Method, device, equipment and storage medium for adjusting plug-in charging power of vehicle

Abstract

The application discloses a method and device for adjusting plug-in charging power of a vehicle, an equipment and a storage medium, and is applied to a server, and comprises the following steps: obtaining server registration data and real-time collection data of the vehicle; the real-time collection data is used to determine historical travel and historical power related data of the vehicle; the server registration data comprises a state label, and the state label is used to reflect the current state of the vehicle; the current state of the vehicle is determined according to the server registration data and / or the real-time collection data; and in the case that the current state is an abnormal state, the plug-in charging power of the vehicle is adjusted according to the abnormal state. Based on the method of the embodiment of the application, the plug-in charging power can be adjusted through the related information of the vehicle, and the problem that the charging and battery replacement cost and the vehicle maintenance cost generally increase due to the frequent use of the charging gun to cause the wear of the battery of the battery replacement new energy vehicle in the related art is solved.

Description

Technical Field

[0001] This application belongs to the field of new energy vehicle technology, specifically relating to a method, apparatus, device, and storage medium for adjusting the plug-in charging power of a vehicle. Background Technology

[0002] The battery swapping mode for new energy vehicles refers to the rapid replacement of the vehicle's battery to achieve energy renewal. The battery of the battery swapping vehicle also has the function of plugging in the charging pile as a temporary energy supplement.

[0003] Currently, the plug-in charging interface for batteries serves as a temporary interface, causing significant wear and tear on the battery equipment of battery-swapping new energy vehicles. Frequent plugging into charging stations accelerates the aging of the equipment, leading to further damage. There is a lack of corresponding technical solutions to address the wear and tear caused to the batteries of battery-swapping new energy vehicles by frequent use of charging guns.

[0004] The aforementioned problems have led to a shortened lifespan of vehicle batteries, resulting in a general increase in charging and battery swapping costs as well as vehicle maintenance costs. Summary of the Invention

[0005] This application aims to provide a method, apparatus, device, and storage medium for adjusting the plug-in charging power of a vehicle, at least to solve the problem of the general increase in charging and battery swapping costs and vehicle maintenance costs caused by the damage to the battery of battery-swapping new energy vehicles due to frequent use of charging guns.

[0006] In a first aspect, embodiments of this application disclose a method for adjusting the plug-in charging power of a vehicle, applied to a server, comprising:

[0007] The system acquires server-side registration data and real-time collected data of the vehicle; the real-time collected data is used to determine the vehicle's historical mileage and historical battery level; the server-side registration data includes status tags, which are used to reflect the vehicle's current status.

[0008] The current status of the vehicle is determined based on the server-registered data and / or the real-time collected data;

[0009] If the current state is abnormal, adjust the plug-in charging power of the vehicle according to the abnormal state.

[0010] Optionally, determining the current status of the vehicle based on the server-registered data includes:

[0011] If the status label is a first target value, the vehicle is determined to be in an abnormal state; the first target value is the parameter value corresponding to the abnormal state corresponding to the status label.

[0012] Optionally, the real-time collected data includes the cumulative number of plug-in charging cycles, and determining the current state of the vehicle based on the real-time collected data includes:

[0013] If the cumulative number of plug-in charging attempts is greater than or equal to the second target value, the vehicle is determined to be in an abnormal state; the second target value is the cumulative number of minimum plug-in charging attempts corresponding to the abnormal state.

[0014] Optionally, the real-time collected data includes battery level change data and mileage change data. The battery level change data includes the vehicle's battery level at the beginning of the sampling period and the vehicle's battery level at the end of the sampling period. The mileage change data is used to characterize the vehicle's mileage during the sampling period. Determining the abnormal state of the vehicle based on the real-time collected data includes:

[0015] The difference between the vehicle's battery level at the beginning of the sampling period and the vehicle's battery level at the end of the sampling period is taken as the vehicle's battery consumption during the sampling period.

[0016] The estimated range of vehicle power consumption is determined based on the mileage traveled; the two endpoints of the estimated range of vehicle power consumption are the maximum and minimum power consumption of the vehicle estimated based on the mileage traveled, and the estimated range of vehicle power consumption is used to estimate the power consumption of the vehicle after traveling the mileage traveled.

[0017] Determine whether the vehicle's power consumption during the sampling period is within the estimated range of the vehicle's power consumption;

[0018] If the battery level change data is outside the estimated range of the vehicle's battery consumption, the vehicle is determined to be in an abnormal state.

[0019] Optionally, the server-side registration data includes battery swapping station location data, and the real-time collected data includes remaining battery power data and vehicle location data. Determining the abnormal status of the vehicle based on the server-side registration data and the real-time collected data includes:

[0020] The estimated return mileage of the vehicle to the battery swapping station is determined based on the battery swapping station location data and the vehicle location data.

[0021] Based on the estimated return mileage, determine the estimated range of vehicle return trip power consumption;

[0022] Compare the remaining battery power data with the estimated range of the vehicle's return trip battery power consumption;

[0023] If the remaining battery power data is outside the estimated range of the vehicle's return trip battery consumption, the vehicle is determined to be in an abnormal state.

[0024] Optionally, the abnormal state includes the cumulative number of vehicle abnormalities, which represents the number of times the vehicle has accumulated abnormalities within a limited period; adjusting the plug-in charging power of the vehicle according to the abnormal state when the current state is abnormal includes:

[0025] The cumulative number of abnormalities of the vehicle was determined statistically;

[0026] The cumulative number of abnormal events of the vehicle is compared with an adjustment threshold; the adjustment threshold is used to determine the method of adjusting the plug-in charging power of the vehicle.

[0027] The plug-in charging power of the vehicle is adjusted based on the relationship between the cumulative number of abnormalities of the vehicle and the adjustment threshold.

[0028] Optionally, the adjustment threshold includes a first adjustment threshold, a second adjustment threshold, and a third adjustment threshold; the first adjustment threshold is less than the second adjustment threshold, and the second adjustment threshold is less than the third adjustment threshold; adjusting the plug-in charging power of the vehicle based on the relationship between the cumulative number of abnormal events and the adjustment threshold includes:

[0029] If the cumulative number of abnormal events of the vehicle is greater than the first adjustment threshold and less than or equal to the second adjustment threshold, the plug-in charging power of the vehicle is adjusted to a first plug-in charging power; the first plug-in charging power is a preset plug-in charging power, and the first plug-in charging power is less than the normal plug-in charging power of the vehicle, but greater than half of the normal plug-in charging power of the vehicle.

[0030] If the cumulative number of abnormal events of the vehicle is greater than the second adjustment threshold and less than or equal to the third adjustment threshold, the plug-in charging power of the vehicle is adjusted to the second plug-in charging power; the second plug-in charging power is a preset plug-in charging power, and the second plug-in charging power is less than or equal to half of the normal plug-in charging power of the vehicle, and greater than zero.

[0031] If the cumulative number of abnormal events of the vehicle exceeds the third adjustment threshold, the plug-in charging power of the vehicle will be adjusted to zero.

[0032] Optionally, the real-time collected data includes vehicle equipment fault data, which is used to characterize the fault status of the vehicle's plug-in charging equipment. The method further includes:

[0033] Determine the equipment fault data of the vehicle;

[0034] If the vehicle's equipment fault data meets a preset fault threshold, the vehicle's plug-in charging power is adjusted to zero; the fault threshold is used to limit fault conditions that prevent the vehicle from plugging in for charging.

[0035] Secondly, embodiments of this application also disclose a device for adjusting the plug-in charging power of a vehicle, the device comprising:

[0036] The data acquisition module is used to acquire the vehicle's server-side registration data and real-time collected data; the real-time collected data is used to determine the vehicle's current status; the server-side registration data is used to record parameter data for determining whether the vehicle is in an abnormal state.

[0037] The status determination module is used to determine the current status of the vehicle based on the server-registered data and / or the real-time collected data.

[0038] A power adjustment module is used to adjust the plug-in charging power of the vehicle according to the abnormal state when the current state is abnormal.

[0039] Thirdly, embodiments of this application also disclose an electronic device, including a processor and a memory, wherein the memory stores a program or instructions that can run on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0040] Fourthly, embodiments of this application also disclose a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0041] In summary, in this embodiment, by acquiring data related to the vehicle's historical mileage, historical battery level, and current vehicle status, the server can analyze and process the vehicle's plug-in charging status. Based on server-registered data and real-time collected data, the vehicle's current status can be determined, thereby identifying any abnormal vehicle conditions. Therefore, the method based on this embodiment can adjust the plug-in charging power of the vehicle based on its abnormal state, solving the problem in related technologies where frequent use of the charging gun causes damage to the battery of battery-swapping new energy vehicles, leading to increased charging and swapping costs and vehicle maintenance costs. Attached Figure Description

[0042] In the attached diagram:

[0043] Figure 1 This is a flowchart illustrating the steps of a method for adjusting the plug-in charging power of a vehicle provided in this embodiment;

[0044] Figure 2 A flowchart illustrating the steps of another method for adjusting the plug-in charging power of a vehicle, as provided in the embodiments of the application;

[0045] Figure 3 This is a block diagram of a device for adjusting the plug-in charging power of a vehicle, as provided in an embodiment of this application.

[0046] Figure 4 This is a block diagram of an electronic device provided in one embodiment of this application;

[0047] Figure 5 This is a block diagram of an electronic device according to another embodiment of the present application. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0049] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0050] Figure 1 This embodiment provides a method for adjusting the plug-in charging power of a vehicle. The method is applied to a server and may include the following steps:

[0051] Step 101: Obtain the vehicle's server registration data and real-time collected data; the real-time collected data is used to determine the vehicle's historical mileage and historical battery level; the server registration data includes status tags, which are used to reflect the vehicle's current status.

[0052] In one embodiment of this application, acquiring the vehicle's server-side registration data and real-time collected data is to provide a data source for judging the vehicle's plug-in charging status and whether the charging power needs to be adjusted. Specifically: the server-side registration data consists of basic information about the vehicle registered on the server, such as the vehicle model, battery capacity, and charging interface type. This data includes a status tag reflecting the vehicle's current status, such as normal, abnormal, or faulty. The status tag can be updated based on the vehicle's real-time collected data or other factors to facilitate timely detection and handling of vehicle problems. The real-time collected data consists of dynamic data continuously uploaded to the server during vehicle operation, including the vehicle's location, speed, remaining battery power, number of plug-in charging attempts, battery level changes, and mileage changes. This real-time collected data can be used to determine the vehicle's historical mileage and historical battery level, such as the vehicle's average daily mileage, average power consumption, and average number of plug-in charging attempts. This data can be used to analyze the vehicle's usage habits and charging / swapping needs, as well as to determine if the vehicle is experiencing any abnormalities or faults.

[0053] For example, in this embodiment of the application, it can be assumed that a battery-swapping new energy vehicle has registered the following data on the server: vehicle model A, battery capacity: 100kWh, charging interface type: Type A, status label: abnormal; and during a certain driving period, vehicle A uploaded the following real-time collected data: vehicle location: coordinates (34.0522, -118.2437), speed: 60km / h, remaining power: 20kWh, cumulative number of plug-in charging times: 5, power data at the beginning of the sampling period: 80kWh, power data at the end of the sampling period: 20kWh, mileage change data: 200km.

[0054] Step 102: Determine the current status of the vehicle based on the server-registered data and / or the real-time collected data.

[0055] In one embodiment of this application, the vehicle is determined to be in a normal or abnormal state based on relevant vehicle data (i.e., server-registered data and / or real-time collected data) in order to promptly detect and address vehicle problems.

[0056] For example, in some embodiments of this application, the current status of a vehicle can be directly determined based on the vehicle's status tag, according to the server-registered data. For instance, if the status tag is normal, it indicates that the vehicle is not experiencing any abnormalities; if the status tag is abnormal, it indicates that the vehicle has some problems and the charging power needs to be adjusted.

[0057] For example, in some other embodiments of this application, based on real-time collected data, it can be determined whether the vehicle is experiencing any abnormalities based on historical mileage and historical battery level data. For instance, if the cumulative number of plug-in charging cycles exceeds a certain threshold, it indicates that the vehicle is using plug-in charging too frequently, which may accelerate the aging of the battery equipment, requiring a reduction in charging power.

[0058] It is important to emphasize that, based on different judgment factors, the data source for making adjustment judgments can be server-registered data, real-time collected data, or a combination of both; and when the server-registered data and real-time collected data contain multiple data of different types, the data source for making adjustment judgments may also be a combination of different types of data.

[0059] Step 103: If the current state is an abnormal state, adjust the plug-in charging power of the vehicle according to the abnormal state.

[0060] In some embodiments of this application, the method of adjusting the vehicle's plug-in charging power can be determined by comparing the cumulative number of abnormal events with a preset adjustment threshold. The adjustment threshold is a set of parameter values ​​determined based on factors such as the vehicle model, battery capacity, and charging interface type, used to divide different adjustment ranges. Based on different adjustment ranges, different plug-in charging powers can be determined, from high to low: normal charging power, first plug-in charging power, second plug-in charging power, and zero charging power.

[0061] In summary, in this embodiment, by analyzing and judging data related to the vehicle's historical mileage, historical battery level, and current vehicle status, the vehicle's plug-in charging usage can be processed from the server. Based on server-registered data and real-time collected data, the vehicle's current status can be determined. Therefore, the method based on this embodiment can adjust the plug-in charging power using relevant vehicle information, solving the problem in related technologies where frequent use of charging guns causes damage to the batteries of battery-swapping new energy vehicles, leading to a general increase in charging and swapping costs and vehicle maintenance costs.

[0062] Figure 2 This embodiment provides a method for adjusting the plug-in charging power of a vehicle. The method is applied to a server and may include the following steps:

[0063] Step 201: Obtain the vehicle's server registration data and real-time collected data; the real-time collected data is used to determine the vehicle's historical mileage and historical battery level; the server registration data includes status tags, which are used to reflect the vehicle's current status.

[0064] The method shown in this step has been explained in step 101 and will not be repeated here.

[0065] Step 202: Determine the current status of the vehicle based on the server-registered data and / or the real-time collected data.

[0066] The method shown in this step has been explained in step 102 and will not be repeated here.

[0067] Optionally, step 202 may specifically include the following sub-steps:

[0068] Sub-step 2021: If the status label is a first target value, determine that the vehicle is in an abnormal state; the first target value is the parameter value corresponding to the abnormal state corresponding to the status label.

[0069] In one embodiment of this application, sub-step 2021 is significant in that it directly determines whether the vehicle is in an abnormal state based on the status tag in the server-registered data. The status tag is a parameter set when the vehicle registers on the server, reflecting the vehicle's current state, such as normal or abnormal. The status tag can be updated based on real-time vehicle data or other factors to facilitate timely detection and handling of vehicle problems. The first target value is the parameter value corresponding to the abnormal state associated with the status tag, used to delineate the boundary between normal and abnormal states. If the status tag equals the first target value, it indicates that the vehicle has reached the conditions for an abnormal state and requires adjustment of charging power or repair; if the status tag does not equal the first target value, it indicates that the vehicle is still in a normal state and does not require adjustment of charging power or repair.

[0070] For example, in this embodiment of the application, suppose a battery-swapping new energy vehicle A has the following data registered on the server: the vehicle's status tag is 1; during a certain driving process, due to a malfunction in the plug-in charging equipment of vehicle A, abnormal power consumption occurs, and the server updates the status tag of vehicle A to 2. Assuming the first target value is 2, the current state of vehicle D is determined according to the following rules: if the status tag is equal to the first target value, the vehicle is judged to be in an abnormal state; if the status tag is not equal to the first target value, the vehicle is judged to be in a normal state. Based on these rules, the following conclusion can be drawn: the status tag of vehicle D is 2, which is equal to the first target value, therefore the vehicle is judged to be in an abnormal state.

[0071] Optionally, the real-time collected data includes the cumulative value of the number of plug-in charging cycles, and step 202 may specifically include the following sub-steps:

[0072] Sub-step 2022: If the cumulative number of plug-in charging times is greater than or equal to the second target value, determine that the vehicle is in an abnormal state; the second target value is the cumulative number of minimum plug-in charging times corresponding to the abnormal state.

[0073] In one embodiment of this application, the vehicle's abnormal state is determined based on the cumulative number of plug-in charging attempts in real-time collected data. The cumulative number of plug-in charging attempts is the total number of times the vehicle uses plug-in charging during operation, reflecting the vehicle's charging and battery swapping needs and usage habits. This cumulative number of attempts can be updated based on data from each time the vehicle is plugged into a charging station, facilitating timely monitoring of the vehicle's charging status. The second target value is the minimum cumulative number of plug-in charging attempts corresponding to the abnormal state, used to delineate the boundary between normal and abnormal states. If the cumulative number of plug-in charging attempts is greater than or equal to the second target value, it indicates that the vehicle is using plug-in charging too frequently, which may accelerate battery aging, requiring a reduction in charging power. If the cumulative number of plug-in charging attempts is less than the second target value, it indicates that the vehicle is using plug-in charging less frequently, and no reduction in charging power is necessary.

[0074] For example, suppose a battery-swapping new energy vehicle A has the following data registered on the server: cumulative number of plug-in charging times: 7. Based on this data, the current state of vehicle A can be determined according to the following rules: if the cumulative number of plug-in charging times is greater than or equal to 5, the vehicle is considered to be in an abnormal state. Based on these rules, the following conclusion can be drawn: the cumulative number of plug-in charging times of vehicle E is 7, which is greater than 5, therefore it is in an abnormal state.

[0075] Optionally, the real-time collected data includes battery level change data and mileage change data. The battery level change data includes the vehicle's battery level at the beginning of the sampling period and the vehicle's battery level at the end of the sampling period. The mileage change data is used to characterize the vehicle's mileage during the sampling period. Step 202 may specifically include the following sub-steps:

[0076] Sub-step 2023: The difference between the vehicle's battery level at the beginning of the sampling period and the vehicle's battery level at the end of the sampling period is taken as the vehicle's battery consumption during the sampling period.

[0077] In one embodiment of this application, the vehicle's power consumption during a sampling period is calculated based on real-time power change data collected to determine if there is any abnormal power consumption. The power change data consists of dynamic data uploaded to the server during vehicle operation, including the vehicle's power level at the start and end of the sampling period. The sampling period is a time interval determined by the server based on the vehicle's driving conditions and charging / swapping needs, such as per hour, per day, or per week. The power change data reflects the vehicle's power level changes during the sampling period, such as increases, decreases, or no change.

[0078] For example, suppose a battery-swapping new energy vehicle A has a battery level of 80 kWh at the beginning of the sampling period and 60 kWh at the end of the sampling period. Then the energy consumption during the sampling period is 20 kWh.

[0079] Sub-step 2024: Determine the estimated range of vehicle power consumption based on the driving mileage; the two endpoints of the estimated range of vehicle power consumption are the maximum and minimum power consumption of the vehicle estimated based on the driving mileage, and the estimated range of vehicle power consumption is used to estimate the power consumption of the vehicle after driving the driving mileage.

[0080] In one embodiment of this application, an estimated range for vehicle battery consumption is determined based on mileage change data collected in real time, in order to determine whether the vehicle has abnormal battery consumption. Mileage change data refers to dynamic data uploaded to the server during vehicle operation, including the vehicle's mileage traveled within a sampling period. The sampling period is a time interval determined by the server based on the vehicle's driving conditions and charging / battery swapping needs, such as per hour, per day, or per week. Mileage change data can be used to reflect the vehicle's distance and speed traveled within the sampling period, as well as factors affecting battery consumption, such as road conditions, weather, and load.

[0081] For example, suppose a battery-swapping new energy vehicle A has a maximum power consumption of 0.3 kWh per kilometer and a minimum power consumption of 0.1 kWh per kilometer, and a driving range of 200 km. Then the estimated range of the vehicle's power consumption is 20 kWh to 60 kWh.

[0082] Sub-step 2025: Determine whether the vehicle's power consumption during the sampling time period is within the estimated range of the vehicle's power consumption;

[0083] In one embodiment of this application, determining whether the power consumption is within the estimated power consumption range is to detect whether the vehicle has abnormal power consumption, that is, whether there is a significant deviation between the actual power consumption and the expected power consumption. If the power consumption is within the estimated power consumption range, it indicates that the vehicle has not experienced any abnormalities or malfunctions; if the power consumption is outside the estimated power consumption range, it indicates that the vehicle has some problems and needs to adjust the charging power or undergo repairs.

[0084] For example, suppose a battery-swapping new energy vehicle A has a maximum power consumption of 0.3 kWh per kilometer and a minimum power consumption of 0.1 kWh per kilometer, and a driving range of 200 km. The estimated range of the vehicle's power consumption is 20 kWh to 60 kWh. However, if the vehicle actually consumes 80 kWh, then the vehicle's power consumption exceeds the estimated range.

[0085] Sub-step 2026: If the power change data is outside the estimated range of the vehicle's power consumption, determine that the vehicle is in an abnormal state.

[0086] In one embodiment of this application, determining whether the power consumption change data is within the estimated power consumption range is to detect whether the vehicle has abnormal power consumption, that is, whether there is a significant deviation between the actual power consumption and the expected power consumption. If the power consumption change data is within the estimated power consumption range, it indicates that the vehicle has not experienced any abnormalities or malfunctions; if the power consumption change data is outside the estimated power consumption range, it indicates that the vehicle has some problems and needs to adjust the charging power or undergo repairs.

[0087] For example, suppose a battery-swapping new energy vehicle A has a maximum power consumption of 0.3 kWh per kilometer and a minimum power consumption of 0.1 kWh per kilometer. If the driving range is 200 km, the estimated range of the vehicle's power consumption is 20 kWh to 60 kWh. However, if the vehicle actually consumes 80 kWh, then the vehicle's power consumption exceeds the estimated range, and it can be determined that the vehicle is abnormal.

[0088] Optionally, the server-side registration data includes battery swapping station location data, and the real-time collected data includes remaining battery power data and vehicle location data. Step 202 may specifically include the following sub-steps:

[0089] Sub-step 2027: Determine the estimated return mileage of the vehicle to the battery swapping station based on the battery swapping station location data and the vehicle location data;

[0090] In one embodiment of this application, an estimated return mileage for the vehicle to the battery swapping station is determined based on the battery swapping station location data in the server-registered data and the vehicle location data in the real-time collected data. This helps to determine if there is a risk of the vehicle returning to the battery swapping station. The battery swapping station location data consists of static data stored on the server, including the name, address, latitude and longitude, and number of available batteries at the battery swapping station. This data reflects the distribution and service capabilities of battery swapping stations and provides the vehicle with the nearest or optimal battery swapping station option. The vehicle location data consists of dynamic data uploaded to the server by the vehicle during its journey, including its latitude and longitude, speed, and direction. This data reflects the vehicle's real-time location and driving status, as well as factors affecting the return mileage, such as road conditions, weather, and traffic. The estimated return mileage is the shortest or optimal travel distance from the vehicle to the battery swapping station determined based on the battery swapping station location data and the vehicle location data. This estimated mileage is used to estimate the amount of electricity the vehicle needs to consume when returning to the battery swapping station. The estimated return mileage can be calculated using algorithms or tools, such as map services and navigation systems, based on data on the location of the battery swapping station and the vehicle's location. This will determine the shortest or optimal route and distance from the vehicle to the battery swapping station.

[0091] For example, in one embodiment of this application, if the location coordinates of the battery swapping station are (40.7128, -74.0060) and the vehicle location data are (39.9526, -75.1652), a map service can be used to calculate the shortest or optimal driving route and distance from the vehicle to the battery swapping station, for example, about 150km.

[0092] Sub-step 2028: Determine the estimated range of vehicle return trip power consumption based on the estimated return trip mileage;

[0093] In one embodiment of this application, an estimated range for the vehicle's return trip power consumption is determined based on the estimated return mileage to assess whether the vehicle faces any return trip risk. This estimated range is defined by the maximum and minimum power consumption predicted based on the return mileage, and is used to estimate the actual power consumption of the vehicle upon returning to the battery swapping station. The estimated range can be determined based on parameters such as the vehicle model, battery capacity, and charging interface type, including maximum and minimum power consumption per kilometer. Then, based on these parameters and the return mileage, the estimated range for the vehicle's return trip power consumption can be calculated.

[0094] For example, suppose a battery-swapping new energy vehicle A has a maximum power consumption of 0.3 kWh per kilometer and a minimum power consumption of 0.1 kWh per kilometer, and the return trip is 150 km. Then the estimated range of power consumption for the vehicle's return trip is 15 kWh to 45 kWh.

[0095] Sub-step 2029: Compare the remaining battery power data with the estimated range of the vehicle's return trip battery power consumption;

[0096] In one embodiment of this application, comparing the remaining power data with the estimated range of power consumption for the return trip is to detect whether there is a risk of the vehicle returning to the battery swapping station, i.e., whether the remaining power is sufficient to support the vehicle's return to the battery swapping station.

[0097] For example, assuming a battery-swapping new energy vehicle A, it can be determined whether the remaining battery power data is greater than or less than the upper limit of the estimated range of battery power consumption for the return trip.

[0098] Sub-step 20210: If the remaining power data is outside the estimated range of the vehicle's return trip power consumption, determine that the vehicle is in an abnormal state.

[0099] In one embodiment of this application, the judgment conclusion obtained from the root sub-step 2029 yields the final judgment of the abnormal state.

[0100] Continuing with the previous example, suppose a battery-swapping new energy vehicle A has a remaining battery level. If the remaining battery level is greater than or equal to the upper limit of the estimated range for the return trip's battery consumption, it means that the vehicle has not encountered any risk and can safely return to the battery swapping station. If the remaining battery level is less than or equal to the lower limit of the estimated range for the return trip's battery consumption, it means that the vehicle is at serious risk and may run out of power en route and be unable to reach the battery swapping station.

[0101] Optionally, the abnormal status includes the cumulative number of vehicle abnormalities, which represents the number of times the vehicle has accumulated abnormalities within a limited period. Step 202 may specifically include the following sub-steps:

[0102] Sub-step 20211: Statistically determine the cumulative number of abnormalities of the vehicle;

[0103] In one embodiment of this application, the cumulative number of vehicle anomalies refers to the number of times a vehicle experiences anomalies within a defined period, reflecting the degree of anomaly, such as the number of plug-in charging / battery swapping attempts or the number of times the return trip distance is exceeded (i.e., return trip risk). The cumulative number of vehicle anomalies can be counted using methods or tools, such as counters, databases, and logs, based on the vehicle's abnormal status.

[0104] For example, suppose a battery-swapping new energy vehicle A has a limited period of one month. If the vehicle experiences three abnormal power consumption events and two return trip risks within that month, then the vehicle's total number of abnormal events is five.

[0105] Sub-step 20212: Compare the cumulative number of abnormal events of the vehicle with the adjustment threshold; the adjustment threshold is used to determine the method of adjusting the plug-in charging power of the vehicle.

[0106] In one embodiment of this application, comparing the cumulative number of vehicle anomalies with the adjustment threshold is to determine the method of adjusting the vehicle's plug-in charging power. That is, based on whether the cumulative number of vehicle anomalies reaches or exceeds the adjustment threshold, it is determined whether the plug-in charging power needs to be reduced or increased.

[0107] For example, suppose a battery-swapping new energy vehicle A has an accumulated number of abnormal incidents that reach or exceed the adjustment threshold. This means that the vehicle needs to reduce the plug-in charging power to reduce abnormal power consumption and return trip risks, and extend battery life. If the accumulated number of abnormal incidents is below the adjustment threshold, the vehicle can continue to use plug-in charging normally.

[0108] Sub-step 20213: Adjust the plug-in charging power of the vehicle according to the relationship between the cumulative number of abnormalities of the vehicle and the adjustment threshold.

[0109] In one embodiment of this application, adjusting the plug-in charging power is determined based on the cumulative number of vehicle anomalies and the adjustment threshold, to decide whether to reduce or increase the plug-in charging power in order to optimize the vehicle's charging efficiency and battery life.

[0110] For example, suppose a battery-swapping new energy vehicle A has a maximum charging power of 20kW per hour, and the vehicle has a cumulative number of abnormal incidents of 6. The adjustment threshold is 5. If the cumulative number of abnormal incidents exceeds the adjustment threshold, the plug-in charging power can be reduced to 15kW.

[0111] Optionally, the adjustment threshold includes a first adjustment threshold, a second adjustment threshold, and a third adjustment threshold; the first adjustment threshold is less than the second adjustment threshold, the second adjustment threshold is less than the third adjustment threshold, and sub-step 20213 may include the following sub-steps:

[0112] Sub-step 202131: If the cumulative number of abnormal events of the vehicle is greater than the first adjustment threshold and less than or equal to the second adjustment threshold, adjust the plug-in charging power of the vehicle to the first plug-in charging power; the first plug-in charging power is a preset plug-in charging power, and the first plug-in charging power is less than the normal plug-in charging power of the vehicle and greater than half of the normal plug-in charging power of the vehicle.

[0113] Sub-step 202132: If the cumulative number of abnormal events of the vehicle is greater than the second adjustment threshold and less than or equal to the third adjustment threshold, adjust the plug-in charging power of the vehicle to the second plug-in charging power; the second plug-in charging power is a preset plug-in charging power, and the second plug-in charging power is less than or equal to half of the normal plug-in charging power of the vehicle, and greater than zero.

[0114] Sub-step 202133: If the cumulative number of abnormalities of the vehicle is greater than the third adjustment threshold, adjust the plug-in charging power of the vehicle to zero.

[0115] In some embodiments of this application, sub-steps 202131-202133 are a further subdivision of sub-step 20213, that is, a detailed refinement of the process of adjusting the plug-in charging power of the vehicle based on the relationship between the cumulative number of abnormalities of the vehicle and the adjustment threshold: by setting a first threshold, a second threshold, and a third threshold, the specific numerical range of the plug-in charging power of the vehicle is specifically defined.

[0116] For example, suppose a battery-swapping new energy vehicle A has a maximum charging power of 20kW per hour, a first adjustment threshold of 2, a first adjustment threshold of 5, and a first adjustment threshold of 8, and the vehicle has accumulated 1, 6, and 9 abnormal occurrences in three weeks, respectively. Then the plug-in charging power for these three weeks can be reduced to 15kW, 8kW, and 0kW, respectively.

[0117] Step 203: The real-time collected data includes vehicle equipment fault data, which is used to characterize the fault status of the vehicle's plug-in charging equipment, and the vehicle's equipment fault data is determined.

[0118] In one embodiment of this application, the fault status of the vehicle's plug-in charging equipment is determined based on real-time data collected from the vehicle, so as to determine whether the vehicle has a major safety hazard and is not suitable for driving on the road.

[0119] For example, as shown in Table 1, Table 1 records some safety hazards related to major vehicle malfunctions, such as pantograph negative contactor failure to disconnect fault alarm, abnormal plug connection signal, rechargeable energy storage system mismatch fault alarm, excessive discharge current during charging, charging current over-limit alarm, charging socket NTC fault, charging socket over-temperature alarm, etc., and provides corresponding countermeasures, such as the normal charging stop procedure and immediately disconnecting the charging contactor when stopping charging, etc.

[0120] Table 1

[0121]

[0122] In this embodiment, the equipment fault data refers to data collected in real time that characterizes the fault status of the vehicle plug-in charging equipment, including the equipment model, serial number, status, fault code, etc. The equipment fault data can be used to reflect the working condition and abnormalities of the vehicle plug-in charging equipment, as well as factors affecting charging efficiency and battery life, such as equipment damage, aging, and overheating.

[0123] Step 204: The real-time collected data includes vehicle equipment fault data, which is used to characterize the fault status of the vehicle's plug-in charging equipment. When the vehicle's equipment fault data meets a preset fault threshold, the vehicle's plug-in charging power is adjusted to zero. The fault threshold is used to restrict fault situations that prevent the vehicle from plugging in.

[0124] In one embodiment of this application, step 204 is intended to adjust the vehicle's plug-in charging power to zero based on the vehicle's equipment fault data and fault threshold, so as to avoid further damage or danger to the vehicle's plug-in charging equipment.

[0125] For example, adjusting the plug-in charging power to zero can be determined by factors such as vehicle model, battery capacity, and charging interface type. For instance, a signal or command can be sent to the vehicle or equipment through the normal charging stop procedure in Table 1 or by immediately disconnecting the charging contactor while stopping charging, so as to disconnect the connection or reduce the charging power to zero.

[0126] Step 205: If the current state is an abnormal state, adjust the plug-in charging power of the vehicle according to the abnormal state.

[0127] The method shown in this step has been explained in step 103 and will not be repeated here.

[0128] In one embodiment of this application, the server adjusts the vehicle's plug-in charging power through the above steps, while the local terminal executes and responds to the adjustment command through the intelligent vehicle terminal (TBOX, Telematics Box) in the vehicle networking system.

[0129] Table 2 shows the specific content and data attributes of the information transmission provided by the TBOX shown in the figure: including the data sequence number, definition, length, and description, etc.

[0130] Table 2

[0131]

[0132] In summary, in this embodiment, by analyzing and judging data related to the vehicle's historical mileage, historical battery level, and current vehicle status, the vehicle's plug-in charging usage can be processed from the server. Based on server-registered data and real-time collected data, the vehicle's current status can be determined. Therefore, the method based on this embodiment can adjust the plug-in charging power using relevant vehicle information, solving the problem in related technologies where frequent use of charging guns causes damage to the batteries of battery-swapping new energy vehicles, leading to a general increase in charging and swapping costs and vehicle maintenance costs.

[0133] refer to Figure 3 This application illustrates an apparatus for adjusting the plug-in charging power of a vehicle, the apparatus 30 comprising:

[0134] The data acquisition module 301 is used to acquire the vehicle's server-side registration data and real-time collected data; the real-time collected data is used to determine the vehicle's current status; the server-side registration data is used to record parameter data for determining whether the vehicle is in an abnormal state.

[0135] The status determination module 302 is used to determine the current status of the vehicle based on the server-registered data and / or the real-time collected data;

[0136] The power adjustment module 303 is used to adjust the plug-in charging power of the vehicle according to the abnormal state when the current state is an abnormal state.

[0137] Optionally, the state determination module 302 includes:

[0138] The first state determination submodule is used to determine that the vehicle is in an abnormal state when the state label is a first target value; the first target value is the parameter value corresponding to the abnormal state corresponding to the state label.

[0139] Optionally, the real-time collected data includes the cumulative number of plug-in charging cycles, and determining the current state of the vehicle based on the real-time collected data includes:

[0140] The second state determination submodule is used to determine that the vehicle is in an abnormal state if the cumulative number of plug-in charging times is greater than or equal to a second target value; the second target value is the cumulative number of minimum plug-in charging times corresponding to the abnormal state.

[0141] Optionally, the real-time collected data includes battery level change data and mileage change data. The battery level change data includes the vehicle's battery level at the beginning of the sampling period and the vehicle's battery level at the end of the sampling period. The mileage change data is used to represent the vehicle's mileage during the sampling period. The status determination module 302 includes:

[0142] The third state judgment submodule is used to take the difference between the vehicle's battery power data at the beginning of the sampling time period and the vehicle's battery power data at the end of the sampling time period as the vehicle's battery power consumption during the sampling time period.

[0143] The fourth state determination submodule is used to determine the estimated range of vehicle power consumption based on the driving mileage; the two endpoints of the estimated range of vehicle power consumption are the maximum power consumption and minimum power consumption of the vehicle estimated based on the driving mileage, and the estimated range of vehicle power consumption is used to estimate the power consumption of the vehicle after driving the driving mileage.

[0144] The fifth state determination submodule is used to determine whether the vehicle's power consumption is within the estimated range of the vehicle's power consumption during the sampling time period.

[0145] The sixth state determination submodule is used to determine that the vehicle is in an abnormal state when the power change data is outside the estimated range of the vehicle's power consumption.

[0146] Optionally, the server-side registration data includes battery swapping station location data, the real-time collected data includes remaining battery power data and vehicle location data, and the status judgment module 302 includes:

[0147] The seventh state determination submodule is used to determine the estimated return mileage of the vehicle to the battery swapping station based on the battery swapping station location data and the vehicle location data.

[0148] The eighth state determination submodule is used to determine the estimated range of vehicle return trip power consumption based on the estimated return trip mileage.

[0149] The ninth state determination submodule is used to compare the remaining battery power data with the estimated range of the vehicle's return trip battery power consumption;

[0150] The tenth state determination submodule is used to determine that the vehicle is in an abnormal state when the remaining power data is outside the estimated range of the vehicle's return trip power consumption.

[0151] Optionally, the abnormal status includes the cumulative number of vehicle abnormalities, which represents the number of times the vehicle has accumulated abnormalities within a limited period; the power adjustment module 303 includes:

[0152] The anomaly statistics submodule is used to count and determine the cumulative number of anomalies of the vehicle.

[0153] The threshold comparison submodule is used to compare the cumulative number of abnormalities of the vehicle with an adjustment threshold; the adjustment threshold is used to determine the method of adjusting the plug-in charging power of the vehicle.

[0154] The power adjustment submodule is used to adjust the plug-in charging power of the vehicle based on the relationship between the cumulative number of abnormalities of the vehicle and the adjustment threshold.

[0155] Optionally, the adjustment threshold includes a first adjustment threshold, a second adjustment threshold, and a third adjustment threshold; the first adjustment threshold is less than the second adjustment threshold, and the second adjustment threshold is less than the third adjustment threshold; the power adjustment submodule includes:

[0156] The first power adjustment unit is used to adjust the plug-in charging power of the vehicle to a first plug-in charging power when the cumulative number of abnormal events of the vehicle is greater than the first adjustment threshold and less than or equal to the second adjustment threshold; the first plug-in charging power is a preset plug-in charging power, and the first plug-in charging power is less than the normal plug-in charging power of the vehicle and greater than half of the normal plug-in charging power of the vehicle.

[0157] The second power adjustment unit is used to adjust the plug-in charging power of the vehicle to a second plug-in charging power when the cumulative number of abnormal events of the vehicle is greater than the second adjustment threshold and less than or equal to the third adjustment threshold; the second plug-in charging power is a preset plug-in charging power, and the second plug-in charging power is less than or equal to half of the normal plug-in charging power of the vehicle and greater than zero.

[0158] The third power adjustment unit is used to adjust the plug-in charging power of the vehicle to zero when the cumulative number of abnormal events of the vehicle exceeds the third adjustment threshold.

[0159] Optionally, the real-time collected data includes vehicle equipment fault data, which is used to characterize the fault status of the vehicle's plug-in charging equipment. The device further includes:

[0160] The fault diagnosis module is used to determine the equipment fault data of the vehicle.

[0161] The charging disable module is used to adjust the vehicle's plug-in charging power to zero when the vehicle's equipment fault data meets a preset fault threshold; the fault threshold is used to limit fault conditions that prevent the vehicle from plugging in.

[0162] In summary, in this embodiment, by analyzing and judging data related to the vehicle's historical mileage, historical battery level, and current vehicle status, the vehicle's plug-in charging usage can be processed from the server. Based on server-registered data and real-time collected data, the vehicle's current status can be determined. Therefore, the method based on this embodiment can adjust the plug-in charging power using relevant vehicle information, solving the problem in related technologies where frequent use of charging guns causes damage to the batteries of battery-swapping new energy vehicles, leading to a general increase in charging and swapping costs and vehicle maintenance costs.

[0163] Reference Figure 4 The electronic device 500 may include one or more of the following components: processing component 502, memory 505, power supply component 506, multimedia component 508, audio component 510, input / output (I / O) interface 512, sensor component 514, and communication component 516.

[0164] Processing component 502 typically controls the overall operation of electronic device 500, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 502 may include one or more modules to facilitate interaction between processing component 502 and other components. For example, processing component 502 may include a multimedia module to facilitate interaction between multimedia component 508 and processing component 502.

[0165] Memory 504 is used to store various types of data to support the operation of electronic device 500. Examples of this data include instructions for any application or method operating on electronic device 500, contact data, phonebook data, messages, pictures, multimedia, etc. Memory 504 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0166] Power supply component 506 provides power to various components of electronic device 500. Power supply component 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 500.

[0167] Multimedia component 508 includes an interface that provides an output interface between electronic device 500 and user. In some embodiments, the interface may include a liquid crystal display (LCD) and a touch panel (TP). If the interface includes a touch panel, the interface may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may not only sense the boundaries of touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 508 includes a front-facing camera and / or a rear-facing camera. When electronic device 500 is in an operating mode, such as shooting mode or multimedia mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0168] Audio component 510 is used to output and / or input audio signals. For example, audio component 510 includes a microphone (MIC) used to receive external audio signals when electronic device 500 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 504 or transmitted via communication component 516. In some embodiments, audio component 510 also includes a speaker for outputting audio signals.

[0169] Input / output (I / O) interface 512 provides an interface between processing component 502 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0170] Sensor assembly 514 includes one or more sensors for providing state assessments of various aspects of electronic device 500. For example, sensor assembly 515 may detect the on / off state of electronic device 500, the relative positioning of components such as the display and keypad of electronic device 500, changes in position of electronic device 500 or a component of electronic device 500, the presence or absence of user contact with electronic device 500, orientation or acceleration / deceleration of electronic device 500, and temperature changes of electronic device 500. Sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 515 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 514 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0171] Communication component 516 facilitates wired or wireless communication between electronic device 500 and other devices. Electronic device 500 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 516 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0172] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to implement a display control method provided in the embodiments of this application.

[0173] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 504 including instructions, which can be executed by a processor 520 of an electronic device 500 to perform the above-described method. For example, the non-transitory storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0174] Figure 5 This is a block diagram of an electronic device 600 according to another embodiment of the present invention. For example, the electronic device 600 may be provided as a server. (See also...) Figure 5 The electronic device 600 includes a processing component 622, which further includes one or more processors, and memory resources represented by memory 632 for storing instructions, such as application programs, that can be executed by the processing component 622. The application programs stored in memory 632 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 622 is configured to execute instructions to perform a display control method provided in embodiments of this application.

[0175] Electronic device 600 may also include a power supply component 626 configured to perform power management of electronic device 600, a wired or wireless network interface 650 configured to connect electronic device 600 to a network, and an input / output (I / O) interface 658. Electronic device 600 may operate on an operating system stored in memory 632, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0176] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0177] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for adjusting the plug-in charging power of a vehicle, applied to a server, characterized in that, include: Obtain vehicle server-side registration data and real-time collected data; The real-time collected data is used to determine the vehicle's historical mileage and historical battery charge data. The server-side registration data includes status tags, which reflect the current status of the vehicle. The current status of the vehicle is determined based on the server-registered data and / or the real-time collected data; If the current state is abnormal, adjust the plug-in charging power of the vehicle according to the abnormal state; Determining the current status of the vehicle based on the server-registered data includes: If the status label is a first target value, the vehicle is determined to be in an abnormal state; the first target value is a parameter value corresponding to the abnormal state of the status label. The real-time collected data includes the cumulative number of plug-in charging cycles. Determining the current state of the vehicle based on the real-time collected data includes: If the cumulative number of plug-in charging attempts is greater than or equal to the second target value, the vehicle is determined to be in an abnormal state; the second target value is the cumulative number of minimum plug-in charging attempts corresponding to the abnormal state.

2. The method as described in claim 1, characterized in that, The real-time collected data includes battery level change data and mileage change data. The battery level change data includes the vehicle's battery level at the beginning of the sampling period and the vehicle's battery level at the end of the sampling period. The mileage change data is used to characterize the vehicle's mileage during the sampling period. The step of determining the abnormal state of the vehicle based on the real-time collected data includes: The difference between the vehicle's battery level at the beginning of the sampling period and the vehicle's battery level at the end of the sampling period is taken as the vehicle's battery consumption during the sampling period. The estimated range of vehicle power consumption is determined based on the mileage traveled; the two endpoints of the estimated range of vehicle power consumption are the maximum and minimum power consumption of the vehicle estimated based on the mileage traveled, and the estimated range of vehicle power consumption is used to estimate the power consumption of the vehicle after traveling the mileage traveled. Determine whether the vehicle's power consumption during the sampling period is within the estimated range of the vehicle's power consumption; If the battery level change data is outside the estimated range of the vehicle's battery consumption, the vehicle is determined to be in an abnormal state.

3. The method as described in claim 1, characterized in that, The server-side registration data includes battery swapping station location data, and the real-time collected data includes remaining battery power data and vehicle location data. Determining the abnormal status of the vehicle based on the server-side registration data and the real-time collected data includes: The estimated return mileage of the vehicle to the battery swapping station is determined based on the battery swapping station location data and the vehicle location data. Based on the estimated return mileage, determine the estimated range of vehicle return trip power consumption; Compare the remaining battery power data with the estimated range of the vehicle's return trip battery power consumption; If the remaining battery power data is outside the estimated range of the vehicle's return trip battery consumption, the vehicle is determined to be in an abnormal state.

4. The method as described in claim 1, characterized in that, The abnormal status includes the cumulative number of vehicle abnormalities, which is used to characterize the number of times the vehicle has accumulated abnormalities within a limited period. When the current state is an abnormal state, adjusting the plug-in charging power of the vehicle according to the abnormal state includes: The cumulative number of abnormalities of the vehicle was determined statistically; The cumulative number of abnormal events of the vehicle is compared with an adjustment threshold; the adjustment threshold is used to determine the method of adjusting the plug-in charging power of the vehicle. The plug-in charging power of the vehicle is adjusted based on the relationship between the cumulative number of abnormalities of the vehicle and the adjustment threshold.

5. The method as described in claim 4, characterized in that, The adjustment thresholds include a first adjustment threshold, a second adjustment threshold, and a third adjustment threshold; the first adjustment threshold is less than the second adjustment threshold, and the second adjustment threshold is less than the third adjustment threshold; adjusting the plug-in charging power of the vehicle based on the relationship between the cumulative number of abnormal events and the adjustment thresholds includes: If the cumulative number of abnormal events of the vehicle is greater than the first adjustment threshold and less than or equal to the second adjustment threshold, the plug-in charging power of the vehicle is adjusted to a first plug-in charging power; the first plug-in charging power is a preset plug-in charging power, and the first plug-in charging power is less than the normal plug-in charging power of the vehicle, but greater than half of the normal plug-in charging power of the vehicle. If the cumulative number of abnormal events of the vehicle is greater than the second adjustment threshold and less than or equal to the third adjustment threshold, the plug-in charging power of the vehicle is adjusted to the second plug-in charging power; the second plug-in charging power is a preset plug-in charging power, and the second plug-in charging power is less than or equal to half of the normal plug-in charging power of the vehicle, and greater than zero. If the cumulative number of abnormal events of the vehicle exceeds the third adjustment threshold, the plug-in charging power of the vehicle will be adjusted to zero.

6. The method as described in claim 1, characterized in that, The real-time collected data includes vehicle equipment fault data, which is used to characterize the fault status of the vehicle's plug-in charging equipment. The method further includes: Determine the equipment fault data of the vehicle; If the vehicle's equipment fault data meets a preset fault threshold, the vehicle's plug-in charging power is adjusted to zero; the fault threshold is used to limit fault conditions that prevent the vehicle from plugging in for charging.

7. A device for adjusting the plug-in charging power of a vehicle, characterized in that, The device includes: The data acquisition module is used to acquire the vehicle's server-side registration data and real-time collected data; the real-time collected data is used to determine the vehicle's historical mileage and historical battery level; the server-side registration data includes status tags, which reflect the vehicle's current status. The status determination module is used to determine the current status of the vehicle based on the server-registered data and / or the real-time collected data. A power adjustment module is used to adjust the plug-in charging power of the vehicle according to the abnormal state when the current state is abnormal. The status determination module includes: The first state determination submodule is used to determine that the vehicle is in an abnormal state when the state label is a first target value; the first target value is the parameter value of the abnormal state corresponding to the state label. The real-time collected data includes the cumulative value of plug-in charging times, and the status judgment module includes: The second state determination submodule is used to determine that the vehicle is in an abnormal state if the cumulative number of plug-in charging times is greater than or equal to a second target value; the second target value is the cumulative number of minimum plug-in charging times corresponding to the abnormal state.

8. An electronic device, characterized in that, include: Processor; memory for storing processor-executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is enabled to perform the method as described in any one of claims 1 to 6.

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