A method for controlling battery charging of a battery swapping cabinet based on the Internet of Things

Abstract

The present invention discloses a method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things, which relates to the technical field of charging control. Compared with the traditional fixed charging mode, the present invention has stronger adaptability in terms of predicting battery swapping demand, switching charging modes, and managing charging safety, and can effectively cope with problems such as sudden increase in battery swapping demand and changes in environmental factors, improving the operation stability of the battery swapping cabinet; by collecting battery status, battery swapping demand, and meteorological data, combining the long short-term memory network (LSTM) and the multiple linear regression model to predict the future battery swapping demand curve, and introducing a meteorological impact factor to adjust the charging plan of the battery swapping cabinet 24 hours before the severe weather warning to match the battery stock with the peak demand; the charging mode is set to three modes: fast charging, normal charging, and slow charging, and is dynamically switched according to the peak value of the battery swapping demand and the daily average battery swapping demand, preferentially using the fast charging mode during the peak period of battery swapping to improve the battery supply rate, and reserving part of the charging resources at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of charging control, and particularly to a method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things. Background Art

[0002] With the rapid development of the food delivery and express delivery industries, the battery swapping cabinet has become the core battery replenishment facility for short-distance travel of electric vehicles. Currently, the common charging strategy for battery swapping cabinets is to charge intensively during the low-peak period of the power grid at night and provide battery swapping services during the day.

[0003] However, the battery swapping demand is not constant. The traditional fixed charging control mode cannot quickly respond to the fluctuations in battery swapping demand. When the battery swapping demand suddenly increases, the batteries in the battery swapping cabinet are taken out within a short time, and the charging system is difficult to replenish in time, resulting in users queuing for a long time and even affecting the rider's itinerary. On the contrary, when the demand drops suddenly, the battery swapping cabinet still charges according to the original plan, resulting in the batteries being in a high state of charge (SOC) for a long time, accelerating aging and reducing operation efficiency. In addition, in bad weather, such as strong winds, heavy rainstorms, etc., users may replace the batteries in advance, causing a shortage of battery supply in the battery swapping cabinet in a short time. Heavy rain or other conditions may also cause riders to reduce their trips, resulting in a sudden drop in battery swapping demand. Some batteries are fully charged but not taken, and the long-term high SOC further accelerates battery aging. In addition, high humidity may affect the battery contact points, increasing the risk of short circuit and easily causing potential safety hazards.

[0004] It can be seen that the current method for controlling the charging of batteries in battery swapping cabinets cannot adapt to the battery swapping demand in bad weather or emergencies, lacks predictive ability, environmental perception and dynamic regulation, and has a lagging regulation method. Therefore, there is an urgent need for a method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things to solve such problems. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides a method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things, which solves the problems that the traditional charging strategy of the battery swapping cabinet is fixed, lacks predictive ability, environmental perception and dynamic regulation, and cannot cope with bad weather and sudden demand fluctuations.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] An embodiment of the present invention provides a method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things, which includes:

[0009] Step S1, collecting battery status data and battery swapping demand data, and monitoring meteorological data;

[0010] Step S2, predicting the future peak battery swapping demand based on the meteorological data and the battery swapping demand data;

[0011] Step S3, adjust the charging plan according to the predicted peak of battery replacement demand in Step S2, and switch the charging mode;

[0012] Step S4, the battery replacement cabinet is equipped with sensors, and the sensors monitor environmental data during the implementation of the charging plan in Step S3;

[0013] Step S5, compare the environmental data collected in Step S4 with the safety threshold. If the humidity exceeds the threshold, adjust the charging power and adjust the fast charging mode; if the temperature is lower than the threshold, enable the fast charging mode.

[0014] As a preferred solution of the method for controlling battery charging of a battery replacement cabinet based on the Internet of Things according to the present invention, wherein: the battery state data and battery replacement demand data include battery power, battery stock, battery replacement frequency, and historical battery replacement records.

[0015] As a preferred solution of the method for controlling battery charging of a battery replacement cabinet based on the Internet of Things according to the present invention, wherein: in Step S1, the steps of collecting battery state data and battery replacement demand data and monitoring meteorological data are as follows:

[0016] Obtain the battery power and remaining stock through the battery management system, and collect the parameters of individual batteries, including: battery voltage, battery current, battery temperature, and remaining available power.

[0017] Collect battery replacement demand data, and record the battery replacement frequency, including battery replacement time, user identification, and battery replacement mode data.

[0018] Statistical historical battery replacement records, including: the average battery replacement cycle, battery replacement time interval, battery replacement mode selection situation of each user, calculate the number of battery replacements per unit time, and form a battery replacement demand prediction curve.

[0019] Deploy temperature, humidity, wind speed, and precipitation sensors in the battery replacement cabinet to monitor the meteorological parameters of the location of the battery replacement cabinet. At the same time, access the third-party meteorological data API to obtain weather warning information, combine historical weather data, analyze the impact of meteorological changes on battery replacement demand, and dynamically adjust the data weight.

[0020] As a preferred solution of the method for controlling battery charging of a battery replacement cabinet based on the Internet of Things according to the present invention, wherein: in Step S2, the steps of predicting the future peak of battery replacement demand based on meteorological data and battery replacement demand data are as follows:

[0021] Establish a battery replacement demand prediction model, use the long short-term memory network LSTM for time series prediction, and establish a battery replacement demand curve, expressed as:

[0022] ,

[0023] Among them, represents the battery swapping demand at time t, represents the battery swapping demand data of the past n time instants, represents the mapping function of the LSTM prediction model;

[0024] Introducing the influence of meteorological factors, a multiple linear regression model is adopted to calculate the influence of meteorological factors on the battery swapping demand. The formula is:

[0025] ,

[0026] Among them, represents the corrected predicted value of the battery swapping demand, represents the temperature at time t, represents the humidity at time t, represents the precipitation at time t, represents the meteorological influence weight coefficient, which is determined by historical data regression analysis,

[0027] Based on meteorological warning data, a weather severity factor is set :

[0028] ,

[0029] Among them, represents the final predicted value of the battery swapping demand, represents the weather severity weight, and its value range is defined as follows:

[0030] Normal weather: ;

[0031] Mild severe weather (light rain, gentle breeze): ;

[0032] Moderate severe weather (heavy rain, strong wind): ;

[0033] Extreme severe weather (typhoon, heavy snow): ;

[0034] Set the prediction time window to the next 24 hours, and calculate the demand peak. The calculation formula is:

[0035] ,

[0036] Among them, represents the peak value of the battery swapping demand within the next 24 hours.

[0037] As a preferred solution of the method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things according to the present invention, wherein: the charging plan includes checking the battery stock within 24 hours before a severe weather warning, adopting a fast charging mode, and adjusting the battery stock.

[0038] As a preferred solution of the method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things according to the present invention, wherein: the charging modes include a fast charging mode, a general charging mode, and a slow charging mode.

[0039] As a preferred solution of the method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things according to the present invention, wherein: in step S3, the step of adjusting the charging plan and switching the charging mode according to the peak value of the predicted battery swapping demand is as follows

[0040] Calculate the current battery stock, and the calculation formula is:

[0041] ,

[0042] wherein represents the current available battery stock, represents the current available battery quantity, represents the minimum inventory battery threshold,

[0043] If ,execute the fast charging mode,

[0044] Based on the predicted peak value of the battery swapping demand ,select the charging mode:

[0045] If ,use the slow charging mode,

[0046] If ,use the general charging mode,

[0047] If ,switch to the fast charging mode,

[0048] wherein represents the daily average battery swapping demand,

[0049] Within 24 hours before a severe weather warning:

[0050] Check the battery stock, and require the inventory battery to be higher than the peak demand,

[0051] Start the fast charging mode to increase the charging rate,

[0052] Reserve some charging resources to cope with sudden battery swapping demands.

[0053] As a preferred solution of the method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things according to the present invention, wherein: the environmental data includes the humidity and temperature of the battery swapping cabinet.

[0054] As a preferred solution of the method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things according to the present invention, wherein: in step S5, the charging power and fast charging mode are adaptively adjusted based on the monitored environmental data. Specifically:

[0055] Let the process of the built-in sensor in the battery swapping cabinet for real-time monitoring data be:

[0056] ,

[0057] wherein, represents the current humidity, represents the current temperature, represents the sensor data reading function,

[0058] If it is detected that , the charging power is reduced, and the adjustment formula is:

[0059] ,

[0060] wherein, represents the original charging power, represents the humidity influence coefficient, and the value range is .

[0061] As a preferred solution of the method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things according to the present invention, wherein: the step of adaptively adjusting the charging power and fast charging mode further includes:

[0062] If , the charging power is increased, and the adjustment formula is:

[0063] ,

[0064] wherein, represents the temperature compensation coefficient, and the value range is ,

[0065] The charging power is adjusted in real time:

[0066] ,

[0067] wherein, represents the adjusted charging power.

[0068] The beneficial effects of the present invention are as follows: Compared with the traditional fixed charging mode, the present invention has stronger adaptability in terms of predicting battery swapping demand, switching charging modes, and charging safety management. It can effectively cope with problems such as sudden increase in battery swapping demand and changes in environmental factors, and improve the operation stability of the battery swapping cabinet.

[0069] The present invention collects battery status, battery swapping demand, and meteorological data, combines the long short-term memory network (LSTM) and the multiple linear regression model to predict the future battery swapping demand curve, and introduces a meteorological impact factor to adjust the charging plan of the battery swapping cabinet 24 hours before the severe weather warning, so as to match the battery stock with the peak demand.

[0070] The present invention sets three charging modes: fast charging, normal charging, and slow charging, and dynamically switches according to the peak value of the battery swapping demand and the daily average battery swapping demand. It preferentially adopts the fast charging mode during the peak period of battery swapping to improve the battery supply rate, and at the same time reserves some charging resources to cope with sudden battery swapping demands, avoiding problems such as insufficient battery supply or waste of battery stock.

[0071] The battery swapping cabinet of the present invention is equipped with environmental sensors to continuously monitor environmental parameters such as temperature and humidity, and dynamically adjusts the charging power in combination with safety thresholds. When the humidity exceeds the threshold, the charging power is reduced and the fast charging is delayed to prevent the risk of short circuit. When the temperature is lower than the threshold, the fast charging mode is enabled to improve the charging efficiency in low-temperature environments, enabling the battery swapping cabinet to operate stably under different environmental conditions.

[0072] In summary, the present invention reduces the resource waste caused by the fixed mode through intelligent charging control, improves the response ability of the charging system to demand changes, and enhances the safety protection in harsh environments. Description of the Drawings

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0074] Figure 1 It is a schematic flow chart of the method for controlling the charging of the battery of the battery swapping cabinet based on the Internet of Things of the present invention. Detailed Embodiments

[0075] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed embodiments of the present invention with reference to the drawings in the specification.

[0076] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0077] Secondly, as used herein, "an embodiment" or "embodiments" refers to specific features, structures, or characteristics that may be included in at least one implementation of the present invention. The phrase "in an embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0078] Embodiment 1, referring to Figure 1 , this embodiment provides a method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things, including the following steps:

[0079] Step S1, collect battery status data and battery swapping demand data, and monitor meteorological data;

[0080] The battery status data and battery swapping demand data include battery power, battery inventory, battery swapping frequency, and historical battery swapping records;

[0081] In step S1, the steps of collecting battery status data and battery swapping demand data and monitoring meteorological data are as follows:

[0082] Obtain the battery power and remaining inventory through the battery management system, and collect the parameters of individual batteries, including: battery voltage, battery current, battery temperature, and remaining available power.

[0083] Collect battery swapping demand data, and record the battery swapping frequency, including the battery swapping time, user identification, and battery swapping mode data.

[0084] Statistical historical battery swapping records, including: the average battery swapping cycle of each user, the battery swapping time interval, the battery swapping mode selection situation, calculate the number of battery swaps per unit time, and form a battery swapping demand prediction curve.

[0085] Deploy temperature, humidity, wind speed, and precipitation sensors in the battery swapping cabinet to monitor the meteorological parameters of the location where the battery swapping cabinet is located. At the same time, access the third-party meteorological data API to obtain weather warning information, combine historical weather data, analyze the impact of meteorological changes on battery swapping demand, and dynamically adjust the data weight.

[0086] Specifically, here, the BMS is used to monitor the battery power, inventory, and parameters of individual batteries, and the database is used to record historical data to provide support for subsequent analysis; collecting battery swapping demand data based on the battery swapping frequency, historical records, and user behavior analysis can effectively reflect the battery swapping demand trend in different time periods.

[0087] In addition, by combining environmental sensors with third-party data APIs to monitor meteorological data, not only the real-time nature is ensured, but also the reliability of the data is enhanced. Meteorological data is crucial for subsequent prediction of battery swapping demands, which can improve the accuracy of prediction, thereby optimizing the charging strategy, reducing energy waste, and enhancing the operation efficiency of battery swapping cabinets.

[0088] Step S2: Predict the future peak battery swapping demand based on meteorological data and battery swapping demand data.

[0089] In step S2, the steps to predict the future peak battery swapping demand based on meteorological data and battery swapping demand data are as follows.

[0090] Establish a battery swapping demand prediction model, use the long short-term memory network (LSTM) for time series prediction, and establish a battery swapping demand curve, expressed as:

[0091] ,

[0092] where represents the battery swapping demand at time t, represents the battery swapping demand data at the past n time moments, represents the mapping function of the LSTM prediction model.

[0093] Introduce the influence of meteorological factors, use a multiple linear regression model to calculate the influence of meteorological factors on the battery swapping demand, and the formula is:

[0094] ,

[0095] where represents the corrected predicted value of the battery swapping demand, represents the temperature at time t, represents the humidity at time t, represents the precipitation at time t, represents the meteorological influence weight coefficient, which is determined by historical data regression analysis.

[0096] Based on meteorological warning data, set the weather severity factor :

[0097] ,

[0098] where represents the final predicted value of the battery swapping demand, represents the weather severity weight, and its value range is defined as follows:

[0099] Normal weather: ;

[0100] Mild severe weather (light rain, gentle breeze): ;

[0101] Moderate severe weather (heavy rain, strong wind): ;

[0102] Extreme severe weather (typhoon, heavy snow): ;

[0103] Set the prediction time window to the next 24 hours, and calculate the peak demand. The calculation formula is:

[0104] ,

[0105] where, represents the peak demand for battery swapping within the next 24 hours;

[0106] Specifically, the core here is to establish a prediction model for battery swapping demand and improve the prediction accuracy;

[0107] Use LSTM for time series prediction, dynamically adjust the changing trend of battery swapping demand, and combine the multiple linear regression method to quantify the impact of meteorological factors on battery swapping demand, so as to improve the adaptability of the prediction model. In addition, determine the weather severity factor by adjusting the battery swapping demand based on historical data analysis and meteorological warning signals, so that the model can adapt to sudden weather changes. Finally, calculate the peak demand for battery swapping as the basis for subsequent charging plan adjustment; it can not only optimize the allocation of battery resources, but also improve the response speed of the battery swapping cabinets;

[0108] Step S3: According to the peak demand for battery swapping predicted in step S2, adjust the charging plan and switch the charging mode;

[0109] The charging plan includes checking the battery stock within 24 hours before the severe weather warning, using the fast charging mode, and adjusting the battery stock;

[0110] The charging modes include the fast charging mode, the normal charging mode, and the slow charging mode;

[0111] In step S3, the steps of adjusting the charging plan and switching the charging mode according to the peak demand for battery swapping predicted in step S2 are as follows

[0112] Calculate the current battery stock. The calculation formula is:

[0113] ,

[0114] where, represents the current available battery stock, represents the current available number of batteries, represents the minimum inventory battery threshold,

[0115] If , then execute the fast charging mode,

[0116] Predicted peak demand for battery swapping , select the charging mode:

[0117] If , use the slow charging mode,

[0118] If , use the normal charging mode,

[0119] If , switch to the fast charging mode,

[0120] Among them, represents the daily average demand for battery swapping,

[0121] 24 hours before the severe weather warning:

[0122] Check the battery inventory, requiring the inventory battery to be higher than the peak demand,

[0123] Start the fast charging mode to increase the charging rate,

[0124] Reserve some charging resources to cope with sudden battery swapping demands;

[0125] Specifically, calculate the current battery inventory here to determine whether it is necessary to execute the fast charging mode in advance to avoid battery shortage problems in the swapping cabinet during the peak demand period;

[0126] Secondly, adopt a demand-driven method to switch the charging mode, determine the charging strategy based on the peak demand for battery swapping and the daily average demand for battery swapping, and achieve intelligent dynamic adjustment; in addition, in response to the severe weather warning, adjust the charging plan 24 hours in advance to minimize the impact of weather factors on the battery swapping service, effectively improve the operating efficiency of the swapping cabinet, and at the same time avoid unnecessary energy waste and improve the stability of battery supply;

[0127] Step S4, the swapping cabinet is equipped with sensors, and the sensors monitor the environmental data during the execution of the charging plan in step S3;

[0128] The environmental data includes the humidity and temperature of the swapping cabinet;

[0129] Step S5, compare the environmental data collected in step S4 with the safety threshold. If the humidity exceeds the threshold, adjust the charging power and adjust the fast charging mode; if the temperature is lower than the threshold, enable the fast charging mode;

[0130] In step S5, adaptively adjust the charging power and the fast charging mode based on the monitored environmental data. Specifically:

[0131] Let the process of the sensors in the swapping cabinet monitoring the real-time data be:

[0132] ,

[0133] Among them, represents the current humidity, represents the current temperature, represents the sensor data reading function,

[0134] If it is detected that , reduce the charging power, and the adjustment formula is:

[0135] ,

[0136] Among them, represents the original charging power, represents the humidity influence coefficient, and the value range is ,

[0137] If , increase the charging power, and the adjustment formula is:

[0138] ,

[0139] Among them, represents the temperature compensation coefficient, and the value range is ,

[0140] Adjust the charging power in real time:

[0141] ,

[0142] Among them, represents the adjusted charging power;

[0143] Specifically, in step S5, during the charging process, the environmental parameters are dynamically monitored and adaptively adjusted to improve charging safety and efficiency. The temperature and humidity are monitored in real time through the sensors built in the battery swapping cabinet. Based on the safety threshold, the charging power can be reduced when the humidity exceeds the standard to reduce potential safety hazards. At the same time, the charging power can be automatically increased in a low-temperature environment to avoid the impact of sudden environmental changes on the charging system, enabling the battery swapping cabinet to operate efficiently under various environmental conditions, extending the battery life, and reducing the instability caused by environmental factors.

[0144] In summary, the present invention:

[0145] Compared with the traditional fixed charging mode, the present invention has stronger adaptability in terms of predicting battery swapping demand, switching charging modes, and managing charging safety, and can effectively address issues such as sudden increases in battery swapping demand and changes in environmental factors, improving the operating stability of the battery swapping cabinet.

[0146] In the present invention, by collecting battery status, battery replacement demand, and meteorological data, combining the long short-term memory network (LSTM) and the multiple linear regression model, the future battery replacement demand curve is predicted, and a meteorological impact factor is introduced. The charging plan of the battery replacement cabinet is adjusted 24 hours before the severe weather warning, and the battery stock is matched with the peak demand.

[0147] In the present invention, three charging modes, namely fast charging, normal charging, and slow charging, are set, and the charging mode is dynamically switched according to the peak value of the battery replacement demand and the daily average battery replacement demand. The fast charging mode is preferentially adopted during the peak period of battery replacement to improve the battery supply rate. At the same time, some charging resources are reserved to cope with sudden battery replacement demands, avoiding problems such as insufficient battery supply or waste of battery stock.

[0148] In the present invention, environmental sensors are installed inside the battery replacement cabinet to monitor environmental parameters such as temperature and humidity in real time, and the charging power is dynamically adjusted in combination with safety thresholds. When the humidity exceeds the threshold, the charging power is reduced and the fast charging is delayed to prevent the risk of short circuit. When the temperature is lower than the threshold, the fast charging mode is enabled to improve the charging efficiency in low-temperature environments, so that the battery replacement cabinet can operate stably under different environmental conditions.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things, characterized in that: including Step S1: Collect battery status data and battery replacement demand data, and monitor meteorological data; Step S2: Based on the meteorological data and battery replacement demand data, predict the future peak of battery replacement demand; Step S3: According to the peak of battery replacement demand predicted in Step S2, adjust the charging plan and switch the charging mode; Step S4: The battery replacement cabinet is equipped with sensors, and the sensors monitor environmental data during the implementation of the charging plan in Step S3; Step S5: Compare the environmental data collected in Step S4 with the safety threshold. If the humidity exceeds the threshold, adjust the charging power and the fast charging mode; if the temperature is lower than the threshold, enable the fast charging mode; In Step S2, the step of predicting the future peak of battery replacement demand based on the meteorological data and battery replacement demand data is as follows: Establish a battery replacement demand prediction model, use the long short-term memory network (LSTM) for time series prediction, and establish a battery replacement demand curve, which is expressed as: ， Among them, represents the battery swapping demand at time t, represents the battery swapping demand data for the past n time instants, represents the mapping function of the LSTM prediction model; Introduce the influence of meteorological factors, and use a multiple linear regression model to calculate the influence of meteorological factors on battery replacement demand. The formula is: ， Among them, represents the predicted value of the battery swapping demand after correction, represents the temperature at time t, represents the humidity at time t, represents the precipitation at time t, represents the meteorological influence weight coefficient, which is determined by regression analysis of historical data. Set the weather severity factor based on meteorological warning data : ， Among them, represents the predicted value of the final battery replacement demand, represents the weight of weather severity; Set the prediction time window to the next 24 hours, and calculate the demand peak. The calculation formula is: ， Among them, represents the peak demand for battery swapping within the next 24 hours.

2. The method for controlling battery charging of a battery swapping cabinet based on the Internet of Things according to claim 1, wherein: The battery status data and battery replacement demand data include battery power, battery stock, battery replacement frequency, and historical battery replacement records.

3. The method for controlling battery charging of a battery swapping cabinet based on the Internet of Things according to claim 2, characterized in that: In Step S1, the step of collecting battery status data and battery replacement demand data and monitoring meteorological data is as follows: Obtain the battery power and remaining stock through the battery management system, and collect the parameters of individual batteries, including: battery voltage, battery current, battery temperature, and remaining available power; Collect battery replacement demand data, and record the battery replacement frequency, including the battery replacement time, user identification, and battery replacement mode data; Statistically analyze the historical battery replacement records, including: the average battery replacement cycle, battery replacement time interval, battery replacement mode selection of each user, and calculate the number of battery replacements per unit time to form a battery replacement demand prediction curve; Deploy temperature, humidity, wind speed, and precipitation sensors in the battery replacement cabinet to monitor the meteorological parameters at the location of the battery replacement cabinet. At the same time, access the third-party meteorological data API to obtain weather warning information, combine the historical weather data, analyze the influence of meteorological changes on battery replacement demand, and dynamically adjust the data weight.

4. The method for controlling the charging of batteries in a battery swapping cabinet based on the Internet of Things according to claim 3, characterized in that: The charging plan includes checking the battery stock within 24 hours before the severe weather warning, using the fast charging mode, and adjusting the battery stock.

5. The method for controlling battery charging of a battery swapping cabinet based on the Internet of Things according to claim 4, characterized in that: The charging modes include fast charging mode, normal charging mode, and slow charging mode.

6. The method for controlling the charging of the battery in the battery swapping cabinet based on the Internet of Things according to claim 5, wherein: The environmental data includes the humidity and temperature of the battery replacement cabinet.

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