Energy storage power supply safety prediction and maintenance method and system based on BMS

Abstract

The invention relates to the technical field of energy storage power supplies, in particular to a BMS-based energy storage power supply safety prediction and maintenance method and system, and the method comprises the steps: obtaining the first internal resistance of a first battery, carrying out the complete discharge operation of the first battery, obtaining an initial battery, and carrying out the charging test operation of the initial battery, the method comprises the steps of obtaining a full-charge battery, a first end point boost rate and a first temperature change rate, executing a power supply test operation on the full-charge battery to obtain a first starting point buck rate, and calculating a current battery SOH according to user use data, an internal resistance prediction function, a boost prediction function, a buck prediction function, a temperature prediction function, an internal resistance weight, a boost weight, a buck weight and a temperature weight, and calculating estimated safety time, and integrating the estimated safety time into a maintenance prompt data packet by using the energy storage power supply control center to complete safety prediction and maintenance of the energy storage power supply. According to the invention, the accuracy of safety prediction of the energy storage power supply can be improved, and the safety and reliability of the energy storage power supply are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage power supply, and particularly relates to a BMS-based energy storage power supply safety prediction and maintenance method and system, an electronic device and a computer readable storage medium. BACKGROUND

[0002] In the energy storage power supply, the battery management system (BMS) plays a crucial role, which monitors and controls the state of the battery pack to ensure the safe operation of the battery system. The traditional BMS mainly focuses on real-time monitoring and management of battery voltage and temperature. However, with the continuous progress of energy storage battery technology, the demand for battery management is becoming more and more complex. How to accurately predict the performance change of the battery and timely maintain and maintain has become an important direction to improve the efficiency and safety of the energy storage power supply.

[0003] In the prior art, the safety and life prediction of the energy storage power supply is usually to use the battery management system to monitor the total power flowing into or out of the battery during the full charge and discharge process to confirm the state of health (SOH) of the battery of the energy storage power supply, and combine the working voltage and temperature to predict the safety state of the energy storage power supply.

[0004] Although the prior art can realize the safety prediction of the energy storage power supply, in the process of using the energy storage battery, it is difficult to appear the full charge and discharge condition, and frequent full charge and discharge test will consume the battery, which is not practical. At the same time, the uncertainty of the environment will also affect the test results. Therefore, how to select some data that is not easily affected by the environment and is convenient to collect to predict the safety of the energy storage power supply has become a problem to be solved. SUMMARY

[0005] The present application provides a BMS-based energy storage power supply safety prediction and maintenance method and computer readable storage medium, which mainly aims to improve the accuracy of safety prediction of the energy storage power supply and ensure the safety and reliability of the energy storage power supply.

[0006] To achieve the above object, the application provides a safety prediction and maintenance method for energy storage power supply based on BMS, which comprises the following steps: obtaining a sample battery set, wherein the sample battery set comprises a first battery, a second battery, a third battery and a fourth battery, and the first SOH of the first battery, the second SOH of the second battery, the third SOH of the third battery and the fourth SOH of the fourth battery are preset, wherein the first SOH > the second SOH > the third SOH > the fourth SOH; obtaining the first internal resistance of the first battery by using a pre-constructed first battery management system, performing a complete discharge operation on the first battery to obtain an initial battery, performing a charging test operation on the initial battery to obtain a full battery, a first terminal voltage rising rate and a first temperature change rate; performing a power supply test operation on the full battery to obtain a first starting voltage falling rate; obtaining a first data set by collecting the first internal resistance, the first terminal voltage rising rate, the first starting voltage falling rate and the first temperature change rate, obtaining a second data set, a third data set and a fourth data set based on the second battery, the third battery and the fourth battery respectively; obtaining an internal resistance sequence, a voltage rising sequence, a voltage falling sequence and a temperature sequence by using the first data set, the second data set, the third data set and the fourth data set, and calculating an internal resistance weight, a voltage rising weight, a voltage falling weight and a temperature weight according to the internal resistance sequence, the voltage rising sequence, the voltage falling sequence and the temperature sequence respectively; constructing an internal resistance prediction function, a voltage rising prediction function, a voltage falling prediction function and a temperature prediction function by using the internal resistance sequence, the voltage rising sequence, the voltage falling sequence and the temperature sequence respectively; receiving a safety prediction instruction, obtaining user usage data based on the safety prediction instruction, wherein the user usage data comprises a user initial temperature, a user voltage rising rate, a user voltage falling rate, a user temperature change rate and a user internal resistance; calculating a current battery SOH according to the user usage data, the internal resistance prediction function, the voltage rising prediction function, the voltage falling prediction function, the temperature prediction function, the internal resistance weight, the voltage rising weight, the voltage falling weight and the temperature weight; calculating an estimated safety time according to the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH; integrating the estimated safety time into a maintenance prompt data packet by using the energy storage power supply control center, and sending the maintenance prompt data packet to a pre-constructed energy storage power supply client to complete the safety prediction and maintenance of the energy storage power supply.

[0007] Optionally, the charging test operation is performed on the initial battery to obtain a full battery, a first end-point voltage rising rate and a first temperature change rate, comprising: starting a pre-constructed charging power supply, recording time in real time from the time when the charging power supply is started to obtain charging time, performing charging operation on the initial battery by using the started charging power supply, and collecting charging voltage and battery temperature of the initial battery in real time by using the first battery management system, when the charging voltage reaches a preset voltage threshold, closing the charging power supply to obtain the full battery, a voltage curve and a temperature curve, wherein the abscissa of the voltage curve and the abscissa of the temperature curve are both charging time, the ordinate of the voltage curve is charging voltage, and the ordinate of the temperature curve is battery temperature; confirming a reference voltage point in the voltage curve based on a preset reference voltage, wherein the ordinate of the reference voltage point is the reference voltage, and the abscissa of the reference voltage point is taken as reference time; confirming a final voltage point in the voltage curve based on the voltage threshold, wherein the ordinate of the final voltage point is the voltage threshold, and the abscissa of the final voltage point is taken as final time; calculating the first end-point voltage rising rate according to the reference time, the final time, the reference voltage and the voltage threshold, and the calculation formula is as follows: , wherein R1 is the first end-point voltage rising rate, Vth is the voltage threshold, Vr is the reference voltage, T1 is the final time, and T2 is the reference time; recording a point with the lowest battery temperature in the temperature curve as a lowest temperature point, recording a point with the highest battery temperature in the temperature curve as a peak temperature point, taking the abscissa of the lowest temperature point as lowest time, taking the ordinate of the lowest temperature point as lowest temperature, taking the abscissa of the peak temperature point as peak time, and taking the ordinate of the peak temperature point as peak temperature; calculating the first temperature change rate according to the peak time, the lowest time, the peak temperature and the lowest temperature, and the calculation formula is as follows: , wherein R2 is the first temperature change rate, , Vp is the peak temperature, , Tp is the peak time, , Tl is the lowest time. , Vp is the peak temperature, , Tl is the lowest time.

[0008] Optionally, the power supply test operation is performed on the full battery to obtain a first start-point voltage falling rate, comprising: connecting a pre-constructed power consumption load unit with the full battery to obtain a power consumption load unit, taking the time when the power consumption load unit is obtained as initial time, recording time in real time from the initial time to obtain real time, performing power supply operation on the power consumption load unit by using the full battery, and monitoring power supply voltage of the full battery in real time by using the first battery management system, when the power supply voltage reaches a preset power supply threshold, recording the real time as current time; calculating the first start-point voltage falling rate according to the current time, the initial time, the voltage threshold and the power supply threshold, and the calculation formula is as follows: wherein, is a first starting voltage drop rate, is a power supply threshold, is a current time, is an initial time.

[0009] Optionally, the obtaining the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence based on the first data set, the second data set, the third data set and the fourth data set comprises: confirming the internal resistance sequence based on the first data set, the second data set, the third data set and the fourth data set, wherein the second data set comprises a second internal resistance, a second terminal voltage rise rate, a second starting voltage drop rate and a second temperature change rate, the third data set comprises a third internal resistance, a third terminal voltage rise rate, a third starting voltage drop rate and a third temperature change rate, the fourth data set comprises a fourth internal resistance, a fourth terminal voltage rise rate, a fourth starting voltage drop rate and a fourth temperature change rate, and the internal resistance sequence is as follows: wherein, is a first internal resistance, is a second internal resistance, is a third internal resistance, is a fourth internal resistance; the voltage rise sequence, the voltage drop sequence and the temperature sequence are obtained based on the first data set, the second data set, the third data set and the fourth data set.

[0010] Optionally, the calculating the internal resistance weight, the voltage rise weight, the voltage drop weight and the temperature weight based on the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence respectively comprises: obtaining a SOH sequence based on the first SOH, the second SOH, the third SOH and the fourth SOH, wherein the SOH sequence is as follows: wherein, is a first SOH, is a second SOH, is a third SOH, is a fourth SOH; a SOH average value is calculated based on the SOH sequence, and a calculation formula is as follows: wherein, is a SOH average value; an internal resistance average value is obtained based on the internal resistance sequence; the internal resistance weight is calculated based on the SOH sequence, the SOH average value, the internal resistance sequence and the internal resistance average value, and a calculation formula is as follows: wherein, is an internal resistance weight, is an i-th term in the SOH sequence, is a SOH average value, is an i-th term in the internal resistance sequence, is an internal resistance average value. ​is the average value of the internal resistance, representing taking absolute value; the boost weight is obtained based on the SOH sequence, the SOH average value and the boost sequence, the drop weight is obtained based on the SOH sequence, the SOH average value and the drop sequence, and the temperature weight is obtained based on the SOH sequence, the SOH average value and the temperature sequence.

[0011] Optionally, the constructing the internal resistance prediction function, the boost prediction function, the drop prediction function and the temperature prediction function respectively by using the internal resistance sequence, the boost sequence, the drop sequence and the temperature sequence comprises: calculating a first coefficient based on the first SOH and the second SOH in the SOH sequence and the first internal resistance and the second internal resistance in the internal resistance sequence, and the calculation formula is as shown below: wherein, is the first coefficient; a first parameter is calculated based on the first SOH in the SOH sequence, the first internal resistance in the internal resistance sequence and the first coefficient, and the calculation formula is as shown below: wherein, is the first parameter; a second coefficient and a second parameter are obtained based on the second SOH and the third SOH in the SOH sequence and the second internal resistance and the third internal resistance in the internal resistance sequence, and a third coefficient and a third parameter are obtained based on the third SOH and the fourth SOH in the SOH sequence and the third internal resistance and the fourth internal resistance in the internal resistance sequence; the internal resistance prediction function is constructed by using the internal resistance sequence, the first coefficient, the first parameter, the second coefficient, the second parameter, the third coefficient and the third parameter, wherein the internal resistance prediction function is as shown below: wherein, is the internal resistance prediction function, is the independent variable of the internal resistance prediction function, , , and the second coefficient, the second parameter, the third coefficient and the third parameter respectively; the boost prediction function is obtained based on the SOH sequence and the boost sequence, the drop prediction function is obtained based on the SOH sequence and the drop sequence, and the temperature prediction function is obtained based on the SOH sequence and the temperature sequence.

[0012] Optionally, the obtaining the user usage data based on the safety prediction instruction comprises: starting the pre-built user battery management system by using the safety prediction instruction, obtaining the user internal resistance of the pre-built user energy storage power supply by using the started user battery management system, performing a charging operation on the user energy storage power supply to obtain a charged energy storage power supply, and obtaining the full-energy energy storage power supply, the user initial temperature, the user voltage rising rate and the user temperature change rate based on the charged energy storage power supply and the user battery management system; obtaining the user voltage falling rate based on the pre-built test load unit and the full-energy energy storage power supply; and integrating the user initial temperature, the user voltage rising rate, the user voltage falling rate, the user temperature change rate and the user internal resistance into the user usage data.

[0013] Optionally, the calculating the current battery SOH according to the user usage data, the internal resistance prediction function, the voltage rising prediction function, the voltage falling prediction function, the temperature prediction function, the internal resistance weight, the voltage rising weight, the voltage falling weight and the temperature weight comprises: taking the user internal resistance in the user usage data as an independent variable of the internal resistance prediction function and substituting the internal resistance prediction function to calculate the internal resistance prediction SOH; obtaining the voltage rising prediction SOH based on the user voltage rising rate in the user usage data and the voltage rising prediction function, obtaining the voltage falling prediction SOH based on the user voltage falling rate in the user usage data and the voltage falling prediction function, and obtaining the temperature prediction SOH based on the user temperature change rate in the user usage data and the temperature prediction function; and calculating the current battery SOH according to the internal resistance prediction SOH, the internal resistance weight, the voltage rising prediction SOH, the voltage rising weight, the voltage falling prediction SOH, the voltage falling weight, the temperature prediction SOH and the temperature weight, and the calculation formula is as follows: , , , , , , , , ,

[0014] Optionally, the calculating the estimated safety time according to the pre-built energy storage power supply control center, the user initial temperature and the current battery SOH comprises: integrating the user initial temperature and the current battery SOH into an analysis data packet, and sending the analysis data packet to the energy storage power supply control center, wherein the energy storage power supply control center comprises a user database; when the energy storage power supply control center receives the analysis data packet, calculating a maximum reference temperature and a minimum reference temperature according to the user initial temperature in the analysis data packet and a preset weighted temperature, wherein the maximum reference temperature is a sum of the user initial temperature and the weighted temperature, and the minimum reference temperature is an absolute difference between the user initial temperature and the weighted temperature; confirming a reference temperature range based on the maximum reference temperature and the minimum reference temperature, wherein a maximum value of the reference temperature range is the maximum reference temperature, and a minimum value of the reference temperature range is the minimum reference temperature; retrieving a plurality of reference user data from the user database based on the reference temperature range, wherein the reference user data comprises a user use time, a reference user temperature and a reference failure SOH; and calculating the estimated safety time according to the plurality of reference user data, the user initial temperature and the current battery SOH, and the calculation formula is as follows: wherein, is the estimated safety time, is the user use time of the i-th reference user data in the plurality of reference user data, is the reference user temperature of the i-th reference user data in the plurality of reference user data, is the reference failure SOH of the i-th reference user data in the plurality of reference user data, is the user initial temperature, is a natural constant.

[0015] ​​​To achieve the above object, the application further provides a BMS-based energy storage power supply safety prediction and maintenance system, comprising: a sample battery acquisition module, configured to acquire a sample battery set, wherein the sample battery set comprises a first battery, a second battery, a third battery and a fourth battery, and the first SOH of the first battery, the second SOH of the second battery, the third SOH of the third battery and the fourth SOH of the fourth battery are preset, wherein the first SOH > the second SOH > the third SOH > the fourth SOH; an energy storage battery detection module, configured to acquire the first internal resistance of the first battery by using a pre-constructed first battery management system, perform a complete discharge operation on the first battery to obtain an initial battery, perform a charging test operation on the initial battery to obtain a full battery, a first terminal voltage boost rate and a first temperature change rate, perform a power supply test operation on the full battery to obtain a first starting voltage drop rate, and obtain a first data set by collecting the first internal resistance, the first terminal voltage boost rate, the first starting voltage drop rate and the first temperature change rate, and acquire a second data set, a third data set and a fourth data set based on the second battery, the third battery and the fourth battery respectively; a prediction function construction module, configured to acquire an internal resistance sequence, a voltage boost sequence, a voltage drop sequence and a temperature sequence based on the first data set, the second data set, the third data set and the fourth data set, calculate an internal resistance weight, a voltage boost weight, a voltage drop weight and a temperature weight according to the internal resistance sequence, the voltage boost sequence, the voltage drop sequence and the temperature sequence respectively, and construct an internal resistance prediction function, a voltage boost prediction function, a voltage drop prediction function and a temperature prediction function by using the internal resistance sequence, the voltage boost sequence, the voltage drop sequence and the temperature sequence respectively; and an energy storage power supply prediction module, configured to receive a safety prediction instruction, acquire user usage data based on the safety prediction instruction, wherein the user usage data comprises a user initial temperature, a user voltage boost rate, a user voltage drop rate, a user temperature change rate and a user internal resistance, calculate a current battery SOH according to the user usage data, the internal resistance prediction function, the voltage boost prediction function, the voltage drop prediction function, the temperature prediction function, the internal resistance weight, the voltage boost weight, the voltage drop weight and the temperature weight, calculate an estimated safety time according to the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH, integrate the estimated safety time into a maintenance prompt data packet by using the energy storage power supply control center, and send the maintenance prompt data packet to a pre-constructed energy storage power supply client to complete the safety prediction and maintenance of the energy storage power supply.

[0016] To solve the above problems, the application further provides an electronic device, comprising: a memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the above-mentioned BMS-based energy storage power supply safety prediction and maintenance method.

[0017] To solve the above problems, the application further provides a computer readable storage medium, wherein at least one instruction is stored in the computer readable storage medium, and the at least one instruction is executed by a processor in an electronic device to implement the BMS-based energy storage power supply safety prediction and maintenance method.

[0018] The application is to solve the problems described in the background art, the application obtains a sample battery set, wherein the sample battery set includes a first battery, a second battery, a third battery and a fourth battery, and the first SOH of the first battery, the second SOH of the second battery, the third SOH of the third battery and the fourth SOH of the fourth battery are preset, wherein the first SOH > the second SOH > the third SOH > the fourth SOH, it can be seen that the embodiments of the application select batteries with different SOHs in advance, which facilitates the subsequent test of the performance difference between batteries with different SOHs, provides a reference for subsequent safety prediction, and then uses the pre-constructed first battery management system to obtain the first internal resistance of the first battery, performs a complete discharge operation on the first battery to obtain an initial battery, performs a charging test operation on the initial battery to obtain a full battery, a first terminal voltage rise rate and a first temperature change rate, performs a power supply test operation on the full battery to obtain a first starting voltage drop rate, it can be seen that the embodiments of the application determine the first internal resistance, the first terminal voltage rise rate, the first starting voltage drop rate and the first temperature change rate of the first battery under the first SOH by performing charging test and power supply test on the first battery, which facilitates subsequent analysis of the relationship between the first SOH and the first internal resistance, the first terminal voltage rise rate, the first starting voltage drop rate and the first temperature change rate, thereby constructing a prediction function, and obtaining a first data set by summarizing the first internal resistance, the first terminal voltage rise rate, the first starting voltage drop rate and the first temperature change rate, obtaining a second data set, a third data set and a fourth data set based on the second battery, the third battery and the fourth battery respectively, it can be seen that the embodiments of the application obtain the data sets corresponding to the first battery, the second battery, the third battery and the fourth battery respectively, which facilitates subsequent analysis of the performance difference of battery SOH, ensures that the evaluation of the battery health status is more comprehensive and accurate, obtains an internal resistance sequence, a voltage rise sequence, a voltage drop sequence and a temperature sequence using the first data set, the second data set, the third data set and the fourth data set, calculates internal resistance weight, voltage rise weight, voltage drop weight and temperature weight according to the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence respectively, it can be seen that the embodiments of the application determine the influence degree of different factors on the battery health status by calculating the internal resistance weight, the voltage rise weight, the voltage drop weight and the temperature weight, improve the accuracy of subsequent evaluation of the current battery SOH, and construct an internal resistance prediction function, a voltage rise prediction function, a voltage drop prediction function and a temperature prediction function using the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence respectively, it can be seen that the embodiments of the application use multiple data sets corresponding to different batteries to construct a prediction function, which clearly reflects the change of internal resistance, voltage rise, voltage drop and temperature with the change of SOH, improves the accuracy of safety prediction of energy storage power supply, receives a safety prediction instruction, and obtains user usage data based on the safety prediction instruction, wherein the user usage data includes: user initial temperature, user voltage rise rate, user voltage drop rate, user temperature change rate and user internal resistance.The current battery SOH is calculated according to the user use data, the internal resistance prediction function, the boost prediction function, the drop prediction function, the temperature prediction function, the internal resistance weight, the boost weight, the drop weight and the temperature weight; it can be seen that the embodiment of the present application considers the influence of the actual working environment on the performance of the energy storage battery by obtaining the user use data, comprehensively considers the user initial temperature, the user boost rate, the user drop rate, the user temperature change rate and the user internal resistance, and combines the prediction functions and the weights to comprehensively evaluate and calculate the current battery SOH, the multi-dimensional analysis method avoids the one-sidedness of the single factor in evaluating the current battery SOH, improves the accuracy of the safety prediction of the energy storage power supply, and calculates the estimated safety time according to the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH; it can be seen that the embodiment of the present application combines the user database in the energy storage power supply control center, uses the multiple user data in the user database to comprehensively calculate the estimated safety time, improves the accuracy of the safety prediction of the energy storage power supply, integrates the estimated safety time into the maintenance prompt data packet by using the energy storage power supply control center, and sends the maintenance prompt data packet to the pre-constructed energy storage power supply client to complete the safety prediction and maintenance of the energy storage power supply; it can be seen that the embodiment of the present application timely reminds the user to maintain or repair the energy storage battery by sending the maintenance prompt data packet, and ensures the safety and reliability of the energy storage power supply. Therefore, the present application can improve the accuracy of the safety prediction of the energy storage power supply, and ensure the safety and reliability of the energy storage power supply. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flowchart of a BMS-based energy storage power supply safety prediction and maintenance method provided by an embodiment of the present application is shown. Figure 2 A function module diagram of a BMS-based energy storage power supply safety prediction and maintenance system provided by an embodiment of the present application is shown. Figure 3 A structural diagram of an electronic device for implementing the BMS-based energy storage power supply safety prediction and maintenance method provided by an embodiment of the present application is shown.

[0020] Explanation of reference signs: 1, electronic device; 10, processor; 11, memory; 12, bus.

[0021] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0023] The embodiment of the present application provides a BMS-based energy storage power supply safety prediction and maintenance method. The execution subject of the BMS-based energy storage power supply safety prediction and maintenance method includes but is not limited to at least one of electronic devices such as a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the present application. In other words, the BMS-based energy storage power supply safety prediction and maintenance method can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster and the like.

[0024] Referring to Figure 1 Fig. 1 is a flowchart of a BMS-based energy storage power supply safety prediction and maintenance method provided by an embodiment of the present application. In the embodiment, the BMS-based energy storage power supply safety prediction and maintenance method includes: S1, obtaining a sample battery set, wherein the sample battery set includes a first battery, a second battery, a third battery and a fourth battery, and a first SOH of the first battery, a second SOH of the second battery, a third SOH of the third battery and a fourth SOH of the fourth battery are preset, wherein the first SOH>the second SOH>the third SOH>the fourth SOH.

[0025] In the embodiment of the present application, the energy storage power supply is a device that can store and release electric energy and is intelligently controlled by a built-in BMS battery system (BMS: Battery Management System), and the energy storage power supply includes an energy storage battery. The energy storage battery is a battery that stores electric energy in the energy storage power supply. Optionally, a lithium battery is used as the energy storage battery. SOH refers to the state of health of the battery, which refers to the ratio of the maximum electric quantity that the current energy storage battery can store to the maximum electric quantity that the brand-new energy storage battery can store. For example, the brand-new energy storage battery can store a maximum electric quantity of 10000 mAh, and due to the loss of the energy storage battery during use, the maximum electric quantity that the energy storage battery can store changes to 8000 mAh after the user uses it for a period of time, and thus the SOH at this time is 80%.

[0026] It can be understood that the existing technology for measuring the SOH of the energy storage battery is generally: fully charging and discharging the energy storage battery, and recording the total amount of electricity discharged during the full discharge process, taking the total amount of electricity as the maximum amount of electricity that the current energy storage battery can store, and then dividing the maximum amount of electricity that the new energy storage battery can store to calculate the SOH of the energy storage battery, but in the process of using the energy storage battery by the user, it is difficult to fully charge and discharge, and frequent full charging and discharging will accelerate the loss of the energy storage battery, therefore, the present application obtains a sample battery set for pre-test, thereby constructing an internal resistance prediction function, a boost prediction function, a step-down prediction function and a temperature prediction function to estimate the current battery SOH in the subsequent, and analyzing the relationship between the current battery SOH and the use time through the user database to infer the estimated safe time, complete safety prediction, and remind the user to replace or maintain the energy storage power supply in time.

[0027] Importantly, the first SOH needs to be greater than or equal to 95%, and the fourth SOH needs to be less than or equal to 70%.

[0028] Generally, when the SOH of the energy storage battery drops to about 70%, the endurance and working efficiency of the energy storage power supply will be greatly reduced, which cannot meet the actual demand and is prone to failure, therefore, the user generally replaces or maintains the energy storage power supply when the SOH of the energy storage battery drops to about 70%, so the first battery and the fourth battery should be selected to ensure that the first SOH is greater than or equal to 95% and the fourth SOH is less than or equal to 70% in the embodiment of the present application, so as to basically cover the range of SOH of the energy storage battery when the user uses the energy storage power supply. Alternatively, when the sample battery set is obtained, the SOH of the energy storage battery can be measured by the existing technology of fully charging and discharging the energy storage battery to select the first battery, the second battery, the third battery and the fourth battery with appropriate SOH.

[0029] For example, Xiao Zhang is a tester of a certain energy storage power supply factory, who needs to test the energy storage power supply produced by the factory to make safety prediction, therefore, Xiao Zhang selects a new energy storage battery produced by the factory as the first battery (SOH is 100%), and tests the SOH of an energy storage battery used by the factory for one year as 90%, so as to select it as the second battery, tests the SOH of an energy storage battery used by the factory for two years as 80%, so as to select it as the third battery, and tests the SOH of a certain energy storage battery recovered by the factory as 70%, so as to select it as the fourth battery.

[0030] S2, obtaining the first internal resistance of the first battery by using the pre-constructed first battery management system, performing full discharge operation on the first battery to obtain an initial battery, performing charging test operation on the initial battery to obtain a full battery, a first terminal boost rate and a first temperature change rate, and performing power supply test operation on the full battery to obtain a first starting step-down rate.

[0031] It should be explained that the first battery management system is a battery management system built-in the first battery, and the obtaining the internal resistance of the first battery by using the pre-built first battery management system refers to detecting the internal resistance of the first battery by using the battery management system built-in the first battery, and the technology of detecting the internal resistance of the first battery by using the battery management system built-in the first battery is prior art, which will not be described herein.

[0032] It can be understood that the performing the complete discharge operation on the first battery refers to making the first battery supply power to the outside, and monitoring the voltage of the first battery in real time by using the first battery management system until the voltage of the first battery drops to a preset cut-off voltage, which is regarded as complete discharge and the first battery is exhausted. The initial battery is the first battery exhausted. The cut-off voltage is related to the physical properties of the first battery and is set when the first battery is produced by the factory. The technology of monitoring the voltage of the first battery in real time by using the first battery management system is prior art, which will not be described herein.

[0033] In detail, the performing the charging test operation on the initial battery to obtain the full battery, the first terminal voltage rising rate and the first temperature change rate comprises: starting the pre-built charging power supply, taking the time when the charging power supply is started as the starting point and recording the time in real time to obtain the charging time, performing the charging operation on the initial battery by using the started charging power supply, and collecting the charging voltage and the battery temperature of the initial battery in real time by using the first battery management system during charging, and closing the charging power supply when the charging voltage reaches a preset voltage threshold to obtain the full battery, a voltage curve and a temperature curve, wherein the abscissa of the voltage curve and the abscissa of the temperature curve are the charging time, the ordinate of the voltage curve is the charging voltage, and the ordinate of the temperature curve is the battery temperature; confirming a reference voltage point in the voltage curve based on a preset reference voltage, wherein the ordinate of the reference voltage point is the reference voltage, and taking the abscissa of the reference voltage point as a reference time; confirming a final voltage point in the voltage curve based on the voltage threshold, wherein the ordinate of the final voltage point is the voltage threshold, and taking the abscissa of the final voltage point as a final time; calculating the first terminal voltage rising rate according to the reference time, the final time, the reference voltage and the voltage threshold, and the calculation formula is as follows: wherein, the first terminal voltage rising rate is, the voltage threshold is, the reference voltage is, the final time is, The reference time is the time at which the reference voltage point is located on the voltage curve. The lowest temperature point is the point at which the battery temperature is lowest on the temperature curve. The peak temperature point is the point at which the battery temperature is highest on the temperature curve. The lowest time is the time at which the lowest temperature point is located on the temperature curve. The lowest temperature is the temperature at which the lowest temperature point is located on the temperature curve. The peak time is the time at which the peak temperature point is located on the temperature curve. The peak temperature is the temperature at which the peak temperature point is located on the temperature curve. The first temperature change rate is calculated according to the peak time, the lowest time, the peak temperature, and the lowest temperature. The calculation formula is as follows: wherein, is the first temperature change rate, is the peak temperature, is the lowest temperature, is the peak time, and is the lowest time.

[0034] It should be explained that the charging power supply is a power supply that can charge the initial battery. When the charging voltage reaches the preset voltage threshold, it is considered that the initial battery has been fully charged, and the full battery is the initial battery that has been fully charged. The voltage threshold is related to the physical properties of the initial battery and is set by the factory.

[0035] For example, if the charging power supply is started at 08:00, then 08:00 is the starting point. When the charging time is 10 minutes at 08:10, and the charging time is 25 minutes at 08:25.

[0036] It should be understood that the charging voltage refers to the voltage across the initial battery during the charging process. The charging temperature refers to the temperature of the surface of the initial battery during the charging process. The technology of collecting the charging voltage and the battery temperature of the initial battery during charging in real time by the first battery management system is a prior art, and will not be described here. During the charging process of the initial battery, the charging voltage and the battery temperature of the initial battery will change with the charging time. Therefore, by drawing a curve of the charging voltage collected by the first battery management system in real time with the change of the charging time, a voltage curve is obtained. By drawing a curve of the battery temperature collected by the first battery management system in real time with the change of the charging time, a temperature curve is obtained. The voltage curve reflects the change of the charging voltage of the initial battery during charging. The temperature curve reflects the change of the battery temperature of the initial battery during charging.

[0037] It should be explained that the reference voltage is less than the voltage threshold and is set by the tester.

[0038] For example, the reference voltage is 4 volts. In the voltage curve, find the point at which the charging voltage is 4 volts, and take this point as the reference voltage point. Read the charging time corresponding to the horizontal coordinate of this point, and take this charging time as the reference time.

[0039] In detail, the power supply test operation is performed on the full battery to obtain the first start voltage drop rate, which comprises: connecting the pre-constructed power consumption load unit with the full battery to obtain the power consumption load unit, taking the time when the power consumption load unit is obtained as the initial time, taking the initial time as the starting point and recording the real-time time, obtaining the real-time time, performing power supply operation on the power consumption load unit by the full battery, and monitoring the power supply voltage of the full battery in power supply by the first battery management system in real time, and recording the real-time time as the current time when the power supply voltage reaches the preset power supply threshold. wherein, the first start voltage drop rate, the power supply threshold, the current time, the initial time.

[0040] It should be explained that the power consumption load unit is a power consumer, and the power of the power consumer when running is the maximum output power that the full battery can provide. The maximum output power is related to the physical properties of the full battery. The power supply voltage refers to the output voltage of the full battery in the power supply process. Optionally, the power supply threshold is 90% of the voltage threshold.

[0041] For example, the time when the pre-constructed power consumption load unit is connected with the full battery to obtain the power consumption load unit is 10:00, then taking 10:00 as the initial time, if the time when the power supply voltage reaches the power supply threshold is 10:20, then taking 10:20 as the current time.

[0042] It can be understood that the first end voltage rise rate reflects the charging rate of the first battery when it is close to full charge, the first start voltage drop rate reflects the rate of the first battery when it is close to full state, and the first temperature change rate reflects the temperature rise rate of the first battery during charging.

[0043] It should be understood that from the prior art, it is known that the SOH of the first battery affects the first end voltage rise rate, the first temperature change rate, the first internal resistance and the first start voltage drop rate of the first battery. Therefore, by obtaining the first end voltage rise rate, the first temperature change rate, the first internal resistance and the first start voltage drop rate, the subsequent construction of the internal resistance prediction function, the voltage rise prediction function, the voltage drop prediction function and the temperature prediction function by using the first internal resistance, the first end voltage rise rate, the first start voltage drop rate and the first temperature change rate is facilitated, so as to inversely estimate the current battery SOH.

[0044] S3, aggregate the first internal resistance, the first terminal voltage rise rate, the first initial voltage drop rate and the first temperature change rate to obtain a first data set, obtain a second data set, a third data set and a fourth data set based on the second battery, the third battery and the fourth battery respectively.

[0045] It should be understood that the method of obtaining the second data set, the third data set and the fourth data set based on the second battery, the third battery and the fourth battery is the same as the method of obtaining the first data set based on the first battery, which will not be repeated here.

[0046] S4, obtain an internal resistance sequence, a voltage rise sequence, a voltage drop sequence and a temperature sequence using the first data set, the second data set, the third data set and the fourth data set, and calculate internal resistance weight, voltage rise weight, voltage drop weight and temperature weight according to the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence respectively.

[0047] In detail, the method of obtaining the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence using the first data set, the second data set, the third data set and the fourth data set comprises: confirming the internal resistance sequence based on the first data set, the second data set, the third data set and the fourth data set, wherein the second data set comprises: a second internal resistance, a second terminal voltage rise rate, a second initial voltage drop rate and a second temperature change rate, the third data set comprises: a third internal resistance, a third terminal voltage rise rate, a third initial voltage drop rate and a third temperature change rate, the fourth data set comprises: a fourth internal resistance, a fourth terminal voltage rise rate, a fourth initial voltage drop rate and a fourth temperature change rate, and the internal resistance sequence is as follows: wherein, is the first internal resistance, is the second internal resistance, is the third internal resistance, is the fourth internal resistance; obtain the voltage rise sequence, the voltage drop sequence and the temperature sequence based on the first data set, the second data set, the third data set and the fourth data set.

[0048] In detail, the voltage rise sequence is as follows: wherein, is the first terminal voltage rise rate, is the second terminal voltage rise rate, is the third terminal voltage rise rate, is the fourth terminal voltage rise rate.

[0049] Further, the voltage drop sequence is as follows: wherein, is the first initial voltage drop rate, is the second initial voltage drop rate, is the third initial voltage drop rate, is a fourth voltage drop rate.

[0050] In detail, the temperature sequence is as follows: , wherein, is a first temperature change rate, is a second temperature change rate, is a third temperature change rate, is a fourth temperature change rate.

[0051] Further, the calculation of the internal resistance weight, the voltage boost weight, the voltage drop weight and the temperature weight according to the internal resistance sequence, the voltage boost sequence, the voltage drop sequence and the temperature sequence respectively comprises: obtaining a SOH sequence by using the first SOH, the second SOH, the third SOH and the fourth SOH, wherein the SOH sequence is as follows: , wherein, is a first SOH, is a second SOH, is a third SOH, is a fourth SOH; calculating a SOH mean value by using the SOH sequence, and the calculation formula is as follows: , wherein, is a SOH mean value; obtaining an internal resistance mean value based on the internal resistance sequence; calculating the internal resistance weight by using the SOH sequence, the SOH mean value, the internal resistance sequence and the internal resistance mean value, and the calculation formula is as follows: , wherein, is an internal resistance weight, is the i-th term in the SOH sequence, is the SOH mean value, is the i-th term in the internal resistance sequence, is the internal resistance mean value, represents taking an absolute value; obtaining the voltage boost weight based on the SOH sequence, the SOH mean value and the voltage boost sequence, obtaining the voltage drop weight based on the SOH sequence, the SOH mean value and the voltage drop sequence, and obtaining the temperature weight based on the SOH sequence, the SOH mean value and the temperature sequence. It can be understood that the internal resistance weight reflects the correlation degree between the current battery SOH and the internal resistance predicted SOH, the greater the internal resistance weight, the higher the correlation degree between the current battery SOH and the internal resistance predicted SOH, and the greater the weight of the internal resistance predicted SOH in calculating the current battery SOH. For specific application of the current battery SOH and the internal resistance predicted SOH, please refer to subsequent embodiments.

[0052] It can be understood that the internal resistance weight reflects the correlation degree between the current battery SOH and the internal resistance predicted SOH, the greater the internal resistance weight, the higher the correlation degree between the current battery SOH and the internal resistance predicted SOH, and the greater the weight of the internal resistance predicted SOH in calculating the current battery SOH. For specific application of the current battery SOH and the internal resistance predicted SOH, please refer to subsequent embodiments.

[0053] ​It should be understood that the method of obtaining the internal resistance mean value based on the internal resistance sequence is the same as the method of obtaining the SOH mean value based on the SOH sequence, which will not be repeated here. The methods of obtaining the boost weight based on the SOH sequence, the SOH mean value and the boost sequence, obtaining the drop weight based on the SOH sequence, the SOH mean value and the drop sequence, and obtaining the temperature weight based on the SOH sequence, the SOH mean value and the temperature sequence are the same as the method of calculating the internal resistance weight based on the SOH sequence, the SOH mean value, the internal resistance sequence and the internal resistance mean value, which will not be repeated here.

[0054] S5, respectively, using the internal resistance sequence, the boost sequence, the drop sequence and the temperature sequence to construct the internal resistance prediction function, the boost prediction function, the drop prediction function and the temperature prediction function.

[0055] In detail, the method of respectively constructing the internal resistance prediction function, the boost prediction function, the drop prediction function and the temperature prediction function based on the internal resistance sequence, the boost sequence, the drop sequence and the temperature sequence comprises: calculating a first coefficient based on the first SOH and the second SOH in the SOH sequence and the first internal resistance and the second internal resistance in the internal resistance sequence, and the calculation formula is as follows: , wherein, is the first coefficient; calculating a first parameter based on the first SOH in the SOH sequence, the first internal resistance in the internal resistance sequence and the first coefficient, and the calculation formula is as follows: , wherein, is the first parameter; obtaining a second coefficient and a second parameter based on the second SOH and the third SOH in the SOH sequence and the second internal resistance and the third internal resistance in the internal resistance sequence, and obtaining a third coefficient and a third parameter based on the third SOH and the fourth SOH in the SOH sequence and the third internal resistance and the fourth internal resistance in the internal resistance sequence; constructing an internal resistance prediction function based on the internal resistance sequence, the first coefficient, the first parameter, the second coefficient, the second parameter, the third coefficient and the third parameter, wherein the internal resistance prediction function is as follows: , wherein, is the internal resistance prediction function, is the independent variable of the internal resistance prediction function, , , and are the second coefficient, the second parameter, the third coefficient and the third parameter respectively; obtaining a boost prediction function based on the SOH sequence and the boost sequence, obtaining a drop prediction function based on the SOH sequence and the drop sequence, and obtaining a temperature prediction function based on the SOH sequence and the temperature sequence.

[0056] It should be understood that the method of obtaining the second coefficient and the second parameter based on the second SOH and the third SOH in the SOH sequence and the second internal resistance and the third internal resistance in the internal resistance sequence, the method of obtaining the third coefficient and the third parameter based on the third SOH and the fourth SOH in the SOH sequence and the third internal resistance and the fourth internal resistance in the internal resistance sequence are the same as the method of calculating the first coefficient and the first parameter based on the first SOH and the second SOH in the SOH sequence and the first internal resistance and the second internal resistance in the internal resistance sequence, which will not be repeated here. The method of obtaining the boost prediction function based on the SOH sequence and the boost sequence, the method of obtaining the drop prediction function based on the SOH sequence and the drop sequence, and the method of obtaining the temperature prediction function based on the SOH sequence and the temperature sequence are the same as the method of obtaining the internal resistance prediction function using the SOH sequence and the internal resistance sequence, which will not be repeated here.

[0057] S6, receiving a safety prediction instruction, obtaining user usage data based on the safety prediction instruction, wherein the user usage data includes: user initial temperature, user boost rate, user drop rate, user temperature change rate and user internal resistance.

[0058] In detail, the method of obtaining user usage data based on the safety prediction instruction includes: starting the pre-constructed user battery management system by using the safety prediction instruction, obtaining the user internal resistance of the pre-constructed user energy storage power supply by using the started user battery management system, performing a charging operation on the user energy storage power supply to obtain a charged energy storage power supply, and obtaining the full-energy energy storage power supply, the user initial temperature, the user boost rate and the user temperature change rate based on the charged energy storage power supply and the user battery management system; obtaining the user drop rate based on the pre-constructed test load unit and the full-energy energy storage power supply; and integrating the user initial temperature, the user boost rate, the user drop rate, the user temperature change rate and the user internal resistance into the user usage data.

[0059] It should be explained that the user energy storage power supply refers to the user's energy storage power supply. The user battery management system refers to the battery management system built-in in the user's energy storage power supply, and the user battery management system can perform data transmission with the energy storage power supply control center. The test load unit is an electric appliance built-in in the user energy storage power supply, and the power of the test load unit when running is the same as the power of the power consumption load unit when running. The safety prediction instruction is initiated by the user of the user energy storage power supply when charging the user energy storage power supply. For example, Xiaowang is the user of the user energy storage power supply, and presses the safety prediction button on the user energy storage power supply to initiate the safety prediction instruction when charging the user energy storage power supply.

[0060] It can be understood that the method of obtaining the user internal resistance of the pre-constructed user energy storage power supply by using the started user battery management system is the same as the method of obtaining the first internal resistance of the first battery by using the pre-constructed first battery management system, which will not be repeated here.

[0061] It should be understood that the charging operation performed on the user energy storage power supply refers to controlling the user energy storage power supply to enter a charging state by using the user battery management system.

[0062] It should be explained that the charging energy storage power supply refers to the user energy storage power supply in the charging process. The full-charge energy storage power supply refers to the user energy storage power supply fully charged, and the method for obtaining the full-charge energy storage power supply, the user initial temperature, the user boost rate, and the user temperature change rate based on the charging energy storage power supply and the user battery management system is the same as the method for obtaining the full-charge battery, the minimum temperature, the first terminal boost rate, and the first temperature change rate by using the initial battery. The method for obtaining the user drop rate based on the pre-constructed test load unit and the full-charge energy storage power supply is the same as the method for obtaining the first starting drop rate by using the power consumption load unit and the full-charge battery. Here, they are not described in detail.

[0063] It can be understood that the user internal resistance refers to the internal resistance of the user energy storage power supply, the user initial temperature refers to the minimum temperature of the user energy storage power supply during charging, the user boost rate reflects the charging rate of the user energy storage power supply when it is close to full charge, the user drop rate reflects the rate of power supply to the outside when the user energy storage power supply is close to the full-charge state, and the user temperature change rate reflects the rate of temperature rise of the user energy storage power supply during the charging process.

[0064] S7. Calculate the current battery SOH according to the user usage data, the internal resistance prediction function, the boost prediction function, the drop prediction function, the temperature prediction function, the internal resistance weight, the boost weight, the drop weight, and the temperature weight.

[0065] In detail, the calculation of the current battery SOH according to the user usage data, the internal resistance prediction function, the boost prediction function, the drop prediction function, the temperature prediction function, the internal resistance weight, the boost weight, the drop weight, and the temperature weight includes: taking the user internal resistance in the user usage data as the independent variable of the internal resistance prediction function and substituting it into the internal resistance prediction function to calculate the internal resistance prediction SOH; obtaining the boost prediction SOH based on the user boost rate in the user usage data and the boost prediction function, obtaining the drop prediction SOH based on the user drop rate in the user usage data and the drop prediction function, and obtaining the temperature prediction SOH based on the user temperature change rate in the user usage data and the temperature prediction function; calculating the current battery SOH according to the internal resistance prediction SOH, the internal resistance weight, the boost prediction SOH, the boost weight, the drop prediction SOH, the drop weight, the temperature prediction SOH, and the temperature weight, and the calculation formula is as follows: wherein, is the current battery SOH, , , and respectively, are the internal resistance predicted SOH, the boost predicted SOH, the drop predicted SOH and the temperature predicted SOH, , and respectively, are the boost weight, the drop weight and the temperature weight.

[0066] It can be understood that the method of obtaining the boost predicted SOH based on the user boost rate in the user usage data and the boost prediction function, the method of obtaining the drop predicted SOH based on the user drop rate in the user usage data and the drop prediction function, and the method of obtaining the temperature predicted SOH based on the user temperature change rate in the user usage data and the temperature prediction function are the same as the method of obtaining the internal resistance predicted SOH by using the user internal resistance in the user usage data, which will not be repeated here.

[0067] It should be understood that the internal resistance predicted SOH is the SOH of the user energy storage battery estimated according to the user internal resistance, the boost predicted SOH is the SOH of the user energy storage battery estimated according to the user boost rate, the drop predicted SOH is the SOH of the user energy storage battery estimated according to the user drop rate, and the temperature predicted SOH is the SOH of the user energy storage battery estimated according to the user temperature change rate. The current battery SOH is the SOH of the user energy storage battery estimated according to the internal resistance predicted SOH, the internal resistance weight, the boost predicted SOH, the boost weight, the drop predicted SOH, the drop weight, the temperature predicted SOH and the temperature weight.

[0068] S8, calculating the estimated safety time according to the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH.

[0069] In detail, the calculating the estimated safety time according to the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH comprises: integrating the user initial temperature and the current battery SOH into an analysis data packet, and sending the analysis data packet to the energy storage power supply control center, wherein the energy storage power supply control center comprises: a user database; when the energy storage power supply control center receives the analysis data packet, calculating the maximum reference temperature and the minimum reference temperature according to the user initial temperature in the analysis data packet and a preset weighted temperature, wherein the maximum reference temperature is the sum of the user initial temperature and the weighted temperature, and the minimum reference temperature is the absolute difference between the user initial temperature and the weighted temperature; determining the reference temperature range based on the maximum reference temperature and the minimum reference temperature, wherein the maximum value of the reference temperature range is the maximum reference temperature, and the minimum value of the reference temperature range is the minimum reference temperature; retrieving a plurality of reference user data from the user database based on the reference temperature range, wherein the reference user data comprises: user use time, reference user temperature and reference failure SOH; calculating the estimated safety time according to the plurality of reference user data, the user initial temperature and the current battery SOH, and the calculation formula is as follows: ,in, To estimate the safety time, For multiple reference user data User usage time of reference user data, For multiple reference user data The reference user temperature of the reference user data, For multiple reference user data Reference fault SOH of reference user data, is the user's initial temperature, is a natural constant.

[0070] It should be explained that the energy storage power supply control center is a software platform built by the energy storage power supply manufacturer and integrated with a user database. Optionally, the energy storage power supply control center is located in a cloud server and is used to receive and analyze analysis data packets sent by the user's battery management system. The user database is a database used to store user data.

[0071] For example, when the manufacturer of the energy storage power supply recycles the energy storage power supply multiple times, the manufacturer will collect data of the recycled energy storage power supply. For example, the time the recycled energy storage power supply has been used is used as the user usage time, the lowest temperature of the recycled energy storage power supply during charging is used as the reference user temperature, and the SOH of the recycled energy storage power supply is used as the reference fault SOH. The user usage time, reference user temperature and reference fault SOH are integrated into user data and stored in a user database, so as to facilitate the prediction of the estimated usage time of the energy storage power supply through user data in the future, thereby providing a reference for more users, facilitating users to replace or maintain the energy storage power supply in a timely manner, and avoiding failures of the energy storage power supply.

[0072] It should be noted that the analysis data packet contains the user's initial temperature and the current battery SOH. Upon receiving the analysis data packet, the energy storage power supply control center automatically parses the data to determine the user's initial temperature and the current battery SOH. The weighted temperature is related to the temperature fluctuation of the energy storage power supply during charging. Optionally, the weighted temperature is set to 5 degrees Celsius.

[0073] It should be understood that retrieving a plurality of reference user data from the user database based on the reference temperature range means that user data whose reference user temperature falls within the reference temperature range is used as reference user data in the user database.

[0074] It is understandable that the estimated safety time refers to the predicted time that the user's energy storage battery can be used safely. For example, if the estimated safety time is 400 days, it means that the user's energy storage battery can be used safely for 400 days. After 400 days, the user needs to replace or repair the energy storage power supply in a timely manner.

[0075] S9, integrating the estimated safe time into a maintenance prompt data packet by the energy storage power supply control center, and sending the maintenance prompt data packet to a pre-constructed energy storage power supply client, completing the safety prediction and maintenance of the energy storage power supply.

[0076] For example, the energy storage power supply client is an energy storage power supply APP on a user's mobile phone, wherein the energy storage power supply APP can be pre-programmed by Java, and the energy storage power supply APP can analyze the estimated safe time contained in the maintenance prompt data packet when receiving the maintenance prompt data packet, and convert the estimated safe time into a piece of text information, and display the text information on the display interface of the user's mobile phone, for example, if the estimated safe time is 400 days, the text information displayed on the display interface is: (the energy storage power supply can be safely used for 400 days).

[0077] It should be understood that the technology of integrating the estimated safe time into a maintenance prompt data packet by the energy storage power supply control center and the technology of the energy storage power supply APP analyzing the estimated safe time contained in the maintenance prompt data packet when receiving the maintenance prompt data packet and converting the estimated safe time into a piece of text information are both prior art, and will not be described here.

[0078] For example, the maintenance of the energy storage battery means that if the estimated safe time is only a few days, the user can send the energy storage power supply to the nearest energy storage power supply maintenance center in time for repair, replace some aged parts or part of the battery monomers in the energy storage battery, so as to avoid the failure of the energy storage power supply.

[0079] The application is to solve the problems described in the background art, the application obtains a sample battery set, wherein the sample battery set includes a first battery, a second battery, a third battery and a fourth battery, and the first SOH of the first battery, the second SOH of the second battery, the third SOH of the third battery and the fourth SOH of the fourth battery are preset, wherein the first SOH > the second SOH > the third SOH > the fourth SOH, it can be seen that the embodiments of the application select batteries with different SOHs in advance, which facilitates the subsequent test of the performance difference between batteries with different SOHs, provides a reference for subsequent safety prediction, and then uses the pre-constructed first battery management system to obtain the first internal resistance of the first battery, performs a complete discharge operation on the first battery to obtain an initial battery, performs a charging test operation on the initial battery to obtain a full battery, a first terminal voltage rise rate and a first temperature change rate, performs a power supply test operation on the full battery to obtain a first starting voltage drop rate, it can be seen that the embodiments of the application determine the first internal resistance, the first terminal voltage rise rate, the first starting voltage drop rate and the first temperature change rate of the first battery under the first SOH by performing charging test and power supply test on the first battery, which facilitates subsequent analysis of the relationship between the first SOH and the first internal resistance, the first terminal voltage rise rate, the first starting voltage drop rate and the first temperature change rate, thereby constructing a prediction function, and obtaining a first data set by summarizing the first internal resistance, the first terminal voltage rise rate, the first starting voltage drop rate and the first temperature change rate, obtaining a second data set, a third data set and a fourth data set based on the second battery, the third battery and the fourth battery respectively, it can be seen that the embodiments of the application obtain the data sets corresponding to the first battery, the second battery, the third battery and the fourth battery respectively, which facilitates subsequent analysis of the performance difference of battery SOH, ensures that the evaluation of the battery health status is more comprehensive and accurate, obtains an internal resistance sequence, a voltage rise sequence, a voltage drop sequence and a temperature sequence using the first data set, the second data set, the third data set and the fourth data set, calculates an internal resistance weight, a voltage rise weight, a voltage drop weight and a temperature weight according to the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence respectively, it can be seen that the embodiments of the application determine the influence degree of different factors on the battery health status by calculating the internal resistance weight, the voltage rise weight, the voltage drop weight and the temperature weight, improve the accuracy of subsequent evaluation of the current battery SOH, and construct an internal resistance prediction function, a voltage rise prediction function, a voltage drop prediction function and a temperature prediction function using the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence respectively, it can be seen that the embodiments of the application use multiple data sets corresponding to different batteries to construct a prediction function, which clearly reflects the change of internal resistance, voltage rise, voltage drop and temperature with the change of SOH, improves the accuracy of safety prediction of energy storage power supply, receives a safety prediction instruction, and obtains user usage data based on the safety prediction instruction, wherein the user usage data includes: user initial temperature, user voltage rise rate, user voltage drop rate, user temperature change rate and user internal resistance.The current battery SOH is calculated according to the user use data, the internal resistance prediction function, the boost prediction function, the drop prediction function, the temperature prediction function, the internal resistance weight, the boost weight, the drop weight and the temperature weight; it can be seen that the embodiment of the present application considers the influence of the actual working environment on the performance of the energy storage battery by obtaining the user use data, and comprehensively considers the user initial temperature, the user boost rate, the user drop rate, the user temperature change rate and the user internal resistance, and combines the prediction functions and the weights to comprehensively evaluate and calculate the current battery SOH, so that the multi-dimensional analysis method avoids the one-sidedness of the single factor in evaluating the current battery SOH, improves the accuracy of the safety prediction of the energy storage power supply, and calculates the estimated safety time according to the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH; it can be seen that the embodiment of the present application combines the user database in the energy storage power supply control center, uses the multiple user data in the user database to comprehensively calculate the estimated safety time, improves the accuracy of the safety prediction of the energy storage power supply, integrates the estimated safety time into the maintenance prompt data packet by using the energy storage power supply control center, and sends the maintenance prompt data packet to the pre-constructed energy storage power supply client, thereby completing the safety prediction and maintenance of the energy storage power supply; it can be seen that the embodiment of the present application timely reminds the user to maintain or repair the energy storage battery by sending the maintenance prompt data packet, and ensures the safety and reliability of the energy storage power supply. Therefore, the present application can improve the accuracy of the safety prediction of the energy storage power supply, and ensure the safety and reliability of the energy storage power supply.

[0080] As Figure 2 shown, it is a functional module diagram of the energy storage power supply safety prediction and maintenance system based on BMS provided by an embodiment of the present application.

[0081] The energy storage power supply safety prediction and maintenance system based on BMS 100 can be installed in an electronic device. According to the realized function, the energy storage power supply safety prediction and maintenance system based on BMS 100 can include a sample battery acquisition module 101, an energy storage battery detection module 102, a prediction function construction module 103 and an energy storage power supply prediction module 104. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, which are stored in the memory of the electronic device.

[0082] The sample battery acquisition module 101 is configured to acquire a sample battery set, wherein the sample battery set comprises a first battery, a second battery, a third battery and a fourth battery, and the first SOH of the first battery, the second SOH of the second battery, the third SOH of the third battery and the fourth SOH of the fourth battery are preset, wherein the first SOH > the second SOH > the third SOH > the fourth SOH; the energy storage battery detection module 102 is configured to acquire the first internal resistance of the first battery by using a pre-constructed first battery management system, perform a complete discharge operation on the first battery to obtain an initial battery, perform a charging test operation on the initial battery to obtain a full battery, a first terminal voltage boost rate and a first temperature change rate, perform a power supply test operation on the full battery to obtain a first starting voltage drop rate, and obtain a first data set by integrating the first internal resistance, the first terminal voltage boost rate, the first starting voltage drop rate and the first temperature change rate, and obtain a second data set, a third data set and a fourth data set based on the second battery, the third battery and the fourth battery, respectively; the prediction function construction module 103 is configured to acquire an internal resistance sequence, a voltage boost sequence, a voltage drop sequence and a temperature sequence based on the first data set, the second data set, the third data set and the fourth data set, calculate an internal resistance weight, a voltage boost weight, a voltage drop weight and a temperature weight according to the internal resistance sequence, the voltage boost sequence, the voltage drop sequence and the temperature sequence, respectively, and construct an internal resistance prediction function, a voltage boost prediction function, a voltage drop prediction function and a temperature prediction function based on the internal resistance sequence, the voltage boost sequence, the voltage drop sequence and the temperature sequence, respectively; the energy storage power prediction module 104 is configured to receive a safety prediction instruction, acquire user usage data based on the safety prediction instruction, wherein the user usage data comprises a user initial temperature, a user voltage boost rate, a user voltage drop rate, a user temperature change rate and a user internal resistance, calculate a current battery SOH according to the user usage data, the internal resistance prediction function, the voltage boost prediction function, the voltage drop prediction function, the temperature prediction function, the internal resistance weight, the voltage boost weight, the voltage drop weight and the temperature weight, calculate an estimated safety time according to the pre-constructed energy storage power control center, the user initial temperature and the current battery SOH, integrate the estimated safety time into a maintenance prompt data packet by using the energy storage power control center, and send the maintenance prompt data packet to a pre-constructed energy storage power client, thereby completing the safety prediction and maintenance of the energy storage power supply.

[0083] In detail, the modules in the BMS-based energy storage power supply safety prediction and maintenance system 100 in the embodiment of the present application use the same technical means as the BMS-based energy storage power supply safety prediction and maintenance method in the above Figure 1 , and can produce the same technical effects, which will not be described here.

[0084] As shown in Figure 3 , it is a structural schematic diagram of an electronic device for implementing the BMS-based energy storage power supply safety prediction and maintenance method according to an embodiment of the present application.

[0085] The electronic device 1 can include a processor 10, a memory 11 and a bus 12, and can further include a computer program, such as the BMS-based energy storage power supply safety prediction and maintenance method program, stored in the memory 11 and executable on the processor 10.

[0086] The memory 11 includes at least one type of readable storage medium, such as a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 11 can include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 can be used not only to store application software and various data installed in the electronic device 1, such as the code of the BMS-based energy storage power supply safety prediction and maintenance method program, but also to temporarily store data that has been output or will be output.

[0087] The processor 10 can be composed of an integrated circuit in some embodiments, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, combinations of various control chips, etc. The processor 10 is the control core of the electronic device, which connects various components of the electronic device through various interfaces and lines, executes or runs programs or modules stored in the memory 11 (such as the BMS-based energy storage power supply safety prediction and maintenance method program, etc.), and calls data stored in the memory 11 to perform various functions and process data of the electronic device 1.

[0088] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable connection and communication between the memory 11, the at least one processor 10, etc.

[0089] Figure 3 Only the electronic device with components is shown, and those skilled in the art can understand that, Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and can include fewer or more components than shown, or combine certain components, or different component arrangements.

[0090] For example, although not shown, the electronic device 1 can also include a power supply (such as a battery) to power each component. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, so that the power management device can implement functions such as charge management, discharge management, and power consumption management. The power supply can also include one or more direct current or alternating current power supplies, recharging devices, power supply fault detection circuits, power supply converters or inverters, power supply status indicators, etc. The electronic device 1 can also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not described here.

[0091] Further, the electronic device 1 can also include a network interface, which can optionally include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is typically used to establish a communication connection between the electronic device 1 and other electronic devices.

[0092] Optionally, the electronic device 1 can also include a user interface, which can be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally a standard wired interface, a wireless interface. Optionally, in some embodiments, the display can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, etc. The display can also be appropriately referred to as a display screen or a display unit, and is used to display information processed in the electronic device 1 and to display a visualized user interface.

[0093] The BMS-based energy storage power safety prediction and maintenance method program stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, which, when running in the processor 10, can achieve: obtaining a sample battery set, wherein the sample battery set includes a first battery, a second battery, a third battery, and a fourth battery, and the first SOH of the first battery, the second SOH of the second battery, the third SOH of the third battery, and the fourth SOH of the fourth battery are preset, wherein the first SOH > the second SOH > the third SOH > the fourth SOH; obtaining the first internal resistance of the first battery by using a pre-constructed first battery management system, performing a complete discharge operation on the first battery to obtain an initial battery, performing a charging test operation on the initial battery to obtain a full battery, a first terminal voltage boost rate, and a first temperature change rate; performing a power supply test operation on the full battery to obtain a first starting voltage drop rate; obtaining a first data set by summarizing the first internal resistance, the first terminal voltage boost rate, the first starting voltage drop rate, and the first temperature change rate, obtaining a second data set, a third data set, and a fourth data set based on the second battery, the third battery, and the fourth battery, respectively; obtaining an internal resistance sequence, a voltage boost sequence, a voltage drop sequence, and a temperature sequence by using the first data set, the second data set, the third data set, and the fourth data set, and calculating internal resistance weight, voltage boost weight, voltage drop weight, and temperature weight according to the internal resistance sequence, the voltage boost sequence, the voltage drop sequence, and the temperature sequence, respectively; constructing an internal resistance prediction function, a voltage boost prediction function, a voltage drop prediction function, and a temperature prediction function by using the internal resistance sequence, the voltage boost sequence, the voltage drop sequence, and the temperature sequence, respectively; receiving a safety prediction instruction, obtaining user usage data based on the safety prediction instruction, wherein the user usage data includes: user initial temperature, user voltage boost rate, user voltage drop rate, user temperature change rate, and user internal resistance; calculating the current battery SOH according to the user usage data, the internal resistance prediction function, the voltage boost prediction function, the voltage drop prediction function, the temperature prediction function, the internal resistance weight, the voltage boost weight, the voltage drop weight, and the temperature weight; calculating the estimated safety time according to the pre-constructed energy storage power control center, the user initial temperature, and the current battery SOH; integrating the estimated safety time into a maintenance prompt data packet by using the energy storage power control center, and sending the maintenance prompt data packet to the pre-constructed energy storage power client to complete the safety prediction and maintenance of the energy storage power.

[0094] Specifically, the specific implementation method of the processor 10 to the above instructions can refer to Figures 1 to 3 The description of related steps in the corresponding embodiments is omitted here.

[0095] Further, the modules / units integrated in the electronic device 1, if implemented in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. The computer readable storage medium can be volatile or non-volatile. For example, the computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM).

[0096] The application further provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor of an electronic device, can implement: obtaining a sample battery set, wherein the sample battery set includes a first battery, a second battery, a third battery and a fourth battery, and a first SOH of the first battery, a second SOH of the second battery, a third SOH of the third battery and a fourth SOH of the fourth battery are preset, wherein the first SOH> the second SOH> the third SOH> the fourth SOH; obtaining a first internal resistance of the first battery by using a pre-constructed first battery management system, performing a complete discharge operation on the first battery to obtain an initial battery, performing a charging test operation on the initial battery to obtain a full battery, a first terminal voltage rising rate and a first temperature change rate; performing a power supply test operation on the full battery to obtain a first starting voltage falling rate; obtaining a first data set by integrating the first internal resistance, the first terminal voltage rising rate, the first starting voltage falling rate and the first temperature change rate, obtaining a second data set, a third data set and a fourth data set based on the second battery, the third battery and the fourth battery respectively; obtaining an internal resistance sequence, a voltage rising sequence, a voltage falling sequence and a temperature sequence by using the first data set, the second data set, the third data set and the fourth data set, and calculating an internal resistance weight, a voltage rising weight, a voltage falling weight and a temperature weight according to the internal resistance sequence, the voltage rising sequence, the voltage falling sequence and the temperature sequence respectively; constructing an internal resistance prediction function, a voltage rising prediction function, a voltage falling prediction function and a temperature prediction function by using the internal resistance sequence, the voltage rising sequence, the voltage falling sequence and the temperature sequence respectively; receiving a safety prediction instruction, and obtaining user usage data based on the safety prediction instruction, wherein the user usage data includes: a user initial temperature, a user voltage rising rate, a user voltage falling rate, a user temperature change rate and a user internal resistance; calculating a current battery SOH according to the user usage data, the internal resistance prediction function, the voltage rising prediction function, the voltage falling prediction function, the temperature prediction function, the internal resistance weight, the voltage rising weight, the voltage falling weight and the temperature weight; calculating an estimated safety time according to the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH; integrating the estimated safety time into a maintenance prompt data packet by using the energy storage power supply control center, and sending the maintenance prompt data packet to a pre-constructed energy storage power supply client to complete the safety prediction and maintenance of the energy storage power supply.

[0097] In several embodiments provided by the present application, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the above-described system embodiments are only illustrative, and actual implementation can have other divisions.

[0098] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical units, i.e. can be located in one place or distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0099] In addition, the functional modules in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0100] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for safety prediction and maintenance of energy storage power supply based on BMS, characterized in that: The method includes: obtaining a sample battery set, wherein the sample battery set includes a first battery, a second battery, a third battery, and a fourth battery, and presetting a first SOH of the first battery, a second SOH of the second battery, a third SOH of the third battery, and a fourth SOH of the fourth battery, wherein the first SOH>the second SOH>the third SOH>the fourth SOH; using a pre-built first battery management system to obtain a first internal resistance of the first battery, performing a full discharge operation on the first battery to obtain an initial battery, performing a charging test operation on the initial battery to obtain a fully charged battery, a first endpoint voltage rise rate, and a first temperature change rate; performing a power supply test operation on the fully charged battery to obtain a first starting point voltage drop rate; summarizing the first internal resistance, the first endpoint voltage rise rate, the first starting point voltage drop rate, and the first temperature change rate to obtain a first data group, and obtaining a second data group, a third data group, and a fourth data group based on the second battery, the third battery, and the fourth battery respectively; using the first data group, the second data group, the third data group, and the fourth data group to obtain an internal resistance series, a voltage rise series, a voltage drop series, and a voltage drop series; The system calculates the internal resistance weight, the boost weight, the buck weight and the temperature weight according to the internal resistance series, the boost series, the buck series and the temperature series respectively; constructs the internal resistance prediction function, the boost prediction function, the buck prediction function and the temperature prediction function respectively using the internal resistance series, the boost series, the buck series and the temperature series; receives a safety prediction instruction, and obtains user usage data based on the safety prediction instruction, wherein the user usage data includes: user initial temperature, user boost rate, user buck rate, user temperature change rate and user internal resistance; calculates the current battery SOH according to the user usage data, the internal resistance prediction function, the boost prediction function, the buck prediction function, the temperature prediction function, the internal resistance weight, the boost weight, the buck weight and the temperature weight; calculates the estimated safety time according to the pre-built energy storage power supply control center, the user's initial temperature and the current battery SOH; uses the energy storage power supply control center to integrate the estimated safety time into a maintenance reminder data packet, and sends the maintenance reminder data packet to the pre-built energy storage power supply client to complete the safety prediction and maintenance of the energy storage power supply.

2. The method for safety prediction and maintenance of energy storage power supply based on BMS according to claim 1, characterized in that: The charging test operation is performed on the initial battery to obtain a fully charged battery, a first endpoint voltage rise rate, and a first temperature change rate, including: starting a pre-built charging power supply, starting at the time when the charging power supply is started and recording the time in real time to obtain the charging time, using the started charging power supply to charge the initial battery, and using the first battery management system to collect the charging voltage and battery temperature of the initial battery in real time; when the charging voltage reaches a preset voltage threshold, the charging power supply is turned off to obtain a fully charged battery, a voltage curve, and a temperature curve, wherein the abscissa of the voltage curve and the abscissa of the temperature curve are both charging time, the ordinate of the voltage curve is charging voltage, and the ordinate of the temperature curve is battery temperature; based on a preset reference voltage, a reference voltage point is identified in the voltage curve, wherein the ordinate of the reference voltage point is the reference voltage, and the abscissa of the reference voltage point is used as a reference time; based on the voltage threshold, a final voltage point is identified in the voltage curve, wherein the ordinate of the final voltage point is the voltage threshold, and the abscissa of the final voltage point is used as the final time; and the first endpoint voltage rise rate is calculated based on the reference time, the final time, the reference voltage, and the voltage threshold, using the following calculation formula: ,in, is the first endpoint pressure increase rate, is the voltage threshold, is the reference voltage, For the final time, is the reference time; the point with the lowest battery temperature in the temperature curve is recorded as the lowest temperature point, the point with the highest battery temperature in the temperature curve is recorded as the peak temperature point, the abscissa of the lowest temperature point is taken as the lowest time, the ordinate of the lowest temperature point is taken as the lowest temperature, the abscissa of the peak temperature point is taken as the peak time, and the ordinate of the peak temperature point is taken as the peak temperature; the first temperature change rate is calculated based on the peak time, the lowest time, the peak temperature and the lowest temperature, and the calculation formula is as follows: ,in, is the first temperature change rate, is the peak temperature, is the lowest temperature, is the peak time, For the minimum time.

3. The BMS-based energy storage power supply safety prediction and maintenance method according to claim 2, characterized in that: The method of performing a power supply test operation on a fully charged battery to obtain a first starting point voltage reduction rate includes: connecting a pre-constructed power consumption load unit to the fully charged battery to obtain a power consumption load unit, using the time when the power consumption load unit is obtained as an initial time, starting from the initial time and recording the time in real time to obtain a real-time time, using the fully charged battery to perform a power supply operation on the power consumption load unit, and using a first battery management system to monitor the power supply voltage of the fully charged battery in real time during power supply, and when the power supply voltage reaches a preset power supply threshold, recording the real-time time as the current time; and calculating the first starting point voltage reduction rate based on the current time, the initial time, the voltage threshold, and the power supply threshold, wherein the calculation formula is as follows: ,in, is the first starting point depressurization rate, is the power supply threshold, is the current time, is the initial time.

4. The BMS-based energy storage power supply safety prediction and maintenance method according to claim 3, characterized in that: The method of obtaining the internal resistance series, the voltage increase series, the voltage decrease series, and the temperature series using the first data group, the second data group, the third data group, and the fourth data group includes: determining the internal resistance series based on the first data group, the second data group, the third data group, and the fourth data group, wherein the second data group includes: a second internal resistance, a second endpoint voltage increase rate, a second starting point voltage decrease rate, and a second temperature change rate; the third data group includes: a third internal resistance, a third endpoint voltage increase rate, a third starting point voltage decrease rate, and a third temperature change rate; and the fourth data group includes: a fourth internal resistance, a fourth endpoint voltage increase rate, a fourth starting point voltage decrease rate, and a fourth temperature change rate, and the internal resistance series is as follows: ,in, is the first internal resistance, is the second internal resistance, is the third internal resistance, is the fourth internal resistance; and obtaining a voltage boost series, a voltage drop series, and a temperature series based on the first data group, the second data group, the third data group, and the fourth data group.

5. The method for safety prediction and maintenance of energy storage power supply based on BMS according to claim 4, characterized in that: The calculation of the internal resistance weight, the boost weight, the buck weight and the temperature weight according to the internal resistance series, the boost series, the buck series and the temperature series respectively includes: obtaining an SOH series using the first SOH, the second SOH, the third SOH and the fourth SOH, wherein the SOH series is as follows: ,in, For the first SOH, For the second SOH, For the third SOH, is the fourth SOH; use the SOH series to calculate the SOH mean, the calculation formula is as follows: ,in, is the SOH mean; the internal resistance mean is obtained based on the internal resistance series; the internal resistance weight is calculated using the SOH series, SOH mean, internal resistance series and internal resistance mean. The calculation formula is as follows: ,in, is the internal resistance weight, is the first in the SOH sequence item, is the mean SOH value, is the first in the internal resistance series item, is the mean internal resistance, The absolute value is taken as the representative; the boost weight is obtained based on the SOH series, the SOH mean, and the boost series; the buck weight is obtained based on the SOH series, the SOH mean, and the buck series; and the temperature weight is obtained based on the SOH series, the SOH mean, and the temperature series.

6. The method for safety prediction and maintenance of energy storage power supply based on BMS according to claim 5, characterized in that: The internal resistance series, the boost series, the buck series and the temperature series are used to construct the internal resistance prediction function, the boost prediction function, the buck prediction function and the temperature prediction function respectively, including: calculating a first coefficient based on the first SOH and the second SOH in the SOH series and the first internal resistance and the second internal resistance in the internal resistance series, and the calculation formula is as follows: ,in, is the first coefficient; the first parameter is calculated based on the first SOH in the SOH series, the first internal resistance in the internal resistance series, and the first coefficient. The calculation formula is as follows: ,in, is the first parameter; based on the second SOH and the third SOH in the SOH series and the second internal resistance and the third internal resistance in the internal resistance series, obtain the second coefficient and the second parameter; based on the third SOH and the fourth SOH in the SOH series and the third internal resistance and the fourth internal resistance in the internal resistance series, obtain the third coefficient and the third parameter; An internal resistance prediction function is constructed using the internal resistance series, the first coefficient, the first parameter, the second coefficient, the second parameter, the third coefficient, and the third parameter, wherein the internal resistance prediction function is as follows: ,in, is the internal resistance prediction function, is the independent variable of the internal resistance prediction function, 、 、 and They are the second coefficient, the second parameter, the third coefficient and the third parameter respectively; the boost prediction function is obtained based on the SOH series and the boost series, the buck prediction function is obtained based on the SOH series and the buck series, and the temperature prediction function is obtained based on the SOH series and the temperature series.

7. The method for safety prediction and maintenance of energy storage power supply based on BMS according to claim 6, characterized in that: The method of obtaining user usage data based on a safety prediction instruction includes: using the safety prediction instruction to start a pre-built user battery management system, using the started user battery management system to obtain a user internal resistance of a pre-built user energy storage power supply, and performing a charging operation on the user energy storage power supply to obtain a charged energy storage power supply, obtaining a fully charged energy storage power supply, a user initial temperature, a user voltage rise rate, and a user temperature change rate based on the charged energy storage power supply and the user battery management system; obtaining a user voltage fall rate based on a pre-built test load unit and the fully charged energy storage power supply; and integrating the user initial temperature, the user voltage rise rate, the user voltage fall rate, the user temperature change rate, and the user internal resistance into the user usage data.

8. The method for safety prediction and maintenance of energy storage power supply based on BMS according to claim 7, characterized in that: The method of calculating the current battery SOH according to the user usage data, the internal resistance prediction function, the boost prediction function, the buck prediction function, the temperature prediction function, the internal resistance weight, the boost weight, the buck weight, and the temperature weight includes: using the user internal resistance in the user usage data as the independent variable of the internal resistance prediction function and substituting it into the internal resistance prediction function to calculate the internal resistance prediction SOH; obtaining the boost prediction SOH based on the user boost rate and the boost prediction function in the user usage data, obtaining the buck prediction SOH based on the user buck rate and the buck prediction function in the user usage data, and obtaining the temperature prediction SOH based on the user temperature change rate and the temperature prediction function in the user usage data; and calculating the current battery SOH according to the internal resistance prediction SOH, the internal resistance weight, the boost prediction SOH, the boost weight, the buck prediction SOH, the buck weight, the temperature prediction SOH, and the temperature weight. The calculation formula is as follows: ,in, is the current battery SOH, 、 、 and They are internal resistance prediction SOH, boost prediction SOH, buck prediction SOH and temperature prediction SOH, 、 and They are boost weight, buck weight and temperature weight respectively.

9. The BMS-based energy storage power supply safety prediction and maintenance method according to claim 8, characterized in that: The method of calculating the estimated safety time based on a pre-built energy storage power supply control center, a user's initial temperature, and a current battery SOH includes: integrating the user's initial temperature and the current battery SOH into an analysis data packet, and sending the analysis data packet to the energy storage power supply control center, wherein the energy storage power supply control center includes: a user database; when the energy storage power supply control center receives the analysis data packet, calculating the maximum reference temperature and the minimum reference temperature based on the user's initial temperature and a preset weighted temperature in the analysis data packet, wherein the maximum reference temperature is the sum of the user's initial temperature and the weighted temperature, and the minimum reference temperature is the absolute difference between the user's initial temperature and the weighted temperature; determining a reference temperature range based on the maximum reference temperature and the minimum reference temperature, wherein the maximum value of the reference temperature range is the maximum reference temperature, and the minimum value of the reference temperature range is the minimum reference temperature; retrieving multiple reference user data from the user database based on the reference temperature range, wherein the reference user data includes: user usage time, reference user temperature, and reference fault SOH; calculating the estimated safety time based on the multiple reference user data, the user's initial temperature, and the current battery SOH, and the calculation formula is as follows: ,in, To estimate the safety time, For multiple reference user data User usage time of reference user data, For multiple reference user data The reference user temperature of the reference user data, For multiple reference user data Reference fault SOH of reference user data, is the user's initial temperature, is a natural constant.

10. A BMS-based energy storage power supply safety prediction and maintenance system, characterized in that: The system includes: a sample battery acquisition module, used to acquire a sample battery set, wherein the sample battery set includes a first battery, a second battery, a third battery, and a fourth battery, and a first SOH of the first battery, a second SOH of the second battery, a third SOH of the third battery, and a fourth SOH of the fourth battery are preset, wherein the first SOH>the second SOH>the third SOH>the fourth SOH; an energy storage battery detection module, used to use a pre-built first battery management system to acquire a first internal resistance of the first battery, perform a full discharge operation on the first battery to obtain an initial battery, perform a charging test operation on the initial battery to obtain a fully charged battery, a first endpoint voltage rise rate, and a first temperature change rate, perform a power supply test operation on the fully charged battery to obtain a first starting point voltage drop rate, summarize the first internal resistance, the first endpoint voltage rise rate, the first starting point voltage drop rate, and the first temperature change rate to obtain a first data group, and obtain a second data group, a third data group, and a fourth data group based on the second battery, the third battery, and the fourth battery respectively; and a prediction function construction module, used to use the first data group, the second data group, the third data group, and the fourth data group to acquire the internal resistance The internal resistance weight, boost weight, buck weight and temperature weight are calculated according to the internal resistance series, boost series, buck series and temperature series respectively, and the internal resistance series, boost series, buck series and temperature series are used to construct the internal resistance prediction function, boost prediction function, buck prediction function and temperature prediction function respectively; the energy storage power supply prediction module is used to receive the safety prediction instruction and obtain the user usage data based on the safety prediction instruction, wherein the user usage data includes: user initial temperature, user boost rate, user buck rate The current battery SOH is calculated based on the user usage data, internal resistance prediction function, boost prediction function, buck prediction function, temperature prediction function, internal resistance weight, boost weight, buck weight, and temperature weight. The estimated safety time is calculated based on the pre-built energy storage power supply control center, the user's initial temperature, and the current battery SOH. The energy storage power supply control center is used to integrate the estimated safety time into a maintenance reminder data package, and the maintenance reminder data package is sent to the pre-built energy storage power supply client to complete the safety prediction and maintenance of the energy storage power supply.

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