Energy storage power supply safety management and control method of Internet of Things

Abstract

The invention, which relates to the technical field of intelligent management, discloses a safety management and control method for an energy storage power supply of the Internet of Things, comprising the following steps: sending a predicted charging duration to a user; and the first charging power is sent, the second charging power of the charging circuit is obtained, the charging pile performs first regulation and control on the first charging power according to the second charging power, and the third charging power of the charging pile after the first regulation and control is obtained. The target charging pile is matched after the charging request of the user is received, long-time waiting is avoided, it is ensured that the charging power is matched with the battery state, the charging efficiency is improved, the charging power is regulated and controlled multiple times, the risk of battery thermal runaway is reduced, the aging associated equation is established in combination with multiple regression analysis, potential safety hazards are found in time, and the charging efficiency is improved. Charging strategies are reasonably arranged, the service life of the battery is prolonged, a large amount of battery operation data are collected through the Internet of Things technology, and real-time monitoring, fault prediction and intelligent decision making of the battery state are achieved.

Description

Technical Field

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

[0002] In recent years, by collecting and integrating historical data, real-time data and simulation data, standardizing the expression of heterogeneous data, unifying data conversion rules, and establishing data fusion standards, we have achieved comprehensive collection, storage, management and sharing of multi-scale heterogeneous data of multiple operating entities, promoted the iteration of multimodal models and the optimization of application services, and the technology for real-time monitoring of parameters such as gas and temperature of various equipment in energy storage systems has become relatively mature. By reducing monitoring blind spots, we can promptly detect faults and abnormally high temperatures, prevent thermal runaway and fire, and effectively improve the safety and reliability of energy storage systems.

[0003] At present, in a Chinese invention patent with publication number CN117410597B, an intelligent monitoring and processing method and system for safety hazards applied to energy storage power sources are disclosed. When the method determines that there are abnormal battery cells in the target battery module, for each abnormal battery cell, the abnormal processing parameters of the abnormal battery cell are determined according to the cell target information of the abnormal battery cell, and a processing operation matching the abnormal processing parameters of the abnormal battery cell is performed on the abnormal battery cell. However, the related technology does not adjust the charging power according to the actual charging efficiency of the battery, which is not conducive to the high efficiency of charging. The battery is not readjusted according to the battery temperature based on the high efficiency data, 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: the related technology does not adjust the charging power according to the actual charging efficiency of the battery, which is not conducive to the high efficiency of charging; the battery is not readjusted according to the battery temperature based on the high efficiency data, which is not conducive to the charging safety and the durability of the battery, and has certain limitations.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for safely managing and controlling energy storage power supply of the Internet of Things, comprising the following steps: Step S100, sending the estimated charging time to the user; Step S200, sending a first charging power, acquiring a second charging power of the charging circuit, and the charging pile performing a first regulation on the first charging power according to the second charging power, and acquiring a third charging power of the charging pile after the first regulation; Step S300, performing a second adjustment on the third charging power according to the battery temperature to obtain a fourth charging power, and after charging is completed, calculating the actual charging time, and obtaining a battery aging ratio according to the actual charging time; Step S400, obtaining battery usage, calculating battery usage parameters, taking battery aging ratio as dependent variable, taking battery usage and battery usage parameters as independent variables, performing multiple regression analysis, obtaining aging correlation equation, setting aging ratio threshold, and calculating usage decision of each dependent variable according to the aging ratio threshold.

[0006] As a preferred solution of the energy storage power supply safety management and control method of the Internet of Things described in the present invention, step S100 includes the following sub-steps: Step S101, the charging service end accepts the user's charging request and obtains the current vehicle location through the GPS positioning system; Step S102, matching the nearest charging station according to the current vehicle position, and obtaining the status of the charging pile in the charging station, where the status of the charging pile includes an idle state and a busy state; Step S103, selecting any charging pile whose working state is an idle state as a target charging pile, and updating the working state of the target charging pile to a busy state; Step S104, obtain the battery SOC of the user, call the charging database, input the battery SOC into the charging database, match the various charging times corresponding to the battery SOC, calculate the average charging time, set the average charging time as the estimated charging time, and send the estimated charging time to the user through the server.

[0007] As a preferred solution of the energy storage power supply safety management and control method of the Internet of Things described in the present invention, step S200 includes the following sub-steps: Step S201, the sensing area of ​​the charging station recognizes the pressure signal and sends a charging start signal to the target charging pile; Step S202: The target charging pile sends a first charging power to the charging circuit, where the first charging power is used to test the charging efficiency of the battery, and the first charging power is distributed between 0 and a constant voltage charging power; Step S203, obtaining a second charging power of the charging circuit, where the second charging power is represented by an actual charging power received by the charging circuit of the battery; Step S204: the target charging pile performs a first regulation on the voltage frequency according to the second charging power, wherein the first regulation is represented by 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, obtaining the transmission power of the target charging pile after the first adjustment, and recording the transmission power of the target charging pile after the first adjustment as the third charging power.

[0008] As a preferred solution of the energy storage power supply safety management and control method of the Internet of Things described in the present invention, wherein: the first time period is set to the recording duration, and the battery temperature is obtained within the first time period after the first regulation; The second control method includes: Retrieving a battery temperature database to obtain a battery model, inputting the battery model into the battery temperature database, matching a temperature threshold corresponding to the battery model, subtracting the battery temperature from the temperature threshold to obtain a first difference value, wherein the first difference value is a positive number, setting a first value as a difference upper limit, and comparing the first difference with the first value; When the first difference is greater than or equal to the first value, the second regulation is not performed; When the first difference is less than the first value, the target charging pile performs a second regulation on the voltage frequency, where the second regulation is represented by continuously reducing the voltage frequency; The transmission power of the target charging pile after the second adjustment is obtained, the transmission power of the target charging pile after the second adjustment is recorded as the fourth charging power, and the fourth charging power is bound to the user's vehicle.

[0009] As a preferred solution of the energy storage power safety management method of the Internet of Things described in the present invention, 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%, a charging completion signal is sent to the user through the server, and the transmitting circuit is turned off; The time from the start of charging to the sending of the charging completion signal is set as the actual charging time, the difference between the actual charging time and the expected charging time is calculated and recorded as the second difference, the ratio of the second difference to the expected charging time is calculated and set as the battery aging ratio, the sensing area of ​​the charging station identifies the vehicle leaving the state, and updates the state of the target charging pile to the idle state.

[0010] As a preferred solution of the energy storage power supply safety management method of the Internet of Things described in the present invention, the battery usage includes service time, deep discharge, overcharge, extreme braking times, extreme acceleration times and extreme high temperature travel conditions, and the service time is expressed as the time the battery has been used since it was last installed on the vehicle. The unit length of the service time is hours, the deep discharge condition is represented by the historical deep discharge time period, the overshoot condition is represented by the historical overshoot time period, the extreme high temperature travel condition is represented by the historical extreme high temperature travel time period, and the extreme high temperature is represented by the natural temperature exceeding 40°C.

[0011] As a preferred solution of the energy storage power safety management method of the Internet of Things described in the present invention, the battery usage parameters include the average duration of deep discharge, the average duration of overcharge and the average duration of extreme high temperature travel; The method for calculating the average duration of deep discharge 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, record it as a third difference, convert the third difference into a time amount in hours, record it as a first time amount, traverse each historical deep discharge time period, calculate each first time amount, calculate the average value of each first time amount, and set the average value of each first time amount as the average duration of deep discharge; The method for calculating the average overshoot duration includes: Obtain any historical overshoot time period, calculate the difference between the end time point and the start time point of the historical overshoot time period, record it as a fourth difference, convert the fourth difference into a time amount in hours, record it as a second time amount, traverse each historical overshoot time period, calculate each second time amount, calculate the average value of each second time amount, and set the average value of each second time amount as the average overshoot duration; The calculation method of the average duration of extreme high temperature travel 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, record it as the fifth difference, convert the fifth difference into a time amount in hours, record it as the third time amount, traverse each historical extreme high temperature travel time period, calculate each third time amount, calculate the average of each third time amount, and set the average of each third time amount as the average duration of extreme high temperature travel.

[0012] As a preferred solution of the energy storage power safety management method of the Internet of Things described in the present invention, wherein: taking the battery aging ratio as the dependent variable, taking the service time, the number of extreme braking times, the number of extreme acceleration times, the average time of deep discharge, the average time of overcharging and the average time of extreme high temperature travel as the independent variables, a multivariate regression analysis is performed to obtain an aging correlation equation; The calculation expression of the aging correlation equation is: ; in, is the battery aging ratio, is the total number of independent variables, For the independent variables, For the The coefficients corresponding to the independent variables.

[0013] As a preferred solution of the energy storage power supply safety management and control method of the Internet of Things described in the present invention, wherein: the second value is set to an aging ratio threshold; The aging ratio threshold is brought into the aging association equation to obtain the limit interval of each independent variable, wherein the limit interval is represented by the value range of the independent variable within the aging ratio threshold after removing other factors.

[0014] As a preferred solution of the energy storage power supply safety management and control method of the Internet of Things described in the present invention, the calculation logic of the use decision of 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, record it as a first sum, take half of the first sum, and set it as the theoretical value of the corresponding independent variable; The theoretical value of the service time is expressed as the remaining battery life, the theoretical value of the number of extreme braking times is expressed as the maximum remaining permissible number of extreme braking times, the theoretical value of the number of extreme acceleration times is expressed as the maximum remaining permissible number of extreme acceleration times, the theoretical value of the average deep discharge time is expressed as the maximum time of a single deep discharge time, the theoretical value of the average overcharge time is expressed as the maximum time of a single overcharge time, and the theoretical value of the average extreme high temperature travel time is expressed as the maximum time of a single extreme high temperature travel; Usage decisions include: replacing the battery after the theoretical value of the service time, the maximum remaining allowable number of extreme braking times and the maximum remaining allowable number of extreme acceleration times are the theoretical values ​​of the extreme braking times and the theoretical values ​​of the extreme acceleration times respectively, the maximum daily average deep discharge time, the maximum daily average overcharge time and the maximum daily average extreme high temperature travel time are the theoretical values ​​of the deep discharge time, the theoretical values ​​of the overcharge time and the theoretical values ​​of the extreme high temperature travel time respectively.

[0015] The beneficial effects of the present invention are as follows: by matching the target charging pile after accepting the user's charging request, and sending the estimated charging time to the user according to the battery SOC, the user can plan the charging time in advance to avoid long waiting. During the charging process, the charging pile can be dynamically adjusted 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 the pressure signal, monitoring the battery temperature and other multi-dimensional data, the charging power is adjusted multiple times, which can effectively prevent problems such as battery overcharging and overheating, and reduce the risk of battery thermal runaway. The battery aging ratio is obtained by statistically analyzing the actual charging time, and the aging correlation equation is established in combination with multivariate regression analysis. The aging ratio threshold is set, the battery aging trend is predicted in advance, and potential safety hazards are discovered in time. Refined management is performed according to the battery aging ratio and usage parameters, and the charging strategy is reasonably arranged to reduce the number of invalid charging and discharging times of the battery and extend the battery life. A large amount of battery operation data is collected by using the Internet of Things technology, and processed 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

[0016] Figure 1 A schematic diagram of the basic flow of a method for safely managing and controlling energy storage power supplies for the Internet of Things provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0018] Example, see Figure 1 , as an embodiment of the present invention, provides a method for safely managing and controlling energy storage power supplies for the Internet of Things, comprising the following steps: Step S100, sending the estimated charging time to the user; Step S200, sending a first charging power, acquiring a second charging power of the charging circuit, and the charging pile performing a first regulation on the first charging power according to the second charging power, and acquiring a third charging power of the charging pile after the first regulation; Step S300, performing a second adjustment on the third charging power according to the battery temperature to obtain a fourth charging power, and after charging is completed, calculating the actual charging time, and obtaining a battery aging ratio according to the actual charging time; Step S400, obtaining battery usage, calculating battery usage parameters, taking battery aging ratio as dependent variable, taking battery usage and battery usage parameters as independent variables, performing multiple regression analysis, obtaining aging correlation equation, setting aging ratio threshold, and calculating usage decision of each dependent variable according to the aging ratio threshold.

[0019] The present invention matches the target charging pile after accepting the user's charging request, and sends the estimated charging time to the user according to the battery SOC. The user plans the charging time in advance to avoid long waiting. During the charging process, the charging pile can be dynamically adjusted 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 multi-dimensional data such as battery temperature, the charging power is adjusted multiple times, which can effectively prevent problems such as battery overcharging and overheating, and reduce the risk of battery thermal runaway. The battery aging ratio is obtained by statistically analyzing the actual charging time, and an aging correlation equation is established in combination with multivariate regression analysis. The aging ratio threshold is set, the battery aging trend is predicted in advance, and potential safety hazards are discovered in time. Refined management is performed according to the battery aging ratio and usage parameters, and the charging strategy is reasonably arranged to reduce the number of invalid charging and discharging times of the battery and extend the battery life. A large amount of battery operation data is collected using the Internet of Things technology, and processed through big data analysis and artificial intelligence algorithms to achieve real-time monitoring of the battery state, fault prediction and intelligent decision-making.

[0020] 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 location through the GPS positioning system; Step S102, matching the nearest charging station according to the current vehicle position, and obtaining the status of the charging pile in the charging station, where the status of the charging pile includes an idle state and a busy state; Step S103, selecting any charging pile whose working state is an idle state as a target charging pile, and updating the working state of the target charging pile to a busy state; Step S104, obtain the battery SOC of the user, call the charging database, input the battery SOC into the charging database, match the various charging times corresponding to the battery SOC, calculate the average charging time, set the average charging time as the estimated charging time, and send the estimated charging time to the user through the server.

[0021] In specific implementation, the vehicle location is obtained through the GPS positioning system, and the nearest charging station is matched, which can quickly provide users with available charging piles, reduce the time users spend looking for charging piles, obtain the status of charging piles in real time, give priority to idle charging piles and update their status to busy, avoid waste of resources, and improve the utilization rate of charging piles. Based on the historical data of battery SOC and charging database, a more accurate estimated charging time is calculated to help users arrange their time reasonably. According to the user's vehicle location, the accuracy of matching the nearest charging station can reach more than 95%, and the charging pile status update time is less than 1 second, ensuring that the information is real-time and accurate.

[0022] The step S200 includes the following sub-steps: Step S201, the sensing area of ​​the charging station recognizes the pressure signal and sends a charging start signal to the target charging pile; Step S202: The target charging pile sends a first charging power to the charging circuit, where the first charging power is used to test the charging efficiency of the battery, and the first charging power is distributed between 0 and a constant voltage charging power; Step S203, obtaining a second charging power of the charging circuit, where the second charging power is represented by an actual charging power received by the charging circuit of the battery; Step S204: the target charging pile performs a first regulation on the voltage frequency according to the second charging power, wherein the first regulation is represented by 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, obtaining the transmission power of the target charging pile after the first adjustment, and recording the transmission power of the target charging pile after the first adjustment as the third charging power.

[0023] In specific implementation, the test of the first charging power can quickly evaluate the charging efficiency of the battery, provide a basis for subsequent charging power adjustments, and dynamically adjust the voltage frequency according to the charging power actually received by the battery (the second charging power) to ensure that the charging power matches the battery status and avoid overcharging or undercharging. The time for the charging station sensing area to recognize the pressure signal and send a charging start signal is less than 0.5 seconds. The first charging power range is 0~constant voltage charging power, and the constant voltage charging power is 80% of the rated power of the battery. The voltage and frequency control accuracy can reach ±1%, ensuring accurate matching of the charging power. The time from starting control 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.

[0024] The first time period is set as the recording duration, and the battery temperature is obtained within the first time period after the first adjustment; The second control method includes: Retrieving a battery temperature database to obtain a battery model, inputting the battery model into the battery temperature database, matching a temperature threshold corresponding to the battery model, subtracting the battery temperature from the temperature threshold to obtain a first difference value, wherein the first difference value is a positive number, setting a first value as a difference upper limit, and comparing the first difference with the first value; When the first difference is greater than or equal to the first value, the second regulation is not performed; When the first difference is less than the first value, the target charging pile performs a second regulation on the voltage frequency, where the second regulation is represented by continuously reducing the voltage frequency; The transmission power of the target charging pile after the second adjustment is obtained, the transmission power of the target charging pile after the second adjustment is recorded as the fourth charging power, and the fourth charging power is bound to the user's vehicle.

[0025] During the specific implementation, the voltage and frequency are dynamically adjusted according to the difference between the battery temperature and the threshold value to ensure that the battery temperature remains within a safe range during charging. When the battery temperature exceeds the threshold, the charging power is automatically reduced to reduce the risk of thermal runaway. The fourth charging power is bound to the user's vehicle to ensure the continuity and consistency of the charging strategy. The time from detecting the temperature abnormality to completing the voltage and frequency regulation is less than 3 seconds. During the charging process, the temperature extremes of the battery system can be controlled within 5°C.

[0026] 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%, a charging completion signal is sent to the user through the server and the transmitting circuit is turned off. The time from the start of charging to the sending of the charging completion signal is set as the actual charging time, the difference between the actual charging time and the expected charging time is calculated and recorded as the second difference, the ratio of the second difference to the expected charging time is calculated and set as the battery aging ratio, the sensing area of ​​the charging station identifies the vehicle leaving the state, and updates the state of the target charging pile to the idle state.

[0027] The battery usage includes service time, deep discharge, overcharge, number of extreme braking, number of extreme acceleration and extreme high temperature travel. The service time is the time from the last time the battery was installed in the vehicle to the current usage time. The unit length of the service time is hours, the deep discharge condition is represented by the historical deep discharge time period, the overshoot condition is represented by the historical overshoot time period, the extreme high temperature travel condition is represented by the historical extreme high temperature travel time period, and the extreme high temperature is represented by the natural temperature exceeding 40°C.

[0028] The battery usage parameters include the average duration of deep discharge, the average duration of overcharge, and the average duration of extreme high temperature travel; The method for calculating the average duration of deep discharge 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, record it as a third difference, convert the third difference into a time amount in hours, record it as a first time amount, traverse each historical deep discharge time period, calculate each first time amount, calculate the average value of each first time amount, and set the average value of each first time amount as the average duration of deep discharge; The method for calculating the average overshoot duration includes: Obtain any historical overshoot time period, calculate the difference between the end time point and the start time point of the historical overshoot time period, record it as a fourth difference, convert the fourth difference into a time amount in hours, record it as a second time amount, traverse each historical overshoot time period, calculate each second time amount, calculate the average value of each second time amount, and set the average value of each second time amount as the average overshoot duration; The calculation method of the average duration of extreme high temperature travel 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, record it as the fifth difference, convert the fifth difference into a time amount in hours, record it as the third time amount, traverse each historical extreme high temperature travel time period, calculate each third time amount, calculate the average of each third time amount, and set the average of each third time amount as the average duration of extreme high temperature travel.

[0029] In specific implementation, by calculating the average duration of deep discharge, the average duration of overcharge and the average duration of extreme high temperature travel, the battery usage is comprehensively evaluated, providing more abundant data support for battery health management, and providing rich independent variables for the battery aging association equation. Through multiple regression analysis, the battery aging trend is more accurately predicted, providing a scientific basis for battery maintenance and replacement.

[0030] Taking the battery aging ratio as the dependent variable and the service time, the number of extreme braking times, the number of extreme acceleration times, the average time of deep discharge, the average time of overcharging and the average time of extreme high temperature travel as independent variables, a multivariate regression analysis was performed to obtain the aging correlation equation. The calculation expression of the aging correlation equation is: ; in, is the battery aging ratio, is the total number of independent variables, For the independent variables, For the The coefficients corresponding to the independent variables.

[0031] The second value is set as the aging ratio threshold; The aging ratio threshold is brought into the aging association equation to obtain the limit interval of each independent variable, wherein the limit interval is represented by the value range of the independent variable within the aging ratio threshold after removing other factors.

[0032] In the specific implementation, by bringing the aging ratio threshold into the aging association equation, the limit range of each independent variable (such as the average duration of deep discharge, the average duration of overcharge, and the average duration of extreme high temperature travel) is accurately calculated. According to the limit range, a personalized use and maintenance strategy is formulated for each battery to extend the battery life. The battery capacity decays to 80% of the initial capacity as the aging ratio threshold. The cross-validation method is used to verify the accuracy of the aging association equation. The model has high accuracy and is experimentally verified under different temperatures and working conditions to ensure the reliability of the limit range. For example, the limit range of the average duration of deep discharge is 0~5 hours, the limit range of the average duration of overcharge is 0~1 hour, and the limit range of the average duration of extreme high temperature travel 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 extreme high temperature travel exceeds 3 hours, the aging ratio of the battery will approach or exceed the threshold of 20%. 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. The calculation logic for the use decision of 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, record it as a first sum, take half of the first sum, and set it as the theoretical value of the corresponding independent variable; The theoretical value of the service time is expressed as the remaining battery life, the theoretical value of the number of extreme braking times is expressed as the maximum remaining permissible number of extreme braking times, the theoretical value of the number of extreme acceleration times is expressed as the maximum remaining permissible number of extreme acceleration times, the theoretical value of the average deep discharge time is expressed as the maximum time of a single deep discharge time, the theoretical value of the average overcharge time is expressed as the maximum time of a single overcharge time, and the theoretical value of the average extreme high temperature travel time is expressed as the maximum time of a single extreme high temperature travel; Usage decisions include: replacing the battery after the theoretical value of the service time, the maximum remaining allowable number of extreme braking times and the maximum remaining allowable number of extreme acceleration times are the theoretical values ​​of the extreme braking times and the theoretical values ​​of the extreme acceleration times respectively, the maximum daily average deep discharge time, the maximum daily average overcharge time and the maximum daily average extreme high temperature travel time are the theoretical values ​​of the deep discharge time, the theoretical values ​​of the overcharge time and the theoretical values ​​of the extreme high temperature travel time respectively.

[0033] In the specific implementation, a data-driven model is used to provide a scientific basis for battery maintenance and replacement, and support is provided for the full life cycle management of the battery through the calculation of theoretical values. Service time: 3 years in use, estimated total life of 5 years, number of extreme braking times: 100 times in use, estimated total life of 200 times, number of extreme acceleration times: 150 times in use, estimated total life of 300 times, average deep discharge time: historical average time of 2 hours, average overcharge time: historical average time of 0.5 hours, average extreme high temperature travel time: historical average time of 1.5 hours, theoretical value of service time: 2.5 years, remaining battery life of 2 years, decision: remaining battery life of 2 years, it is recommended to replace the battery after 2 years, theoretical value of extreme braking times: 100 times, decision: The maximum remaining allowable number of extreme braking times is 100 times. Users are advised to reduce the number of extreme braking times. The theoretical value of extreme acceleration times: 150 times. Decision: The maximum remaining allowable number of extreme acceleration times is 150 times. Users are advised to reduce the number of extreme acceleration times. The theoretical value of the average deep discharge time: 2.5 hours. Decision: The maximum single deep discharge time is 2.5 hours. Users are advised to avoid deep discharge of more than 2.5 hours. The theoretical value of the average overcharge time: 0.5 hours. Decision: The maximum single overcharge time is 0.5 hours. Users are advised to avoid overcharge of more than 0.5 hours. The theoretical value of the average extreme high temperature travel time: 1.5 hours. Decision: The maximum single extreme high temperature travel time is 1.5 hours. Users are advised to avoid extreme high temperature travel of more than 1.5 hours.

[0034] It should be understood by those skilled in the art that the embodiments of the present invention can be provided as methods, systems or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Wherein, 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions can also be stored in a computer readable memory that can direct a computer or other data processing device that can be programmed to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0035] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for safely managing and controlling energy storage power supply of the Internet of Things, characterized in that: The following steps are involved: Step S100, sending the estimated charging time to the user; Step S200, sending a first charging power, acquiring a second charging power of the charging circuit, and the charging pile performing a first regulation on the first charging power according to the second charging power, and acquiring a third charging power of the charging pile after the first regulation; Step S300, performing a second adjustment on the third charging power according to the battery temperature to obtain a fourth charging power, and after charging is completed, calculating the actual charging time, and obtaining a battery aging ratio according to the actual charging time; Step S400, obtaining battery usage, calculating battery usage parameters, taking battery aging ratio as dependent variable, taking battery usage and battery usage parameters as independent variables, performing multiple regression analysis, obtaining aging correlation equation, setting aging ratio threshold, and calculating usage decision of each dependent variable according to the aging ratio threshold.

2. The method for safely managing and controlling energy storage power supply of 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 location through the GPS positioning system; Step S102, matching the nearest charging station according to the current vehicle position, and obtaining the status of the charging pile in the charging station, where the status of the charging pile includes an idle state and a busy state; Step S103, selecting any charging pile whose working state is an idle state as a target charging pile, and updating the working state of the target charging pile to a busy state; Step S104, obtain the battery SOC of the user, call the charging database, input the battery SOC into the charging database, match the various charging times corresponding to the battery SOC, calculate the average charging time, set the average charging time as the estimated charging time, and send the estimated charging time to the user through the server.

3. The method for safely managing and controlling energy storage power supply of the Internet of Things according to claim 1, characterized in that: The step S200 includes the following sub-steps: Step S201, the sensing area of ​​the charging station recognizes the pressure signal and sends a charging start signal to the target charging pile; Step S202: The target charging pile sends a first charging power to the charging circuit, where the first charging power is used to test the charging efficiency of the battery, and the first charging power is distributed between 0 and a constant voltage charging power; Step S203, obtaining a second charging power of the charging circuit, where the second charging power is represented by an actual charging power received by the charging circuit of the battery; Step S204: the target charging pile performs a first regulation on the voltage frequency according to the second charging power, wherein the first regulation is represented by 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, obtaining the transmission power of the target charging pile after the first adjustment, and recording the transmission power of the target charging pile after the first adjustment as the third charging power.

4. The method for safely managing and controlling energy storage power supply of the Internet of Things according to claim 1, characterized in that: The first time period is set as the recording duration, and the battery temperature is obtained within the first time period after the first adjustment; The second control method includes: Retrieving a battery temperature database to obtain a battery model, inputting the battery model into the battery temperature database, matching a temperature threshold corresponding to the battery model, subtracting the battery temperature from the temperature threshold to obtain a first difference value, wherein the first difference value is a positive number, setting a first value as a difference upper limit, and comparing the first difference with the first value; When the first difference is greater than or equal to the first value, the second regulation is not performed; When the first difference is less than the first value, the target charging pile performs a second regulation on the voltage frequency, where the second regulation is represented by continuously reducing the voltage frequency; The transmission power of the target charging pile after the second adjustment is obtained, the transmission power of the target charging pile after the second adjustment is recorded as the fourth charging power, and the fourth charging power is bound to the user's vehicle.

5. A method for safely managing and controlling energy storage power supplies for the Internet of Things as claimed in claim 4, characterized in that: 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%, a charging completion signal is sent to the user through the server and the transmitting circuit is turned off. The time from the start of charging to the sending of the charging completion signal is set as the actual charging time, the difference between the actual charging time and the expected charging time is calculated and recorded as the second difference, the ratio of the second difference to the expected charging time is calculated and set as the battery aging ratio, the sensing area of ​​the charging station identifies the vehicle leaving the state, and updates the state of the target charging pile to the idle state.

6. The method for safely managing and controlling energy storage power supply of the Internet of Things according to claim 1, characterized in that: The battery usage includes service time, deep discharge, overcharge, number of extreme braking, number of extreme acceleration and extreme high temperature travel. The service time is the time from the last time the battery was installed in the vehicle to the current usage time. The unit length of the service time is hours, the deep discharge condition is represented by the historical deep discharge time period, the overshoot condition is represented by the historical overshoot time period, the extreme high temperature travel condition is represented by the historical extreme high temperature travel time period, and the extreme high temperature is represented by the natural temperature exceeding 40°C.

7. The method for safely managing and controlling energy storage power supply of the Internet of Things according to claim 1, characterized in that: The battery usage parameters include the average duration of deep discharge, the average duration of overcharge, and the average duration of extreme high temperature travel; The method for calculating the average duration of deep discharge 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, record it as a third difference, convert the third difference into a time amount in hours, record it as a first time amount, traverse each historical deep discharge time period, calculate each first time amount, calculate the average value of each first time amount, and set the average value of each first time amount as the average duration of deep discharge; The method for calculating the average overshoot duration includes: Obtain any historical overshoot time period, calculate the difference between the end time point and the start time point of the historical overshoot time period, record it as a fourth difference, convert the fourth difference into a time amount in hours, record it as a second time amount, traverse each historical overshoot time period, calculate each second time amount, calculate the average value of each second time amount, and set the average value of each second time amount as the average overshoot duration; The calculation method of the average duration of extreme high temperature travel 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, record it as the fifth difference, convert the fifth difference into a time amount in hours, record it as the third time amount, traverse each historical extreme high temperature travel time period, calculate each third time amount, calculate the average of each third time amount, and set the average of each third time amount as the average duration of extreme high temperature travel.

8. The method for safely managing and controlling energy storage power supply of the Internet of Things according to claim 1, characterized in that: Taking the battery aging ratio as the dependent variable and the service time, the number of extreme braking times, the number of extreme acceleration times, the average time of deep discharge, the average time of overcharging and the average time of extreme high temperature travel as independent variables, a multivariate regression analysis was performed to obtain the aging correlation equation. The calculation expression of the aging correlation equation is: ； in, is the battery aging ratio, is the total number of independent variables, For the independent variables, For the The coefficients corresponding to the independent variables.

9. The method for safely managing and controlling energy storage power supply of the Internet of Things according to claim 1, characterized in that: The second value is set as the aging ratio threshold; The aging ratio threshold is brought into the aging association equation to obtain the limit interval of each independent variable, wherein the limit interval is represented by the value range of the independent variable within the aging ratio threshold after removing other factors.

10. The method for safely managing and controlling energy storage power supply of the Internet of Things according to claim 1, characterized in that: The calculation logic for the use decision of 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, record it as a first sum, take half of the first sum, and set it as the theoretical value of the corresponding independent variable; The theoretical value of the service time is expressed as the remaining battery life, the theoretical value of the number of extreme braking times is expressed as the maximum remaining permissible number of extreme braking times, the theoretical value of the number of extreme acceleration times is expressed as the maximum remaining permissible number of extreme acceleration times, the theoretical value of the average deep discharge time is expressed as the maximum time of a single deep discharge time, the theoretical value of the average overcharge time is expressed as the maximum time of a single overcharge time, and the theoretical value of the average extreme high temperature travel time is expressed as the maximum time of a single extreme high temperature travel; Usage decisions include: replacing the battery after the theoretical value of the service time, the maximum remaining allowable number of extreme braking times and the maximum remaining allowable number of extreme acceleration times are the theoretical values ​​of the extreme braking times and the theoretical values ​​of the extreme acceleration times respectively, the maximum daily average deep discharge time, the maximum daily average overcharge time and the maximum daily average extreme high temperature travel time are the theoretical values ​​of the deep discharge time, the theoretical values ​​of the overcharge time and the theoretical values ​​of the extreme high temperature travel time respectively.

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