Battery intelligent scheduling method and system for battery swap cabinet

Abstract

The present application relates to the technical field of battery swap cabinet management, and discloses a battery swap cabinet battery intelligent scheduling method and system, which performs live monitoring on the battery state of the batteries in the battery swap cabinet and electric vehicles, obtains battery state information uploaded to a cloud platform, records the battery state information of the batteries at each time through the cloud platform to obtain battery state monitoring records, analyzes the health state characteristics of the batteries based on the state monitoring records, acquires service demand information of the battery swap cabinet at the current time, drives the battery swap cabinet to charge the batteries in combination with the battery state information of each battery in the battery swap cabinet, performs charging restriction processing and adjustment of the battery swap priority of the batteries in the battery swap cabinet according to the health state characteristics of the batteries, acquires a battery swap request of an electric vehicle, analyzes the battery swap request according to the battery state information and the battery swap priority of each battery in the battery swap cabinet, and generates a battery scheduling scheme, thereby solving the problem that adaptive adjustment cannot be made according to the fluctuation of the battery swap demand.

Description

Technical Field

[0001] This invention relates to the technical field of battery swapping cabinet management, and in particular to a method and system for intelligent battery scheduling in battery swapping cabinets. Background Technology

[0002] With the development of battery swapping services, the number of battery swapping cabinets is constantly increasing, and the complexity of management is also increasing. Traditional management methods use fixed charging power and fixed order to respond to battery swapping requests, which cannot adapt to the off-peak and peak periods of battery swapping demand. They cannot meet the battery swapping demand during peak periods, and it is also difficult to take into account the management of battery health. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for intelligent battery scheduling in battery swapping cabinets, which aims to solve the problem that existing technologies cannot make adaptive adjustments based on fluctuations in battery swapping demand.

[0004] The present invention is implemented as follows: Firstly, the present invention provides a method for intelligent battery scheduling in a battery swapping cabinet, comprising: Real-time monitoring of battery status in battery swapping cabinets and electric vehicles is performed to obtain battery status information, which is then uploaded to the cloud platform. The battery status information at various times is recorded by the cloud platform to obtain the battery status monitoring record, and the health status characteristics of the battery are obtained by analyzing the status monitoring record. Obtain the business demand information of the battery swapping cabinet at the current time, and combine it with the battery status information of each battery in the battery swapping cabinet to drive the battery swapping cabinet to charge the batteries, and perform charging restriction processing and battery swapping priority adjustment of the batteries in the battery swapping cabinet according to the health status characteristics of the batteries. The system obtains battery swapping requests from electric vehicles and analyzes these requests based on the battery status information and swapping priorities of each battery in the battery swapping cabinet to generate a battery scheduling scheme corresponding to the battery swapping request.

[0005] Secondly, the present invention provides a battery intelligent scheduling system for a battery swapping cabinet, used to implement the battery intelligent scheduling method for a battery swapping cabinet as described in any one of the first aspects.

[0006] This invention provides a method for intelligent battery scheduling in battery swapping cabinets, which has the following beneficial effects: This invention can quickly respond to battery swapping requests, provide personalized services, shorten waiting time, improve satisfaction, achieve automated management, improve efficiency, reduce costs, optimize charging strategies based on electricity prices, provide real-time monitoring and early warning for battery protection to avoid safety accidents, adjust strategies based on battery health to extend battery life, optimize battery resource allocation to avoid idle waste, and rationally use power resources in conjunction with grid conditions, thus comprehensively improving the quality and efficiency of battery swapping services. Attached Figure Description

[0007] Figure 1 This is a schematic diagram illustrating the steps of a battery intelligent scheduling method for a battery swapping cabinet provided in an embodiment of the present invention. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0009] The implementation of the present invention will be described in detail below with reference to specific embodiments.

[0010] Reference Figure 1 The diagram shows a preferred embodiment of the present invention.

[0011] In a first aspect, the present invention provides a method for intelligent battery scheduling in a battery swapping cabinet, comprising: S1: Monitor the battery status of the battery in the battery swapping cabinet and electric vehicle in real time, obtain battery status information, and upload the battery status information to the cloud platform; S2: Record the battery status information at various times through the cloud platform to obtain the battery status monitoring record, and analyze the battery health status characteristics based on the status monitoring record. S3: Obtain the business demand information of the battery swapping cabinet at the current time, and combine it with the battery status information of each battery in the battery swapping cabinet to drive the battery swapping cabinet to charge the battery, and perform charging restriction processing and battery swapping priority adjustment of the batteries in the battery swapping cabinet according to the health status characteristics of the battery. S4: Obtain the battery swapping request from the electric vehicle, and analyze the battery swapping request based on the battery status information and swapping priority of each battery in the battery swapping cabinet, so as to generate a battery scheduling scheme corresponding to the battery swapping request.

[0012] Specifically, a fuel gauge chip can be used, which can measure the remaining battery power in real time. Through a circuit connection with the battery, it outputs the power data as a digital signal. For example, some high-precision fuel gauge chips can measure power accuracy down to 1%. Temperature sensors, such as thermistors or thermocouples, can also be used. These sensors are mounted on or near the battery surface to sense temperature changes in real time and convert the temperature data into an electrical signal for transmission.

[0013] More specifically, by integrating power and temperature data collected at the same time through a data acquisition system, this data can be stored in a local database, and a timestamp can be added to each data record for subsequent querying and analysis. For example, an SQL database can be used to store and manage this data.

[0014] More specifically, similar to the data collection method for batteries in the battery swapping cabinet, a power meter chip and a temperature sensor are used to collect power and temperature data respectively. The electrical performance of the battery is reflected by monitoring parameters such as voltage, current, and internal resistance. Data can be collected using devices such as voltage sensors, current sensors, and internal resistance testers. For example, the charging and discharging current of the battery, as well as the voltage changes at both ends of the battery, can be monitored in real time.

[0015] More specifically, the same data acquisition system integrates the power consumption, temperature, and electrical performance data at the same time and stores them in a local database. A timestamp is added, and professional data acquisition software can be used to collect, process, and store the data.

[0016] More specifically, battery status information stored in the local database is uploaded to the cloud platform via wireless communication modules such as 4G and WiFi. Protocols such as MQTT can be used for data transmission to ensure reliable and efficient transmission. The cloud platform can centrally store and manage large amounts of battery status information, and perform unified analysis and processing of the data.

[0017] Specifically, cloud platforms typically store historical order data for battery swapping stations. By writing database queries, historical order records for battery swapping stations can be extracted from the cloud platform's database. These records include order time, battery number, user information, etc. Data analysis and machine learning algorithms are then used to analyze these historical order records. For example, time series analysis methods can be used to predict current battery swapping demand based on the time distribution patterns of historical orders. Other factors, such as weekdays, holidays, and weather, can also be combined to improve the accuracy of predictions.

[0018] More specifically, historical order records reflect the usage patterns and regularities of the battery swapping cabinets. By analyzing these records, we can understand the peak and trough demand for battery swapping at different times, thereby better predicting the current demand. Accurate demand forecasting helps to formulate reasonable charging strategies. If the current demand for battery swapping is predicted to be high, more batteries need to be charged in advance to meet the user's needs; conversely, if the demand is low, the charging amount can be appropriately reduced to lower energy consumption.

[0019] More specifically, based on preset standards, the business demand information is analyzed to determine the period type (off-peak and peak electricity consumption) and period level to which the battery swapping cabinet belongs at the current time. Different time periods can be set as off-peak and peak periods according to local electricity pricing policies and electricity consumption patterns, and the levels can be divided according to the level of business demand. Based on the determined period type and level, the corresponding charging management mode is selected from the preset management mode library. The management mode library contains different charging strategies, such as fast charging, slow charging, and equalization charging.

[0020] More specifically, electricity prices vary at different times of day. By distinguishing between off-peak and peak electricity consumption periods, more charging can be done during off-peak periods, reducing charging costs. At the same time, selecting the appropriate charging management mode based on the level of business needs can improve energy utilization efficiency.

[0021] More specifically, power data is extracted from battery status information, and the theoretical charging power of the battery is calculated according to the requirements of the charging management mode. For example, if a fast charging mode is used, the appropriate charging power needs to be calculated based on the remaining battery power and charging time requirements. Based on the calculated theoretical charging power and combined with other battery status information, such as temperature and health status, a specific charging strategy is generated. The charging strategy includes parameters such as charging current, charging time, and charging stage.

[0022] More specifically, different battery states require different charging powers. By analyzing battery power data and determining the appropriate theoretical charging power, overcharging or undercharging can be avoided, ensuring battery safety and performance. Considering other battery state information, personalized charging strategies can be generated to better meet battery charging needs and extend battery life.

[0023] More specifically, the charging control system of the battery swapping cabinet adjusts the output power of the charging equipment according to the generated charging strategy, so that the battery is charged according to the theoretical charging power. The charging control system can control the charging power by adjusting parameters such as charging current and voltage. During the charging process, it monitors the battery status information in real time, such as charge level and temperature. If any abnormalities are detected in the battery, such as excessively high temperature or slow charging speed, the charging strategy will be adjusted or charging will be stopped in a timely manner to ensure charging safety.

[0024] More specifically, charging according to the theoretical charging power can ensure that the battery reaches the appropriate level of charge within a specified time to meet the battery swapping needs. Real-time monitoring of the battery status and timely handling of abnormal situations can prevent battery damage or safety accidents caused by problems during the charging process.

[0025] Specifically, the cloud platform uses databases (such as relational databases like MySQL or non-relational databases like MongoDB) to store the status information of batteries in battery swapping cabinets and electric vehicles. It adds precise timestamps to each data record to reflect the time sequence of the data. These timestamped data are arranged in chronological order to form status monitoring records. Scripts can be written using programming languages ​​(such as Python) to extract and sort the data from the database.

[0026] More specifically, by recording the battery's status information in different scenarios (battery swapping cabinets and electric vehicles), we can gain a comprehensive understanding of the battery's usage history and changes. The status monitoring records are an important basis for subsequent long-term operational performance characteristic identification and health status analysis. The time-series data arrangement helps to discover the changing patterns of battery status over time.

[0027] More specifically, key features, such as the rate of change of battery capacity, temperature fluctuation range, and number of charge / discharge cycles, can be extracted from the status monitoring records. Data mining and machine learning algorithms, such as principal component analysis (PCA), can be used to extract the most representative features. Historical data can be used to train machine learning models (such as support vector machines and decision trees) so that the models can learn the characteristic patterns of normal and abnormal battery operation. The current status monitoring records can be input into the trained model to identify the long-term operating performance of the battery and obtain health supervision information, such as whether the battery has problems such as capacity decay or increased internal resistance.

[0028] More specifically, long-term performance feature identification can help identify potential problems and malfunctions in the battery during use, allowing for proactive measures to address these issues and prevent serious battery damage. Health monitoring information can serve as an important indicator for assessing battery health status, providing a basis for subsequent charging restrictions and battery swapping priority adjustments.

[0029] More specifically, a real-time health scoring model based on health monitoring information and current battery status information can be established. A weighted summation method can be used to assign different weights to different features to calculate the real-time health score of the battery. Based on the real-time health score, different charging power limit thresholds can be set. When the battery health score is low, the charging power is reduced to reduce damage to the battery; when the health score is high, the charging power can be appropriately increased to shorten the charging time.

[0030] More specifically, limiting charging power through real-time health scoring can prevent damage to the battery caused by overcharging or high-current charging, extend battery life, and reduce charging power for batteries in poor health to reduce safety hazards such as overheating and ensure the safety of the charging process.

[0031] More specifically, by statistically analyzing the number of charge-discharge cycles of the battery from the status monitoring records, and analyzing the time interval and frequency distribution of the charge-discharge cycles, data analysis tools (such as Excel and Python's Pandas library) can be used to perform data statistics and analysis, and extract relevant features of the charge-discharge frequency, such as average charge-discharge frequency, maximum charge-discharge frequency, and fluctuation of charge-discharge frequency.

[0032] More specifically, charge and discharge frequency characteristics can reflect the intensity of battery use. Frequent charge and discharge will accelerate battery aging. By analyzing charge and discharge frequency characteristics, we can understand the battery's usage and provide a reference for subsequent battery replacement priority adjustments. Charge and discharge frequency is one of the important factors affecting battery life. By analyzing charge and discharge frequency characteristics, we can predict the remaining battery life and arrange battery replacement in advance.

[0033] More specifically, different charging and discharging frequency thresholds and battery swapping priority rules are set according to the battery type, performance, and usage requirements. For example, when the battery's charging and discharging frequency exceeds a certain threshold, its battery swapping priority is increased. Based on the charging and discharging frequency characteristics and preset standards, the battery swapping priority is adjusted. Algorithms or rule engines can be used to achieve automatic priority adjustment. By adjusting the battery swapping priority, batteries with higher charging and discharging frequencies and more severe aging can be replaced first, ensuring that users use batteries with better performance and improving the quality of battery swapping services.

[0034] Specifically, battery swapping requests are typically sent to the battery swapping system via wireless communication methods (such as Bluetooth, 4G, WiFi, etc.). After receiving the request, the system uses signal processing technology and protocol parsing to extract the unique identification code of the electric vehicle. Using the identification code as an index, the system searches for the corresponding owner profile information in the database. The database can be a relational database (such as MySQL) or a non-relational database (such as MongoDB) to store the owner's relevant information, including driving habits, battery swapping frequency, credit records, etc.

[0035] More specifically, obtaining vehicle owner profile information helps to understand the personalized needs and usage habits of vehicle owners, thereby providing them with more precise battery swapping services. For example, for vehicle owners who frequently travel long distances, they can be given priority to be provided with batteries with more power. Information such as the vehicle owner's credit record can be used to assess the risk of battery swapping. Vehicle owners with good credit can be provided with more convenient battery swapping services, while certain restrictive measures can be taken for vehicle owners with poor credit.

[0036] More specifically, based on the needs of the battery swapping business and the performance of the batteries, a battery swapping threshold is preset, such as a minimum power requirement. The power data of each battery is compared with the preset battery swapping threshold, and batteries that meet the power requirements are selected as candidate batteries. Algorithms can be written using programming languages ​​(such as Python) to implement data comparison and filtering.

[0037] More specifically, by setting a battery swapping threshold, it can be ensured that the batteries provided to users have sufficient power to meet their needs and avoid inconvenience caused by insufficient battery power. Selecting only batteries with the required power as candidates can avoid unnecessary battery scheduling and improve the resource utilization rate of the battery swapping cabinet.

[0038] More specifically, based on the preset scheduling strategy, combined with the vehicle owner profile information and the battery swapping priority of the candidate batteries, the adaptability of battery use is analyzed. For example, considering factors such as the vehicle owner's driving habits, mileage requirements, and the remaining power and health status of the battery, the adaptability parameters of the vehicle owner profile information corresponding to each candidate battery are calculated. Based on the adaptability parameters, the candidate battery with the highest adaptability is selected, and a battery scheduling scheme is generated. Optimization algorithms (such as genetic algorithms, simulated annealing algorithms, etc.) can be used to find the optimal scheduling scheme.

[0039] More specifically, by combining vehicle owner profile information and battery swapping priority analysis, we can provide vehicle owners with the most suitable batteries for their needs, improve user satisfaction, and generate the optimal battery scheduling plan to rationally allocate battery resources in the battery swapping cabinet, improve swapping efficiency, and reduce operating costs.

[0040] More specifically, the status information of each battery in the battery swapping cabinet is collected regularly, including charge level, temperature, and health status. Data analysis techniques (such as statistical analysis and machine learning) are used to analyze this data to understand the overall usage and performance changes of the batteries. Based on the analysis results, the preset battery swapping threshold is dynamically adjusted. For example, if the overall performance of the batteries is found to be declining, the battery swapping threshold can be appropriately increased to ensure the quality of batteries provided to users.

[0041] More specifically, battery performance changes with usage time and frequency. Dynamically adjusting the battery swapping threshold can adapt to this change and ensure that the quality of the battery swapping service remains at a high level. Adjusting the battery swapping threshold according to the actual condition of the battery can make more rational use of the battery resources in the battery swapping cabinet and avoid waste or shortage of resources.

[0042] Preferably, the steps for real-time monitoring of the battery status in the battery swapping cabinet and the electric vehicle to obtain battery status information include: S11: Collect data on the battery charge and temperature in the battery swapping cabinet to obtain charge and temperature data; S12: Combine the power data and temperature data at the same time to obtain the battery status information of the battery in the battery swapping cabinet; S13: Collect data on the battery capacity, temperature, and electrical performance of the electric vehicle to obtain data on the battery capacity, temperature, and electrical performance. S14: Combine the power data, temperature data, and electrical performance data at the same time to obtain the battery status information of the battery in the electric vehicle.

[0043] Specifically, each battery in the battery swapping cabinet is equipped with a fuel gauge chip, such as the Texas Instruments (TI) BQ27441. The fuel gauge chip is connected to the positive and negative terminals of the battery to monitor parameters such as battery voltage and current in real time. It uses an internal algorithm to calculate the remaining battery power and usually outputs the power data in the form of a digital signal. It can be transmitted to the main control board of the battery swapping cabinet through communication interfaces such as I2C or SPI. Some battery swapping cabinets use an integrated BMS to manage the batteries. The BMS collects various parameters of the batteries in real time, including the power level. The BMS can send the power data to the control system of the battery swapping cabinet through the CAN bus.

[0044] More specifically, a thermistor is installed on or near the battery surface. The thermistor's resistance changes with temperature. By measuring the thermistor's resistance and then converting it into a corresponding temperature value based on its resistance-temperature characteristic curve, a digital temperature sensor such as the DS18B20 can directly output a digital temperature signal. When installed near the battery, the temperature data is transmitted to the main control board of the battery swapping cabinet via a single-bus communication method.

[0045] More specifically, the main control board of the battery swapping cabinet adds timestamps to the collected power and temperature data to ensure that the two data are measured at the same time. The power and temperature data with the same timestamp are combined into a data record, which can be stored in the form of a structure or data object. For example, an object containing power and temperature attributes can be defined in a programming language, and the power and temperature values ​​at the same time can be assigned to the corresponding attributes of the object. The combined battery status information is stored in the local storage of the battery swapping cabinet (such as SD card, flash memory, etc.) for later uploading to the cloud platform for further analysis.

[0046] More specifically, electric vehicles are usually equipped with an onboard BMS, which monitors the battery level in real time. The BMS transmits the power data to the electric vehicle's electronic control unit (ECU) via the CAN bus, and the ECU then sends the power data to the cloud platform via a wireless communication module (such as 4G, Bluetooth, etc.).

[0047] More specifically, multiple temperature sensors are placed in the electric vehicle battery pack to monitor the temperature of different parts of the battery in real time. These sensors transmit temperature data to the BMS via CAN bus or other communication methods, and then the BMS summarizes the data and sends it to the ECU.

[0048] More specifically, the battery voltage is measured to reflect the battery's potential state. The voltage sensor converts the measured voltage signal into a digital signal and transmits it to the BMS through a communication interface. The charging and discharging current of the battery is monitored to understand the battery's operating status. Similarly, the current sensor converts the current signal into a digital signal and transmits it to the BMS along with other data. The battery's internal resistance is measured through specific circuits and algorithms. Internal resistance is an important indicator reflecting the battery's internal performance, and the measured internal resistance data is also integrated into the BMS.

[0049] More specifically, the ECU synchronizes the collected battery power, temperature, and electrical performance data to ensure that these data are measured at the same time. Then, it integrates these data into a single data record, which can be stored in the form of a structure or data object. The ECU uploads the combined battery status information to the cloud platform via a wireless communication module for remote monitoring and analysis.

[0050] Preferably, the steps of recording battery status information at various times through a cloud platform to obtain battery status monitoring records, and analyzing the battery health status characteristics based on the status monitoring records, include: S21: Based on the cloud platform, the battery status information of the specified battery in the battery swapping cabinet and electric vehicle is recorded according to the time relationship to obtain the battery status monitoring record. S22: Based on the status monitoring records, identify the characteristics of the battery's long-term operating performance to obtain the battery's health monitoring information; S23: Analyze the battery status information for the current time period based on the health monitoring information to generate the health status characteristics for battery replacement.

[0051] Preferably, the step of identifying features of the battery's long-term operating performance based on the status monitoring records to obtain battery health monitoring information includes: S221: Divide the status monitoring record into time periods, and identify the characteristics of the operation performance of each time period of the status monitoring record to obtain the operation performance feature set of each time period. S222: Perform correlation analysis on the performance feature sets of adjacent time periods, and based on the analysis results, perform a transformation trend analysis on the performance of adjacent time periods to obtain transformation trend information between each time period; S223: Based on the conversion trend information, simulate the changes in the battery's health status to obtain several health change prediction curves as health monitoring information for the battery.

[0052] Specifically, various sensors are installed on the battery swapping cabinets and electric vehicles to monitor the battery status information in real time. For example, temperature sensors and humidity sensors are installed in the battery swapping cabinets to monitor the temperature and humidity of the battery storage environment; voltage sensors, current sensors, and SOC (State of Charge) sensors are installed on the electric vehicles to obtain information such as battery voltage, current, and remaining charge in real time.

[0053] More specifically, the sensor collects battery status data at a certain sampling frequency (e.g., once per second) and transmits the data to the cloud platform via a wireless communication module (e.g., 4G, LoRa, etc.). After receiving the data, the cloud platform sorts and stores the data according to the timestamp to form a complete status monitoring record. The battery status monitoring record is stored in a database (e.g., MySQL, MongoDB, etc.) for easy subsequent querying and analysis.

[0054] More specifically, by deploying sensors on battery swapping cabinets and electric vehicles, it is possible to comprehensively record the battery's status information under different usage scenarios, providing a rich data foundation for subsequent health status analysis. Recording battery status information according to time relationships makes the data have temporal continuity, which facilitates the analysis of the changes in battery status over time.

[0055] More specifically, based on the battery's usage characteristics and analysis needs, the status monitoring records are divided into different time periods, such as days, weeks, and months. For the status monitoring records within each time period, features related to battery performance are extracted. Common features include voltage features such as average voltage, maximum voltage, minimum voltage, and voltage change rate; current features such as average current, maximum current, minimum current, and charge / discharge current ratio; temperature features such as average temperature, highest temperature, lowest temperature, and temperature change rate; and SOC features such as initial SOC, final SOC, and SOC change range.

[0056] More specifically, dividing the status monitoring records into time periods allows for a more detailed analysis of the battery's performance in different time periods, revealing the periodic patterns of battery status changes. By extracting the performance characteristics of each time period, complex status monitoring records can be transformed into a representative feature set, facilitating subsequent correlation analysis and health status assessment.

[0057] More specifically, statistical analysis methods (such as correlation analysis and regression analysis) are used to conduct correlation analysis on the characteristic sets of operation performance in adjacent time periods. For example, the correlation of battery voltage characteristics in adjacent time periods is calculated to determine whether the voltage change trend is consistent. Based on the results of the correlation analysis, the conversion trend of operation performance between adjacent time periods is analyzed. For example, if the SOC of the battery shows a downward trend in adjacent time periods and the magnitude of the decline gradually increases, it indicates that the self-discharge rate of the battery is increasing.

[0058] More specifically, by analyzing correlations and transformation trends, we can discover the changing trends of battery performance over time, provide early warnings of potential battery problems, and help to gain a deeper understanding of the state transition mechanisms of batteries at different time periods, thus providing a more accurate basis for battery health management.

[0059] More specifically, a suitable prediction model (such as a time series model, machine learning model, etc.) is selected to simulate changes in the battery's state of health. For example, the ARIMA (Autoregressive Integrated Moving Average) model can be used to predict changes in the battery's state of charge (SOC). The prediction model can be trained using historical state monitoring records and conversion trend information, and the model's parameters can be adjusted to improve the model's prediction accuracy. The trained model can then be used to predict the future state of the battery, generating several health change prediction curves. These curves can reflect the trend of the battery's state of health changes under different conditions.

[0060] More specifically, by generating a health change prediction curve, changes in the battery's health status can be predicted in advance, providing a basis for decision-making regarding battery maintenance and replacement. The health change prediction curve can intuitively display the trend of battery health status changes, making it easier for users to understand and manage.

[0061] More specifically, the battery status information for the current time period is compared with the health change prediction curve in the health monitoring information to determine whether the actual state of the battery deviates from the predicted trend. Based on the results of the comparative analysis, a health status feature for battery replacement is generated. For example, if the actual SOC of the battery decreases significantly faster than the predicted curve and the internal resistance of the battery increases, a health status feature of "severe battery performance degradation, replacement recommended" can be generated.

[0062] More specifically, by combining health monitoring information with battery status information for the current time period, it is possible to more accurately determine whether the battery needs to be replaced, avoid unnecessary battery replacements, reduce costs, and promptly detect and replace battery health problems, thereby improving battery safety and reducing the occurrence of safety accidents.

[0063] Preferably, the steps of obtaining the current time's business demand information for the battery swapping cabinet, combining it with the battery status information of each battery in the cabinet to drive the cabinet to charge the batteries, and adjusting the charging restrictions and battery swapping priorities of the batteries in the cabinet based on the battery health status characteristics include: S21: Retrieve historical order records of the battery swapping cabinet through the cloud platform, and predict business demand for the current time based on the historical order records to obtain business demand information of the battery swapping cabinet at the current time. S22: Analyze the business demand information according to preset standards to obtain the charging management mode of the battery swapping cabinet corresponding to the business demand information; S23: Based on the charging management mode, the power data contained in the battery status information is parsed to obtain the theoretical charging power of the battery, so as to generate a corresponding charging strategy. S24: Drive the battery swapping cabinet to charge the battery with the theoretical charging power according to the charging strategy.

[0064] S25: Perform real-time health scoring on the batteries in the battery swapping cabinet based on the health status characteristics, so as to limit the charging power of the batteries. S26: Based on the state monitoring records, perform frequency analysis of the battery charge-discharge cycles to obtain the battery charge-discharge frequency characteristics; S27: Analyze the charging and discharging frequency characteristics according to preset standards to adjust the battery swapping priority.

[0065] Specifically, cloud platforms typically use databases to store historical order records for battery swapping cabinets. SQL statements can be used to query historical order data for a specific battery swapping cabinet from the database. For example, the pymysql library in Python can be used to connect to the database, execute query statements to obtain the required data, and time series analysis algorithms, such as ARIMA (Autoregressive Integral Moving Average) and LSTM (Long Short-Term Memory), can be used to arrange historical order records in chronological order as input data. The model is then trained to predict current business needs. For example, the statsmodels library in Python can be used to implement the ARIMA model, and the Keras library can be used to implement the LSTM model.

[0066] More specifically, the preset standards can be formulated based on factors such as local electricity pricing policies, peak and off-peak electricity consumption times, and the peak and off-peak seasons for battery swapping services. For example, during off-peak electricity consumption periods, charging costs are lower, so a fast charging mode can be used; during peak electricity consumption periods, a slow charging mode can be used to reduce costs. The business demand information is compared with the preset standards, and a suitable mode is selected from the preset charging management mode library based on factors such as the level of business demand and the time period. Conditional statements (such as if-else statements) can be used to achieve mode matching.

[0067] More specifically, power data is extracted from battery status information. Based on the requirements of the charging management mode and parameters such as battery capacity and current power level, the theoretical charging power of the battery is calculated. For example, if the charging management mode is fast charging, the battery capacity is 50Ah, the current power level is 20%, and the target power level is 80%, the required charging power can be calculated within the specified fast charging time. Based on the theoretical charging power, the charging current, voltage, and other parameters are determined, and a specific charging strategy is generated. The charging strategy can be stored in the form of a table or configuration file, which is convenient for the charging control system of the battery swapping cabinet to read and execute.

[0068] More specifically, the charging control system of the battery swapping cabinet receives the charging strategy and adjusts the parameters of the charging circuit, such as current and voltage, to charge the battery according to the theoretical charging power. The charging control system can use a microcontroller (such as Arduino, STM32, etc.) to achieve precise control of the charging process. During the charging process, the system monitors the battery status information in real time, such as charge level and temperature. If any abnormalities are found in the battery, such as excessively high temperature or slow charging speed, the system will adjust the charging strategy or stop charging in time to ensure charging safety.

[0069] Specifically, the cloud platform receives real-time battery status information, including charge, temperature, voltage, and current, through communication interfaces with the battery swapping cabinet and electric vehicle. This information is accompanied by precise timestamps to indicate the specific moment of data collection. The received battery status information is stored in the cloud platform's database, arranged according to the battery's unique identifier and time sequence. Relational databases (such as MySQL) or non-relational databases (such as MongoDB) can be used to store this data. All status information for a specific battery is extracted from the database and arranged into a continuous sequence, i.e., a status monitoring record, in chronological order. Scripts written using programming languages ​​(such as Python) can be used to query and process data from the database to generate status monitoring records.

[0070] More specifically, by recording the battery's status information in the battery swapping cabinet and electric vehicle, a comprehensive understanding of the battery's usage history and changes can be obtained. The battery's performance and health status will change with different usage time and methods. Complete status monitoring records can provide a rich data foundation for subsequent analysis. Status monitoring records are an important basis for identifying long-term operational performance characteristics and analyzing health status. The chronological arrangement of data helps to discover the pattern of battery status changes over time, thereby more accurately assessing the battery's health status.

[0071] More specifically, key features such as power change rate, temperature fluctuation range, number of charge / discharge cycles, and voltage stability can be extracted from status monitoring records. Data mining and machine learning algorithms can be used to extract these features. For example, the power change rate can be obtained by calculating the power difference between adjacent time points, and the temperature fluctuation range can be evaluated by using statistical methods to calculate the standard deviation of temperature.

[0072] More specifically, historical data is used to train machine learning models (such as support vector machines, decision trees, neural networks, etc.) so that the models can learn the characteristic patterns of normal and abnormal battery operation. The current state monitoring records are input into the trained model, and the model will identify the long-term operating performance of the battery based on the features and output the battery's health monitoring information, such as whether the battery has capacity decay, increased internal resistance, or thermal runaway risk.

[0073] More specifically, long-term performance feature identification can help identify potential problems and malfunctions in batteries during use. Some battery issues may not show obvious symptoms in the short term, but analysis of long-term data can help detect these problems early, allowing for timely intervention and preventing serious battery damage. Health monitoring information can serve as an important indicator for assessing battery health. By analyzing the long-term performance of batteries, we can gain a more accurate understanding of their actual health status, providing a basis for subsequent charging limits and battery swapping priority adjustments.

[0074] More specifically, a real-time health scoring model based on health monitoring information and current battery status information is established. A weighted summation method can be used to assign different weights to different features, calculating the battery's real-time health score. For example, features such as charge level, temperature, and capacity decay can all be used as the basis for scoring. The weight of each feature is determined according to its impact on battery health. Based on the real-time health score, different charging power limit thresholds are set. When the battery health score is low, the charging power is reduced to minimize damage to the battery; when the health score is high, the charging power can be appropriately increased to shorten the charging time. The charging power limitation can be implemented through the charging control system of the battery swapping cabinet, which will automatically adjust the charging parameters based on the scoring results.

[0075] More specifically, limiting charging power through real-time health scoring can prevent damage to the battery caused by overcharging or high-current charging, and extend the battery's lifespan. For batteries in poor health, reducing charging power can slow down the rate of internal chemical reactions, reducing the risk of battery overheating and aging. For batteries with potential faults or health problems, reducing charging power can reduce safety hazards and prevent safety accidents such as battery fires and explosions caused by abnormal conditions during charging.

[0076] More specifically, by statistically analyzing the number of charge-discharge cycles recorded in the battery status monitoring logs, and examining the time intervals and frequency distribution of these cycles, data analysis tools (such as Python's Pandas library) can be used to process and statistically analyze the data. For example, the start and end points of a charge-discharge cycle can be determined by analyzing the rising and falling trends of the battery level. Then, the number of cycles can be counted, and relevant characteristics of the charge-discharge frequency can be extracted, such as the average charge-discharge frequency, the maximum charge-discharge frequency, and the fluctuation of the charge-discharge frequency. These characteristics can reflect the battery's usage intensity and usage patterns.

[0077] More specifically, charge / discharge frequency characteristics reflect battery usage intensity. Frequent charge / discharge accelerates battery aging. Analyzing these characteristics allows us to understand battery usage and provides a reference for adjusting battery swapping priorities. Charge / discharge frequency is one of the key factors affecting battery lifespan. By analyzing charge / discharge frequency characteristics, we can predict the remaining battery lifespan, schedule battery replacements in advance, and avoid disrupting battery swapping services due to sudden battery failure.

[0078] More specifically, different charging and discharging frequency thresholds and battery swapping priority rules are set according to the battery type, performance, and usage requirements. For example, when the battery's charging and discharging frequency exceeds a certain threshold, its battery swapping priority is increased; when the charging and discharging frequency is low, its battery swapping priority is decreased. The battery swapping priority is adjusted according to the charging and discharging frequency characteristics and preset standards. Algorithms or rule engines can be used to achieve automatic priority adjustment. For example, batteries can be sorted from high to low charging and discharging frequency, and batteries with higher charging and discharging frequencies can be given higher battery swapping priority.

[0079] More specifically, by adjusting the battery swapping priority, batteries with higher charging and discharging frequencies and more severe aging can be replaced first, ensuring that users use batteries with better performance, improving the quality of battery swapping services, and avoiding the overuse of aging batteries, which helps to extend the overall lifespan of batteries and reduce operating costs. At the same time, timely replacement of aging batteries can reduce the occurrence of battery failures and improve the reliability and stability of battery swapping cabinets.

[0080] Preferably, the step of analyzing the business demand information according to preset standards to obtain the charging management mode of the battery swapping cabinet corresponding to the business demand information includes: S221: Analyze the business demand information according to preset standards to obtain the period type and period level to which the battery swapping cabinet belongs at the current time; wherein, the period type includes off-peak electricity consumption period and peak electricity consumption period; S222: Perform mode scheduling on the preset management mode library according to the period type and the period level to obtain the charging management mode of the battery swapping cabinet corresponding to the business demand information.

[0081] Specifically, the preset standards are usually formulated in combination with local power policies, historical business data and the operational needs of the battery swapping cabinets. For example, based on the local power grid's peak and off-peak electricity consumption periods, the off-peak electricity consumption period (such as 0:00-6:00 AM) and peak electricity consumption period (such as 11:00-2:00 PM and 6:00-10:00 PM) are determined. At the same time, the period is divided into three levels: high, medium and low, according to the level of business demand. The threshold for the level division can be determined based on data such as the number of historical orders and the frequency of battery swapping.

[0082] More specifically, the current business demand information (such as the expected number of battery swapping orders, battery usage frequency, etc.) is compared with preset standards. Data processing software (such as Python's Pandas library) can be used to process and analyze the business demand data. Conditional statements can be used to determine the period type and period level of the battery swapping cabinet at the current time. For example, if the current time is 2 a.m. and the expected number of battery swapping orders is small, it can be judged as an off-peak period and a low period level according to the preset standards.

[0083] More specifically, clearly defining the period type and level of the battery swapping station at the current time allows for more accurate matching of the corresponding charging management mode, thereby rationally allocating resources and improving resource utilization efficiency. Different period types and levels correspond to different electricity costs and business needs. During off-peak hours, when electricity prices are lower, a more aggressive charging strategy can be adopted; during peak hours, the charging power can be appropriately reduced to lower costs.

[0084] More specifically, the preset management mode library contains charging management modes for different period types and levels. Each mode defines specific charging parameters, such as charging power, charging time, and charging stage. These modes can be stored in a database or configuration file for easy querying and retrieval. Based on the determined period type and level, the corresponding charging management mode can be found in the management mode library. The scheduling of modes can be implemented using database query statements or programming logic. For example, if the current period is a low-demand period and the level is low, the corresponding low-demand low-peak mode can be found in the management mode library.

[0085] More specifically, different period types and levels have different requirements for charging management. Through mode scheduling, the most suitable charging management mode can be selected according to the actual situation, the charging strategy can be optimized, and the charging efficiency and quality can be improved.

[0086] Preferably, the step of analyzing the battery swapping request based on the battery status information and swapping priority of each battery in the battery swapping cabinet to generate a battery scheduling scheme corresponding to the battery swapping request includes: S41: Identify the signal source of the battery swapping request, obtain the identification code of the electric vehicle that issued the battery swapping request, and retrieve the owner's profile information from the database based on the identification code. S42: Based on the preset battery swapping threshold, perform feasibility calculations on the power data contained in the battery status information of each battery to select batteries that can respond to the battery swapping request as candidate batteries. S43: Analyze the profile information and the battery swapping priority of each candidate battery according to the preset scheduling strategy to generate a battery scheduling scheme corresponding to the battery swapping request.

[0087] Specifically, battery swapping requests are typically sent to the battery swapping system via wireless communication methods (such as Bluetooth, 4G, WiFi, etc.). After receiving the request, the system uses signal processing technology and protocol parsing to extract the unique identification code of the electric vehicle from the signal. This can be achieved with the help of the parsing function of the communication module and related protocol stacks. For example, if 4G communication is used, the request can be received and the identification code can be parsed through the 4G module.

[0088] More specifically, the identification code is used as an index to search for the corresponding car owner profile information in the database. The database can be a relational database (such as MySQL) or a non-relational database (such as MongoDB). SQL query statements (for relational databases) or corresponding database query interfaces (for non-relational databases) are used to obtain the car owner profile information, which includes the car owner's driving habits (such as long-distance driving, short-distance commuting), battery swapping frequency, credit records, etc.

[0089] More specifically, obtaining vehicle owner profile information helps to understand the personalized needs and usage habits of vehicle owners, thereby providing them with more precise battery swapping services. For example, for vehicle owners who frequently travel long distances, they can be given priority to be provided with batteries with more charge; for vehicle owners with good credit, a more convenient battery swapping process can be provided; vehicle owner credit records and other information can be used to assess battery swapping risks; for vehicle owners with poor credit, certain restrictive measures can be taken, such as requiring additional guarantees or deposits, to reduce operational risks.

[0090] More specifically, based on the needs of the battery swapping service and the performance of the batteries, a battery swapping threshold is preset, usually the minimum requirement for battery capacity. For example, it is stipulated that the battery capacity must reach more than 80% before it can be used for battery swapping. The capacity data of each battery in the battery swapping cabinet is compared with the preset battery swapping threshold. An algorithm can be written using a programming language (such as Python) to traverse the capacity data in the battery status information and filter out batteries whose capacity meets the threshold requirement as candidate batteries.

[0091] More specifically, by setting a battery swapping threshold, it can be ensured that the batteries provided to users have sufficient power to meet their needs and avoid inconvenience caused by insufficient battery power. Selecting only batteries with the required power as candidates can avoid unnecessary battery scheduling, improve the resource utilization rate of the battery swapping cabinet, and reduce energy waste.

[0092] More specifically, based on the preset scheduling strategy, combined with the vehicle owner profile information and the battery swapping priority of the candidate batteries, the adaptability of battery use is analyzed. For example, considering factors such as the vehicle owner's driving habits, mileage requirements, and the remaining charge and health status of the battery, the adaptability parameters of the vehicle owner profile information corresponding to each candidate battery are calculated. A weighted summation method can be used to assign different weights to different factors to calculate the adaptability score. Based on the adaptability parameters, the candidate battery with the highest adaptability is selected to generate a battery scheduling plan. A sorting algorithm can be used to sort the adaptability scores of each candidate battery, and the battery with the highest score is selected as the scheduling plan. At the same time, the scheduling plan is sent to the control system of the battery swapping cabinet to control the battery swapping cabinet to perform the battery swapping operation.

[0093] More specifically, by combining vehicle owner profile information and battery swapping priority analysis, we can provide vehicle owners with the most suitable batteries for their needs, thereby improving user satisfaction. Different vehicle owners have different usage needs, and personalized scheduling schemes can better meet these needs. By generating the optimal battery scheduling scheme, we can rationally allocate battery resources in the battery swapping cabinet, improve swapping efficiency, reduce operating costs, and prioritize the scheduling of highly adaptable batteries, thereby reducing battery idleness and waste and improving resource utilization efficiency.

[0094] Preferably, the battery status information of each battery in the battery swapping cabinet is analyzed as a whole to dynamically adjust the preset battery swapping threshold.

[0095] Specifically, the status information of each battery, including charge level, temperature, charge / discharge cycles, and internal resistance, is obtained from the battery management system of the battery swapping station. This data can be collected in real time by sensors and transmitted to a cloud platform or local server for storage. The collected status information of different batteries is then integrated and organized according to dimensions such as battery number and time for subsequent analysis. Database management systems (such as MySQL and MongoDB) can be used to store and manage this data.

[0096] More specifically, statistical methods are used to analyze battery state information, calculating statistical quantities such as the average, standard deviation, maximum, and minimum values ​​of various indicators. For example, the average capacity and temperature fluctuation range of all batteries can be calculated, and the changing trend of battery state information over time can be observed to determine changes in battery performance. Time series analysis methods (such as moving average and exponential smoothing) can be used to predict future changes in battery state and analyze the correlations between different battery state indicators, such as the relationship between capacity and temperature, and the relationship between charge / discharge cycles and internal resistance. Correlation analysis and regression analysis can be used to determine these correlations.

[0097] More specifically, based on the data analysis results, adjustment rules for preset battery swapping thresholds are formulated. For example, if it is found that most batteries have generally high charge levels and stable temperatures, the battery swapping threshold can be appropriately increased; if the battery has undergone many charge-discharge cycles and its internal resistance has increased, the battery swapping threshold needs to be lowered to ensure the battery's safety and reliability. According to the adjustment rules, the preset battery swapping thresholds are adjusted in real time or periodically. Automated scripts or algorithms can be used to achieve automatic threshold adjustment to ensure that the thresholds can reflect the actual state of the battery in a timely manner.

[0098] Preferably, the step of analyzing the profile information and the battery swapping priority of each candidate battery according to a preset scheduling strategy to generate a battery scheduling scheme corresponding to the battery swapping request includes: S431: Based on the preset scheduling strategy, perform an adaptive analysis of battery usage between the profile information and the battery swapping priority of each candidate battery to obtain adaptive parameters of each candidate battery corresponding to the profile information. S432: Select the best candidate battery based on the adaptive parameters to generate a battery scheduling scheme corresponding to the battery swapping request.

[0099] Specifically, the preset scheduling strategy is the foundation of the entire analysis. It specifies how to combine profile information and battery swapping priority to evaluate battery adaptability. For example, the strategy will set the weight of different factors, such as the owner's driving habits, mileage requirements, remaining battery power, health status, charging and discharging frequency, etc.

[0100] More specifically, the user profile information and relevant information about the candidate batteries are quantified. For example, the driver's driving habits (long-distance and short-distance) are converted into specific mileage requirements; the battery's health status is quantified using a scoring system. This data is then standardized to eliminate differences in measurement units between different data points for subsequent calculations.

[0101] More specifically, based on the weights of each factor in the preset scheduling strategy, the profile information and the relevant information of the candidate batteries are weighted and calculated. For example, assuming that the weight of the owner's mileage demand is 0.4, the weight of the remaining battery power is 0.3, the weight of the battery health status is 0.2, and the weight of the charging and discharging frequency is 0.1, for a certain candidate battery, an adaptability score is calculated based on its corresponding data and weights. This score is the adaptability parameter of the profile information corresponding to the candidate battery.

[0102] More specifically, different car owners have different usage habits and needs. By combining user profile information with adaptive analysis, we can provide car owners with batteries that better meet their actual needs. For example, for car owners who drive long distances, we can prioritize batteries with sufficient charge and good health. By considering the battery swapping priority and its own condition, we can allocate battery resources more rationally, giving priority to suitable car owners with highly adaptable batteries, avoiding resource waste and improving the overall operational efficiency of battery swapping stations.

[0103] More specifically, the adaptability parameters of each candidate battery are sorted, usually in descending order. This can be achieved using a sorting function in a programming language (such as Python). The candidate battery with the highest adaptability parameter is selected from the sorted list as the best battery, which is the battery to be allocated to the car owner who made the battery swap request.

[0104] More specifically, the optimal battery information (such as battery number, location, etc.) and related battery swapping operation instructions (such as opening the corresponding battery compartment) are integrated to generate a complete battery scheduling plan. This plan can be executed through the battery swapping cabinet's control system. By selecting the battery with the highest adaptability parameters, the needs of car owners can be met to the greatest extent, improving the quality of battery swapping services and user satisfaction. A clear scheduling plan can guide the operation of the battery swapping cabinet, making the battery swapping process more efficient and accurate, reducing manual intervention, and lowering the probability of errors.

[0105] Secondly, the present invention provides a battery intelligent scheduling system for a battery swapping cabinet, used to implement the battery intelligent scheduling method for a battery swapping cabinet as described in any one of the first aspects.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent battery scheduling in a battery swapping cabinet, characterized in that, include: Real-time monitoring of battery status in battery swapping cabinets and electric vehicles is performed to obtain battery status information, which is then uploaded to the cloud platform. The battery status information at various times is recorded by the cloud platform to obtain the battery status monitoring record, and the health status characteristics of the battery are obtained by analyzing the status monitoring record. Obtain the business demand information of the battery swapping cabinet at the current time, and combine it with the battery status information of each battery in the battery swapping cabinet to drive the battery swapping cabinet to charge the batteries, and perform charging restriction processing and battery swapping priority adjustment of the batteries in the battery swapping cabinet according to the health status characteristics of the batteries. The system obtains battery swapping requests from electric vehicles and analyzes these requests based on the battery status information and swapping priorities of each battery in the battery swapping cabinet to generate a battery scheduling scheme corresponding to the battery swapping request.

2. The intelligent battery scheduling method for battery swapping cabinets as described in claim 1, characterized in that, The steps for real-time monitoring of battery status in battery swapping cabinets and electric vehicles to obtain battery status information include: Data on battery charge and temperature are collected in the battery swapping cabinet to obtain charge and temperature data. By combining the power data and temperature data at the same time, the battery status information of the batteries in the battery swapping cabinet can be obtained. Data on the battery capacity, temperature, and electrical performance of electric vehicles are collected to obtain data on battery capacity, temperature, and electrical performance. By combining the power data, temperature data, and electrical performance data at the same time, the battery status information of the electric vehicle's battery can be obtained.

3. The intelligent battery scheduling method for battery swapping cabinets as described in claim 1, characterized in that, The steps of recording battery status information at various times through a cloud platform to obtain battery status monitoring records, and analyzing the battery health status characteristics based on these records, include: Based on the cloud platform, the battery status information of the specified battery in the battery swapping cabinet and electric vehicle is recorded according to the time relationship to obtain the battery status monitoring record. Based on the aforementioned status monitoring records, the long-term operating performance characteristics of the battery are identified to obtain battery health monitoring information. Based on the health monitoring information, the battery status information for the current time period is analyzed to generate the health status characteristics for battery replacement.

4. The intelligent battery scheduling method for battery swapping cabinets as described in claim 3, characterized in that, The steps for identifying long-term operational performance characteristics of the battery based on the aforementioned status monitoring records to obtain battery health monitoring information include: The status monitoring records are divided into time periods, and the operational performance features of each time period are identified to obtain the operational performance feature set for each time period. A correlation analysis is performed on the performance feature sets of adjacent time periods, and a conversion trend analysis of the performance of adjacent time periods is performed based on the analysis results to obtain the conversion trend information between each time period. Based on the aforementioned conversion trend information, the battery's health status changes are simulated to obtain several health change prediction curves, which serve as health monitoring information for the battery.

5. The intelligent battery scheduling method for battery swapping cabinets as described in claim 1, characterized in that, The steps of obtaining the current business demand information of the battery swapping cabinet, combining it with the battery status information of each battery in the cabinet to drive the cabinet to charge the batteries, and adjusting the charging restrictions and swapping priorities of the batteries in the cabinet based on the battery health status characteristics include: By retrieving historical order records of the battery swapping cabinets through the cloud platform, and based on the historical order records, the business demand for the current time is predicted to obtain the business demand information of the battery swapping cabinets at the current time. The business demand information is analyzed according to preset standards to obtain the charging management mode of the battery swapping cabinet corresponding to the business demand information. Based on the charging management mode, the power data contained in the battery status information is parsed to obtain the theoretical charging power of the battery, so as to generate a corresponding charging strategy. According to the charging strategy, the battery swapping cabinet is driven to charge the battery with the theoretical charging power. The batteries in the battery swapping cabinet are scored in real time based on the health status characteristics in order to limit the charging power of the batteries. Based on the state monitoring records, the frequency of charge-discharge cycles of the battery is analyzed to obtain the charge-discharge frequency characteristics of the battery. The charging and discharging frequency characteristics are analyzed according to preset standards to adjust the battery swapping priority.

6. The intelligent battery scheduling method for battery swapping cabinets as described in claim 5, characterized in that, The steps for analyzing the business demand information according to preset standards to obtain the charging management mode of the battery swapping cabinet corresponding to the business demand information include: The business demand information is analyzed according to preset standards to obtain the period type and period level to which the battery swapping cabinet belongs at the current time; wherein, the period type includes the off-peak electricity consumption period and the peak electricity consumption period; Based on the period type and period level, a preset management mode library is used for mode scheduling to obtain the charging management mode of the battery swapping cabinet corresponding to the business demand information.

7. The intelligent battery scheduling method for battery swapping cabinets as described in claim 1, characterized in that, The steps for analyzing battery swapping requests based on the battery status information and swapping priority of each battery in the battery swapping cabinet to generate a battery scheduling scheme corresponding to the battery swapping request include: The source of the battery swapping request signal is identified to obtain the identification code of the electric vehicle that issued the battery swapping request, and the owner's profile information is retrieved from the database based on the identification code. Based on the preset battery swapping threshold, the feasibility of order response is calculated using the power data contained in the battery status information of each battery, so as to select batteries that can respond to battery swapping requests as candidate batteries. The profile information and the battery swapping priority of each candidate battery are analyzed according to the preset scheduling strategy to generate a battery scheduling scheme corresponding to the battery swapping request.

8. The intelligent battery scheduling method for battery swapping cabinets as described in claim 7, characterized in that, The battery status information of each battery in the battery swapping cabinet is analyzed as a whole in order to dynamically adjust the preset battery swapping threshold.

9. The intelligent battery scheduling method for battery swapping cabinets as described in claim 7, characterized in that, The steps of analyzing the profile information and the battery swapping priority of each candidate battery according to a preset scheduling strategy to generate a battery scheduling scheme corresponding to the battery swapping request include: Based on the preset scheduling strategy, an adaptive analysis of battery usage is performed on the profile information and the battery swapping priority of each candidate battery to obtain the adaptive parameters of each candidate battery corresponding to the profile information. The optimal candidate battery is selected based on the adaptive parameters to generate a battery scheduling scheme corresponding to the battery swapping request.

10. A battery intelligent scheduling system for a battery swapping cabinet, characterized in that, This method is used to implement the intelligent battery scheduling method for a battery swapping cabinet as described in any one of claims 1-9.

Images