Specifically, it first receives a query request sent by a client, which carries query range parameters for charging stations and vehicle information of the vehicle to be charged. Then, it obtains a list of charging stations that meet the query conditions based on the query range parameters. It also determines the current available power of each charging station in the list and obtains the historical charging demand information of the vehicle to be charged based on the vehicle information. Then, based on the current SOC value of the vehicle to be charged contained in the vehicle information, and combined with the current available power of each charging station and the historical charging demand information of the vehicle to be charged, it performs a matching analysis to obtain the estimated charging time corresponding to each charging station for the vehicle to be charged. Finally, it determines the query result based on the list of charging stations and the estimated charging time corresponding to each charging station in the list, and sends the query result to the client. By applying the technical solution disclosed herein, since the query results are determined based on the estimated charging time corresponding to the charging station, users can choose a charging station with higher charging efficiency according to their actual needs, saving the time spent in the charging process, improving vehicle charging efficiency, and thus effectively enhancing the user's service experience.

[0028] The above description is merely an overview of the technical solution disclosed herein. In order to better understand the technical means of this disclosure and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this disclosure more apparent and understandable, specific embodiments of this disclosure are described below. Attached Figure Description

[0029] 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.

[0030] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A schematic diagram of the structure of a charging query system provided in an embodiment of this disclosure is shown;

[0032] Figure 2 A flowchart illustrating a charging query method provided in an embodiment of this disclosure is shown;

[0033] Figure 3 A flowchart illustrating a charging query method provided in an embodiment of this disclosure is shown;

[0034] Figure 4 A schematic diagram illustrating an example provided by an embodiment of this disclosure is shown;

[0035] Figure 5 A schematic diagram illustrating an example provided by an embodiment of this disclosure is shown;

[0036] Figure 6 A schematic diagram illustrating an example provided by an embodiment of this disclosure is shown;

[0037] Figure 7 A schematic diagram illustrating an example provided by an embodiment of this disclosure is shown;

[0038] Figure 8 A flowchart illustrating a charging query method provided in an embodiment of this disclosure is shown;

[0039] Figure 9 A schematic diagram of the structure of a charging query device provided in an embodiment of this disclosure is shown.

[0040] Figure 10 A schematic diagram of the structure of a charging query device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0041] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0042] To address the technical problem in current related technologies where charging query results only indicate the distance to each charging station but not the estimated charging time, which makes it easy for users to select charging stations with low charging efficiency, leading to excessive charging time and affecting vehicle charging efficiency, this disclosure provides a charging query system, such as... Figure 1 As shown, the system includes a server 11 and a client 12.

[0043] Communication connections are established between the server 11 and different clients 12. In some embodiments, the server 11 may be a server platform responsible for managing and coordinating various charging station systems, which can be used to provide query results of charging stations to the clients 12. In some examples, the server 11 may adopt cloud computing technology, utilizing distributed systems and microservice architecture to ensure high availability and scalability. This can better cope with large-scale user access and high-concurrency scenarios, ensuring the stable operation of the system.

[0044] In some embodiments, client 12 can provide users with a charging station query service. It can be software or hardware that connects to server 11 via a network and requests corresponding resources and services from server 11. In some examples, client 12 can be a mobile client, in-vehicle client, tablet client, etc.

[0045] For example, when a user's vehicle needs charging, the client 12 can send a query request to the server 11 to query charging stations. The server 11 returns query results based on the query request, which are marked with the estimated charging time for each charging station. The client 12 can output and display the query results marked with the estimated charging time for each charging station.

[0046] By applying the technical solutions of the embodiments of this disclosure, users can intuitively understand the estimated charging time corresponding to each charging station, and choose a charging station with higher charging efficiency according to their actual needs, saving the time spent in the charging process, improving vehicle charging efficiency, and thus effectively enhancing the user's service experience.

[0047] Furthermore, to illustrate the specific execution process of the aforementioned charging query system, Figure 2 A schematic diagram of a charging query method according to an embodiment of the present disclosure is shown. This method is applied to the aforementioned charging query system, such as... Figure 2 As shown, the following steps may be included:

[0048] Step 201: The client sends a query request to the server.

[0049] In some embodiments, the client may send a query request to the server according to the instructions entered by the user, or send a query request to the server when a preset event is met, such as when the vehicle's battery level is less than a preset threshold, or when the current time is a preset target time, or when the vehicle enters a preset target location.

[0050] A query request can be used to request the server to search for charging stations. The query request can include the query range parameters for charging stations and vehicle information for the vehicle to be charged. The query range parameters are used to determine the query criteria, i.e., to search according to these criteria and find charging stations that meet the requirements. The query range parameters can include the query location (such as the client's current location or a user-specified location), the distance range from the query location (such as searching within 500 meters or 1000 meters of the current location), and the type of charging equipment (such as DC fast charging piles, AC slow charging piles, integrated charging stations, split charging stations, etc.).

[0051] The vehicle information of the vehicle to be charged can be pre-entered in the client. For example, the vehicle information can be determined based on the information entered by the user or the vehicle registration certificate uploaded by the user. Specifically, it may include the vehicle identification number (VIN), the current SOC value, and the vehicle model information.

[0052] Step 202: The server receives the query request sent by the client and sends the query results corresponding to the query request to the client.

[0053] The server can retrieve a list of charging stations that meet the query criteria based on the query range parameters. For example, it can search for charging stations within an 800-meter radius of the query location and generate a list of the found charging stations. After obtaining this list, the server can determine the current usable power of each charging station. For instance, real-time data on the usage of each charging station can be uploaded, and the server can retrieve this data. This data can be used to determine the current usage of each charging gun in these charging stations, and combined with the equipment information of these charging devices, the server can analyze the current usable power of each charging station in the list.

[0054] The server can obtain the historical charging demand information of the vehicle to be charged based on the vehicle information. This historical charging demand information may include the vehicle's historical charging requirements, such as required voltage, required current, and required power. This embodiment of the disclosure can pre-record the historical charging demand information corresponding to different vehicles, and subsequently query the historical charging demand information of the vehicle to be charged using the VIN code.

[0055] In some embodiments, the server can perform a matching analysis based on the current SOC value of the vehicle to be charged, which is included in the vehicle information, and the current available power of each charging station in the list, as well as the historical charging demand information of the vehicle to be charged, to obtain the estimated charging time for the vehicle to be charged at each charging station. For example, the server can estimate the amount of electricity required to charge the vehicle to 100% based on its current SOC value, and perform a matching analysis based on the current available power of each charging station in the list and the vehicle's historical charging power demand information to estimate the charging time for the vehicle to go to these charging stations.

[0056] The server determines the query result based on the list of charging stations and the estimated charging time for each charging station in the list, and sends the query result to the client.

[0057] Step 203: The client receives the query results sent by the server and outputs the query results.

[0058] In some embodiments, the query results may be a list of charging stations that meet the query criteria. The estimated charging time for each charging station may be marked in the list. The estimated charging time may be actively displayed in the list, or it may be displayed when the user clicks to enter the details page of the charging station. In this way, the user can intuitively understand the estimated charging time for each charging station and choose a charging station with higher charging efficiency according to their actual needs.

[0059] In some examples, the ability to sort charging stations by their estimated charging duration can be provided.

[0060] In some examples, the server can send the charging stations with the shortest estimated charging time in the list, or charging stations with charging time less than a certain threshold, as query results to the client for recommendation or priority display.

[0061] Compared with existing related technologies, the embodiments disclosed herein can help users choose charging stations with higher charging efficiency, save time spent in the charging process, improve vehicle charging efficiency, and thus effectively enhance the user's service experience.

[0062] Furthermore, to illustrate the specific execution process on the server side, Figure 3 A flowchart illustrating a charging query method according to an embodiment of this disclosure is shown. When executed on the server side, it may include the following steps.

[0063] Step 301: The server receives the query request sent by the client.

[0064] For example, such as Figure 4 As shown, the embodiments of this disclosure can be divided into three stages: the stage of analyzing the battery demand of all vehicles on the platform, the stage of user charging prediction and matching, and the stage of outputting matching site solutions.

[0065] In the platform's full-vehicle battery demand analysis phase, relevant information for all vehicles on the platform is first collected, including but not limited to vehicle type, battery capacity, charging current requirement, charging voltage requirement, charging capacity, and charging SOC. This data is then comprehensively analyzed to understand the overall charging demand across the platform. This step lays the foundation for subsequent personalized services, ensuring comprehensive coverage of various potential user needs. In the user charging prediction and matching phase, based on the data obtained in the full-vehicle battery demand analysis phase, the calculation delves deeper into the individual user level, estimating each user's charging needs and outputting core prediction indicators, such as the estimated charging time at charging stations. This phase corresponds to the server-side execution of steps 301 to 304. In the charging station matching output phase, the query results for charging stations are determined based on the relevant core indicators calculated in the user charging prediction and matching phase and provided to the user for selection. This phase corresponds to the server-side execution of step 305.

[0066] To illustrate the implementation process of analyzing the battery demand of all vehicles on the platform, in some embodiments, the server can pre-collect charging process data, vehicle battery data, and charging bill data for different vehicles; then, based on the charging process data, vehicle battery data, and charging bill data, it records the average power demand for each vehicle in each SOC range during historical charging, as well as the battery degradation rate for each vehicle.

[0067] Among them, charging process data refers to the real-time charging status data of the vehicle and the charging pile when the charging pile is charging the vehicle. It mainly includes information such as charging amount, meter reading, vehicle battery SOC, vehicle required voltage and current, and pile output voltage and current.

[0068] Vehicle battery data refers to the basic information about the vehicle battery reported by the vehicle battery management system (BMS) when the charging station is charging the vehicle. This information is used by the charging station to adjust the output according to the vehicle battery requirements. The data mainly includes the battery's rated total capacity, maximum allowable voltage, current and temperature, vehicle VIN, and the vehicle battery's current SOC.

[0069] Charging bill data refers to the overall charging situation reported by the charging station when the vehicle is fully charged. This data mainly includes information such as the total amount of electricity charged, the start and end times of charging, the start and end SOC, and the reason for ending the charging process.

[0070] In some examples, the server records the average power demand for different vehicles in various SOC intervals during historical charging processes, as well as the battery degradation rate for different vehicles, based on charging process data, vehicle battery data, and charging bill data. Specifically, this may include: first, filtering the charging process data, vehicle battery data, and charging bill data; then merging the filtered charging process data, vehicle battery data, and charging bill data to obtain a dataset, wherein the dataset includes at least one or more of the following: vehicle identification number, SOC value at the start of charging, SOC value at the end of charging, charging duration, charging amount, vehicle battery rated total capacity, and real-time status data during charging; and then, based on the dataset, calculating and recording the average power demand for different vehicles in various SOC intervals during historical charging processes, as well as the battery degradation rate for different vehicles.

[0071] For example, such as Figure 5 The diagram shows the flowchart for the platform's full vehicle battery demand analysis phase. First, all charging pile protocol log messages are collected. Based on this, three core instruction data are extracted: charging process data, vehicle battery data, and charging bill data. For charging bill data and vehicle battery data, each charging order has only one record. However, for charging process data, the charging pile reports the current charging status of the vehicle in real-time at fixed intervals (generally 30 seconds) during the charging period; therefore, each charging order has multiple records.

[0072] Based on the extracted three core instruction data, further filtering is performed on the charging bill data. The filtering rules are: charging capacity (e) > 10 kWh, initial SOC value <= 15%, final SOC value >= 95%, and charging duration (t) > 10 minutes. This filters out charging bill data corresponding to charging orders that meet these criteria. Simultaneously, the number of status data entries (n) in each charging order is calculated at 30-second intervals, requiring n >= t * 2 * 0.75, meaning the actual number of reports must account for more than 75% of the theoretically reported number. Furthermore, the three core instruction data are merged with vehicle battery data, using the vehicle's VIN as the unique identification code, to obtain the basic dataset L0 = [v, s1, s2, t, e, w, T = [{nv, nc, ov, oc, soc, tt}]]. If a vehicle has multiple charging order records on the same day, the basic dataset L0 is obtained by selecting the three core instruction data corresponding to the most recently generated charging order based on the order time.

[0073] In the basic dataset L0, v represents the vehicle's unique identification code (VIN), s1 represents the SOC value at the start of charging, s2 represents the SOC value at the end of charging, t represents the charging duration, e represents the charging amount, w represents the vehicle's rated total battery capacity, and T represents the real-time status data reported during charging (multiple records exist). Here, nc represents the charging vehicle's required current, nv represents the charging vehicle's required voltage, ov represents the charging pile's output voltage, oc represents the charging pile's output current, soc represents the vehicle's real-time battery SOC value, and tt represents the timestamp. Based on the basic dataset L0, the server can calculate the average power demand for different vehicles across various SOC intervals during historical charging processes, as well as the battery degradation rate for different vehicles.

[0074] In some examples, the server calculates the average power demand for different vehicles in each SOC range during historical charging, as well as the battery degradation rate for different vehicles, based on the dataset. Specifically, this may include: using the SOC value at the start of charging, the SOC value at the end of charging, the charging amount, and the rated total capacity of the vehicle's battery in the dataset as parameters to calculate the battery degradation rate for the vehicle; and using the real-time status data of the vehicle in the dataset as parameters to calculate the average power demand for the vehicle in each SOC range during historical charging, wherein the real-time status data includes at least one or more of the following: the real-time SOC value of the vehicle's battery, the vehicle's required voltage, the vehicle's required current, the charging device's output voltage, and the charging device's output current.

[0075] For example, such as Figure 5 As shown, after data cleaning and validation, the battery degradation rate (SOH) is calculated based on the base dataset L0. Specifically, based on the SOC values ​​and charging capacity at the start and end of the actual charging order, the amount of charge required for a complete charge (i.e., charging from SOC 0 to 100%) can be calculated as (e0) = e / (s2-s1) / 100%. Therefore, the battery degradation rate SOH = (e0 / w)*100%, which is the ratio of the current actual total chargeable capacity to the battery's rated total capacity. The symbols in the formula can be found in the symbol explanation section above and will not be repeated here.

[0076] This embodiment of the disclosure can also calculate the average power demand of the vehicle (P1 to P4) within each SOC interval. The SOC interval can be multiple SOC intervals divided within the range of SOC from 0 to 100%, such as the 0-20% SOC interval, the 20%-80% SOC interval, the 80%-90% SOC interval, and the 90%-100% SOC interval. It should be noted that, depending on the performance of different vehicles during charging, the vehicle's charging power demand does not remain consistently near a fixed value. During charging, the vehicle's charging power demand changes in a curve. This embodiment of the disclosure uses this division method based on the changes in the vehicle's charging power demand; that is, by analyzing the curve changes, it is divided into these four SOC intervals. Within the corresponding SOC interval, the change in the vehicle's charging power demand is relatively small. The purpose is to improve the accuracy of the estimated charging time calculation during the user charging prediction and matching stage.

[0077] Based on the basic dataset L0, this embodiment acquires real-time status data T during the charging process for each vehicle. According to the four SOC intervals (0–20%, 20%–80%, 80%–90%, and 90%–100%), the average power demand of the vehicle within each corresponding SOC interval is calculated using the following formula:

[0078]

[0079] Where P represents the average power demand, n represents the number of data points in the corresponding SOC interval, nc represents the demand current of the charging vehicle, and nv represents the demand voltage of the charging vehicle.

[0080] The above calculations yield the average power demand P1 for each vehicle in the 0-20% range, P2 in the 20%-80% range, P3 in the 80%-90% range, and P4 in the 90%-100% range. Combining P1 to P4 with the vehicle's corresponding State of Charge (SOH) results in the vehicle demand model data table M, which will be used in the subsequent user charging prediction and matching phase.

[0081] Step 302: The server obtains a list of charging stations that meet the query conditions based on the query range parameter of the charging stations carried in the query request.

[0082] For example, retrieve the latitude and longitude information of the user's location from the query range parameter, and match a list of charging stations within 2000 meters.

[0083] Step 303: The server determines the current available power of each charging station in the charging station list, and obtains the historical charging demand information of the vehicles to be charged based on the vehicle information of the vehicles to be charged carried in the query request.

[0084] In some embodiments, taking one of the charging stations as an example, the process of the server determining the current usable power of the charging station may specifically include: firstly obtaining a list of first charging guns that are currently idle and available at the charging station; then calculating the current maximum allocable output power of each first charging gun in the list of first charging guns; and then obtaining the maximum value from the current maximum allocable output power of each first charging gun as the current usable power of the charging station.

[0085] In some examples, calculating the current maximum allocable output power of each first charging gun in the first charging gun list may specifically include: first, obtaining a second charging gun list, where the second charging guns in the second charging gun list are the charging guns currently charging under the same charging device as the first charging guns; then calculating the current output power of each second charging gun in the second charging gun list; then, based on the current output power of each second charging gun and the rated power corresponding to the charging device, calculating the current usable power of the charging device; and finally, obtaining the minimum value between the current usable power of the charging device and the maximum output power of the first charging gun as the current maximum allocable output power of the first charging gun.

[0086] In some examples, calculating the current output power of each second charging gun in the second charging gun list may include: first, obtaining the charging start time corresponding to the second charging gun; then, determining the charging start time based on the charging start time; obtaining the deviation coefficient corresponding to the charging start time; and then using the deviation coefficient, the current output voltage and output current of the second charging gun as parameters to calculate the current output power of the second charging gun.

[0087] For example, query the list of currently available charging guns at the charging station, i.e., the list of available charging guns (the first list of charging guns), and iterate through the list of available charging guns to calculate the maximum allocable output power P0 for the corresponding available charging gun (the first charging gun). The calculation method is as follows:

[0088] The maximum output power P of the idle gun is obtained from the idle gun search device information table. maxThe system also retrieves a list of all charging guns currently charging under the same device (the second charging gun list). Based on this, it obtains the real-time output current (oc), output voltage (ov), and order start time (st) of the corresponding gun (second charging gun). The corresponding real-time power op = oc * ov * deviation coefficient is calculated. According to the actual situation, the power is in the rising phase in the first 90 seconds, and the power will gradually increase. After 90 seconds, the power tends to stabilize. Therefore, the deviation coefficient is matched according to the order start time. If the order start time exceeds 90 seconds, the deviation coefficient can be set to 1.1. If the order start time is within 90 seconds, the deviation coefficient can be set to 2.

[0089] Obtain the rated power EP of the charging device, and calculate the theoretical usable power of the current charging device using the following formula:

[0090]

[0091] In the formula, n represents the number of second charging guns, and P free This indicates the theoretically usable power of the charging device, that is, the current usable power of the charging device.

[0092] Based on the calculations above, the theoretical maximum allocable output power P0 for the corresponding idle gun (first charging gun) is calculated as: P0 = min(P max P free ), which is the maximum allocatable output power of the first charging gun at present.

[0093] Based on the above, aggregate the data according to charging stations, and find the maximum value of all idle guns P0 under each charging station, which is the theoretically maximum output power SP that the charging station can allocate. max .

[0094] In some embodiments, the server obtains the historical charging demand information of the vehicle to be charged based on the vehicle information of the vehicle to be charged, including: obtaining the vehicle identification code, such as the VIN code, of the vehicle to be charged from the vehicle information of the vehicle to be charged; then, based on the VIN code, querying the average demand power corresponding to each SOC range of the vehicle to be charged during the historical charging process, as well as the battery degradation degree corresponding to the vehicle to be charged, such as using the VIN code to query the vehicle demand model data table M corresponding to the vehicle, and then querying the P1~P4 and SOH corresponding to the vehicle to be charged from the data table M; and then determining the historical charging demand information of the vehicle to be charged based on the queried average demand power (P1~P4) and battery degradation degree (SOH).

[0095] In some examples, if the average power demand (P1-P4) and battery degradation (SOH) of the vehicle to be charged are not found based on the vehicle identification number, then the average power demand and battery degradation of similar vehicles are queried based on the vehicle information of the vehicle to be charged. For example, the average power demand (P1-P4) and battery degradation (SOH) of similar vehicles can be queried based on the vehicle model information, vehicle registration date, vehicle manufacturer, battery model, etc. Furthermore, if information such as the vehicle's production date and mileage can be obtained, the average power demand (P1-P4) and battery degradation (SOH) of even more similar vehicles can be queried. The average power demand (P1-P4) and battery degradation (SOH) of similar vehicles found are then identified as the historical charging demand information of the vehicle to be charged.

[0096] As an example, based on the vehicle information of the vehicle to be charged, the average power demand and battery degradation of similar vehicles can be queried. Specifically, this may include: obtaining the vehicle model information of the vehicle to be charged from the vehicle information; then obtaining target vehicles with the same vehicle model information and corresponding records of average power demand (P1-P4) and battery degradation (SOH); and accordingly, determining the average power demand (P1-P4) and battery degradation (SOH) of the target vehicle as the historical charging demand information of the vehicle to be charged. In this way, even if the corresponding P1-P4 and SOH cannot be found based on the vehicle identification number of the vehicle to be charged, the corresponding P1-P4 and SOH of similar vehicles can be found through the vehicle model information, thereby determining the historical charging demand information of the vehicle to be charged.

[0097] In some embodiments, during the full-vehicle battery demand analysis phase of the platform, charging process data, vehicle battery data, and charging bill data for different vehicles can be updated periodically. Based on these updated core instruction data, the vehicle demand model data table M corresponding to each vehicle is updated to improve the accuracy of estimated charging time calculations during the subsequent user charging prediction and matching phase. Specifically, the server can calculate and record the average power demand for each vehicle in different SOC intervals during historical charging processes, as well as the battery degradation rate for each vehicle, based on the latest collected charging process data, vehicle battery data, and charging bill data for different vehicles at preset time intervals. Correspondingly, the server queries the average power demand for the vehicle to be charged in different SOC intervals during historical charging processes, as well as the battery degradation rate for the vehicle to be charged, based on the vehicle identification code. Specifically, this may include: using the vehicle identification code of the vehicle to be charged, querying the average power demand (P1-P4) and battery degradation rate (SOH) for the vehicle to be charged from the latest recorded average power demand (P1-P4) and battery degradation rate (SOH) for different vehicles. In this way, the average power demand (P1 to P4) and battery degradation (SOH) of the vehicle to be charged can be obtained using the latest data, which can improve the accuracy of the estimated charging time calculation.

[0098] Step 304: The server performs a matching analysis based on the current SOC value of the vehicle to be charged contained in the vehicle information, and in combination with the current available power of each charging station and the historical charging demand information of the vehicle to be charged, to obtain the estimated charging time of the vehicle to be charged for each charging station.

[0099] Based on the retrieved P1 to P4 and SOH of the vehicle to be charged, in some embodiments, step 304 may specifically include: the server determining at least one SOC interval experienced during the process of charging from the current SOC value of the vehicle to 100%; then, based on the corresponding battery degradation (SOH) and the average power demand corresponding to each of the at least one SOC interval, and in conjunction with the current available power (SP) of the charging station... max A matching analysis is performed to obtain the estimated charging time for the vehicle to be charged at the corresponding charging station.

[0100] For example, if the current SOC value is 18%, then the process of charging to 100% will go through four SOC ranges: 0-20%, 20%-80%, 80%-90%, and 90%-100%. This depends on the vehicle's corresponding SOH and the corresponding P1-P4 values, combined with the charging station's SP (Service Level). maxMatching analysis is performed to obtain the estimated charging time for the vehicle at the given charging station. If the current SOC value is 25%, then the charging process to 100% will involve three SOC intervals: 20%–80%, 80%–90%, and 90%–100%. This is determined based on the vehicle's corresponding SOH and P2–P4 values, combined with the charging station's SP (Special Charge). max A matching analysis is performed to obtain the estimated charging time for the vehicle to be charged at the corresponding charging station.

[0101] In some examples, the server performs a matching analysis based on the battery degradation of the vehicle to be charged and the average power demand corresponding to at least one SOC interval, combined with the current available power of the charging station, to obtain the estimated charging time for the vehicle to be charged at the charging station. Specifically, this may include: first, using the current SOC value and battery degradation as parameters, calculating the target amount of electricity required to charge from the current SOC value to 100%; then, using the target amount of electricity, the current SOC value of the vehicle to be charged, the current available power of the charging station, and the average power demand corresponding to at least one SOC interval as parameters, calculating the charging stage time corresponding to each of the at least one SOC interval; then, summing the charging stage times corresponding to each of the at least one SOC interval to obtain the estimated charging time for the vehicle to be charged at the charging station. For example, a weighted summation method can be used to calculate the estimated charging time by summing the charging stage times corresponding to each SOC interval, where each SOC interval has its own corresponding weight value; or the charging stage times corresponding to each SOC interval can be directly summed to obtain the estimated charging time, etc.

[0102] For example, based on the current SOC value of the vehicle to be charged, the required charge RE for a full charge can be calculated using the following formula:

[0103] RE = (100 - soc0) * SOH * w.

[0104] Where soc0 represents the current SOC value of the vehicle to be charged, SOH represents the battery degradation rate of the vehicle to be charged, and w represents the rated total capacity of the battery of the vehicle to be charged. Simultaneously, based on soc0, the time required for each stage of charging is matched and accumulated to obtain the total estimated charging time. A calculation example is as follows:

[0105] If the average power demand of the vehicle to be charged is P1 in the 0-20% range, P2 in the 20%-80% range, P3 in the 80%-90% range, and P4 in the 90%-100% range, then the charging time for each range from SOC0 to 100% is as follows:

[0106] (1) For the charging phase time t1 in the 0-20% range, if soc0>=20%, t1=0; if soc0<20%, t1=(20%-soc0)*RE / min(P1,SP) max ), SP max This indicates the maximum output power that a charging station can allocate;

[0107] (2) For the charging phase time t2 in the 20% to 80% range, if soc0>=80%, t2=0; if soc0<80%, t2=(80%-max(soc0,20%))*RE / min(P2,SP) max );

[0108] (3) For the charging phase time t3 in the 80% to 90% range, if soc0>=90%, t3=0; if soc0<90%, t3=(90%-max(soc0,80%))*RE / min(P3,SP) max );

[0109] (4) For the charging phase in the 90% to 100% range, the time t4 is: t4 = (100% - max(soc0, 90%)) * RE / min(P4, SP) max );

[0110] Total time T = t1 + t2 + t3 + t4

[0111] The embodiments disclosed herein can determine the estimated charging time for a vehicle to be charged at a charging station by using the total time T.

[0112] Furthermore, this embodiment of the disclosure can estimate the user's charging cost calculation: based on the estimated charging time T and power RE calculated above, the billing strategy model data of the corresponding charging station is matched to calculate the estimated user charging cost. This embodiment of the disclosure can also calculate the distance between the user and the corresponding charging station, matching the latitude and longitude information of the user's location with the latitude and longitude of the corresponding charging station, and calculating the corresponding distance according to the distance calculation formula, etc.

[0113] For example, such as Figure 6The diagram illustrates a flowchart of the user charging prediction and matching phase. Based on the user's location coordinates (latitude and longitude), nearby charging stations are matched. Then, for each matched station, a list of available charging guns is obtained. This list is used to determine the station's maximum available output power for the vehicle, along with the user's historical charging needs. Finally, based on this maximum available output power and historical charging needs, the charging duration and amount of electricity to be charged are estimated. Furthermore, this phase can also estimate the user's charging cost based on the estimated charging duration and amount of electricity, combined with the charging station's pricing strategy, and calculate the distance to the charging station using its latitude and longitude coordinates.

[0114] Step 305: The server determines the query result based on the list of charging stations and the estimated charging time for each charging station in the list, and sends the query result to the client.

[0115] As an optional approach, the server determines the query results based on the list of charging stations and the estimated charging time for each station in the list. Specifically, this may include marking the estimated charging time for each charging station in the list and presenting the marked list of charging stations as the query results. In this way, users can intuitively understand the estimated charging time for each charging station and thus select the most efficient charging station based on their actual needs, saving time and improving vehicle charging efficiency.

[0116] As an alternative approach, the server determines the query results based on the list of charging stations and the estimated charging time for each charging station in the list. Specifically, this may include filtering the charging station list according to the estimated charging time and determining the query results based on the filtered list of charging stations.

[0117] For example, charging stations with estimated charging times less than a certain target threshold can be filtered out and compiled into a new list of charging stations. This new list can then be sent to the client as a query result. The target threshold can be set by system default or by the user on the client side, such as including it in the query range parameters for charging stations, and then sending the query request to the server.

[0118] For example, the system can filter out charging stations with the shortest estimated charging time and send the results to the client.

[0119] In some embodiments, in addition to determining the query results based on the estimated charging time, the query results can also be determined by combining factors such as the user's charging cost and the distance to the charging station, and then sent to the client.

[0120] For example, in the stage of matching site solutions output, such as Figure 7 As shown, users can sort the found charging stations by charging time, by estimated charging cost, or by distance.

[0121] Compared with existing related technologies, the embodiments disclosed herein can help users choose charging stations with higher charging efficiency, save time spent in the charging process, improve vehicle charging efficiency, and thus effectively enhance the user's service experience.

[0122] Figure 8 A flowchart illustrating a charging query method according to an embodiment of this disclosure is shown. Figure 8 As shown, this method is applied to the client side and may include the following steps.

[0123] Step 401: The client sends a query request to the server.

[0124] The query request includes a query range parameter for charging stations and vehicle information for the vehicle to be charged. The query range parameter is used to determine the list of charging stations that meet the query conditions, and the vehicle information is used to determine the historical charging demand information of the vehicle to be charged. The current SOC value of the vehicle to be charged, which is included in the vehicle information, is used to match and analyze the currently available power information of each charging station in the charging station list with the historical charging demand information of the vehicle to be charged, so as to obtain the estimated charging time for the vehicle to be charged for each charging station.

[0125] In some examples, the current SOC value of the vehicle to be charged can be input by the user, and the client receives the current SOC value input by the user.

[0126] Step 402: The client receives and outputs the query results sent by the server.

[0127] The query results can be determined based on a list of charging stations and the estimated charging time for each station. For example, the query results might include a tagged list of charging stations, each marked with its estimated charging time. Alternatively, the query results could include charging stations with estimated charging times less than a certain target threshold, or charging stations with the shortest estimated charging times.

[0128] For example, by integrating and sorting relevant data based on the user's selection strategy, the output examples include:

[0129] 1. XXXX1 charging station, distance 0.5KM, estimated charging capacity 25KW·H, estimated charging time 25 minutes

[0130] 2. XXXX2 charging station, distance 0.7KM, estimated charging capacity 25KW·H, estimated charging time 30 minutes 3、......

[0132] Compared with existing related technologies, the embodiments disclosed herein can help users choose charging stations with higher charging efficiency, save time spent in the charging process, improve vehicle charging efficiency, and thus effectively enhance the user's service experience.

[0133] Furthermore, as a specific implementation of a server-side example, this disclosure provides a charging query device that can be applied to the server side, such as... Figure 9 As shown, the device includes: a receiving module 51, an acquisition module 52, a determining module 53, an analysis module 54, and a sending module 55.

[0134] The receiving module 51 is configured to receive a query request sent by a client, wherein the query request carries the query range parameters of the charging station and the vehicle information of the vehicle to be charged.

[0135] The acquisition module 52 is configured to acquire a list of charging stations that meet the query conditions based on the query range parameters.

[0136] The determination module 53 is configured to determine the current available power of each charging station in the charging station list, and to obtain the historical charging demand information of the vehicle to be charged based on the vehicle information.

[0137] The analysis module 54 is configured to perform a matching analysis based on the current SOC value of the vehicle to be charged contained in the vehicle information, and in combination with the current available power of each charging station and the historical charging demand information of the vehicle to be charged, to obtain the estimated charging time of the vehicle to be charged for each charging station.

[0138] The sending module 55 is configured to determine the query result based on the list of charging stations and the estimated charging time corresponding to each charging station in the list, and send the query result to the client.

[0139] In some embodiments, the determining module 53 is specifically configured to obtain the vehicle identification code of the vehicle to be charged from the vehicle information; query the average power demand of the vehicle to be charged for each SOC range during historical charging, and the battery degradation degree of the vehicle to be charged, based on the vehicle identification code; and determine the historical charging demand information based on the queried average power demand and the battery degradation degree.

[0140] In some embodiments, the SOC interval is a plurality of SOC intervals divided within the range of SOC from 0 to 100%; correspondingly, the analysis module 54 is specifically configured to determine at least one SOC interval experienced during the process of charging from the current SOC value to 100%; and to obtain the estimated charging time of the vehicle to be charged for the charging station by performing a matching analysis based on the battery degradation degree and the average demand power corresponding to the at least one SOC interval, combined with the current available power of the charging station.

[0141] In some embodiments, the analysis module 54 is further configured to: use the current SOC value and the battery degradation rate as parameters to calculate the target amount of electricity required to charge from the current SOC value to 100%; use the target amount of electricity, the current SOC value, the current available power of the charging station, and the average power demand corresponding to each of the at least one SOC interval as parameters to calculate the charging stage time corresponding to each of the at least one SOC interval; and sum the charging stage times corresponding to each of the at least one SOC interval to obtain the estimated charging time for the vehicle to be charged at the charging station.

[0142] In some embodiments, the determining module 53 is further configured to, if the average power demand and battery degradation corresponding to the vehicle to be charged are not found according to the vehicle identification code, then, based on the vehicle information, query the average power demand and battery degradation corresponding to similar vehicles of the vehicle to be charged.

[0143] Accordingly, the determining module 53 is further configured to determine the average power demand and battery degradation of the similar vehicles found in the query as the historical charging demand information of the vehicle to be charged.

[0144] In some embodiments, the determining module 53 is further configured to obtain vehicle model information corresponding to the vehicle to be charged from the vehicle information; and to obtain a target vehicle that is the same as the vehicle model information and has a record corresponding to the average power demand and the battery degradation.

[0145] Accordingly, the determining module 53 is further configured to determine the average power demand and battery degradation corresponding to the target vehicle as the historical charging demand information of the vehicle to be charged.

[0146] In some embodiments, the acquisition module 52 is further configured to collect charging process data, vehicle battery data, and charging bill data of different vehicles; and based on the charging process data, vehicle battery data, and charging bill data, record the average power demand of the different vehicles for each SOC range during historical charging, as well as the battery degradation degree of the different vehicles.

[0147] In some embodiments, the acquisition module 52 is specifically configured to filter the charging process data, vehicle battery data, and charging bill data; merge the filtered charging process data, vehicle battery data, and charging bill data to obtain a dataset, the dataset including at least one or more of the following: vehicle identification number corresponding to each vehicle, SOC value at the start of charging, SOC value at the end of charging, charging duration, charging amount, rated total capacity of the vehicle battery, and real-time status data during charging; based on the dataset, calculate and record the average power demand corresponding to each SOC interval for each vehicle during historical charging, and the battery degradation degree corresponding to each vehicle.

[0148] In some embodiments, the acquisition module 52 is further configured to use the SOC value at the start of charging, the SOC value at the end of charging, the charging amount, and the rated total capacity of the vehicle battery in the dataset as parameters to calculate the battery degradation degree corresponding to the vehicle; and to use the real-time status data corresponding to the vehicle in the dataset as parameters to calculate the average power demand of the vehicle for each SOC interval during the historical charging process, wherein the real-time status data includes at least one or more of the following: the real-time SOC value of the vehicle battery, the vehicle demand voltage, the vehicle demand current, the charging device output voltage, and the charging device output current.

[0149] In some embodiments, the acquisition module 52 is further configured to calculate and record the average power demand for each SOC range during the historical charging process of different vehicles and the battery degradation degree of different vehicles, based on the latest charging process data, vehicle battery data and charging bill data collected for different vehicles at preset time intervals.

[0150] Accordingly, the determining module 53 is further configured to query the average power demand and battery degradation of the vehicle to be charged from the latest recorded average power demand and battery degradation of different vehicles, using the vehicle identification number of the vehicle to be charged.

[0151] In some embodiments, the determining module 53 is further configured to: obtain a list of first charging guns that are currently idle and available at the charging station; calculate the current maximum allocable output power of each first charging gun in the first charging gun list; and obtain the maximum value from the current maximum allocable output power of each first charging gun as the current usable power of the charging station.

[0152] In some embodiments, the determining module 53 is further configured to: obtain a second charging gun list, wherein the second charging guns in the second charging gun list are charging guns currently being charged under the same charging device as the first charging gun; calculate the current output power of each second charging gun in the second charging gun list; calculate the current usable power of the charging device based on the current output power of each second charging gun and the rated power corresponding to the charging device; and obtain the minimum value between the current usable power of the charging device and the maximum output power of the first charging gun as the current maximum allocable output power of the first charging gun.

[0153] In some embodiments, the determining module 53 is further configured to: obtain the charging start time corresponding to the second charging gun; determine the charging start time based on the charging start time; obtain the deviation coefficient corresponding to the charging start time; and calculate the current output power of the second charging gun using the deviation coefficient, the current output voltage and output current of the second charging gun as parameters.

[0154] In some embodiments, the sending module 55 is specifically configured to mark the estimated charging time corresponding to each charging station in the charging station list, and use the marked charging station list as the query result; or, to filter the charging station list according to the estimated charging time, and determine the query result based on the filtered charging station list.

[0155] It should be noted that other corresponding descriptions of the functional units involved in the charging query device provided in this embodiment can be found in the following references. Figures 1 to 7 The corresponding descriptions in [the document] will not be repeated here.

[0156] Furthermore, as a specific implementation of a client example, this disclosure provides a charging query device that can be applied to a client, such as... Figure 10 As shown, the device includes: a transmitting module 61 and a receiving module 62.

[0157] The sending module 61 is configured to send a query request to the server. The query request carries a query range parameter for charging stations and vehicle information of the vehicle to be charged. The query range parameter is used to determine a list of charging stations that meet the query conditions. The vehicle information is used to determine the historical charging demand information of the vehicle to be charged. The current state of charge (SOC) value of the vehicle to be charged, which is included in the vehicle information, is used to perform matching analysis with the currently available power information of each charging station in the charging station list and the historical charging demand information of the vehicle to be charged to obtain the estimated charging time of the vehicle to be charged for each charging station.

[0158] The receiving module 62 is configured to receive and output the query results sent by the server, the query results being determined based on the list of charging stations and the estimated charging time corresponding to each charging station.

[0159] It should be noted that other corresponding descriptions of the functional units involved in the charging query device provided in this embodiment can be found in the following references. Figures 1 to 8 The corresponding descriptions in [the document] will not be repeated here.

[0160] Based on the above, Figures 1 to 8 As illustrated in the example, correspondingly, embodiments of this disclosure also provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the above-described... Figures 1 to 8 The example method shown.

[0161] Based on the above, Figures 1 to 8 As illustrated, correspondingly, this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described... Figures 1 to 8 The example method shown.

[0162] Based on this understanding, the technical solutions of the embodiments of this disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this disclosure.

[0163] Based on the above, Figures 1 to 8 The method shown, and Figure 9 or Figure 10 To achieve the above objectives, this disclosure also provides an electronic device, comprising a storage medium and a processor; the storage medium for storing a computer program; and the processor for executing the computer program to implement the above-described virtual device embodiments. Figures 1 to 8 The method shown.

[0164] Optionally, the aforementioned electronic device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, an input unit, etc.

[0165] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0166] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0167] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented through hardware. Compared with the current related technologies, the embodiments of this disclosure can help users choose charging stations with higher charging efficiency, save time spent in the charging process, improve vehicle charging efficiency, and thus effectively enhance the user's service experience.

[0168] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0169] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A charging query method, characterized in that, include: Receive a query request sent by the client, the query request carrying the query range parameters of the charging station and the vehicle information of the vehicle to be charged; A list of charging stations that meet the query conditions is obtained based on the query range parameters; Determine the current available power of each charging station in the charging station list, and obtain the historical charging demand information of the vehicle to be charged based on the vehicle information; Based on the current state of charge (SOC) value of the vehicle to be charged contained in the vehicle information, and combined with the current available power of each charging station and the historical charging demand information of the vehicle to be charged, a matching analysis is performed to obtain the estimated charging time of the vehicle to be charged for each charging station. The query result is determined based on the list of charging stations and the estimated charging time for each charging station, and the query result is sent to the client.

2. The method according to claim 1, characterized in that, Based on the vehicle information, obtain the historical charging demand information of the vehicle to be charged, including: Obtain the vehicle identification number of the vehicle to be charged from the vehicle information; Based on the vehicle identification number, query the average power demand of the vehicle to be charged for each SOC range during the historical charging process, as well as the battery degradation rate of the vehicle to be charged. The historical charging demand information is determined based on the average power demand and the battery degradation.

3. The method according to claim 2, characterized in that, The SOC interval is a set of multiple SOC intervals divided within the range of SOC from 0 to 100%. Based on the current SOC value of the vehicle to be charged contained in the vehicle information, and combined with the current available power of each charging station and the historical charging demand information of the vehicle to be charged, a matching analysis is performed to obtain the estimated charging time of the vehicle to be charged for each charging station, including: Determine at least one SOC interval experienced during the process of charging from the current SOC value to 100%; Based on the battery degradation and the average power demand corresponding to each of the at least one SOC range, and combined with the current available power of the charging station, a matching analysis is performed to obtain the estimated charging time for the vehicle to be charged at the charging station.

4. The method according to claim 3, characterized in that, Based on the battery degradation and the average power demand corresponding to each of the at least one SOC range, and combined with the current available power of the charging station, a matching analysis is performed to obtain the estimated charging time for the vehicle to be charged at the corresponding charging station, including: Using the current SOC value and the battery degradation rate as parameters, calculate the target charge required to charge from the current SOC value to 100%; Using the target power, the current SOC value, the current usable power of the charging station, and the average demand power corresponding to the at least one SOC interval as parameters, the charging stage time corresponding to the at least one SOC interval is calculated. The estimated charging time for the vehicle to be charged at the charging station is obtained by summing the charging time corresponding to each of the at least one SOC interval.

5. The method according to claim 2, characterized in that, Based on the vehicle identification number, query the average power demand of the vehicle to be charged for each SOC range during historical charging processes, and the battery degradation rate of the vehicle to be charged, including: If the average power demand and battery degradation of the vehicle to be charged are not found based on the vehicle identification code, then the average power demand and battery degradation of similar vehicles to the vehicle to be charged are queried based on the vehicle information. The step of determining the historical charging demand information based on the retrieved average power demand and battery degradation includes: The average power demand and battery degradation of the similar vehicles found in the query are used to determine the historical charging demand information of the vehicle to be charged.

6. The method according to claim 5, characterized in that, Based on the vehicle information, query the average power demand and battery degradation of similar vehicles to the vehicle to be charged, including: Obtain the vehicle model information corresponding to the vehicle to be charged from the vehicle information; Obtain a target vehicle that has the same vehicle model information as the vehicle and that has corresponding records for the average power demand and the battery degradation. The step of determining the average power demand and battery degradation of the similar vehicles found in the query as the historical charging demand information of the vehicle to be charged includes: The average power demand and battery degradation of the target vehicle are used to determine the historical charging demand information of the vehicle to be charged.

7. The method according to claim 2, characterized in that, The method further includes: Collect charging process data, vehicle battery data, and charging bill data for different vehicles; Based on the charging process data, vehicle battery data, and charging bill data, the average power demand of different vehicles for each SOC range during historical charging processes, as well as the battery degradation rate of different vehicles, are recorded.

8. The method according to claim 7, characterized in that, Based on the charging process data, vehicle battery data, and charging bill data, the average power demand for each vehicle in each SOC range during historical charging processes is recorded, as well as the battery degradation rate for each vehicle, including: The charging process data, vehicle battery data, and charging bill data are filtered. The dataset is obtained by merging the filtered charging process data, vehicle battery data, and charging bill data. The dataset includes at least one or more of the following: vehicle identification number corresponding to each vehicle, SOC value at the start of charging, SOC value at the end of charging, charging duration, charging amount, rated total capacity of vehicle battery, and real-time status data during charging. Based on the dataset, the average power demand for each vehicle in each SOC range during historical charging is calculated and recorded, as well as the battery degradation rate for each vehicle.

9. The method according to claim 8, characterized in that, Based on the dataset, the average power demand for each vehicle in each SOC range during historical charging is calculated, as well as the battery degradation rate for each vehicle, including: Using the SOC value at the start of charging, the SOC value at the end of charging, the charging amount, and the vehicle's rated total battery capacity in the dataset as parameters, the battery degradation rate for each vehicle is calculated; and, Using the real-time status data of the vehicles in the dataset as parameters, the average power demand of the vehicles for each SOC range during the historical charging process is calculated. The real-time status data includes at least one or more of the following: the real-time SOC value of the vehicle battery, the vehicle's required voltage, the vehicle's required current, the charging equipment's output voltage, and the charging equipment's output current.

10. The method according to claim 7, characterized in that, The method further includes: Based on the latest charging process data, vehicle battery data and charging bill data collected for different vehicles at preset time intervals, the average power demand for each vehicle in each SOC range during the historical charging process, as well as the battery degradation rate for each vehicle, are calculated and recorded. The step of querying the average power demand of the vehicle to be charged for each SOC range during historical charging processes, based on the vehicle identification number, and the battery degradation rate of the vehicle to be charged, includes: Using the vehicle identification number of the vehicle to be charged, the average power demand and battery degradation of the vehicle to be charged are retrieved from the latest records of average power demand and battery degradation of the different vehicles.

11. The method according to any one of claims 1 to 10, characterized in that, Determining the current usable power of each charging station in the charging station list includes: Get the list of the first available charging guns at the charging station; Calculate the current maximum allocable output power of each first charging gun in the first charging gun list; The maximum value is obtained from the current maximum allocable output power of each of the first charging guns, and is taken as the current usable power of the charging station.

12. The method according to claim 11, characterized in that, Calculate the current maximum allocable output power of each first charging gun in the first charging gun list, including: Get the second charging gun list. The second charging gun in the second charging gun list is the charging gun that is currently charging under the same charging device as the first charging gun. Calculate the current output power of each of the second charging guns in the second charging gun list; Based on the current output power of each of the second charging guns and the rated power of the charging device, calculate the current usable power of the charging device; The minimum value between the current usable power of the charging device and the maximum output power of the first charging gun is obtained as the current maximum allocable output power of the first charging gun.

13. The method according to claim 12, characterized in that, Calculate the current output power of each second charging gun in the second charging gun list, including: Obtain the charging start time corresponding to the second charging gun; The duration of charging initiation is determined based on the charging start time. Obtain the deviation coefficient corresponding to the charging start time; Using the deviation coefficient, the current output voltage and output current of the second charging gun as parameters, the current output power of the second charging gun is calculated.

14. The method according to claim 1, characterized in that, The query results are determined based on the list of charging stations and the estimated charging time for each charging station, including: Mark the estimated charging time for each charging station in the list of charging stations, and use the marked list of charging stations as the query result; or... The list of charging stations is filtered according to the estimated charging time, and the query result is determined based on the filtered list of charging stations.

15. A charging query method, characterized in that, include: A query request is sent to the server. The query request carries the query range parameters of charging stations and the vehicle information of the vehicle to be charged. The query range parameters are used to determine the list of charging stations that meet the query conditions. The vehicle information is used to determine the historical charging demand information of the vehicle to be charged. The current state of charge (SOC) value of the vehicle to be charged, which is included in the vehicle information, is used to match and analyze the currently available power information of each charging station in the charging station list with the historical charging demand information of the vehicle to be charged, so as to obtain the estimated charging time of the vehicle to be charged for each charging station. The system receives and outputs the query results sent by the server, which are determined based on the list of charging stations and the estimated charging time for each charging station.

16. A charging query device, characterized in that, include: The receiving module is configured to receive a query request sent by a client, the query request carrying the query range parameters of the charging station and the vehicle information of the vehicle to be charged. The acquisition module is configured to acquire a list of charging stations that meet the query conditions based on the query range parameters; The determination module is configured to determine the current available power of each charging station in the charging station list, and to obtain the historical charging demand information of the vehicle to be charged based on the vehicle information. The analysis module is configured to perform a matching analysis based on the current state of charge (SOC) value of the vehicle to be charged contained in the vehicle information, and in combination with the current available power of each charging station and the historical charging demand information of the vehicle to be charged, to obtain the estimated charging time of the vehicle to be charged for each charging station. The sending module is configured to determine the query result based on the list of charging stations and the estimated charging time corresponding to each charging station, and send the query result to the client.

17. A charging query device, characterized in that, include: The sending module is configured to send a query request to the server. The query request carries a query range parameter for charging stations and vehicle information of the vehicle to be charged. The query range parameter is used to determine a list of charging stations that meet the query conditions. The vehicle information is used to determine the historical charging demand information of the vehicle to be charged. The current state of charge (SOC) value of the vehicle to be charged, which is included in the vehicle information, is used to perform matching analysis with the currently available power information of each charging station in the charging station list and the historical charging demand information of the vehicle to be charged to obtain the estimated charging time of the vehicle to be charged for each charging station. The receiving module is configured to receive and output the query results sent by the server, the query results being determined based on the list of charging stations and the estimated charging time corresponding to each charging station.

18. A charging query system, characterized in that, include: A server and a client, wherein the server is configured to implement the method of any one of claims 1 to 14, and the client is configured to implement the method of claim 15.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 15.

20. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 15.

21. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 15.

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