A Method for Safety Control of Energy Storage Power Sources in the Internet of Things

Abstract

The present invention discloses a method for safely controlling an energy storage power supply in the Internet of Things, which relates to the technical field of intelligent management and includes the following steps: sending an estimated charging duration to the user; sending a first charging power, obtaining a second charging power of the charging circuit, and the charging pile performs a first regulation on the first charging power according to the second charging power to obtain a third charging power of the charging pile after the first regulation. By matching the target charging pile after receiving the user's charging request, the present invention avoids long waiting times, ensures that the charging power matches the battery state, improves the charging efficiency, performs multiple regulations on the charging power, reduces the risk of battery thermal runaway, establishes an aging correlation equation by combining multiple regression analysis, discovers potential safety hazards in a timely manner, reasonably arranges the charging strategy, extends the service life of the battery, and uses Internet of Things technology to collect a large amount of battery operation data to realize real-time monitoring, fault prediction and intelligent decision-making of the battery state.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management, and in particular to a method for safely controlling an energy storage power supply in the Internet of Things. Background Art

[0002] In recent years, by collecting, fusing historical data, real-time data, and simulation data, standardizing the expression of heterogeneous data, unifying data conversion rules, establishing data fusion standards, realizing the comprehensive collection, storage, management, and sharing of multi-scale heterogeneous data of multiple operating entities, promoting the iteration of multi-modal models and the optimization of application services, the technology of real-time monitoring of parameters such as gas and the temperature of each part of the equipment in the energy storage system has been relatively mature. By reducing monitoring blind spots, faults and abnormal high temperatures can be detected in time, thermal runaway and fires can be prevented, and the safety and reliability of the energy storage system can be effectively improved.

[0003] Currently, in the Chinese invention patent with the publication number CN117410597B, a method and system for intelligent monitoring and processing of potential safety hazards applied to an energy storage power supply are disclosed. When it is determined that there is an abnormal battery cell in the target battery module, for each abnormal battery cell, according to the monomer target information of the abnormal battery cell, the abnormal processing parameters of the abnormal battery cell are determined, and a processing operation matching the abnormal processing parameters of the abnormal battery cell is performed on the abnormal battery cell. However, in the related technology, the charging power is not adjusted according to the actual charging efficiency of the battery, which is not conducive to the high efficiency of charging. On the basis of high-efficiency data, the battery is not adjusted again according to the battery temperature, which is not conducive to the safety of charging and the durability of the battery, and there are certain limitations. Summary of the Invention

[0004] The technical problem solved by the present invention is that in the related technology, the charging power is not adjusted according to the actual charging efficiency of the battery, which is not conducive to the high efficiency of charging. On the basis of high-efficiency data, the battery is not adjusted again according to the battery temperature, which is not conducive to the safety of charging and the durability of the battery, and there are certain limitations.

[0005] To solve the above technical problem, the present invention provides the following technical solution: A method for safely controlling an energy storage power supply in the Internet of Things, including the following steps:

[0006] Step S100, sending the expected charging duration to the user;

[0007] Step S200, sending the first charging power, obtaining the second charging power of the charging circuit, and the charging pile performs a first regulation on the first charging power according to the second charging power to obtain the third charging power of the charging pile after the first regulation;

[0008] Step S300, perform a second regulation on the third charging power according to the battery temperature to obtain the fourth charging power. After charging is completed, count the actual charging duration, and obtain the battery aging ratio based on the actual charging duration;

[0009] Step S400, obtain the battery usage situation, calculate the battery usage parameters. Taking the battery aging ratio as the dependent variable and the battery usage situation and battery usage parameters as the independent variables, perform multiple regression analysis to obtain the aging correlation equation, set the aging ratio threshold, and calculate the usage decisions of each dependent variable according to the aging ratio threshold.

[0010] As a preferred solution of an energy storage power supply safety control method for the Internet of Things according to the present invention, wherein: the step S100 includes the following sub-steps:

[0011] Step S101, the charging server receives the user's charging request and obtains the current vehicle position through the GPS positioning system;

[0012] Step S102, match the nearest charging station according to the current vehicle position, and obtain the charging pile status in the charging station. The charging pile status includes an idle status and a busy status;

[0013] Step S103, select any charging pile with a working status of idle as the target charging pile, and update the working status of the target charging pile to busy;

[0014] Step S104, obtain the battery SOC of the user, retrieve the charging database, input the battery SOC into the charging database, match the respective charging durations corresponding to the battery SOC, calculate the average value of the charging durations, set the average value of the charging durations as the estimated charging duration, and send the estimated charging duration to the user through the server.

[0015] As a preferred solution of an energy storage power supply safety control method for the Internet of Things according to the present invention, wherein: the step S200 includes the following sub-steps:

[0016] Step S201, the induction area of the charging station identifies the pressure signal and sends a start charging signal to the target charging pile;

[0017] Step S202, the target charging pile sends the first charging power to the charging circuit. The first charging power is used to test the charging efficiency of the battery, and the first charging power is distributed between 0 and the constant voltage charging power;

[0018] Step S203, obtain the second charging power of the charging circuit. The second charging power represents the actual charging power received by the charging circuit of the battery;

[0019] Step S204: The target charging pile performs a first regulation on the voltage frequency according to the second charging power. The first regulation means continuously increasing the voltage frequency, continuously calculating the actual charging power of the charging current, and stopping the regulation until the actual power of the charging circuit reaches the first charging power;

[0020] Step S205: Obtain the transmission power of the target charging pile after the first regulation, and record the transmission power of the target charging pile after the first regulation as the third charging power.

[0021] As a preferred solution of the energy storage power supply safety control method for the Internet of Things according to the present invention, wherein: set the first time period as the recording duration, and obtain the battery temperature within the first time period after the first regulation;

[0022] The regulation method of the second regulation includes:

[0023] Retrieve the battery temperature database, obtain the battery model, input the battery model into the battery temperature database, match the temperature threshold corresponding to the battery model, subtract the battery temperature from the temperature threshold to obtain a first difference. The first difference is a positive number. Set the first value as the difference upper limit, and compare the first difference with the first value;

[0024] When the first difference is greater than or equal to the first value, no second regulation is performed;

[0025] When the first difference is less than the first value, the target charging pile performs a second regulation on the voltage frequency. The second regulation means continuously decreasing the voltage frequency;

[0026] Obtain the transmission power of the target charging pile after the second regulation, record the transmission power of the target charging pile after the second regulation as the fourth charging power, and bind the fourth charging power to the user's vehicle.

[0027] As a preferred solution of the energy storage power supply safety control method for the Internet of Things according to the present invention, wherein: the charging pile charges the vehicle at the fourth power, and continuously obtains the SOC of the vehicle. When the SOC of the vehicle exceeds 80%, a charging completion signal is sent to the user through the server, and the transmission circuit is closed;

[0028] Set the duration from the start of charging to the sending of the charging completion signal as the actual charging duration, calculate the difference between the actual charging duration and the expected charging duration, record it as the second difference, calculate the ratio of the second difference to the expected charging duration, set it as the battery aging ratio, and the induction area of the charging station identifies the driving-away state of the vehicle, and updates the status of the target charging pile to the idle state.

[0029] As a preferred solution of a method for controlling the safety of an energy storage power supply in the Internet of Things according to the present invention, wherein: the battery usage conditions include service duration, deep discharge condition, overcharge condition, number of rapid brakings, number of rapid accelerations, and extreme high temperature travel condition, and the service duration is expressed as the usage duration experienced from the latest installation of the battery on the vehicle to the present;

[0030] The unit length of the service duration is hours, the deep discharge condition is expressed as a historical deep discharge time period, the overcharge condition is expressed as a historical overcharge time period, the extreme high temperature travel condition is expressed as a historical extreme high temperature travel time period, and the extreme high temperature is expressed as a natural temperature exceeding 40°C.

[0031] As a preferred solution of a method for controlling the safety of an energy storage power supply in the Internet of Things according to the present invention, wherein: the battery usage parameters include average deep discharge duration, average overcharge duration, and average extreme high temperature travel duration;

[0032] The calculation method of the average deep discharge duration includes:

[0033] Obtain any historical deep discharge time period, calculate the difference between the end time point and the start time point of the historical deep discharge time period, denoted as the third difference, convert the third difference into a time quantity in hours, denoted as the first time quantity, traverse each historical deep discharge time period, calculate each first time quantity, calculate the average value of each first time quantity, and set the average value of each first time quantity as the average deep discharge duration;

[0034] The calculation method of the average overcharge duration includes:

[0035] Obtain any historical overcharge time period, calculate the difference between the end time point and the start time point of the historical overcharge time period, denoted as the fourth difference, convert the fourth difference into a time quantity in hours, denoted as the second time quantity, traverse each historical overcharge time period, calculate each second time quantity, calculate the average value of each second time quantity, and set the average value of each second time quantity as the average overcharge duration;

[0036] The calculation method of the average extreme high temperature travel duration includes:

[0037] Obtain any historical extreme high temperature travel time period, calculate the difference between the end time point and the start time point of the historical extreme high temperature travel time period, denoted as the fifth difference, convert the fifth difference into a time quantity in hours, denoted as the third time quantity, traverse each historical extreme high temperature travel time period, calculate each third time quantity, calculate the average value of each third time quantity, and set the average value of each third time quantity as the average extreme high temperature travel duration.

[0038] As a preferred solution of a method for safely controlling an energy storage power supply of the Internet of Things according to the present invention, wherein: taking the battery aging ratio as the dependent variable, and taking the service duration, the number of emergency brakes, the number of emergency accelerations, the average duration of deep discharge, the average duration of overcharge, and the average duration of traveling in extremely high temperatures as independent variables, performing multiple regression analysis to obtain an aging correlation equation;

[0039] The calculation expression of the aging correlation equation is:

[0040] ;

[0041] Wherein, is the battery aging ratio, is the total number of types of independent variables, is the th independent variable, is the th coefficient corresponding to the independent variable.

[0042] As a preferred solution of a method for safely controlling an energy storage power supply of the Internet of Things according to the present invention, wherein: setting the second value as the aging ratio threshold;

[0043] Substituting the aging ratio threshold into the aging correlation equation to obtain the limit intervals of each independent variable. The limit intervals represent the numerical ranges of the independent variables within the aging ratio threshold after removing other factors.

[0044] As a preferred solution of a method for safely controlling an energy storage power supply of the Internet of Things according to the present invention, wherein: the calculation logic of the usage decision for each independent variable includes:

[0045] Obtain the numerical ranges of each independent variable, calculate the sum of the upper limit of the numerical range of each independent variable and the lower limit of the numerical range of each independent variable, denote it as the first sum, take half of the value of the first sum, and set it as the theoretical value of the corresponding independent variable;

[0046] The theoretical value of the service duration is expressed as the remaining battery usage duration, the theoretical value of the number of emergency brakes is expressed as the maximum remaining allowable number of emergency brakes, the theoretical value of the number of emergency accelerations is expressed as the maximum remaining allowable number of emergency accelerations, the theoretical value of the average duration of deep discharge is expressed as the longest time for a single deep discharge duration, the theoretical value of the average duration of overcharge is expressed as the longest time for a single overcharge duration, and the theoretical value of the average duration of traveling in extremely high temperatures is expressed as the longest time for a single extremely high temperature travel;

[0047] Usage decisions include: after using the theoretical value of the service life, replacing the battery. The maximum remaining number of allowable rapid braking times and the maximum remaining number of allowable rapid acceleration times are the theoretical values of the rapid braking times and the rapid acceleration times respectively. The maximum daily duration of the average deep discharge duration, the maximum daily duration of the average overcharge duration, and the maximum daily duration of the average extreme high temperature travel duration are the theoretical values of the average deep discharge duration, the average overcharge duration, and the average extreme high temperature travel duration respectively.

[0048] Advantages of the present invention: After receiving the user's charging request, match the target charging pile, and send the estimated charging duration to the user according to the battery SOC, enabling the user to plan the charging time in advance and avoid long waiting times. During the charging process, the charging pile can dynamically adjust according to the actual feedback power of the charging circuit to ensure that the charging power matches the battery state, improving the charging efficiency and reducing the charging time. During the charging process, by identifying pressure signals, monitoring battery temperature and other multi-dimensional data, and performing multiple regulations on the charging power, it can effectively prevent problems such as overcharging and overheating of the battery, reducing the risk of battery thermal runaway. By statistically obtaining the battery aging ratio based on the actual charging duration, and combining multiple regression analysis to establish an aging correlation equation, setting an aging ratio threshold, predicting the aging trend of the battery in advance, timely discovering potential safety hazards, and performing refined management according to the battery aging ratio and usage parameters, reasonably arranging the charging strategy, reducing the number of ineffective charge and discharge cycles of the battery, and extending the service life of the battery. Using Internet of Things technology to collect a large amount of battery operation data, and processing it through big data analysis and artificial intelligence algorithms to achieve real-time monitoring of the battery state, fault prediction and intelligent decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the basic process of a method for safely controlling an energy storage power supply of the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0051] Embodiment, referring to Figure 1 , which is an embodiment of the present invention, provides a method for safely controlling an energy storage power supply of the Internet of Things, including the following steps:

[0052] Step S100, sending the estimated charging duration to the user;

[0053] Step S200: Send the first charging power, obtain the second charging power of the charging circuit, and the charging pile performs a first regulation on the first charging power according to the second charging power to obtain the third charging power of the charging pile after the first regulation.

[0054] Step S300: Perform a second regulation on the third charging power according to the battery temperature to obtain the fourth charging power. After charging is completed, count the actual charging duration, and obtain the battery aging ratio according to the actual charging duration.

[0055] Step S400: Obtain the battery usage situation, calculate the battery usage parameters. Using the battery aging ratio as the dependent variable and the battery usage situation and battery usage parameters as independent variables, perform multiple regression analysis to obtain the aging correlation equation, set the aging ratio threshold, and calculate the usage decisions of each dependent variable according to the aging ratio threshold.

[0056] After receiving the user's charging request, the present invention matches the target charging pile, and sends the estimated charging duration to the user according to the battery SOC, enabling the user to plan the charging time in advance and avoid long waiting times. During the charging process, the charging pile can perform dynamic regulation according to the actual feedback power of the charging circuit to ensure that the charging power matches the battery state, improve the charging efficiency, and reduce the charging time. During the charging process, by identifying pressure signals, monitoring battery temperature and other multi-dimensional data, and performing multiple regulations on the charging power, it can effectively prevent problems such as overcharging and overheating of the battery, and reduce the risk of battery thermal runaway. By counting the actual charging duration, the battery aging ratio is obtained, and combined with multiple regression analysis, an aging correlation equation is established, the aging ratio threshold is set, the aging trend of the battery is predicted in advance, potential safety hazards are discovered in a timely manner, refined management is carried out according to the battery aging ratio and usage parameters, the charging strategy is reasonably arranged, the number of ineffective charge and discharge cycles of the battery is reduced, and the service life of the battery is extended. By using Internet of Things technology to collect a large amount of battery operation data and processing it through big data analysis and artificial intelligence algorithms, real-time monitoring, fault prediction and intelligent decision-making of the battery state are realized.

[0057] The step S100 includes the following sub-steps:

[0058] Step S101: The charging service end receives the user's charging request and obtains the current vehicle position through the GPS positioning system.

[0059] Step S102: Match the nearest charging station according to the current vehicle position, and obtain the status of the charging piles in the charging station. The status of the charging piles includes the idle state and the busy state.

[0060] Step S103: Select any charging pile with the working state of idle and set it as the target charging pile, and update the working state of the target charging pile to busy.

[0061] In step S104, obtain the battery SOC of the user, retrieve the charging database, input the battery SOC into the charging database, match each charging duration corresponding to the battery SOC, calculate the average value of the charging durations, set the average value of the charging durations as the estimated charging duration, and send the estimated charging duration to the user through the server.

[0062] In specific implementation, the vehicle position is obtained through the GPS positioning system, and the nearest charging station is matched, which can quickly provide the user with available charging piles, reduce the time for the user to search for charging piles, obtain the status of the charging piles in real time, preferentially select idle charging piles and update their status to busy, avoid resource waste, improve the utilization rate of the charging piles, calculate a more accurate estimated charging duration based on the historical data of the battery SOC and the charging database, help the user arrange time reasonably, and the accuracy rate of matching the nearest charging station according to the user's vehicle position can reach more than 95%, and the update time of the charging pile status is less than 1 second, ensuring real-time and accurate information.

[0063] The step S200 includes the following sub-steps:

[0064] In step S201, the induction area of the charging station identifies the pressure signal and sends a start charging signal to the target charging pile;

[0065] In step S202, the target charging pile sends a first charging power to the charging circuit, and the first charging power is used to test the charging efficiency of the battery, and the first charging power is distributed between 0 and the constant voltage charging power;

[0066] In step S203, obtain the second charging power of the charging circuit, and the second charging power represents the actual charging power received by the charging circuit of the battery;

[0067] In step S204, the target charging pile performs a first regulation on the voltage frequency according to the second charging power, and the first regulation means continuously increasing the voltage frequency and continuously calculating the actual charging power of the charging current, and stopping the regulation until the actual power of the charging circuit reaches the first charging power;

[0068] In step S205, obtain the transmission power of the target charging pile after the first regulation, and record the transmission power of the target charging pile after the first regulation as the third charging power.

[0069] In specific implementation, the test of the first charging power can quickly evaluate the charging efficiency of the battery, providing a basis for subsequent charging power adjustment. Dynamically adjust the voltage frequency according to the actual charging power received by the battery (the second charging power) to ensure that the charging power matches the battery state, avoiding overcharging or undercharging. The time for the charging station sensing area to identify the pressure signal and send the start charging signal is less than 0.5 seconds. The range of the first charging power is 0 to the constant voltage charging power, and the constant voltage charging power is 80% of the battery rated power. The regulation accuracy of the voltage frequency can reach ±1%, ensuring the precise matching of the charging power. The time from starting the regulation to reaching the first charging power is usually completed within 3 to 5 seconds. The fluctuation range of the third charging power is controlled within ±2%, ensuring the stability of the subsequent charging process.

[0070] Set the first time period as the recording duration, and obtain the battery temperature within the first time period after the first regulation;

[0071] The regulation method of the second regulation includes:

[0072] Retrieve the battery temperature database, obtain the battery model, input the battery model into the battery temperature database, match the temperature threshold corresponding to the battery model, subtract the battery temperature from the temperature threshold to obtain the first difference. The first difference is a positive number. Set the first value as the difference upper limit, and compare the first difference with the first value;

[0073] When the first difference is greater than or equal to the first value, no second regulation is performed;

[0074] When the first difference is less than the first value, the target charging pile performs a second regulation on the voltage frequency, and the second regulation is expressed as continuously decreasing the voltage frequency;

[0075] Obtain the emission power of the target charging pile after the second regulation, record the emission power of the target charging pile after the second regulation as the fourth charging power, and bind the fourth charging power to the user's vehicle.

[0076] In specific implementation, dynamically adjust the voltage frequency according to the difference between the battery temperature and the threshold to ensure that the battery temperature remains within a safe range during the charging process. When the battery temperature exceeds the threshold, automatically reduce the charging power to reduce the risk of thermal runaway. Bind the fourth charging power to the user's vehicle to ensure the continuity and consistency of the charging strategy. The time from detecting the temperature anomaly to completing the voltage frequency regulation is less than 3 seconds. During the charging process, the temperature difference of the battery system can be controlled within 5°C.

[0077] The charging pile charges the vehicle at the fourth power and continuously obtains the SOC of the vehicle. When the SOC of the vehicle exceeds 80%, send a charging complete signal to the user through the server and turn off the emission circuit;

[0078] Set the duration from the start of charging to the sending of the charging completion signal as the actual charging duration, calculate the difference between the actual charging duration and the expected charging duration, denoted as the second difference, calculate the ratio of the second difference to the expected charging duration, and set it as the battery aging ratio. The induction area of the charging station identifies the vehicle departure state and updates the status of the target charging pile to the idle state.

[0079] The battery usage conditions include service duration, deep discharge situation, overcharge situation, number of rapid brakings, number of rapid accelerations, and extreme high-temperature travel situation. The service duration is expressed as the usage duration experienced from the latest installation of the battery in the vehicle to the present.

[0080] The unit of the service duration is hours. The deep discharge situation is expressed as the historical deep discharge time period. The overcharge situation is expressed as the historical overcharge time period. The extreme high-temperature travel situation is expressed as the historical extreme high-temperature travel time period. The extreme high temperature is defined as the natural temperature exceeding 40°C.

[0081] The battery usage parameters include the average deep discharge duration, average overcharge duration, and average extreme high-temperature travel duration.

[0082] The calculation method of the average deep discharge duration includes:

[0083] Obtain any historical deep discharge time period, calculate the difference between the end time point and the start time point of the historical deep discharge time period, denoted as the third difference, convert the third difference into a time quantity in hours, denoted as the first time quantity, traverse each historical deep discharge time period, calculate each first time quantity, calculate the average value of each first time quantity, and set the average value of each first time quantity as the average deep discharge duration.

[0084] The calculation method of the average overcharge duration includes:

[0085] Obtain any historical overcharge time period, calculate the difference between the end time point and the start time point of the historical overcharge time period, denoted as the fourth difference, convert the fourth difference into a time quantity in hours, denoted as the second time quantity, traverse each historical overcharge time period, calculate each second time quantity, calculate the average value of each second time quantity, and set the average value of each second time quantity as the average overcharge duration.

[0086] The calculation method of the average extreme high-temperature travel duration includes:

[0087] Obtain any historical extreme high temperature travel time period, calculate the difference between the end time point and the start time point of the historical extreme high temperature travel time period, denoted as the fifth difference, convert the fifth difference into a time quantity in hours, denoted as the third time quantity, traverse each historical extreme high temperature travel time period, calculate each third time quantity, calculate the average value of each third time quantity, and set the average value of each third time quantity as the average travel duration under extreme high temperature.

[0088] In specific implementation, by calculating the average deep discharge duration, overcharge average duration, and average travel duration under extreme high temperature, comprehensively evaluate the battery usage situation, provide richer data support for battery health management, provide rich independent variables for the battery aging correlation equation, and through multiple regression analysis, more accurately predict the battery aging trend, providing a scientific basis for battery maintenance and replacement.

[0089] Taking the battery aging ratio as the dependent variable, and the service duration, number of extreme braking times, number of extreme acceleration times, average deep discharge duration, overcharge average duration, and average travel duration under extreme high temperature as independent variables, conduct multiple regression analysis to obtain the aging correlation equation;

[0090] The calculation expression of the aging correlation equation is:

[0091] ;

[0092] Among them, is the battery aging ratio, is the total number of types of independent variables, is the th independent variable, is the th coefficient corresponding to the independent variable.

[0093] Set the second value as the aging ratio threshold;

[0094] Substitute the aging ratio threshold into the aging correlation equation to obtain the limit intervals of each independent variable. The limit intervals represent the numerical ranges of the independent variables within the aging ratio threshold after removing other factors.

[0095] In specific implementation, by substituting the aging ratio threshold into the aging correlation equation, the limit intervals of each independent variable (such as the average duration of deep discharge, the average duration of overcharge, and the average duration of travel in extremely high temperatures) are accurately calculated. According to the limit intervals, personalized usage and maintenance strategies are formulated for each battery to extend the battery life. Taking the battery capacity decay to 80% of the initial capacity as the aging ratio threshold, the cross-validation method is used to verify the accuracy of the aging correlation equation, and the model has a high accuracy. Experimental verification is carried out under different temperatures and working conditions to ensure the reliability of the limit intervals. For example, the limit interval of the average duration of deep discharge is 0 - 5 hours, the limit interval of the average duration of overcharge is 0 - 1 hour, and the limit interval of the average duration of travel in extremely high temperatures is 0 - 3 hours. When the average duration of deep discharge of the battery exceeds 5 hours, the average duration of overcharge exceeds 1 hour, or the average duration of travel in extremely high temperatures exceeds 3 hours, the aging ratio of the battery will approach or exceed the 20% threshold. At this time, the system will remind the user to perform battery maintenance or replacement to ensure the safety and service life of the battery

[0096] The calculation logic of the usage decision for each independent variable includes:

[0097] Obtain the numerical range of each independent variable, calculate the sum value of the upper limit of the numerical range of each independent variable and the lower limit of the numerical range of each independent variable, denoted as the first sum value, take half of the value of the first sum value, and set it as the theoretical value of the corresponding independent variable;

[0098] The theoretical value of the service duration is expressed as the remaining service duration of the battery, the theoretical value of the number of extreme braking times is expressed as the maximum remaining number of allowable extreme braking times, the theoretical value of the number of extreme acceleration times is expressed as the maximum remaining number of allowable extreme acceleration times, the theoretical value of the average duration of deep discharge is expressed as the longest time for a single deep discharge, the theoretical value of the average duration of overcharge is expressed as the longest time for a single overcharge, and the theoretical value of the average duration of travel in extremely high temperatures is expressed as the longest time for a single travel in extremely high temperatures;

[0099] The usage decision includes: after the theoretical value of the service duration, replace the battery. The maximum remaining number of allowable extreme braking times and the maximum remaining number of allowable extreme acceleration times are the theoretical values of the number of extreme braking times and the number of extreme acceleration times respectively. The longest daily duration of the average duration of deep discharge, the longest daily duration of the average duration of overcharge, and the longest daily duration of the average duration of travel in extremely high temperatures are the theoretical values of the average duration of deep discharge, the average duration of overcharge, and the average duration of travel in extremely high temperatures respectively.

[0100] In specific implementation, a data-driven model is used to provide a scientific basis for battery maintenance and replacement, and theoretical value calculations are used to support the full life cycle management of the battery. Service duration: It has been used for 3 years, and the expected total life is 5 years. Number of emergency brakes: It has been used 100 times, and the expected total life is 200 times. Number of rapid accelerations: It has been used 150 times, and the expected total life is 300 times. Average duration of deep discharge: The historical average duration is 2 hours. Average duration of overcharge: The historical average duration is 0.5 hours. Average duration of travel in extremely high temperatures: The historical average duration is 1.5 hours. Theoretical value of service duration: 2.5 years. Remaining service duration of the battery: 2 years. Decision: The remaining service duration of the battery is 2 years. It is recommended to replace the battery after 2 years. Theoretical value of the number of emergency brakes: 100 times. Decision: The maximum allowable remaining number of emergency brakes is 100 times. It is recommended that users reduce the number of emergency brakes. Theoretical value of the number of rapid accelerations: 150 times. Decision: The maximum allowable remaining number of rapid accelerations is 150 times. It is recommended that users reduce the number of rapid accelerations. Theoretical value of the average duration of deep discharge: 2.5 hours. Decision: The maximum single duration of deep discharge is 2.5 hours. It is recommended that users avoid deep discharge exceeding 2.5 hours. Theoretical value of the average duration of overcharge: 0.5 hours. Decision: The maximum single duration of overcharge is 0.5 hours. It is recommended that users avoid overcharge exceeding 0.5 hours. Theoretical value of the average duration of travel in extremely high temperatures: 1.5 hours. Decision: The maximum single duration of travel in extremely high temperatures is 1.5 hours. It is recommended that users avoid travel in extremely high temperatures exceeding 1.5 hours.

[0101] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-readable storage media that contain computer-usable program code. Among them, the storage medium is implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in the process Figure 1 a process or multiple processes and / or blocks Figure 1 the functions specified in a block or multiple blocks.

[0102] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not 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 modifications or equivalent replacements can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for safely controlling an energy storage power supply in the Internet of Things, characterized in that, It includes the following steps: Step S100: Send the estimated charging duration to the user; Step S200: Send the first charging power, obtain the second charging power of the charging circuit, and the charging pile performs the first regulation on the first charging power according to the second charging power to obtain the third charging power of the charging pile after the first regulation; Step S300: Perform the second regulation on the third charging power according to the battery temperature to obtain the fourth charging power. After charging is completed, count the actual charging duration and obtain the battery aging ratio according to the actual charging duration; Step S400: Obtain the battery usage situation, calculate the battery usage parameters, use the battery aging ratio as the dependent variable, and the battery usage situation and battery usage parameters as the independent variables to perform multiple regression analysis to obtain the aging correlation equation, set the aging ratio threshold, and calculate the usage decisions of each dependent variable according to the aging ratio threshold; The said Step S200 includes the following sub-steps: Step S201: The induction area of the charging station identifies the pressure signal and sends a start charging signal to the target charging pile; Step S202: The target charging pile sends the first charging power to the charging circuit. The first charging power is used to test the charging efficiency of the battery, and the first charging power is distributed between 0 and the constant voltage charging power; Step S203: Obtain the second charging power of the charging circuit, and the second charging power represents the actual charging power received by the charging circuit of the battery; Step S204: The target charging pile performs the first regulation on the voltage frequency according to the second charging power. The first regulation means continuously increasing the voltage frequency and continuously calculating the actual charging power of the charging current, and stopping the regulation until the actual power of the charging circuit reaches the first charging power; Step S205: Obtain the transmission power of the target charging pile after the first regulation, and record the transmission power of the target charging pile after the first regulation as the third charging power; The said battery usage situation includes the service duration, deep discharge situation, overcharge situation, number of extreme braking times, number of extreme acceleration times, and extreme high temperature travel situation. The service duration represents the usage duration experienced from the latest installation of the battery in the vehicle to the present; The unit of the service duration is hours. The deep discharge situation represents the historical deep discharge time period, the overcharge situation represents the historical overcharge time period, the extreme high temperature travel situation represents the historical extreme high temperature travel time period, and the extreme high temperature means the natural temperature exceeds 40°C; The said battery usage parameters include the average deep discharge duration, average overcharge duration, and average extreme high temperature travel duration; The calculation method of the average deep discharge duration includes: Obtain any historical deep discharge time period, calculate the difference between the end time point and the start time point of the historical deep discharge time period, and record it as the third difference. Convert the third difference into a time quantity in hours, and record it as the first time quantity. Traverse each historical deep discharge time period, calculate each first time quantity, calculate the average value of each first time quantity, and set the average value of each first time quantity as the average deep discharge duration; The calculation method of the average overcharge duration includes: Obtain any historical overcharge time period, calculate the difference between the end time point and the start time point of the historical overcharge time period, denoted as the fourth difference, convert the fourth difference into a time quantity in hours, denoted as the second time quantity, traverse each historical overcharge time period, calculate each second time quantity, calculate the average value of each second time quantity, and set the average value of each second time quantity as the average overcharge duration; The calculation method of the average extreme high temperature travel duration includes: Obtain any historical extreme high temperature travel time period, calculate the difference between the end time point and the start time point of the historical extreme high temperature travel time period, denoted as the fifth difference, convert the fifth difference into a time quantity in hours, denoted as the third time quantity, traverse each historical extreme high temperature travel time period, calculate each third time quantity, calculate the average value of each third time quantity, and set the average value of each third time quantity as the average extreme high temperature travel duration; Taking the battery aging ratio as the dependent variable and the service duration, the number of extreme braking times, the number of extreme acceleration times, the average deep discharge duration, the average overcharge duration, and the average extreme high temperature travel duration as independent variables, conduct multiple regression analysis to obtain the aging correlation equation; The calculation expression of the aging correlation equation is: ； Among them, is the battery aging ratio, is the total number of types of independent variables, is the th independent variable, is the th coefficient corresponding to the independent variable.

2. The energy storage power supply safety control method for the Internet of Things according to claim 1, characterized in that: The step S100 includes the following sub-steps: Step S101, the charging service end accepts the user's charging request and obtains the current vehicle position through the GPS positioning system; Step S102, match the nearest charging station according to the current vehicle position, and obtain the charging pile status in the charging station, where the charging pile status includes the idle status and the busy status; Step S103, select any charging pile with the working status of idle as the target charging pile, and update the working status of the target charging pile to busy; Step S104, obtain the battery SOC of the user, retrieve the charging database, input the battery SOC into the charging database, match each charging duration corresponding to the battery SOC, calculate the average value of the charging duration, set the average value of the charging duration as the estimated charging duration, and send the estimated charging duration to the user through the server.

3. The energy storage power supply safety control method for the Internet of Things according to claim 1, characterized in that: Set the first time period as the recording duration, and obtain the battery temperature within the first regulated first time period; The regulation method of the second regulation includes: Retrieve the battery temperature database, obtain the battery model, input the battery model into the battery temperature database, match the temperature threshold corresponding to the battery model, subtract the battery temperature from the temperature threshold to obtain the first difference. The first difference is a positive number. Set the first value as the difference upper limit, and compare the first difference with the first value; When the first difference is greater than or equal to the first value, no second regulation is performed; When the first difference is less than the first value, the target charging pile performs a second regulation on the voltage frequency, and the second regulation is expressed as continuously decreasing the voltage frequency; Obtain the emission power of the target charging pile after the second regulation, denote the emission power of the target charging pile after the second regulation as the fourth charging power, and bind the fourth charging power to the user's vehicle.

4. The energy storage power supply safety control method for the Internet of Things according to claim 3, wherein: The charging pile charges the vehicle at the fourth power and obtains the SOC of the vehicle in real time. When the SOC of the vehicle exceeds 80%, it sends a charging completion signal to the user through the server and turns off the transmitting circuit; Set the duration from the start of charging to the sending of the charging completion signal as the actual charging duration, calculate the difference between the actual charging duration and the expected charging duration, denoted as the second difference, calculate the ratio of the second difference to the expected charging duration, and set it as the battery aging ratio. The induction area of the charging station identifies the driving-away state of the vehicle and updates the status of the target charging pile to the idle state.

5. The energy storage power supply safety control method for the Internet of Things according to claim 1, characterized in that: Set the second value as the aging ratio threshold; Substitute the aging ratio threshold into the aging correlation equation to obtain the limit intervals of each independent variable. The limit interval represents the numerical range of the independent variable within the aging ratio threshold after removing other factors.

6. The safety control method for the energy storage power supply of the Internet of Things according to claim 1, characterized in that: The calculation logic of the usage decision for each independent variable includes: Obtain the numerical range of each independent variable, calculate the sum of the upper limit of the numerical range of each independent variable and the lower limit of the numerical range of each independent variable, denoted as the first sum, take half of the value of the first sum, and set it as the theoretical value of the corresponding independent variable; The theoretical value of the service duration is expressed as the remaining battery usage duration, the theoretical value of the number of extreme braking times is expressed as the maximum remaining number of allowable extreme braking times, the theoretical value of the number of extreme acceleration times is expressed as the maximum remaining number of allowable extreme acceleration times, the theoretical value of the average deep discharge duration is expressed as the longest time for a single deep discharge duration, the theoretical value of the average overcharge duration is expressed as the longest time for a single overcharge duration, and the theoretical value of the average extreme high temperature travel duration is expressed as the longest time for a single extreme high temperature travel; The usage decision includes: after the theoretical value of the service duration, replace the battery. The maximum remaining number of allowable extreme braking times and the maximum remaining number of allowable extreme acceleration times are the theoretical values of the number of extreme braking times and the number of extreme acceleration times respectively. The longest daily duration of the average deep discharge duration, the longest daily duration of the average overcharge duration, and the longest daily duration of the average extreme high temperature travel duration are the theoretical values of the average deep discharge duration, the average overcharge duration, and the average extreme high temperature travel duration respectively.

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