A method and system for safety prediction and maintenance of energy storage power supply based on BMS

Abstract

The application relates to the technical field of energy storage power supply, and relates to an energy storage power supply safety prediction and maintenance method and system based on a BMS, which comprises the following steps: obtaining the first internal resistance of a first battery, 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 terminal voltage falling rate, calculating the current battery SOH according to user use data, an internal resistance prediction function, a voltage rising prediction function, a voltage falling prediction function, a temperature prediction function, an internal resistance weight, a voltage rising weight, a voltage falling weight and a temperature weight, calculating the estimated safety time, integrating the estimated safety time into maintenance prompt data packets by using an energy storage power supply control center, and completing the safety prediction and maintenance of the energy storage power supply. The application can improve the accuracy of safety prediction of the energy storage power supply, and ensures the safety and reliability of the energy storage power supply.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage power supply, in particular to an energy storage power supply safety prediction and maintenance method and system based on BMS, 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 achieved by monitoring 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 combining 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 actual use of 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 an energy storage power supply safety prediction and maintenance method based on BMS, a 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 purpose, the present application provides an energy storage power supply safety prediction and maintenance method based on BMS, which comprises:

[0007] Obtain 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;

[0008] Obtaining a first internal resistance of the first battery by using a pre-constructed first battery management system, 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 full battery, a first end-point boost rate and a first temperature change rate;

[0009] Performing a power supply test operation on the full battery to obtain a first start-point drop rate;

[0010] Obtaining a first data set by summarizing the first internal resistance, the first end-point boost rate, the first start-point drop rate and the first temperature change rate, obtaining a second data set, a third data set and a fourth data set based on a second battery, a third battery and a fourth battery respectively;

[0011] Obtaining an internal resistance sequence, a boost sequence, a 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, calculating an internal resistance weight, a boost weight, a drop weight and a temperature weight according to the internal resistance sequence, the boost sequence, the drop sequence and the temperature sequence respectively;

[0012] Constructing an internal resistance prediction function, a boost prediction function, a drop prediction function and a temperature prediction function by using the internal resistance sequence, the boost sequence, the drop sequence and the temperature sequence respectively;

[0013] Receiving a safety prediction instruction, obtaining user usage data based on the safety prediction instruction, wherein the user usage data includes a user initial temperature, a user boost rate, a user drop rate, a user temperature change rate and a user internal resistance;

[0014] Calculating a 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;

[0015] Calculating an estimated safety time according to the pre-constructed energy storage power control center, the user initial temperature and the current battery SOH;

[0016] Integrating the estimated safety time into a maintenance prompt data packet by using the energy storage power control center, sending the maintenance prompt data packet to a pre-constructed energy storage power client, and completing the safety prediction and maintenance of the energy storage power.

[0017] Optionally, the charging test operation on the initial battery to obtain the full battery, the first end-point boost rate and the first temperature change rate includes:

[0018] start the pre-constructed charging power supply, record the time from the time when the charging power supply is started and in real time, obtain the charging time, perform a charging operation on the initial battery by using the started charging power supply, and collect the charging voltage and the 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, turn off the charging power supply, obtain the full-electricity battery, the voltage curve and the 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;

[0019] confirm 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 a reference time;

[0020] confirm 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 a final time;

[0021] calculate a 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:

[0022] wherein, the first terminal voltage-rising rate is, the voltage threshold is, the reference voltage is, the final time is, the reference time is;

[0023] record a point with the lowest battery temperature in the temperature curve as a lowest temperature point, record a point with the highest battery temperature in the temperature curve as a peak temperature point, take the abscissa of the lowest temperature point as a lowest time, take the ordinate of the lowest temperature point as a lowest temperature, take the abscissa of the peak temperature point as a peak time, and take the ordinate of the peak temperature point as a peak temperature;

[0024] calculate a 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:

[0025] wherein, the first temperature change rate is, the peak temperature is, the lowest temperature is, the peak time is, the lowest time is.

[0026] Optionally, the power supply test operation is performed on the full-electricity battery to obtain a first starting voltage-falling rate, and the method comprises the following steps:

[0027] The pre-constructed power consumption load unit is connected with the full power battery to obtain a power consumption load unit, a time when the power consumption load unit is obtained is an initial time, a real-time time is recorded starting from the initial time, a power supply operation is performed on the power consumption load unit by using the full power battery, and a power supply voltage of the full power battery in the power supply operation is monitored in real time by using the first battery management system, and when the power supply voltage reaches a preset power supply threshold, the real-time time is recorded as a current time;

[0028] A first starting point voltage drop rate is calculated according to the current time, the initial time, the voltage threshold and the power supply threshold, and a calculation formula is as follows:

[0029] wherein, the first starting point voltage drop rate, the voltage threshold, the power supply threshold, the current time, the initial time.

[0030] Optionally, the obtaining of the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence by using the first data group, the second data group, the third data group and the fourth data group comprises:

[0031] The internal resistance sequence is determined based on the first data group, the second data group, the third data group and the fourth data group, wherein the second data group comprises a second internal resistance, a second terminal voltage rise rate, a second starting point voltage drop rate and a second temperature change rate, the third data group comprises a third internal resistance, a third terminal voltage rise rate, a third starting point voltage drop rate and a third temperature change rate, the fourth data group comprises a fourth internal resistance, a fourth terminal voltage rise rate, a fourth starting point voltage drop rate and a fourth temperature change rate, and the internal resistance sequence is as follows:

[0032] wherein, the first internal resistance, the second internal resistance, the third internal resistance, the fourth internal resistance;

[0033] The voltage rise sequence, the voltage drop sequence and the temperature sequence are obtained based on the first data group, the second data group, the third data group and the fourth data group.

[0034] Optionally, the internal resistance weight, the voltage rise weight, the voltage drop weight and the temperature weight are calculated according to the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence respectively, and the calculation comprises:

[0035] The SOH sequence is obtained by using the first SOH, the second SOH, the third SOH and the fourth SOH, and the SOH sequence is as follows:

[0036] in, The first SOH, For the second SOH, It is the third SOH. It is the fourth SOH;

[0037] The mean of SOH can be calculated using the SOH series, as shown in the following formula:

[0038] in, The mean SOH value;

[0039] Obtain the mean internal resistance based on the internal resistance series;

[0040] The internal resistance weight is calculated using the SOH series, the mean of SOH, the internal resistance series, and the mean of internal resistance. The calculation formula is shown below:

[0041] in, As the internal resistance weight, The first number in the SOH sequence item, The mean SOH value The first in the internal resistance sequence item, The average internal resistance, This represents taking the absolute value;

[0042] The pressure boosting weight is obtained based on the SOH series, SOH mean, and pressure boosting series; the pressure depressurization weight is obtained based on the SOH series, SOH mean, and pressure depressurization series; and the temperature weight is obtained based on the SOH series, SOH mean, and temperature series.

[0043] Optionally, the construction of internal resistance prediction functions, voltage boost prediction functions, voltage drop prediction functions, and temperature prediction functions using internal resistance series, voltage boost series, voltage drop series, and temperature series respectively includes:

[0044] The first coefficient is calculated based on the first and second SOHs in the SOH sequence and the first and second internal resistances in the internal resistance sequence. The calculation formula is as follows:

[0045] in, The first coefficient;

[0046] The 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. The calculation formula is as follows:

[0047] in, The first parameter;

[0048] The second coefficient and the 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 the third coefficient and the 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;

[0049] 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 follows:

[0050] wherein, is the internal resistance prediction function, is an independent variable of the internal resistance prediction function, , , and the second coefficient, the second parameter, the third coefficient and the third parameter respectively;

[0051] 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.

[0052] Optionally, the user usage data is obtained based on the safety prediction instruction, and the user usage data comprises:

[0053] The pre-constructed user battery management system is started by using the safety prediction instruction, the user internal resistance of the pre-constructed user energy storage power supply is obtained by using the started user battery management system, a charging operation is performed on the user energy storage power supply, a charged energy storage power supply is obtained, and the full-energy energy storage power supply, the user initial temperature, the user boost rate and the user temperature change rate are obtained based on the charged energy storage power supply and the user battery management system;

[0054] The user drop rate is obtained based on the pre-constructed test load unit and the full-energy energy storage power supply;

[0055] The user initial temperature, the user boost rate, the user drop rate, the user temperature change rate and the user internal resistance are integrated into the user usage data.

[0056] Optionally, the current battery SOH is calculated 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, and the calculation comprises:

[0057] The user internal resistance in the user usage data is taken as the independent variable of the internal resistance prediction function and substituted into the internal resistance prediction function to calculate the internal resistance prediction SOH;

[0058] The voltage increase prediction SOH is obtained based on a user voltage increase rate in the user use data and a voltage increase prediction function, the voltage decrease prediction SOH is obtained based on a user voltage decrease rate in the user use data and a voltage decrease prediction function, and the temperature prediction SOH is obtained based on a user temperature change rate in the user use data and a temperature prediction function;

[0059] The current battery SOH is calculated according to the internal resistance prediction SOH, the internal resistance weight, the voltage increase prediction SOH, the voltage increase weight, the voltage decrease prediction SOH, the voltage decrease weight, the temperature prediction SOH and the temperature weight, and the calculation formula is as follows:

[0060] wherein, is the current battery SOH, , , and the internal resistance prediction SOH, the voltage increase prediction SOH, the voltage decrease prediction SOH and the temperature prediction SOH, , and the voltage increase weight, the voltage decrease weight and the temperature weight.

[0061] Optionally, the estimated safety time is calculated according to the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH, and the method comprises the following steps:

[0062] The user initial temperature and the current battery SOH are integrated into an analysis data packet, and the analysis data packet is sent to the energy storage power supply control center, wherein the energy storage power supply control center comprises a user database;

[0063] When the energy storage power supply control center receives the analysis data packet, the maximum reference temperature and the minimum reference temperature are calculated 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;

[0064] The reference temperature range is determined 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;

[0065] The plurality of reference user data is retrieved 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;

[0066] The estimated safety time is calculated according to the plurality of reference user data, the user initial temperature and the current battery SOH, and the calculation formula is as follows:

[0067] wherein, to estimate the safety time, a user usage time of the i-th reference user data in the plurality of reference user data, a reference user temperature of the i-th reference user data in the plurality of reference user data, a reference failure SOH of the i-th reference user data in the plurality of reference user data, an initial temperature of the user, a natural constant. To achieve the above object, the present application also 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;

[0068] 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 end-point boost rate and a first temperature change rate, perform a power supply test operation on the full battery to obtain a first start-point drop rate, and aggregate the first internal resistance, the first end-point boost rate, the first start-point drop rate and the first temperature change rate to obtain a first data set, 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;

[0069] a prediction function construction module, configured to acquire an internal resistance sequence, a boost sequence, a 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 boost weight, a drop weight and a temperature weight according to the internal resistance sequence, the boost sequence, the drop sequence and the temperature sequence respectively, and construct an internal resistance prediction function, a boost prediction function, a drop prediction function and a temperature prediction function based on the internal resistance sequence, the boost sequence, the drop sequence and the temperature sequence respectively;

[0070]

[0071]

[0072] ​​​The energy storage power prediction module is configured to receive a safety prediction instruction, obtain user usage data based on the safety prediction instruction, wherein the user usage data comprises a user initial temperature, a user boost rate, a user drop rate, a user temperature change rate and a user internal resistance, calculate a current battery SOH according to the user usage data, an internal resistance prediction function, a boost prediction function, a drop prediction function, a temperature prediction function, an internal resistance weight, a boost weight, a drop weight and a temperature weight, calculate an estimated safety time according to a 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.

[0073] To solve the above problems, the present application further provides an electronic device, which comprises:

[0074] a memory, configured to store at least one instruction; and

[0075] a processor, configured to execute the instruction stored in the memory to implement the above-mentioned BMS-based safety prediction and maintenance method of the energy storage power.

[0076] To solve the above problems, the present application further provides a computer readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned BMS-based safety prediction and maintenance method of the energy storage power.

[0077] 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, 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 on the evaluation of 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

[0078] Figure 1 The flowchart of the BMS-based energy storage power supply safety prediction and maintenance method provided by an embodiment of the present application is shown.

[0079] Figure 2 The function module diagram of the BMS-based energy storage power supply safety prediction and maintenance system provided by an embodiment of the present application is shown.

[0080] Figure 3 The structural diagram of the 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.

[0081] Explanation of reference signs:

[0082] 1, electronic device; 10, processor; 11, memory; 12, bus.

[0083] 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

[0084] 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.

[0085] 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 and a terminal, 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.

[0086] 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 the following steps.

[0087] S1, acquiring 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.

[0088] 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 can be stored by the current energy storage battery to the maximum electric quantity that can be stored by a brand-new energy storage battery. 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 can be stored by the energy storage battery becomes 8000 mAh after the user uses it for a period of time, and thus the SOH is 80%.

[0089] 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.

[0090] 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%.

[0091] 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 embodiments 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.

[0092] For example, Xiao Zhang is a test operator 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.

[0093] 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.

[0094] 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 here.

[0095] 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 power of the first battery is exhausted. The initial battery is the first battery with exhausted power. 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 here.

[0096] In detail, the performing the charging test operation on the initial battery to obtain the full-power battery, the first terminal voltage rising rate and the first temperature change rate comprises:

[0097] starting the pre-built charging power supply, recording the time in real time from the time when the charging power supply is started 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-power battery, the voltage curve and the 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;

[0098] 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 a reference time;

[0099] 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 a final time;

[0100] 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:

[0101] wherein, the first terminal voltage rising rate is, the voltage threshold is, the reference voltage is, the final time is, Reference time;

[0102] The point of the lowest battery temperature in the temperature curve is recorded as the lowest temperature point, the point of the highest battery temperature in the temperature curve is recorded as the peak temperature point, the abscissa of the lowest temperature point is recorded as the lowest time, the ordinate of the lowest temperature point is recorded as the lowest temperature, the abscissa of the peak temperature point is recorded as the peak time, and the ordinate of the peak temperature point is recorded as the peak temperature;

[0103] The first temperature change rate is calculated according to the peak time, the lowest time, the peak temperature and the lowest temperature, and the calculation formula is as follows:

[0104] wherein, the first temperature change rate, the peak temperature, the lowest temperature, the peak time, the lowest time.

[0105] 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 fully charged, and the voltage threshold is related to the physical properties of the initial battery and is set by the factory.

[0106] For example, the charging power supply is started at 08:00, and 08:00 is taken as the starting point. When 08:10, the charging time is 10 minutes, and when 08:25, the charging time is 25 minutes.

[0107] It should be understood that the charging voltage refers to the voltage across the initial battery during the charging process of the initial battery. The charging temperature refers to the temperature of the surface of the initial battery during the charging process of the initial battery. The technology of collecting the charging voltage and the battery temperature of the initial battery in the charging process in real time by the first battery management system is a prior art, which 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 change of the charging time, so by drawing a curve of the process 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 process 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 in the charging process. The temperature curve reflects the change of the battery temperature of the initial battery in the charging process.

[0108] It should be explained that the reference voltage is less than the voltage threshold and is set by the tester.

[0109] For example, the reference voltage is 4 volts, in the voltage curve, find the point of the charging voltage is 4 volts, the point as the reference voltage point, and read the horizontal coordinate corresponding to the charging time of the point, the charging time as the reference time.

[0110] In detail, the power supply test operation is performed on the full battery to obtain the first start voltage drop rate, including:

[0111] The pre-constructed power consumption load unit is connected with the full battery to obtain the power consumption load unit, and the time of the power consumption load unit is the initial time, and the initial time is taken as the starting point and the real-time time is recorded to obtain the real-time time, and the power supply operation is performed on the power consumption load unit by the full battery, and the power supply voltage of the full battery in the power supply is monitored in real time by the first battery management system, and when the power supply voltage reaches the preset power supply threshold, the real-time time is recorded as the current time.

[0112] The first start voltage drop rate is calculated according to the current time, the initial time, the voltage threshold and the power supply threshold, and the calculation formula is as follows:

[0113] Among them, The first start voltage drop rate, The voltage threshold, The power supply threshold, The current time, The initial time.

[0114] It should be explained that the power consumption load unit is an electrical appliance, and the power of the electrical appliance when running is the maximum output power that the full battery can provide, and 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.

[0115] For example, the pre-constructed power consumption load unit is connected with the full battery to obtain the power consumption load unit, and the time of the power consumption load unit is 10:00, then 10:00 is taken as the initial time, and if the time when the power supply voltage reaches the power supply threshold is 10:20, then 10:20 is taken as the current time.

[0116] 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 charge, and the first temperature change rate reflects the temperature rise rate of the first battery during charging.

[0117] It should be understood that, from the prior art, the SOH of the first battery affects the first terminal voltage rising rate, the first temperature change rate, the first internal resistance and the first initial voltage falling rate of the first battery, so that the first terminal voltage rising rate, the first temperature change rate, the first internal resistance and the first initial voltage falling rate are obtained, and the internal resistance prediction function, the voltage rising prediction function, the voltage falling prediction function and the temperature prediction function are constructed by using the first internal resistance, the first terminal voltage rising rate, the first initial voltage falling rate and the first temperature change rate, so as to inversely estimate the current battery SOH.

[0118] S3, the first internal resistance, the first terminal voltage rising rate, the first initial voltage falling rate and the first temperature change rate are summarized to obtain the first data set, and the second data set, the third data set and the fourth data set are obtained based on the second battery, the third battery and the fourth battery.

[0119] 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 by using the first battery, which will not be described here.

[0120] S4, the internal resistance series, the voltage rising series, the voltage falling series and the temperature series are obtained by using the first data set, the second data set, the third data set and the fourth data set, and the internal resistance weight, the voltage rising weight, the voltage falling weight and the temperature weight are calculated according to the internal resistance series, the voltage rising series, the voltage falling series and the temperature series.

[0121] In detail, the internal resistance series, the voltage rising series, the voltage falling series and the temperature series are obtained by using the first data set, the second data set, the third data set and the fourth data set, and the internal resistance weight, the voltage rising weight, the voltage falling weight and the temperature weight are calculated according to the internal resistance series, the voltage rising series, the voltage falling series and the temperature series.

[0122] The internal resistance series is determined based on the first data set, the second data set, the third data set and the fourth data set, wherein the second data set includes the second internal resistance, the second terminal voltage rising rate, the second initial voltage falling rate and the second temperature change rate, the third data set includes the third internal resistance, the third terminal voltage rising rate, the third initial voltage falling rate and the third temperature change rate, the fourth data set includes the fourth internal resistance, the fourth terminal voltage rising rate, the fourth initial voltage falling rate and the fourth temperature change rate, and the internal resistance series is as follows:

[0123] Wherein, is the first internal resistance, is the second internal resistance, is the third internal resistance, is the fourth internal resistance;

[0124] The voltage rising series, the voltage falling series and the temperature series are obtained based on the first data set, the second data set, the third data set and the fourth data set.

[0125] In detail, the boost sequence is as follows:

[0126] wherein, is a first end-point boost rate, is a second end-point boost rate, is a third end-point boost rate, is a fourth end-point boost rate.

[0127] Further, the boost sequence is as follows:

[0128] wherein, is a first start-point boost rate, is a second start-point boost rate, is a third start-point boost rate, is a fourth start-point boost rate.

[0129] In detail, the temperature sequence is as follows:

[0130] 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.

[0131] Further, the resistance weight, the boost weight, the boost weight, and the temperature weight are calculated according to the resistance sequence, the boost sequence, the boost sequence, and the temperature sequence, respectively, and the calculation includes:

[0132] The SOH sequence is obtained by using the first SOH, the second SOH, the third SOH, and the fourth SOH, wherein the SOH sequence is as follows:

[0133] wherein, is a first SOH, is a second SOH, is a third SOH, is a fourth SOH.

[0134] The SOH average is calculated by using the SOH sequence, and the calculation formula is as follows:

[0135] wherein, is the SOH average;

[0136] The resistance average is obtained based on the resistance sequence;

[0137] The resistance weight is calculated by using the SOH sequence, the SOH average, the resistance sequence, and the resistance average, and the calculation formula is as follows:

[0138] in, As the internal resistance weight, The first number in the SOH sequence item, The mean SOH value The first in the internal resistance sequence item, The average internal resistance, This represents taking the absolute value;

[0139] The pressure boosting weight is obtained based on the SOH series, SOH mean, and pressure boosting series; the pressure depressurization weight is obtained based on the SOH series, SOH mean, and pressure depressurization series; and the temperature weight is obtained based on the SOH series, SOH mean, and temperature series.

[0140] Understandably, the internal resistance weight reflects the correlation between the current battery SOH and the predicted internal resistance SOH. A higher internal resistance weight indicates a stronger correlation between the current battery SOH and the predicted internal resistance SOH, thus placing a greater weight on the predicted internal resistance SOH when calculating the current battery SOH. For specific applications of the current battery SOH and the predicted internal resistance SOH, please refer to the following embodiments.

[0141] It should be understood that the method for obtaining the mean internal resistance based on the internal resistance series is the same as the method for obtaining the mean SOH using the SOH series, and will not be repeated here. The methods for obtaining the boost weight based on the SOH series, the mean SOH, and the boost series; the methods for obtaining the buck weight based on the SOH series, the mean SOH, and the buck series; and the methods for obtaining the temperature weight based on the SOH series, the mean SOH, and the temperature series are all the same as the method for calculating the internal resistance weight using the SOH series, the mean SOH, the internal resistance series, and the mean internal resistance, and will not be repeated here.

[0142] S5. Construct internal resistance prediction function, voltage rise prediction function, voltage drop prediction function and temperature prediction function respectively using internal resistance series, voltage rise series, voltage drop series and temperature series.

[0143] Specifically, the construction of internal resistance prediction functions, voltage rise prediction functions, voltage fall prediction functions, and temperature prediction functions using internal resistance series, voltage rise series, voltage fall series, and temperature series respectively includes:

[0144] The first coefficient is calculated based on the first and second SOHs in the SOH sequence and the first and second internal resistances in the internal resistance sequence. The calculation formula is as follows:

[0145] in, The first coefficient;

[0146] The 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. The calculation formula is as follows:

[0147] wherein, is the first parameter;

[0148] the second coefficient and the 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 the third coefficient and the 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;

[0149] 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 follows:

[0150] wherein, is the internal resistance prediction function, is an independent variable of the internal resistance prediction function, , , and the second coefficient, the second parameter, the third coefficient and the third parameter respectively;

[0151] 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.

[0152] 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, and 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 by using the SOH sequence and the internal resistance sequence, which will not be repeated here.

[0153] 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.

[0154] In detail, the user usage data is obtained based on the safety prediction instruction, including:

[0155] The pre-built user battery management system is started by using the safety prediction instruction, the user internal resistance of the pre-built user energy storage power is obtained by using the started user battery management system, and the charging operation is performed on the user energy storage power to obtain a charged energy storage power. The full-charge energy storage power, the user initial temperature, the user voltage rising rate and the user temperature change rate are obtained based on the charged energy storage power and the user battery management system.

[0156] The user voltage falling rate is obtained based on the pre-built test load unit and the full-charge energy storage power.

[0157] The user initial temperature, the user voltage rising rate, the user voltage falling rate, the user temperature change rate and the user internal resistance are integrated as user use data.

[0158] It should be explained that the user energy storage power refers to the energy storage power of the user. The user battery management system refers to the battery management system built in the energy storage power of the user, and the user battery management system can transmit data with the energy storage power control center. The test load unit is an electrical appliance built in the user energy storage power, 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 when charging the user energy storage power. For example, Xiaowang is the user of the user energy storage power, presses the safety prediction button on the user energy storage power when charging the user energy storage power, and initiates the safety prediction instruction. It can be understood that the method for obtaining the user internal resistance of the pre-built user energy storage power by using the started user battery management system is the same as the method for obtaining the first internal resistance of the first battery by using the pre-built first battery management system, which will not be repeated here.

[0159] It should be understood that the charging operation performed on the user energy storage power refers to controlling the user energy storage power to enter the charging state by using the user battery management system.

[0160] It should be explained that the charged energy storage power refers to the user energy storage power in charging. The full-charge energy storage power refers to the user energy storage power fully charged, and the method for obtaining the full-charge energy storage power, the user initial temperature, the user voltage rising rate and the user temperature change rate based on the charged energy storage power and the user battery management system is the same as the method for obtaining the full-charge battery, the minimum temperature, the first terminal voltage rising rate and the first temperature change rate by using the initial battery, and the method for obtaining the user voltage falling rate based on the pre-built test load unit and the full-charge energy storage power is the same as the method for obtaining the first starting voltage falling rate by using the power consumption load unit and the full-charge battery, which will not be repeated here.

[0161] 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 lowest 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 buck rate reflects the rate of the user energy storage power supply when it is close to full charge, and the user temperature change rate reflects the rate of temperature rise of the user energy storage power supply during charging.

[0162] S7, 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.

[0163] 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 buck prediction function, the temperature prediction function, the internal resistance weight, the boost weight, the buck weight and the temperature weight comprises:

[0164] The user internal resistance in the user usage data is taken as an independent variable of the internal resistance prediction function and substituted into the internal resistance prediction function to calculate the internal resistance prediction SOH.

[0165] The boost prediction SOH is obtained based on the user boost rate in the user usage data and the boost prediction function, the buck prediction SOH is obtained based on the user buck rate in the user usage data and the buck prediction function, and the temperature prediction SOH is obtained based on the user temperature change rate in the user usage data and the temperature prediction function.

[0166] The current battery SOH is calculated 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, and the calculation formula is as follows:

[0167] wherein, is the current battery SOH, , , and are the internal resistance prediction SOH, the boost prediction SOH, the buck prediction SOH and the temperature prediction SOH respectively, , and are the boost weight, the buck weight and the temperature weight respectively.

[0168] It can be understood that the method of obtaining the voltage rise prediction SOH based on the user voltage rise rate in the user use data and the voltage rise prediction function, the method of obtaining the voltage drop prediction SOH based on the user voltage drop rate in the user use data and the voltage drop prediction function, and the method of obtaining the temperature prediction SOH based on the user temperature change rate in the user use data and the temperature prediction function are the same as the method of obtaining the internal resistance prediction SOH based on the user internal resistance in the user use data, and details are not repeated here.

[0169] It should be understood that the internal resistance prediction SOH is the SOH of the user energy storage battery estimated according to the user internal resistance, the voltage rise prediction SOH is the SOH of the user energy storage battery estimated according to the user voltage rise rate, the voltage drop prediction SOH is the SOH of the user energy storage battery estimated according to the user voltage drop rate, and the temperature prediction 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 prediction SOH, the internal resistance weight, the voltage rise prediction SOH, the voltage rise weight, the voltage drop prediction SOH, the voltage drop weight, the temperature prediction SOH and the temperature weight.

[0170] 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.

[0171] 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:

[0172] The user initial temperature and the current battery SOH are integrated into an analysis data packet, and the analysis data packet is sent to the energy storage power supply control center, wherein the energy storage power supply control center comprises a user database;

[0173] When the energy storage power supply control center receives the analysis data packet, the maximum reference temperature and the minimum reference temperature are calculated 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;

[0174] The reference temperature range is determined 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;

[0175] A plurality of reference user data are retrieved 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;

[0176] The estimated safety time is calculated according to the plurality of reference user data, the user initial temperature and the current battery SOH, and the calculation formula is as follows:

[0177] in, To estimate the safe time, For multiple reference user data, the first User usage time based on reference user data. For multiple reference user data, the first Reference user temperature based on reference user data. For multiple reference user data, the first Reference fault SOH for each reference user data, The user's initial temperature. It is a natural constant.

[0178] It should be explained that the energy storage power control center is a software platform built by the energy storage power manufacturer that integrates a user database. Optionally, the energy storage power control center is located on a cloud server, and it 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.

[0179] For example, when energy storage power supply manufacturers recycle energy storage power supplies multiple times, they collect data from the recycled power supplies. For instance, they use the time the recycled power supply has been used as the user's usage time, the lowest temperature the recycled power supply reached during charging as the reference user temperature, and the State of Emergency (SOH) of the recycled power supply as the reference Fault SOH. The user's usage time, reference user temperature, and reference Fault SOH are integrated into user data and stored in a user database. This data is then used to predict the estimated usage time of the energy storage power supply in the future, providing a reference for more users and facilitating timely replacement or maintenance of the energy storage power supply to prevent failures.

[0180] It should be explained that the analysis data packet is a data packet containing information about the user's initial temperature and the current battery SOH. When the energy storage power control center receives the analysis data packet, it can automatically parse out the user's initial temperature and the current battery SOH from the data packet. The weighted temperature is related to the temperature fluctuation of the energy storage power supply during charging. Optionally, the weighted temperature can be set to 5 degrees Celsius.

[0181] It should be understood that retrieving multiple 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.

[0182] It can be understood that the estimated safe time refers to the predicted time that the user's energy storage battery can be safely used, for example, if the estimated safe time is 400 days, it means that the user's energy storage battery can still be safely used for 400 days, and after 400 days, the user needs to replace or repair the energy storage power supply in time.

[0183] S9, integrating the estimated safe 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.

[0184] For example, the energy storage power supply client is an energy storage power supply APP on the user's mobile phone, wherein the energy storage power supply APP can be pre-programmed by Java, and when the maintenance prompt data packet is received, the energy storage power supply APP can analyze the estimated safe time contained in the maintenance prompt data packet and convert the estimated safe time into a piece of text information to be displayed 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).

[0185] It should be understood that the technology of integrating the estimated safe time into a maintenance prompt data packet by using the energy storage power supply control center and the technology of converting the estimated safe time into a piece of text information when the maintenance prompt data packet is received by the energy storage power supply APP are both prior art, and will not be described here.

[0186] 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 repair center 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.

[0187] 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, by acquiring the user use data, the embodiment of the present application considers the influence of the actual working environment on the performance of the energy storage battery, 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; the multi-dimensional analysis method avoids the one-sidedness of evaluating the current battery SOH by a single factor, 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, by combining the user database in the energy storage power supply control center, the embodiment of the present application 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, by sending the maintenance prompt data packet, the embodiment of the present application timely reminds the user to maintain or repair the energy storage battery, 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.

[0188] As shown in Figure 2 , 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.

[0189] The energy storage power supply safety prediction and maintenance system based on BMS 100 can be installed in an electronic device. According to the functions to be realized, 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 fixed functions, and are stored in the memory of the electronic device.

[0190] The sample battery acquisition module 101 is 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 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 pre-set, wherein the first SOH > the second SOH > the third SOH > the fourth SOH;

[0191] The energy storage battery detection module 102 is configured to obtain a first internal resistance of a 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 summarizing the first internal resistance, the first terminal voltage boost rate, the first starting voltage drop rate and the first temperature change rate, and obtain second, third and fourth data sets based on second, third and fourth batteries respectively.

[0192] The prediction function construction module 103 is configured to obtain an internal resistance sequence, a voltage boost sequence, a voltage drop sequence and a temperature sequence based on the first, second, third and fourth data sets, calculate internal resistance weight, voltage boost weight, voltage drop weight and temperature weight based on the internal resistance sequence, the voltage boost sequence, the voltage drop sequence and the temperature sequence respectively, and construct internal resistance prediction function, voltage boost prediction function, voltage drop prediction function and temperature prediction function based on the internal resistance sequence, the voltage boost sequence, the voltage drop sequence and the temperature sequence respectively.

[0193] The energy storage power prediction module 104 is configured to receive a safety prediction instruction, obtain 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, calculate current battery SOH based on 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 estimated safety time based on the pre-constructed energy storage power control center, the user initial temperature and the current battery SOH, integrate the estimated safety time into maintenance prompt data package by using the energy storage power control center, and send the maintenance prompt data package to the pre-constructed energy storage power client, thereby completing the safety prediction and maintenance of the energy storage power supply.

[0194] 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 described in the above Figure 1 , and can produce the same technical effects, which will not be described here.

[0195] As Figure 3 shown 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.

[0196] 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.

[0197] 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. equipped on the electronic device 1. 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 on 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.

[0198] 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 and combinations of various control chips, etc. The processor 10 is the control core of the electronic device, which connects various components of the entire 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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 realize:

[0205] Obtain 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;

[0206] Obtain the first internal resistance of the first battery using a pre-constructed first battery management system, perform a full discharge operation on the first battery to obtain an initial battery, and perform a charging test operation on the initial battery to obtain a full battery, a first end-point boost rate, and a first temperature change rate;

[0207] Perform a power supply test operation on the full battery to obtain a first start-point step-down rate;

[0208] Obtain a first data set by summarizing the first internal resistance, the first end-point boost rate, the first start-point step-down 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;

[0209] Obtain an internal resistance series, a boost series, a step-down series, and a temperature series using the first data set, the second data set, the third data set, and the fourth data set, and calculate internal resistance weights, boost weights, step-down weights, and temperature weights according to the internal resistance series, the boost series, the step-down series, and the temperature series, respectively;

[0210] Construct internal resistance prediction functions, boost prediction functions, step-down prediction functions, and temperature prediction functions using the internal resistance series, the boost series, the step-down series, and the temperature series, respectively;

[0211] Receive a safety prediction instruction and obtain user usage data based on the safety prediction instruction, wherein the user usage data includes user initial temperature, user boost rate, user step-down rate, user temperature change rate, and user internal resistance;

[0212] Calculate the current battery SOH according to the user usage data, the internal resistance prediction functions, the boost prediction functions, the step-down prediction functions, the temperature prediction functions, the internal resistance weights, the boost weights, the step-down weights, and the temperature weights;

[0213] Calculate the estimated safety time according to the pre-constructed energy storage power control center, the user initial temperature, and the current battery SOH;

[0214] The energy storage power control center integrates the estimated safety time into a maintenance prompt data packet, and sends 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.

[0215] Specifically, the specific implementation method of the processor 10 to the above instructions can refer to Figures 1 to 3 The description of the related steps in the corresponding embodiments will not be repeated here.

[0216] Further, the modules / units integrated in the electronic device 1, if realized 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, Read-Only Memory).

[0217] The application also provides a computer readable storage medium, which stores a computer program, and the computer program can realize the following when executed by a processor of an electronic device:

[0218] Obtain 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;

[0219] Obtain a 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, and perform a charging test operation on the initial battery to obtain a full battery, a first end-point boost rate, and a first temperature change rate;

[0220] Perform a power supply test operation on the full battery to obtain a first start-point step-down rate;

[0221] Obtain a first data set by summarizing the first internal resistance, the first end-point boost rate, the first start-point step-down 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;

[0222] Obtain an internal resistance sequence, a boost sequence, a step-down 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 calculate an internal resistance weight, a boost weight, a step-down weight, and a temperature weight according to the internal resistance sequence, the boost sequence, the step-down sequence, and the temperature sequence, respectively;

[0223] The internal resistance prediction function, the boost prediction function, the drop prediction function and the temperature prediction function are respectively constructed by using the internal resistance sequence, the boost sequence, the drop sequence and the temperature sequence;

[0224] The safety prediction instruction is received, and user usage data is obtained 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;

[0225] The current battery SOH is calculated 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;

[0226] The estimated safety time is calculated according to the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH;

[0227] The estimated safety time is integrated into maintenance prompt data packets by using the energy storage power supply control center, and the maintenance prompt data packets are sent to the pre-constructed energy storage power supply client, and the safety prediction and maintenance of the energy storage power supply are completed.

[0228] 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 merely illustrative; actual implementation can have another division manner.

[0229] 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., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs.

[0230] 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, 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.

[0231] 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.

[0232] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A BMS-based energy storage power supply safety prediction and maintenance method, characterized in that, The method comprises: acquiring a sample battery set, wherein the sample battery set comprises 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; acquiring 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 terminal voltage falling rate; obtaining a first data set by summarizing the first internal resistance, the first terminal voltage rising rate, the first terminal voltage falling rate and the first temperature change rate, and 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; acquiring 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 acquiring 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 safety prediction and maintenance of the energy storage power supply.

2. The BMS-based energy storage power supply safety prediction and maintenance method of claim 1, wherein, The method comprises: starting a pre-constructed charging power supply, recording time from a starting time of the charging power supply in real time to obtain charging time, performing a 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, and when the charging voltage reaches a preset voltage threshold, the charging power supply is turned off to obtain a 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 a reference time; Confirming a final voltage point in the voltage curve based on a 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 a 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, is a first end point rate of rise of voltage, is a voltage threshold, is a reference voltage, is a final time, is a reference time; Taking a point with the lowest battery temperature in the temperature curve as a lowest temperature point, a point with the highest battery temperature in the temperature curve as a peak temperature point, the abscissa of the lowest temperature point as a lowest time, the ordinate of the lowest temperature point as a lowest temperature, the abscissa of the peak temperature point as a peak time, and the ordinate of the peak temperature point as a peak temperature; Calculating a 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, is the first temperature rate, is the peak temperature, is the minimum temperature, is the peak time, is the minimum time.

3. The BMS-based energy storage power supply safety prediction and maintenance method of claim 2, wherein, The method further includes the following steps: Connecting the pre-constructed power consumption load unit with the full battery to obtain a power consumption load unit, taking a time when the power consumption load unit is connected as an initial time, taking the initial time as a starting point and recording a real-time time, performing a power supply operation on the power consumption load unit by the full battery, and monitoring a power supply voltage of the full battery in real time by the first battery management system, and taking a real-time time when the power supply voltage reaches a preset power supply threshold as a current time; Calculating a first starting 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 start rate of decrease in voltage, is a voltage threshold, is a supply threshold, is a current time, is an initial time.

4. The BMS-based energy storage power supply safety prediction and maintenance method of claim 3, wherein, The method further includes the following steps: Confirming the internal resistance number 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 includes a second internal resistance, a second terminal voltage rising rate, a second starting voltage falling rate and a second temperature change rate, the third data set includes a third internal resistance, a third terminal voltage rising rate, a third starting voltage falling rate and a third temperature change rate, the fourth data set includes a fourth internal resistance, a fourth terminal voltage rising rate, a fourth starting voltage falling rate and a fourth temperature change rate, and the internal resistance number 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; Obtaining the voltage rising number sequence, the voltage falling number sequence and the temperature number sequence based on the first data set, the second data set, the third data set and the fourth data set.

5. The BMS-based energy storage power supply safety prediction and maintenance method of claim 4, wherein, The method further includes the following steps: Obtaining the SOH number sequence by using the first SOH, the second SOH, the third SOH and the fourth SOH, wherein the SOH number sequence is as follows: wherein, is a first SOH, is a second SOH, is a third SOH, is a fourth SOH; Calculating an SOH average value by using the SOH number sequence, and the calculation formula is as follows: wherein, is the SOH average; Obtaining an internal resistance average value based on the internal resistance number sequence; Calculating the internal resistance weight by using the SOH number sequence, the SOH average value, the internal resistance number sequence and the internal resistance average value, and the calculation formula is as follows: wherein, is an internal resistance weight, is the i-th item in the SOH series, is the i-th item in the SOH series, is an SOH mean value, is the i-th item in the internal resistance series, is the i-th item in the internal resistance series, is an internal resistance mean value, represents taking the absolute value; Obtaining the voltage rising weight based on the SOH number sequence, the SOH average value and the voltage rising number sequence, obtaining the voltage falling weight based on the SOH number sequence, the SOH average value and the voltage falling number sequence, and obtaining the temperature weight based on the SOH number sequence, the SOH average value and the temperature number sequence.

6. The BMS-based energy storage power supply safety prediction and maintenance method of claim 5, wherein, The internal resistance prediction function, the boost prediction function, the drop prediction function and the temperature prediction function are respectively constructed by using the internal resistance sequence, the boost sequence, the drop sequence and the temperature sequence, and the construction includes: The first coefficient is calculated 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; The 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 follows: wherein, is a first parameter; The second coefficient and the 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 the third coefficient and the 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 follows: wherein, is an internal resistance prediction function, is an argument of the internal resistance prediction function, , , and are a second coefficient, a second parameter, a third coefficient, and a 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.

7. The BMS-based energy storage power supply safety prediction and maintenance method of claim 6, wherein, The user usage data is obtained based on the safety prediction instruction, and the user usage data includes: The pre-constructed user battery management system is started by using the safety prediction instruction, the user internal resistance of the pre-constructed user energy storage power supply is obtained by using the started user battery management system, and a charging operation is performed on the user energy storage power supply to obtain a charged energy storage power supply, and the full-energy energy storage power supply, the user initial temperature, the user boost rate and the user temperature change rate are obtained based on the charged energy storage power supply and the user battery management system; The user drop rate is obtained based on the pre-constructed test load unit and the full-energy energy storage power supply; The user initial temperature, the user boost rate, the user drop rate, the user temperature change rate and the user internal resistance are integrated into the user usage data.

8. The BMS-based energy storage power supply safety prediction and maintenance method of claim 7, wherein, The current battery SOH is calculated based on 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, and the calculation includes: The user internal resistance in the user usage data is taken as the independent variable of the internal resistance prediction function and substituted into the internal resistance prediction function to calculate the internal resistance prediction SOH; The boost prediction SOH is obtained based on the user boost rate in the user usage data and the boost prediction function, the drop prediction SOH is obtained based on the user drop rate in the user usage data and the drop prediction function, and the temperature prediction SOH is obtained based on the user temperature change rate in the user usage data and the temperature prediction function; The current battery SOH is calculated based on 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 are the internal resistance predicted SOH, the boost predicted SOH, the buck predicted SOH and the temperature predicted SOH, respectively, , and are the boost weight, the buck weight and the temperature weight, respectively.

9. The BMS-based energy storage power supply safety prediction and maintenance method of claim 8, wherein, The estimated safety time is calculated based on the pre-constructed energy storage power supply control center, the user initial temperature and the current battery SOH, and the calculation includes: The user initial temperature and the current battery SOH are integrated into an analysis data packet, and the analysis data packet is sent 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, the maximum reference temperature and the minimum reference temperature are calculated according to the user initial temperature and the preset weighted temperature in the analysis data packet, 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; The reference temperature range is confirmed 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; The plurality of reference user data is retrieved from the user database based on the reference temperature range, wherein the reference user data includes: user use time, reference user temperature and reference fault SOH; The estimated safety time is calculated 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 usage time of the i-th reference user data of the plurality of reference user data, is the user usage time of the i-th reference user data of the plurality of reference user data, is the reference user temperature of the i-th reference user data of the plurality of reference user data, is the reference user temperature of the i-th reference user data of the plurality of reference user data, is the reference failure SOH of the i-th reference user data of the plurality of reference user data, is the reference failure SOH of the i-th reference user data of the plurality of reference user data, is the user initial temperature, is the natural constant.

10. A BMS-based energy storage power supply safety prediction and maintenance system, characterized in that, The system comprises: A sample battery acquisition module 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; An energy storage battery detection module 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 rise 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 aggregate the first internal resistance, the first terminal voltage rise rate, the first starting voltage drop rate and the first temperature change rate to obtain a first data set, 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 is configured to acquire an internal resistance sequence, a voltage rise 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, calculate 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, and construct an internal resistance prediction function, a voltage rise prediction function, a voltage drop prediction function and a temperature prediction function by using the internal resistance sequence, the voltage rise sequence, the voltage drop sequence and the temperature sequence, respectively. The energy storage power prediction module is configured to receive a safety prediction instruction, obtain user usage data based on the safety prediction instruction, wherein the user usage data includes a user initial temperature, a user boost rate, a user drop rate, a user temperature change rate, and a user internal resistance, calculate a current battery SOH according to the user usage data, an internal resistance prediction function, a boost prediction function, a drop prediction function, a temperature prediction function, an internal resistance weight, a boost weight, a drop weight, and a temperature weight, calculate an estimated safety time according to a 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 safety prediction and maintenance of the energy storage power.

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