Method and device for measuring and calculating charging capacity of battery swap station and medium

Abstract

The invention discloses a method and equipment for measuring and calculating the charging capacity of a battery swap station and a medium, and aims to solve the technical problems that the passenger flow volume and charging requirements in different time periods cannot be accurately predicted and the power requirements of battery swap and charging services cannot be balanced according to different operation habits and energy complementation habits of drivers in the prior art. The method comprises the steps of predicting a battery replacement demand of a battery replacement station in a preset time period; obtaining available electric power of the battery swap station and charging bin power of the battery swap station, and calculating residual charging capacity of the battery swap station in combination with the battery swap demand; and determining the number of openable charging piles of the battery swap station according to the residual charging capacity. According to the invention, the number of the openable charging piles can be flexibly adjusted according to the real-time data and the prediction result, so as to cope with the fluctuation and change of the battery replacement demand, and ensure the stable operation and efficient service of the battery replacement station.

Description

[0001] This application claims priority based on the invention patent application with the application number 202311867842.2 and the invention title "A Method, Device and Medium for Calculating the Charging Capacity of a Battery Swap Station" filed on December 29, 2023. This application incorporates the entire text of the above-mentioned Chinese patent application by reference. Technical Field

[0002] This application relates to the technical field of power data processing, and particularly to a method, device and medium for calculating the charging capacity of a battery swap station. Background Art

[0003] Currently, in the operation of battery swap stations, the differences in drivers' operation habits and energy replenishment habits lead to peak and valley differences in passenger flow. In order to meet the user needs during peak periods and at the same time maximize the power allocation capacity during valley hours and generate economic benefits, battery swap stations need to adopt effective strategies to improve profitability. An effective solution is to use the power capacity of the battery swap station during idle time to provide charging services for users. However, in view of different operation habits and energy replenishment habits of drivers, how to accurately predict the passenger flow and charging demand at different time periods and balance the power demand for battery swapping and charging services is a key issue. Summary of the Invention

[0004] Embodiments of this application provide a method, device and medium for calculating the charging capacity of a battery swap station, which are used to solve the technical problem that in the prior art, in view of different operation habits and energy replenishment habits of drivers, it is impossible to accurately predict the passenger flow and charging demand at different time periods and balance the power demand for battery swapping and charging services.

[0005] On the one hand, embodiments of this application provide a method for calculating the charging capacity of a battery swap station. The battery swap station is provided with charging piles and includes:

[0006] Predict the battery swapping demand of the battery swap station in a preset time period;

[0007] Obtain the available power of the battery swap station and the power of the charging bins of the battery swap station, and calculate the remaining charging capacity of the battery swap station in combination with the battery swapping demand;

[0008] Determine the number of available charging piles of the battery swap station according to the remaining charging capacity.

[0009] In one implementation manner of this application, predicting the battery swapping demand of the battery swap station in a preset time period specifically includes:

[0010] Based on the previously determined data acquisition requirements corresponding to the battery swap station, determine the designated positions where the Internet of Things devices in the battery swap station need to be set, and install corresponding sensors and monitoring devices in the battery swap station according to the designated positions;

[0011] Through the sensors and monitoring devices, monitor the operating status of the charging piles and the charging behaviors of the charging vehicles in the battery swapping station, and obtain corresponding supply and demand data; the supply and demand data further includes: the battery charging status, arrival time, and departure time of the charging vehicles, the working status, charging speed of the charging piles, and the current number of fully charged batteries in the battery swapping station;

[0012] According to the supply and demand data, predict the number of battery swapping vehicles in the battery swapping station within a preset time period, and calculate the difference between the number of battery swapping vehicles within the preset time period and the current number of fully charged batteries, so as to obtain the battery swapping demand of the battery swapping station within the preset time period.

[0013] Based on the pre-determined data acquisition requirements, the embodiments of the present application rationally arrange Internet of Things devices (such as sensors and monitoring devices), which can ensure the comprehensive and accurate collection of key information in the battery swapping station, providing a solid data foundation for subsequent battery swapping demand prediction and operation management; by precisely setting the specified positions of the Internet of Things devices, the processes of device installation, debugging, and maintenance can be simplified, the operation and maintenance efficiency can be improved, and the operation and maintenance costs can be reduced. With the development of the battery swapping station business and the change of requirements, the layout and configuration of the Internet of Things devices can be flexibly adjusted to adapt to new data acquisition requirements and maintain the continuous optimization and upgrading of the system; through the sensors and monitoring devices, the operating status of the charging piles and the charging behaviors of the charging vehicles are monitored in real time, potential problems and abnormalities can be detected in time, and the smooth progress of the battery swapping process can be ensured. The obtained supply and demand data not only includes the basic information of the charging vehicles (such as battery charging status, arrival and departure times), but also covers key information such as the working status and charging speed of the charging piles, providing a rich data source for subsequent data analysis and decision-making; based on the real-time monitored data, the battery swapping station can dynamically adjust the charging strategy, such as optimizing the charging time, adjusting the charging power, etc., to improve the charging efficiency and user satisfaction; by predicting the number of battery swapping vehicles in the battery swapping station within a preset time period according to the supply and demand data and calculating the difference from the current number of fully charged batteries, the battery swapping demand can be scientifically predicted, providing strong decision-making support for the power distribution, battery inventory management, and personnel arrangement of the battery swapping station; accurate prediction of the battery swapping demand helps to avoid over-reservation or shortage of power and battery resources, reduce resource waste, and improve economic benefits. Through advance preparation and planning, the battery swapping station can better meet the battery swapping needs of users, reduce the waiting time of users, and improve user satisfaction and loyalty.

[0014] In an implementation manner of the present application, predicting the number of battery swapping vehicles in the battery swapping station within a preset time period according to the supply and demand data specifically includes:

[0015] Perform data cleaning on the supply and demand data in the battery swapping station, and perform denoising operations on the supply and demand data after cleaning to obtain effective supply and demand data;

[0016] Normalize the effective supply and demand data to obtain standard supply and demand data, and analyze the supply and demand data corresponding to the swap station based on machine learning algorithms and data mining techniques;

[0017] Determine the actual business needs of the swap station, and extract corresponding effective features from the standard supply and demand data according to the actual business needs of the swap station;

[0018] Predict the number of battery swapping vehicles at the swap station within a preset time period based on the corresponding effective features.

[0019] Through data cleaning and denoising operations in the embodiments of the present application, errors, duplicates, anomalies or irrelevant information in the supply and demand data can be removed, ensuring the accuracy and reliability of the data, and providing a high-quality data source for subsequent data analysis; Removing noise data can reduce the interference of the machine learning model during training, improve the stability and prediction accuracy of the model, and make the prediction results more reliable; The cleaned effective supply and demand data can simplify the complexity of data analysis, improve the efficiency of data processing and analysis, and shorten the decision-making cycle; Normalization processing can convert supply and demand data with different dimensions into standard data under the same scale, eliminate the influence of dimension differences on the data analysis results, and make the analysis more accurate; Analyzing based on the normalized standard supply and demand data using machine learning algorithms and data mining techniques can accelerate the model convergence speed, improve the model training efficiency, and may enhance the prediction performance of the model; Through in-depth analysis of the standard supply and demand data, potential laws and correlations between the supply and demand of the swap station can be discovered, providing strong support for optimizing the operation of the swap station; Extracting corresponding effective features according to the actual business needs of the swap station can ensure a high degree of matching between data analysis and business objectives, and improve the pertinence and practicality of the analysis results; By extracting effective features, the battery swapping demand of the swap station at different time periods can be predicted more accurately, so as to optimize the allocation of power, batteries and human resources and improve resource utilization efficiency; Based on the analysis results of the effective features, more accurate and scientific decision-making support can be provided for the operation and management of the swap station, helping managers make more reasonable decisions; By predicting the number of battery swapping vehicles within a preset time period, the swap station can plan and prepare power, batteries and human resources in advance to ensure a rapid response during peak periods or demand growth, and avoid service interruptions or delays; Accurate prediction of the number of battery swapping vehicles helps the swap station reasonably arrange charging resources and service times, reduce user waiting times, and improve user experience and satisfaction; Guiding the daily operation of the swap station through the prediction results can optimize the service process, improve the operation efficiency, and reduce the operation cost, achieving the maximization of economic benefits.

[0020] In an implementation manner of the present application, the available power of the battery swapping station and the power of the charging bins of the battery swapping station are obtained, and in combination with the battery swapping demand, the remaining charging capacity of the battery swapping station is calculated, specifically including:

[0021] Obtain the available power of the battery swapping station and the power of the charging bins of the battery swapping station;

[0022] Determine the number of batteries to be charged within a preset time period according to the battery swapping demand;

[0023] Determine the charging occupied power of the battery swapping station according to the number of batteries to be charged and the power of the charging bins of the battery swapping station;

[0024] Calculate the available power of the battery swapping station and the charging occupied power to determine the remaining charging capacity of the battery swapping station.

[0025] In the embodiments of the present application, by obtaining the available power of the battery swapping station and the power of the charging bins, the system can grasp the power resource status of the battery swapping station in real time, including the total available power and the charging capacity of each charging bin currently, which helps the manager to allocate and schedule the power resources reasonably; determining the number of batteries to be charged within a preset time period according to the battery swapping demand, this step enables the system to predict and plan the future charging demand in advance; through accurate prediction, the battery swapping station can arrange the charging plan more effectively, avoiding waste and shortage of power resources; determining the charging occupied power according to the number of batteries to be charged and the power of the charging bins of the battery swapping station, this step helps the system to optimize the charging strategy; by reasonably allocating the charging power, the system can ensure that each charging bin can charge at the optimal power, thereby improving the charging efficiency and shortening the charging time; calculating the available power of the battery swapping station and the charging occupied power to determine the remaining charging capacity; by monitoring the remaining charging capacity in real time, the system can issue an early warning in time when the power resources are tense, avoiding overload operation and power failures; the battery swapping station can predict and meet the battery swapping demand of users more accurately, reduce the waiting time of users, improve the battery swapping efficiency, help to improve the user experience and satisfaction, and enhance the trust and dependence of users on the battery swapping station; the real-time and accurate power resource data and charging demand prediction provide strong support for the intelligent decision-making and operation management of the battery swapping station; the manager can formulate more scientific and reasonable operation strategies based on these data, optimize the resource allocation, improve the operation efficiency, and reduce the operation cost.

[0026] In an implementation manner of the present application, according to the remaining charging capacity, determine the number of available charging piles of the battery swapping station, specifically including:

[0027] Obtain the number of charging piles in use and the power of the charging piles in the battery swapping station;

[0028] Determine the number of available charging piles of the battery swapping station according to the power of the charging piles and the remaining charging capacity;

[0029] Determine the number of available charging piles in the battery swapping station according to the number of available charging piles and the number of charging piles in use.

[0030] In the embodiments of the present application, by obtaining the number and power of the charging piles in use in the battery swapping station in real time, the currently available charging pile resources can be quickly calculated, which helps users understand the charging situation of the battery swapping station in advance and avoid the embarrassing situation of finding no available charging piles after arrival, thereby improving the user experience and satisfaction; determining the number of available charging piles according to the charging pile power and the remaining charging capacity can intelligently manage the charging pile resources; when the power resources are in short supply, the use of high-power charging piles can be preferentially guaranteed to meet the fast charging needs; when the power resources are abundant, more charging piles can be opened for users to use, improving the resource utilization rate; by calculating the number of available charging piles, real-time operation data support can be provided for the operation managers of the battery swapping station; the managers can adjust the operation strategies according to these data, such as increasing or decreasing the number of charging piles, adjusting the charging price, etc., to improve the operation efficiency and economic benefits, which is not only applicable to the current operation management of the battery swapping station, but also has good flexibility and scalability; with the increase in the number of electric vehicles and the growth of battery swapping demand, the number and power of the charging piles can be easily expanded to meet the needs of future development; by optimizing the allocation and utilization of the charging pile resources, the energy waste caused by the idle or overuse of the charging piles can be reduced, which helps to reduce the operation cost of the battery swapping station, and at the same time promotes energy conservation, emission reduction and sustainable development; by improving the charging efficiency and service quality, more electric vehicle users can be attracted to use the battery swapping service, further promoting the development of the electric vehicle industry.

[0031] In one implementation manner of the present application, after determining the number of available charging piles in the battery swapping station according to the number of available charging piles and the number of charging piles in use, the method further includes:

[0032] Determine the number of idle charging piles in the battery swapping station according to the number of configured charging piles and the number of charging piles in use in the battery swapping station;

[0033] If the number of available charging piles exceeds the number of idle charging piles, open all the idle charging piles; if the number of available charging piles does not exceed the number of idle charging piles, open the number of idle charging piles equal to the number of available charging piles.

[0034] In the embodiment of the present application, by calculating the number of idle charging piles in the swapping station in real time and opening the corresponding charging piles according to the limit of the number of available charging piles, it can ensure that users can quickly find available charging piles for charging when they arrive at the swapping station, avoiding wasting time or generating dissatisfaction due to the inability to find idle charging piles, thus improving the user experience; by intelligently opening charging piles based on the comparison result between the number of available charging piles and the number of idle charging piles, it avoids over-opening or idling of charging piles, helps optimize the utilization of charging pile resources, ensures that power resources are utilized to the greatest extent, and reduces resource waste at the same time; the strategy allows the system to flexibly adjust the number of available charging piles according to real-time conditions; whether the power resources are tight or abundant, it can make the optimal decision according to the current conditions, thus enhancing the flexibility and adaptability of the system; by intelligently managing the opening and closing of charging piles, it can reduce operation problems caused by improper management of charging piles, such as user complaints, power failures, etc., helps improve the operation efficiency of the swapping station, reduce operation costs, and improve the overall service quality; by optimizing the utilization of charging pile resources and reducing resource waste, this embodiment also helps to promote sustainable development; by reducing power consumption and carbon emissions, the swapping station can contribute to environmental protection and promote the green development of the electric vehicle industry.

[0035] In one implementation manner of the present application, after determining the number of charging piles in use in the swapping station, the method further includes:

[0036] Determine the operating charging piles in the swapping station, and input the supply-demand data corresponding to each operating charging pile into a pre-constructed charging vehicle behavior analysis model respectively to realize the prediction of the charging demand of charging vehicles;

[0037] Determine the charging demand priority levels corresponding to each charging vehicle, and adjust the charging power and charging speed of each operating charging pile according to the charging demand priority levels.

[0038] In the embodiments of the present application, by inputting the supply and demand data of each operating charging pile into a pre-constructed charging vehicle behavior analysis model, the charging demand of charging vehicles can be accurately predicted, enabling the swapping station to plan and prepare in advance, thereby optimizing the allocation of charging resources, significantly improving the charging efficiency, and avoiding the situation of idle or overly congested charging piles caused by uneven distribution of charging resources; dynamically adjusting the charging power and charging speed of each operating charging pile according to the priority of charging demand can ensure that high-priority charging vehicles (such as vehicles with extremely low battery levels or for emergency purposes) obtain charging services first, enhancing user satisfaction and loyalty. Especially during peak periods or when resources are scarce, the waiting time and anxiety of users will be significantly reduced; through the accurate prediction of charging demand and the reasonable allocation of charging resources, energy waste can be reduced, the charging power of the charging pile can be adjusted according to actual demand, and the situation of still operating at maximum power when the charging demand is low can be avoided, thereby achieving energy conservation and efficient utilization, which is of great significance for promoting green travel and energy conservation and emission reduction; it can improve the overall operation efficiency of the swapping station. Through an automated prediction and scheduling system, the need for manual intervention is reduced, and the operation cost is lowered. At the same time, through real-time monitoring and data analysis, potential problems such as charging pile failures and supply-demand imbalances can be discovered and solved in a timely manner to ensure the stable operation of the swapping station; through the linkage with other intelligent systems such as the urban traffic management system and the power grid management system, the intelligent level of urban traffic can be further improved.

[0039] In one implementation manner of the present application, after determining the number of idle charging piles in the swapping station, the method further includes:

[0040] Respectively obtain the historical supply and demand records of each idle charging pile within a preset time interval;

[0041] Respectively input the historical supply and demand records corresponding to each of the idle charging piles into a preset charging pile operation status prediction model to analyze the historical supply records and determine the charging power and charging speed of each of the idle charging piles within the preset time interval;

[0042] According to the charging power and charging speed of each of the idle charging piles, determine whether there are any faulty idle charging piles, and add a fault label to the faulty idle charging piles to determine the number of effective idle charging piles in the swapping station.

[0043] In the embodiments of the present application, by regularly obtaining the historical supply and demand records of each idle charging pile within a preset time interval and inputting them into a preset prediction model for the operating state of the charging pile for analysis, potential fault signs of the charging pile can be detected in advance, which helps to reduce the interruption of charging services caused by sudden failures and improve the reliability and availability of the charging pile; determining the charging power and charging speed of each idle charging pile within a preset time interval not only helps to identify faulty charging piles, but also provides an important basis for the resource management and scheduling of the battery swapping station; by accurately understanding the actual performance of each charging pile, charging tasks can be more reasonably allocated to ensure the continuity and efficiency of charging services; by promptly identifying and eliminating faulty charging piles, it can be ensured that users can always find available charging facilities when using, avoiding the waste of time and energy for users when looking for idle charging piles due to encountering faulty equipment, thereby enhancing the overall satisfaction and experience of users; detecting and handling charging pile faults in advance can reduce the maintenance costs and time caused by equipment damage. In addition, by optimizing resource allocation and reducing unnecessary service interruptions, the operating costs of the battery swapping station can also be effectively controlled; this process relies on in-depth analysis of historical data and the prediction ability of machine learning models, enhancing the data-driven decision-making ability of the battery swapping station; by continuously accumulating and analyzing data, the prediction model and scheduling strategy can be continuously optimized to cope with the changing charging demands and market environments; by integrating advanced prediction models, data analysis techniques, and automated control systems, the battery swapping station can achieve more efficient resource management, more accurate fault detection, and more flexible service scheduling, providing strong support for the development of intelligent transportation systems.

[0044] In one implementation manner of the present application, after determining the number of available charging piles of the battery swapping station according to the remaining charging capacity, the method further includes:

[0045] Determining a plurality of vehicles to be charged in the battery swapping station, determining the charging influencing factors corresponding to the plurality of vehicles to be charged based on historical charging records, and determining the charging priorities corresponding to the plurality of vehicles to be charged; the charging influencing factors at least include: arrival time, required power value, and vehicle level;

[0046] Based on the charging priorities corresponding to the plurality of vehicles to be charged, allocating available idle charging piles to the corresponding vehicles to be charged, and obtaining the charging conditions of the vehicles to be charged;

[0047] Adjusting the charging power and charging speed of the corresponding charging pile according to the charging conditions of the vehicles to be charged and the power supply conditions of the battery swapping station to optimize the power control strategy.

[0048] By comprehensively considering multiple charging influencing factors such as arrival time, required power value, and vehicle level, the embodiments of the present application can more fairly determine the charging priority of the vehicle to be charged, ensuring the reasonable allocation of charging resources, meeting both emergency needs and taking into account long-term stability and user satisfaction, thereby improving the overall efficiency and fairness of charging services; according to the charging priority of the vehicle to be charged and the power supply situation of the swapping station, dynamically adjusting the charging power and charging speed of the charging pile helps to maximize the utilization of power resources; when the power supply is tense, it can preferentially meet the charging needs of high-priority vehicles, while avoiding inefficient or unnecessary charging operations and reducing energy waste; users can reasonably arrange the charging time according to the actual situation of the vehicle and the charging priority, reducing the waiting time. At the same time, adjusting the charging power and speed in real time according to the charging situation ensures the high efficiency and stability of the charging process and improves the user's charging experience; the swapping station can quickly adjust the charging strategy and resource allocation according to real-time data and user needs, enhancing the flexibility and response speed of the swapping station, helping to cope with emergencies and changing needs, and improving the overall operation efficiency; by integrating intelligent technologies such as data analysis, priority setting, and dynamic adjustment, the swapping station can achieve more efficient and automated management, not only reducing the cost and error rate of manual intervention, but also improving the management efficiency and service quality, providing strong support for the development of intelligent transportation systems; by optimizing the charging power and speed, reducing energy waste and emissions, it helps to promote green travel and sustainable development; by providing more intelligent and efficient charging services, the swapping station provides a strong guarantee for the popularization and promotion of electric vehicles, promoting the application of clean energy and environmental protection.

[0049] On the other hand, the embodiments of the present application also provide a device for calculating the charging capacity of a swapping station. The swapping station is provided with charging piles, and the device includes:

[0050] A prediction module for predicting the swapping demand of the swapping station in a preset time period;

[0051] An acquisition module for acquiring the available power of the swapping station and the power of the charging bins of the swapping station, and calculating the remaining charging capacity of the swapping station in combination with the swapping demand;

[0052] A determination module for determining the number of available charging piles of the swapping station according to the remaining charging capacity.

[0053] On the other hand, the embodiments of the present application also provide an electronic device. The swapping station is provided with charging piles, and the device includes:

[0054] At least one processor;

[0055] And a memory communicatively connected to the at least one processor;

[0056] Among them, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for calculating the charging capacity of the battery swapping station as described in any one of the above.

[0057] On the other hand, an embodiment of the present application also provides a non-volatile computer storage medium storing computer-executable instructions. The battery swapping station is provided with charging piles, and when the computer executes the executable instructions, the method for calculating the charging capacity of the battery swapping station as described in any one of the above is implemented.

[0058] The embodiment of the present application provides a method, device and medium for calculating the charging capacity of a battery swapping station, which at least include the following beneficial effects:

[0059] By accurately predicting the battery swapping demand of the battery swapping station within a preset time period, the battery swapping station can plan and prepare power, battery inventory and human resources in advance to ensure rapid response during peak periods or demand growth, and avoid service interruption or delay; based on the prediction of the battery swapping demand, the battery swapping station can allocate resources more accurately, such as dispatching fully charged batteries in advance, reducing the waiting time of users caused by insufficient batteries, and improving user satisfaction; by predicting the battery swapping demand, the battery swapping station can avoid unnecessary power waste and battery inventory backlog, thereby reducing operating costs and improving economic benefits; by obtaining the available power and charging bin power of the battery swapping station in real time and calculating the remaining charging capacity in combination with the battery swapping demand, it can ensure that the power supply of the battery swapping station operates within a safe range and prevent equipment damage or safety accidents caused by power overload; reasonably arranging the number of charging piles to be turned on and the charging power according to the remaining charging capacity can ensure that the charging piles operate in an efficient range, improve the charging efficiency and shorten the waiting time of users; combining real-time data and prediction results, the battery swapping station can dynamically adjust the charging strategy, such as increasing or decreasing the number of charging piles to be turned on, adjusting the charging power, etc., to cope with the changes in battery swapping demand in different time periods and scenarios; by determining the number of charging piles that can be opened according to the remaining charging capacity, the battery swapping station can ensure that there are enough charging piles for users, reduce the waiting time of users and improve the user experience; reasonably opening the number of charging piles can avoid the idle and waste of charging piles, improve the utilization rate of resources and economic benefits; the battery swapping station can flexibly adjust the number of charging piles that can be opened according to real-time data and prediction results to cope with the fluctuations and changes in battery swapping demand, and ensure the stable operation and efficient service of the battery swapping station. Description of the Drawings

[0060] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0061] Figure 1 Schematic flowchart of a method for calculating the charging capacity of a battery swapping station provided by an embodiment of the present application;

[0062] Figure 2 Internal structure schematic diagram of a device for calculating the charging capacity of a battery swapping station provided by an embodiment of the present application;

[0063] Figure 3 Internal structure schematic diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0064] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0065] An embodiment of the present application discloses a method, device, and medium for calculating the charging capacity of a battery swapping station to solve the technical problem in the prior art that it is impossible to accurately predict the passenger flow and charging demand during different time periods and balance the power demand for battery swapping and charging services according to different operation habits and energy replenishment habits of drivers.

[0066] The following will detail the technical solutions provided by each embodiment of the present application in conjunction with the drawings.

[0067] Figure 1 Schematic flowchart of a method for calculating the charging capacity of a battery swapping station provided by an embodiment of the present application.

[0068] The implementation of the analysis method involved in an embodiment of the present application can be a terminal device or a server, and the present application does not make special restrictions on this. For the convenience of understanding and description, the following embodiments will be described in detail using a server as an example.

[0069] It should be noted that the server can be a single device or a system composed of multiple devices, that is, a distributed server, and the present application does not make specific limitations on this.

[0070] As Figure 1 shown, a method for calculating the charging capacity of a battery swapping station provided by an embodiment of the present application includes:

[0071] 101. Predict the battery swapping demand of the battery swapping station during a preset time period.

[0072] In an embodiment of the present application, predicting the battery swapping demand of the battery swapping station during a preset time period specifically includes:

[0073] Based on the pre-determined data acquisition requirements corresponding to the battery swapping station, determine the designated locations where Internet of Things (IoT) devices need to be set up in the battery swapping station, and install corresponding sensors and monitoring devices in the battery swapping station according to the designated locations;

[0074] Monitor the operating status of charging piles and the charging behaviors of charging vehicles in the battery swapping station through sensors and monitoring devices, and obtain corresponding supply and demand data; the supply and demand data also includes: the battery charging status, arrival time, and departure time of the charging vehicle, the working status, charging speed of the charging pile, and the current number of fully charged batteries in the battery swapping station;

[0075] Predict the number of battery swapping vehicles in the battery swapping station within a preset time period based on the supply and demand data, and calculate the difference between the number of battery swapping vehicles within the preset time period and the current number of fully charged batteries to obtain the battery swapping demand of the battery swapping station within the preset time period.

[0076] Based on the pre-determined data acquisition requirements, this application embodiment reasonably arranges IoT devices (such as sensors and monitoring devices), which can ensure the comprehensive and accurate collection of key information in the battery swapping station, providing a solid data foundation for subsequent battery swapping demand prediction and operation management. By precisely setting the designated locations of IoT devices, the processes of device installation, debugging, and maintenance can be simplified, the operation and maintenance efficiency can be improved, and the operation and maintenance costs can be reduced. With the development of the battery swapping station business and the change of requirements, the layout and configuration of IoT devices can be flexibly adjusted to adapt to new data acquisition requirements and maintain the continuous optimization and upgrade of the system.

[0077] By using sensors and monitoring devices to monitor the operating status of charging piles and the charging behaviors of charging vehicles in real time, potential problems and abnormalities can be detected in a timely manner to ensure the smooth progress of the battery swapping process. The obtained supply and demand data not only includes the basic information of the charging vehicle (such as battery charging status, arrival and departure times), but also covers key information such as the working status and charging speed of the charging pile, providing a rich data source for subsequent data analysis and decision-making. Based on the real-time monitored data, the battery swapping station can dynamically adjust the charging strategy, such as optimizing the charging time, adjusting the charging power, etc., to improve the charging efficiency and user satisfaction.

[0078] By predicting the number of battery swapping vehicles in the battery swapping station within a preset time period based on the supply and demand data and calculating the difference from the current number of fully charged batteries, the battery swapping demand can be scientifically predicted, providing strong decision-making support for the power distribution, battery inventory management, and personnel arrangement of the battery swapping station. Accurate prediction of the battery swapping demand helps to avoid over-reservation or shortage of power and battery resources, reduce resource waste, and improve economic benefits. By making preparations and plans in advance, the battery swapping station can better meet the battery swapping needs of users, reduce the waiting time of users, and improve user satisfaction and loyalty.

[0079] In one embodiment, the usage of electric vehicles in a certain urban area has been growing rapidly. To meet the increasing demand for battery swapping, an intelligent battery swapping station has been built. To effectively manage the operation of the battery swapping station, improve the battery swapping efficiency, and optimize resource allocation, the battery swapping station has decided to implement an Internet of Things (IoT)-based monitoring and prediction system.

[0080] First, based on the layout diagram and service process of the battery swapping station, the data acquisition requirements are determined in advance, including but not limited to the working status of charging piles, charging efficiency, battery inventory, and the flow of charging vehicles. Then, according to these requirements, the designated locations of IoT devices (such as temperature sensors, current sensors, cameras, RFID readers, etc.) within the battery swapping station are determined. For example, current sensors and cameras are installed beside each charging pile to monitor the charging status and vehicle information; RFID readers and inventory management systems are installed in the battery storage area to track the battery status and quantity in real time. According to the designated locations, these sensors and monitoring devices are installed and debugged within the battery swapping station to ensure that they can collect data accurately and stably.

[0081] Through the installed sensors and monitoring devices, the operating status of the charging piles in the battery swapping station (such as whether they are idle, charging power, etc.), the charging behaviors of charging vehicles (such as battery charging status, connection and disconnection times of charging, etc.), and the battery inventory are monitored in real time. The system regularly collects and organizes this data to form a detailed supply and demand data report, including the battery charging status, arrival time, departure time of charging vehicles, the working status and charging speed of charging piles, and the current number of fully charged batteries in the battery swapping station.

[0082] Based on the collected supply and demand data, data mining and predictive analysis are carried out using data analysis tools (such as machine learning algorithms). Analyze historical data to identify the correlations between battery swapping demands and factors such as time periods, weather conditions, holidays, etc. According to the analysis results, predict the number of battery swapping vehicles within a preset time period (such as the next 24 hours, a week, or a month) for the battery swapping station. Calculate the difference between the predicted number of battery swapping vehicles within the preset time period and the current number of fully charged batteries to determine the battery swapping demand of the battery swapping station within that time period. According to the prediction results, allocate fully charged batteries in advance, adjust the charging power, or optimize the service process to meet the expected battery swapping demand.

[0083] In one embodiment of the present application, predicting the number of battery swapping vehicles within a preset time period based on the supply and demand data specifically includes:

[0084] Clean the supply and demand data in the battery swapping station and perform denoising operations on the cleaned supply and demand data to obtain effective supply and demand data;

[0085] Normalize the effective supply and demand data to obtain standard supply and demand data, and analyze the supply and demand data corresponding to the battery swapping station based on machine learning algorithms and data mining techniques;

[0086] Determine the actual business needs of the battery swapping station, and extract corresponding effective features from the standard supply and demand data according to the actual business needs of the battery swapping station;

[0087] Predict the number of battery swapping vehicles at the battery swapping station within a preset time period based on the corresponding effective features.

[0088] Through data cleaning and denoising operations in the embodiments of the present application, errors, duplicates, anomalies, or irrelevant information in the supply and demand data can be removed, ensuring the accuracy and reliability of the data, and providing a high-quality data source for subsequent data analysis. Removing noise data can reduce the interference of the machine learning model during training, improve the stability and prediction accuracy of the model, and make the prediction results more reliable. The cleaned effective supply and demand data can simplify the complexity of data analysis, improve the efficiency of data processing and analysis, and shorten the decision-making cycle.

[0089] Normalization processing can convert supply and demand data with different dimensions into standard data under the same scale, eliminate the influence of dimension differences on the data analysis results, and make the analysis more accurate. Analyzing based on the normalized standard supply and demand data using machine learning algorithms and data mining techniques can accelerate the model convergence speed, improve the model training efficiency, and may enhance the prediction performance of the model. Through in-depth analysis of the standard supply and demand data, potential laws and correlations between the supply and demand of the battery swapping station can be discovered, providing strong support for optimizing the operation of the battery swapping station.

[0090] Extracting corresponding effective features according to the actual business needs of the battery swapping station can ensure a high degree of matching between data analysis and business goals, and improve the pertinence and practicality of the analysis results. By extracting effective features, the battery swapping demand of the battery swapping station at different time periods can be predicted more accurately, thereby optimizing the allocation of power, batteries, and human resources, and improving resource utilization efficiency. Based on the analysis results of effective features, more accurate and scientific decision-making support can be provided for the operation and management of the battery swapping station, helping managers make more reasonable decisions.

[0091] By predicting the number of battery swapping vehicles within a preset time period, the battery swapping station can plan and prepare power, batteries, and human resources in advance to ensure rapid response during peak periods or demand growth, and avoid service interruptions or delays. Accurate prediction of the number of battery swapping vehicles helps the battery swapping station reasonably arrange charging resources and service times, reduce user waiting times, and improve user experience and satisfaction. Guiding the daily operation of the battery swapping station through the prediction results can optimize the service process, improve operation efficiency, and reduce operation costs, achieving the maximization of economic benefits.

[0092] In one embodiment, with the popularization of electric vehicles, the swapping station, as an important facility for energy replenishment of electric vehicles, its operation efficiency and service quality have an important impact on user experience and industry development. In order to accurately predict the swapping demand of the swapping station at different time periods, improve resource utilization rate and user satisfaction, a certain swapping station decides to implement a set of intelligent swapping demand prediction system.

[0093] First, collect the original supply and demand data from the Internet of Things devices and monitoring systems of the swapping station, including the battery charging status, arrival time, departure time of the charging vehicles, the working status and charging speed of the charging piles, and the current number of fully charged batteries of the swapping station. Perform data cleaning on the collected original supply and demand data, remove duplicate records, correct error data, fill in missing values, etc., to ensure the integrity and accuracy of the data. Perform denoising operations on the cleaned supply and demand data, and identify and remove noise data, such as outliers and abnormal points, through statistical methods or machine learning algorithms to obtain effective supply and demand data.

[0094] Perform normalization processing on the obtained effective supply and demand data, convert all data to the same scale, and eliminate the influence of dimension differences on data analysis. Based on machine learning algorithms (such as random forest, neural network, support vector machine, etc.) and data mining techniques (such as association rule mining, clustering analysis, etc.), conduct in-depth analysis on the normalized standard supply and demand data, and explore the potential laws and correlations in the data.

[0095] According to the actual operation situation and business requirements of the swapping station, clarify the prediction target, that is, the number of swapping vehicles at the swapping station within a preset time period. According to the business requirements, extract the corresponding effective features from the standard supply and demand data. These features may include the historical number of swapping vehicles, the utilization rate of charging piles, the change in battery inventory, weather conditions, holiday factors, etc.

[0096] Based on the extracted effective features, select a suitable machine learning model for training. Through the training process, the model can learn the complex relationship between the features and the number of swapping vehicles. Use the trained model to predict the number of swapping vehicles at the swapping station within a preset time period. The prediction result will be an important basis for the operation decision of the swapping station. According to the prediction result, the swapping station can carry out resource allocation and service optimization in advance, such as increasing or decreasing the number of charging piles, adjusting the charging power, replenishing fully charged batteries, etc., to meet the expected swapping demand.

[0097] 102. Obtain the available power of the swapping station and the power of the charging bins of the swapping station, and combine the swapping demand to calculate the remaining charging capacity of the swapping station.

[0098] In one embodiment of the present application, obtaining the available power of the swapping station and the power of the charging bins of the swapping station, and combining the swapping demand to calculate the remaining charging capacity of the swapping station specifically includes:

[0099] Obtain the available power of the battery swapping station and the power of the charging bins in the battery swapping station;

[0100] Determine the number of batteries to be charged within a preset time period according to the battery swapping demand;

[0101] Determine the charging occupied power of the battery swapping station according to the number of batteries to be charged and the power of the charging bins in the battery swapping station;

[0102] Calculate the available power of the battery swapping station and the charging occupied power to determine the remaining charging capacity of the battery swapping station.

[0103] In the embodiments of the present application, by obtaining the available power of the battery swapping station and the power of the charging bins, the system can grasp the power resource status of the battery swapping station in real time, including the total available power and the charging capacity of each charging bin. This helps the manager to reasonably allocate and schedule the power resources. Determining the number of batteries to be charged within a preset time period according to the battery swapping demand enables the system to predict and plan future charging demands in advance. Through accurate prediction, the battery swapping station can arrange the charging plan more effectively, avoiding waste and shortage of power resources. Determining the charging occupied power according to the number of batteries to be charged and the power of the charging bins in the battery swapping station helps the system to optimize the charging strategy. By reasonably allocating the charging power, the system can ensure that each charging bin can charge with the optimal power, thereby improving the charging efficiency and shortening the charging time.

[0104] Calculating the available power of the battery swapping station and the charging occupied power to determine the remaining charging capacity is crucial for ensuring the safety and stability of power resources. By monitoring the remaining charging capacity in real time, the system can issue early warnings in a timely manner when the power resources are in short supply, avoiding overloading and power failures. Through the above steps, the battery swapping station can more accurately predict and meet the battery swapping demands of users, reduce the waiting time of users, and improve the battery swapping efficiency. This helps to enhance the user experience and satisfaction, and strengthen the trust and dependence of users on the battery swapping station. Real-time and accurate power resource data and charging demand prediction provide strong support for the intelligent decision-making and operation management of the battery swapping station. The manager can formulate more scientific and reasonable operation strategies based on these data, optimize the resource allocation, improve the operation efficiency, and reduce the operation cost.

[0105] In one embodiment, with the popularization of electric vehicles, as an important facility for the energy supply of electric vehicles, the management of the power resources of the battery swapping station and the evaluation of the remaining charging capacity are crucial for ensuring the continuity and efficiency of the battery swapping service. In order to achieve the refined management of the power resources of the battery swapping station, a certain battery swapping station decides to implement a set of intelligent power resource management and remaining charging capacity evaluation system.

[0106] First, obtain the real-time available power of the swapping station through the Internet of Things device or the power management system interface, which represents the maximum power output capacity that the swapping station can currently provide. At the same time, the system also collects the power information of each charging bin in the swapping station, and these information reflect the charging capacity and efficiency of the charging bin. Based on the previously mentioned prediction results of the swapping demand (such as predicting the number of swapping vehicles in the swapping station within a preset time period through machine learning algorithms), the system further analyzes the number of batteries that need to be charged in these swapping demands. This usually involves the assessment of the battery status of the swapping vehicles to determine which batteries need to be charged. According to the number of batteries to be charged and the power of each charging bin, the system calculates the total power required by the charging bins within the preset time period, that is, the charging occupancy power. This step takes into account the charging efficiency of the charging bin, the battery charging speed, and the possible concurrent charging demands.

[0107] The system compares the available power of the swapping station with the charging occupancy power to determine the remaining charging capacity of the swapping station. The remaining charging capacity refers to the additional charging power that the swapping station can still provide without exceeding the power load limit of the swapping station. If the remaining charging capacity is sufficient, the system can normally execute the charging plan; if the remaining charging capacity is insufficient, the system may trigger an early warning mechanism to prompt the manager to take corresponding measures, such as adjusting the charging plan, optimizing the power distribution, or increasing the power supply, etc.

[0108] 103. Determine the number of available charging piles in the swapping station according to the remaining charging capacity.

[0109] In an embodiment of the present application, determining the number of available charging piles in the swapping station according to the remaining charging capacity specifically includes:

[0110] Obtain the number of charging piles in use and the power of the charging piles in the swapping station;

[0111] Determine the number of available charging piles in the swapping station according to the power of the charging piles and the remaining charging capacity;

[0112] Determine the number of available charging piles in the swapping station according to the number of available charging piles and the number of charging piles in use.

[0113] In the embodiments of the present application, by obtaining the number of charging piles in use and the charging power of the charging piles in the swapping station in real time, the system can quickly calculate the currently available charging pile resources. This helps users understand the charging situation of the swapping station in advance, avoiding the embarrassing situation of arriving at the station only to find that there are no available charging piles, thus enhancing the user experience and satisfaction. By determining the available number of charging piles based on the charging power and the remaining charging capacity, the system can intelligently manage the charging pile resources. When power resources are in short supply, the system can give priority to ensuring the use of high-power charging piles to meet the fast-charging demand; when power resources are abundant, more charging piles can be opened for users to use, improving the resource utilization rate. By calculating the number of charging piles that can be opened, the system can provide real-time operation data support for the operation managers of the swapping station. The managers can adjust the operation strategies according to these data, such as increasing or decreasing the number of charging piles, adjusting the charging price, etc., to improve the operation efficiency and economic benefits.

[0114] This method is not only applicable to the current operation management of the swapping station, but also has good flexibility and scalability. With the increase in the number of electric vehicles and the growth of swapping demand, the system can easily expand the number and power of the charging piles to meet the needs of future development. By optimizing the allocation and utilization of the charging pile resources, the system can reduce the energy waste caused by the idleness or overuse of the charging piles. This helps to reduce the operation cost of the swapping station, while promoting energy conservation, emission reduction and sustainable development. In addition, by improving the charging efficiency and service quality, the system can also attract more electric vehicle users to use the swapping service, further promoting the development of the electric vehicle industry.

[0115] In one embodiment, today with the increasing popularity of electric vehicles, as an important facility for electric vehicle energy replenishment, the management and opening strategy of the charging piles in the swapping station directly affects the user experience and operation efficiency. In order to achieve the dynamic management and intelligent opening of the charging piles in the swapping station, a certain swapping station decides to implement a set of intelligent charging pile management systems.

[0116] The system uses Internet of Things technology to monitor the usage status of each charging pile in the swapping station in real time, including the number of charging piles in use and the real-time power output of each charging pile. These data are aggregated to the central processing unit through wireless transmission for analysis and processing. As mentioned above, the system already has the ability to evaluate the remaining charging capacity of the swapping station. This step will evaluate the additional charging power that the swapping station can support under the current power conditions based on the current power load situation and the remaining power resources of the swapping station.

[0117] Based on the real-time monitored charging pile power and remaining charging capacity, the system calculates the number of available charging piles that the battery swapping station can provide under the current power limit. This calculation process takes into account the power differences and charging efficiencies of the charging piles to ensure that all available charging piles can operate under safe and stable conditions. Based on the determined number of available charging piles, the system further calculates the number of charging piles that can be opened for new users according to the number of charging piles currently in use. This step aims to balance the relationship between user demand and power resource supply, ensuring that while the battery swapping station meets the charging needs of existing users, it can also provide sufficient charging resources for potential users.

[0118] The system dynamically adjusts the number of available charging piles according to the changes in real-time data. When power resources are abundant, the number of available charging piles is increased to meet the charging needs of more users; when power resources are scarce, the number of available charging piles is reduced to ensure the charging quality and safety of existing users. At the same time, the system can also formulate more scientific and reasonable opening strategies based on historical data and user behavior patterns, such as setting up reservation charging, priority allocation and other mechanisms.

[0119] In an embodiment of the present application, after determining the number of available charging piles of the battery swapping station according to the number of available charging piles and the number of charging piles currently in use, the method further includes:

[0120] Determine the number of idle charging piles in the battery swapping station according to the number of configured charging piles and the number of charging piles currently in use in the battery swapping station;

[0121] If the number of available charging piles exceeds the number of idle charging piles, all idle charging piles are opened; if the number of available charging piles does not exceed the number of idle charging piles, the number of available charging piles of idle charging piles is opened.

[0122] By calculating the number of idle charging piles in the battery swapping station in real time and opening the corresponding charging piles according to the limit of the number of available charging piles, the system of this embodiment of the present application can ensure that users can quickly find available charging piles for charging when they arrive at the battery swapping station. This avoids users wasting time or being dissatisfied due to not finding idle charging piles, thus improving the user experience. The system intelligently opens charging piles according to the comparison result of the number of available charging piles and the number of idle charging piles, avoiding over-opening or idling of charging piles. This dynamic adjustment strategy helps to optimize the utilization of charging pile resources, ensure that power resources are utilized to the greatest extent, and reduce resource waste.

[0123] The strategy in this embodiment allows the system to flexibly adjust the number of chargers that can be opened according to real-time situations. Whether the power resources are tight or abundant, the system can make optimal decisions based on the current conditions, thus enhancing the flexibility and adaptability of the system. By intelligently managing the opening and closing of chargers, the system can reduce operation problems caused by improper charger management, such as user complaints and power failures. This helps to improve the operation efficiency of the swapping station, reduce operation costs, and enhance the overall service quality. By optimizing the utilization of charger resources and reducing resource waste, this embodiment also helps to promote sustainable development. By reducing power consumption and carbon emissions, the swapping station can contribute to environmental protection and promote the green development of the electric vehicle industry.

[0124] In one embodiment, with the continuous increase in the number of electric vehicles, the usage situation of chargers in the swapping station becomes increasingly complex. To ensure that users can use chargers in a timely and effective manner while avoiding waste of charger resources, a swapping station decides to implement a dynamic opening strategy for intelligent idle chargers.

[0125] First, the Internet of Things technology is used to collect data on the configured number of chargers and the number of chargers in use in the swapping station in real time. These data are the basis for subsequent calculation of the number of idle chargers and formulation of the opening strategy. Based on the collected configured number of chargers and the number of chargers in use, the system calculates the number of idle chargers in the current swapping station. This step is the prerequisite for determining the number of chargers that can be opened. As mentioned before, the system has already evaluated the number of chargers that can be opened according to the remaining charging capacity of the swapping station and other relevant factors. This number represents the number of additional chargers that the swapping station can open under the current power and resource conditions.

[0126] The system compares the number of idle chargers with the number of chargers that can be opened. If the number of chargers that can be opened exceeds the number of idle chargers, the system chooses to open all the idle chargers to meet the charging needs of users. If the number of chargers that can be opened does not exceed the number of idle chargers, the system chooses to open the corresponding number of idle chargers according to the number of chargers that can be opened. The system monitors the usage situation of chargers in real time and dynamically adjusts the opening strategy according to real-time data. For example, when a new user arrives at the swapping station and requests charging, the system will re-formulate the opening strategy according to the changes in the current number of idle chargers and the number of chargers that can be opened. At the same time, the system also provides real-time feedback to users, informing them of the number and location information of available chargers.

[0127] In one embodiment of the present application, after determining the number of chargers in use in the swapping station, the method further includes:

[0128] Identify the operating charging piles in the battery swapping station, and input the supply and demand data corresponding to each operating charging pile into a pre-constructed charging vehicle behavior analysis model respectively to achieve the prediction of the charging demand of charging vehicles;

[0129] Determine the charging demand priority corresponding to each charging vehicle, and adjust the charging power and charging speed of each operating charging pile according to the charging demand priority.

[0130] In one embodiment, the server can determine the currently operating (in use) charging piles based on the collected supply and demand data. These operating charging piles are the facilities that are providing services to charging vehicles. Input the supply and demand data of each operating charging pile into a pre-constructed charging vehicle behavior analysis model. Based on historical data and current situations, this model can predict the charging demand of charging vehicles. Through model analysis, key information such as the expected charging amount and charging duration of each charging vehicle can be understood.

[0131] Based on the charging demand prediction results, a charging demand priority can be determined for each charging vehicle. The priority considers various factors such as the vehicle arrival time, required power, battery status, etc. Vehicles with a higher priority will receive faster charging services. According to the charging demand priority, the charging power and charging speed of each operating charging pile can be dynamically adjusted. Vehicles with a higher priority will obtain a higher charging power and a faster charging speed to meet their urgent charging needs. While vehicles with a lower priority may need to wait or receive a lower charging power and speed. Throughout the process, continuously monitor the operating conditions of the battery swapping station and the charging behavior of vehicles. According to real-time data, the charging power and speed can be adjusted in a timely manner to ensure that high-priority vehicles are served in a timely manner and at the same time balance the workload of each charging pile.

[0132] In one embodiment of the present application, after determining the number of idle charging piles in the battery swapping station, the method further includes:

[0133] Respectively obtain the historical supply and demand records of each idle charging pile within a preset time interval;

[0134] Input the historical supply and demand records corresponding to each idle charging pile into a preset charging pile operating status prediction model respectively to analyze the historical supply records and determine the charging power and charging speed of each idle charging pile within a preset time interval;

[0135] According to the charging power and charging speed of each idle charging pile, determine whether there are faulty idle charging piles, and add a fault label to the faulty idle charging piles to determine the number of effective idle charging piles in the battery swapping station.

[0136] In one embodiment, the server obtains the historical supply and demand records of each idle charging pile within a preset time interval. These records include key data such as the working status, charging power, and charging speed of the charging pile. The historical supply and demand records of each idle charging pile are input into a pre-constructed prediction model for the operating status of the charging pile. This model analyzes based on historical data and can predict the charging power and charging speed of each idle charging pile within a preset time interval. Through model analysis, the working status and performance of each idle charging pile can be understood.

[0137] Based on the charging power and charging speed of each idle charging pile, it is possible to detect whether there are any faulty idle charging piles. If an abnormal condition or performance degradation occurs in a certain idle charging pile, a fault label will be added to it. After determining the number of faulty idle charging piles with fault labels, the corresponding number of available idle charging piles can be further determined. The number of available idle charging piles is the total number of idle charging piles minus the number of faulty idle charging piles. Throughout the process, the operating condition of the battery swapping station and the working status of the idle charging piles are continuously monitored. For faulty idle charging piles, early warning and handling can be carried out in a timely manner to ensure the normal operation of the battery swapping station. At the same time, based on the number and performance of the available idle charging piles, resource allocation can be optimized to improve the operation efficiency.

[0138] In one embodiment of the present application, after determining the number of available charging piles of the battery swapping station according to the remaining charging capacity, the method further includes:

[0139] Determine multiple vehicles to be charged in the battery swapping station, and based on the historical charging records, determine the charging influencing factors corresponding to the multiple vehicles to be charged, and determine the charging priorities of the multiple vehicles to be charged; the charging influencing factors at least include: arrival time, required power value, and vehicle level;

[0140] Based on the charging priorities corresponding to the multiple vehicles to be charged, allocate the available idle charging piles to the corresponding vehicles to be charged, and obtain the charging situation of the vehicles to be charged;

[0141] According to the charging situation of the vehicles to be charged and the power supply situation of the battery swapping station, adjust the charging power and charging speed of the corresponding charging piles to optimize the power control strategy.

[0142] In one embodiment, in a battery swapping station, multiple vehicles waiting to be charged are identified. These vehicles are currently waiting for charging. According to the charging influencing factors of each vehicle waiting to be charged, such as arrival time, required power value, and vehicle level, corresponding charging priorities are determined for them. Vehicles with higher priorities will be given priority to obtain charging services. Based on the determined charging priorities, available idle charging piles are allocated to the corresponding vehicles waiting to be charged to ensure that vehicles with high priorities can obtain charging services in a timely manner. Continuously monitor the charging status of each vehicle waiting to be charged, including key data such as charging power, charging speed, and battery status.

[0143] According to the charging status of the vehicles waiting to be charged and the power supply situation of the battery swapping station, dynamically adjust the charging power and charging speed of the corresponding charging piles. This adjustment aims to optimize the power control strategy to ensure that vehicles with high priorities can be quickly charged while balancing the workload of each charging pile.

[0144] Throughout the process, continuously monitor the operating status of the battery swapping station and the charging behavior of the vehicles. According to the real-time data, the charging strategy can be adjusted in a timely manner to ensure efficient and fair charging services for the vehicles waiting to be charged. The system combines the pre-determined charging priorities and the real-time power supply situation to achieve reasonable allocation of idle charging piles and dynamic optimization of the power control strategy. This intelligent management method helps to improve the operation efficiency, user experience, and resource utilization rate of the battery swapping station, providing strong support for the development of electric vehicle charging and swapping facilities.

[0145] The above is the method embodiment proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a device for calculating the charging capacity of a battery swapping station, and its structure is as Figure 2 shown.

[0146] Figure 2 is the internal structure schematic diagram of a device for calculating the charging capacity of a battery swapping station provided by the embodiment of this application. As Figure 2 shown, the battery swapping station is provided with charging piles, and the device includes:

[0147] A prediction module 201, configured to predict the battery swapping demand of the battery swapping station in a preset time period;

[0148] An acquisition module 202, configured to acquire the available power of the battery swapping station and the power of the charging bins of the battery swapping station, and calculate the remaining charging capacity of the battery swapping station in combination with the battery swapping demand;

[0149] A determination module 203, configured to determine the number of available charging piles that can be opened in the battery swapping station according to the remaining charging capacity.

[0150] Figure 3 is the internal structure schematic diagram of an electronic device provided by the embodiment of this application. As Figure 3 shown, the battery swapping station is provided with charging piles, and the device includes:

[0151] At least one processor;

[0152] And a memory communicatively connected to the at least one processor;

[0153] Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for calculating the charging capacity of the battery swapping station as described in any of the above embodiments.

[0154] In one embodiment of the present application, the above-mentioned processor is capable of: predicting the battery swapping demand of the battery swapping station in a preset time period;

[0155] Obtaining the available power of the battery swapping station and the power of the charging bins of the battery swapping station, and combining the battery swapping demand to calculate the remaining charging capacity of the battery swapping station;

[0156] Determining the number of available charging piles of the battery swapping station according to the remaining charging capacity.

[0157] The embodiment of the present application also provides a non-volatile computer storage medium storing computer-executable instructions. The battery swapping station is provided with charging piles, and the computer can execute the method for calculating the charging capacity of the battery swapping station as described in any of the above embodiments when executing the executable instructions.

[0158] In one embodiment of the present application, the above-mentioned computer is capable of: predicting the battery swapping demand of the battery swapping station in a preset time period;

[0159] Obtaining the available power of the battery swapping station and the power of the charging bins of the battery swapping station, and combining the battery swapping demand to calculate the remaining charging capacity of the battery swapping station;

[0160] Determining the number of available charging piles of the battery swapping station according to the remaining charging capacity.

[0161] The various embodiments in the present application are all described in a progressive manner. The same or similar parts among the various embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments.

[0162] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0163] The device, medium, and method provided by the embodiments of the present application correspond one by one. Therefore, the device and the medium also have beneficial technical effects similar to those of their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and the medium will not be elaborated here.

[0164] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0166] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0168] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0169] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0170] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0171] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0172] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for calculating the charging capacity of a battery swapping station, characterized in that, The battery swapping station is provided with charging piles, and the method includes: Predicting the battery swapping demand of the battery swapping station within a preset time period; Obtaining the available power of the battery swapping station and the power of the charging bins of the battery swapping station, and calculating the remaining charging capacity of the battery swapping station in combination with the battery swapping demand; Determining the number of available charging piles of the battery swapping station according to the remaining charging capacity.

2. The method for calculating the charging capacity of a battery swapping station according to claim 1, wherein Predicting the battery swapping demand of the battery swapping station within a preset time period specifically includes: Based on the pre-determined data acquisition requirements corresponding to the battery swapping station, determining the specified locations where Internet of Things devices need to be set in the battery swapping station, and installing corresponding sensors and monitoring devices in the battery swapping station according to the specified locations; Monitoring the operating status of the charging piles and the charging behaviors of the charging vehicles in the battery swapping station through the sensors and monitoring devices, and obtaining corresponding supply and demand data; the supply and demand data further includes: the battery charging status, arrival time, departure time of the charging vehicles, the working status, charging speed of the charging piles, and the current number of fully charged batteries in the battery swapping station; According to the supply and demand data, predicting the number of battery swapping vehicles in the battery swapping station within a preset time period, and calculating the difference between the number of battery swapping vehicles within the preset time period and the current number of fully charged batteries, so as to obtain the battery swapping demand of the battery swapping station within the preset time period.

3. The method for calculating the charging capacity of a battery swapping station according to claim 2, wherein Predicting the number of battery swapping vehicles in the battery swapping station within a preset time period according to the supply and demand data specifically includes: Performing data cleaning on the supply and demand data in the battery swapping station, and performing a denoising operation on the supply and demand data after cleaning to obtain effective supply and demand data; Performing normalization processing on the effective supply and demand data to obtain standard supply and demand data, and analyzing the supply and demand data corresponding to the battery swapping station based on machine learning algorithms and data mining techniques; Determining the actual business requirements of the battery swapping station, and extracting corresponding effective features from the standard supply and demand data according to the actual business requirements of the battery swapping station; Predicting the number of battery swapping vehicles in the battery swapping station within a preset time period based on the corresponding effective features.

4. The method for calculating the charging capacity of a battery swapping station according to claim 1, wherein Obtaining the available power of the battery swapping station and the power of the charging bins of the battery swapping station, and calculating the remaining charging capacity of the battery swapping station in combination with the battery swapping demand specifically includes: Obtaining the available power of the battery swapping station and the power of the charging bins of the battery swapping station; Determining the number of batteries to be charged within a preset time period according to the battery swapping demand; Determining the charging occupancy power of the battery swapping station according to the number of batteries to be charged and the power of the charging bins of the battery swapping station; Calculating the available power of the battery swapping station and the charging occupancy power to determine the remaining charging capacity of the battery swapping station.

5. A method for calculating the charging capacity of a battery swapping station according to claim 1, wherein, Determining the number of available charging piles of the battery swapping station according to the remaining charging capacity specifically includes: Obtaining the number of charging piles in use and the power of the charging piles in the battery swapping station; Determining the number of available charging piles of the battery swapping station according to the power of the charging piles and the remaining charging capacity; Determining the number of available charging piles of the battery swapping station according to the number of available charging piles and the number of charging piles in use.

6. The method for calculating the charging capacity of a battery swapping station according to claim 5, wherein After determining the number of available charging piles of the battery swapping station according to the number of available charging piles and the number of charging piles in use, the method further includes: Determine the number of idle charging piles in the battery swapping station according to the number of configured charging piles in the battery swapping station and the number of charging piles in use; If the number of chargeable charging piles that can be opened exceeds the number of idle charging piles, open all idle charging piles. If the number of chargeable charging piles that can be opened does not exceed the number of idle charging piles, open the number of idle charging piles equal to the number of chargeable charging piles that can be opened.

7. A method for calculating the charging capacity of a battery swapping station according to claim 5, characterized in that, After determining the number of charging piles in use in the battery swapping station, the method further includes: Determine the operating charging piles in the battery swapping station, and input the supply and demand data corresponding to each operating charging pile into a pre-constructed charging vehicle behavior analysis model respectively to predict the charging demand of the charging vehicles; Determine the charging demand priority levels corresponding to each charging vehicle, and adjust the charging power and charging speed of each operating charging pile according to the charging demand priority levels.

8. A method for calculating the charging capacity of a battery swapping station according to claim 6, characterized in that After determining the number of idle charging piles in the battery swapping station, the method further includes: Obtain the historical supply and demand records of each idle charging pile within a preset time interval respectively; Input the historical supply and demand records corresponding to each idle charging pile into a preset charging pile operation status prediction model respectively to analyze the historical supply records and determine the charging power and charging speed of each idle charging pile within the preset time interval; Determine whether there are faulty idle charging piles according to the charging power and charging speed of each idle charging pile, and add a fault label to the faulty idle charging piles to determine the number of effective idle charging piles in the battery swapping station.

9. The method for calculating the charging capacity of a battery swapping station according to claim 1, wherein After determining the number of chargeable charging piles that can be opened by the battery swapping station according to the remaining charging capacity, the method further includes: Determine multiple vehicles to be charged in the battery swapping station, determine the charging influence factors corresponding to the multiple vehicles to be charged based on the historical charging records, and determine the charging priority levels corresponding to the multiple vehicles to be charged; the charging influence factors at least include: arrival time, required power value, and vehicle level; Based on the charging priority levels corresponding to the multiple vehicles to be charged, allocate the available idle charging piles to the corresponding vehicles to be charged, and obtain the charging status of the vehicles to be charged; Adjust the charging power and charging speed of the corresponding charging piles according to the charging status of the vehicles to be charged and the power supply status of the battery swapping station to optimize the power control strategy.

10. A charging capacity measurement device for a battery swapping station, characterized in that, The battery swapping station is provided with charging piles, and the device includes: A prediction module for predicting the battery swapping demand of the battery swapping station within a preset time period; An acquisition module for acquiring the available power of the battery swapping station and the power of the charging bins of the battery swapping station, and calculating the remaining charging capacity of the battery swapping station in combination with the battery swapping demand; A determination module for determining the number of chargeable charging piles that can be opened by the battery swapping station according to the remaining charging capacity.

11. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The battery swapping station is provided with charging piles, and when the processor executes the computer program, it implements a method for calculating the charging capacity of a battery swapping station according to any one of claims 1-9.

12. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The battery swapping station is provided with charging piles, and when the computer executes the executable instructions, it implements a method for calculating the charging capacity of a battery swapping station according to any one of claims 1-9.

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