CN116577679B - Thermal runaway early warning method and system for large energy storage battery, electronic equipment and medium - Google Patents
Thermal runaway early warning method and system for large energy storage battery, electronic equipment and medium Download PDFInfo
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- H—ELECTRICITY
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
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- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
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Abstract
The application relates to a thermal runaway early warning method and system of a large energy storage battery, electronic equipment and medium, wherein the method comprises the following steps: receiving the detected temperature and voltage of the battery core in the energy storage battery in real time; acquiring the current running state of the battery cell; taking the running state, voltage and temperature as input of an expansion force prediction model to obtain a corresponding expansion force value in the current state; receiving the detected expansion force value of the battery cell in real time; comparing the received expansion force value with the corresponding expansion force value in the current state; if the expansion force difference is not greater than the first threshold value, updating the SOC and the SOH; if the difference of the expansion forces is not greater than a second threshold value, when the accumulation of the abnormal times exceeds a preset value, carrying out abnormal alarm on the battery cell; and if the expansion force difference is larger than a second threshold value, carrying out abnormal alarm on the battery cell. According to the application, the expansion force of the battery cell is monitored, a battery cell database is established for automatic parameter learning, and risks such as battery cell failure or battery cell thermal runaway and the like can be judged and early-warned.
Description
Technical Field
The application relates to the technical field of energy storage batteries, in particular to a thermal runaway early warning method and system of a large-scale energy storage battery, electronic equipment and medium.
Background
Thermal runaway refers to a chain reaction phenomenon induced by various causes, resulting in the emission of a large amount of heat and harmful gases from the battery in a short time, and even causing ignition and explosion of the battery in severe cases. The fire suppression scheme of the electrochemical energy storage cabin takes early discovery and early treatment as the principle, and early warning and accurate suppression treatment are carried out in the primary stage of thermal runaway, so that the loss of the electrochemical energy storage cabin can be reduced as much as possible.
Most battery thermal runaway early warning only comprises data acquisition and monitoring of three dimensions of voltage, current and temperature, the state of a battery cell in a system cannot be visually predicted from data, failure early warning is carried out, risks cannot be timely perceived before failure, and obvious time difference problems and detection errors exist.
In order to solve the above problems, there are currently proposals for alerting by detecting parameters related to battery expansion. Such as: the application discloses a method and a device for early warning a battery system, electronic equipment and a storage medium, wherein the method and the device are disclosed in China patent application publication number CN115079030A, and the method and the device are used for acquiring current actual environment parameters and actual early warning parameters of the battery system; the actual early warning parameters comprise the current battery expansion force and the battery expansion force change rate; searching early warning thresholds under all environmental parameters according to the actual environmental parameters to obtain the early warning thresholds under the actual environmental parameters; the early warning threshold under each environmental parameter is obtained by constructing a battery system simulation model, selecting a plurality of environmental parameters to input the battery system simulation model, and performing simulation test; and if at least one actual early warning parameter exceeds the corresponding early warning threshold value under the actual environment parameter, executing alarm processing. According to the application, the initial pretightening force, the battery temperature, the battery charge state and the battery health degree of the battery are input into the battery system simulation model to obtain the working parameter value of the battery system in normal working, because the battery charging and discharging involve complex chemical and physical processes, the accurate estimation of the SOC and the SOH is very complex and difficult under different working conditions, the internal structure of the battery is gradually aged along with the accumulation of the service time and the influence of the environment, the SOC and the SOH also change, the initial SOC and the SOH cannot represent the current real level of the battery, the current real level of the battery needs to be updated in time, otherwise, the inaccurate environment parameter value can cause great error on the estimation result of the expansion force predicted value; the multiple groups of environment parameters disclosed by the application are only part of experimental data with limited quantity under limited conditions, and cannot accurately represent the actual mathematical rules among the initial pretightening force, the battery temperature, the battery state of charge, the battery health and the expansion force of the battery, so that the effect of accurately estimating the expansion force cannot be achieved; in addition, most of large-scale energy storage batteries are used in large-scale factories, and work interruption of the large-scale energy storage batteries can cause interruption of engineering projects in the process of the factories to influence production of the factories, so that the false judgment rate of early warning needs to be reduced under the actual application scene, and stable running of the engineering of the factories is ensured.
Disclosure of Invention
To achieve the above and other advantages and in accordance with the purpose of the present application, a first object of the present application is to provide a thermal runaway warning method of a large-sized energy storage battery, comprising the steps of:
s1, receiving the temperature of the battery cell in the energy storage battery detected by the temperature detection unit and the voltage of the battery cell in the energy storage battery detected by the voltage detection unit in real time;
s2, acquiring the current running state of the battery cell in the energy storage battery;
s3, taking the running state, the received voltage and the received temperature as input of an expansion force prediction model to obtain a corresponding expansion force value in the current state;
s4, receiving the expansion force value of the battery core in the energy storage battery detected by the pressure detection unit in real time;
s5, comparing the received expansion force value with the corresponding expansion force value in the current state, and calculating to obtain an expansion force difference value;
s6, judging whether the expansion force difference value is larger than a first threshold value;
s7, otherwise, taking the running state, the received voltage, the temperature and the expansion force value as input of a battery cell SOC-SOH prediction model, obtaining corresponding SOC and SOH, and updating the current SOC and SOH of the battery cell;
s8, judging whether the expansion force difference value is larger than a second threshold value or not;
s9, otherwise counting the abnormal times, jumping to the step S1, repeatedly executing until the accumulated abnormal times exceed a preset value, and carrying out battery cell abnormality alarm;
and S10, if yes, alarming for abnormal battery cell.
Further, the step S4 includes the steps of:
dividing the test position of the battery cell into a plurality of subareas;
establishing a corresponding relation between each sub-area of the battery cell and a sub-unit in a test pattern of the battery cell;
and receiving the expansion force values acquired by the pressure sensors corresponding to each sub-area of the battery cell in real time.
Further, the step S4 further includes the following steps:
displaying the expansion force corresponding to the subarea of the battery cell in a color mode in a subunit in a test pattern of the battery cell; wherein the expansion force is displayed in the same color, and the magnitude of the expansion force has a positive correlation with the shade of the color.
Further, the step S5 further includes the steps of:
comparing the received expansion force value corresponding to each sub-area of the battery cell with the expansion force value corresponding to the current state, calculating to obtain the expansion force difference value of each sub-area of the battery cell, and taking the maximum expansion force difference value as the final expansion force difference value.
Further, the construction of the expansion force prediction model in the step S3 includes the following steps:
controlling the temperatures of a plurality of groups of SOC and SOH cell samples;
performing charge and discharge tests on the electric core sample in various preset states, and recording the expansion force of the electric core;
processing the temperature, voltage and expansion force of the battery cell to generate a database;
in a database, counting the distribution of the expansion force of the battery cell in a temperature and voltage two-dimensional space;
fitting the distribution trend of the expansion force of the battery cell in the temperature and voltage two-dimensional space to obtain an expansion force prediction model.
Further, the construction of the battery cell SOC-SOH model in the step S7 comprises the following steps:
judging whether the temperature, voltage and expansion force of the power core are effective or not;
otherwise, jumping to the temperature step of controlling a plurality of groups of SOC and SOH cell samples to continue to be executed;
if yes, in a database, counting the distribution of the SOC and the SOH in the three-dimensional space of temperature, voltage and expansion force;
calculating partial differentiation of the temperature, the voltage and the expansion force on the SOC and the SOH respectively;
and fitting the SOC and the SOH with the corresponding partial differential calculation results to obtain a battery cell SOC-SOH prediction model.
Further, before the step of distributing the statistical SOCs and SOHs in the three-dimensional space of temperature, voltage and expansion force, the method further comprises the following steps of:
if the battery cell is at the charge and discharge end, collecting expansion force data and judging whether the graphic data is complete;
if yes, jumping to the step of counting the distribution of the SOH and the SOC in the three-dimensional space of temperature, voltage and expansion force;
otherwise, jumping to the step of collecting the expansion force data and judging whether the graphic data is complete;
the judging process of the battery cell at the charge and discharge terminal comprises the following steps:
calculating the expansion force change rate of each sub-area of the battery cell, wherein the expansion force change rate of each sub-area of the battery cell is the differential of the acquired expansion force and capacity of each sub-area of the battery cell;
displaying the calculated expansion force change rate of each sub-area of the battery cell in a corresponding sub-unit in a test pattern of the battery cell;
acquiring the running state of the battery cell;
judging whether the expansion force change rate in the charged state of the power core is less than 0;
if yes, determining that the battery cell is at the charging end;
otherwise, judging that the battery cell is not at the charging end;
judging whether the change rate of the expansion force in the discharging state of the power core is larger than a preset value or not;
if yes, determining that the battery cell is at the discharge end;
otherwise, determining that the battery cell is not at the discharge end
The step of judging whether the graphic data is complete comprises the following steps:
judging whether all the subunits displayed in color form a complete test pattern of the battery cell;
if yes, judging that the graphic data is complete;
otherwise, judging that the graphic data is incomplete.
A second object of the present application is to provide an electronic device including: a memory having program code stored thereon; a processor coupled to the memory and which, when executed by the processor, implements the above-described method.
A third object of the present application is to provide a computer readable storage medium having stored thereon program instructions which, when executed, implement the above-described method.
The application provides a thermal runaway early warning system for a large-sized energy storage battery, which comprises a main controller, a voltage detection unit, a temperature detection unit, a pressure detection unit and a communication isolation unit, wherein the main controller is communicated with external equipment through the communication isolation unit, the voltage detection unit is used for detecting the voltage of a battery cell, the temperature detection unit is used for detecting the temperature of the battery cell, the pressure detection unit is used for detecting the expansion force of the battery cell, the main controller is used for storing an expansion force prediction model and a battery cell SOC-SOH prediction model, processing acquired data, updating the current SOC and SOH of the battery cell through the acquired data, the expansion force prediction model and the battery cell SOC-SOH prediction model and carrying out abnormal battery cell alarm.
Compared with the prior art, the application has the beneficial effects that:
the application provides a thermal runaway early warning method and system of a large energy storage battery, electronic equipment and medium, which can monitor the expansion force of a battery cell, establish a battery cell database for automatic parameter learning, and serve as calibration and comparison of later data; the system can monitor and analyze data in real time, respond to the data requirement of the main controller, actively report abnormal states, greatly promote accurate SOC/SOH prediction of the system state, confirm the safety state of the current system in real time, and protect the navigation for the system safety; the expansion force monitoring reaction is quick, the acquisition range is wide, the precision is high, the reliability is high, the method is very suitable for the application of large-scale energy storage products, and the target requirement for improving the safety of the products is met.
The application takes the current running state, the collected voltage and the collected temperature of the battery core as the input of the expansion force prediction model, wherein the acquisition of the running state, the voltage and the temperature parameters is more direct and more accurate than the acquisition of the initial pretightening force, the charge state and the health degree of the battery, thereby avoiding larger error of the estimation result of the expansion force prediction value, realizing the technical effect of early intervention for safety protection before the thermal runaway of the energy storage battery and preventing large-scale thermal failure.
The expansion force prediction model disclosed by the application is used for grasping the internal regularity of expansion force, temperature and voltage on the basis of deeply researching the theoretical relationship between the expansion force and the temperature and voltage, and carrying out mathematical statistics fit on the regularity so as to realize the effect of accurately predicting the expansion force.
On the basis of further researching theoretical relations among the SOC and the SOH, the temperature, the voltage and the expansion force, the application grasps the intrinsic regularity of the SOC and the SOH, the temperature, the voltage and the expansion force, carries out mathematical statistics, data processing and fitting on the regularity, realizes accurate prediction and updating of the SOC and the SOH, realizes accurate monitoring of the expansion force of the battery core, and simultaneously takes account of updating and calibration of the SOC and the SOH, has important significance for safe and stable operation of the whole large-scale energy storage battery, and protects the system for safe driving and navigation.
According to the application, the early warning threshold is graded according to theoretical basis and actual experience, so that the early warning threshold is inserted in advance for safety protection before thermal runaway of the energy storage battery is ensured, and the false judgment rate is further reduced on the premise of preventing large-scale failure, so that the stable operation of factory engineering is ensured.
The application monitors the expansion force change of different positions of the surface of the battery core in the charge and discharge process, further analyzes the expansion force change trend of each subarea, quantitatively characterizes the expansion force distribution difference of the surface of the battery core, more accurately and timely finds the expansion force change degree of the battery core, timely and accurately warns the occurrence of thermal runaway, and carries out safety protection in advance to prevent large-scale thermal failure.
The foregoing description is only an overview of the present application, and is intended to provide a better understanding of the present application, as it is embodied in the following description, with reference to the preferred embodiments of the present application and the accompanying drawings. Specific embodiments of the present application are given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 is an internal schematic view of an energy storage battery of example 1;
FIG. 2 is a schematic diagram of a thermal runaway warning system of a large energy storage battery according to embodiment 1;
FIG. 3 is a schematic diagram of a thermal runaway warning system of a large energy storage battery according to embodiment 1;
FIG. 4 is a flow chart of a thermal runaway warning method for a large energy storage battery according to embodiment 2;
FIG. 5 is a flow chart showing the construction of an expansion force prediction model according to example 2;
FIG. 6 is a graph of voltage versus expansion force for example 2;
FIG. 7 is a graph of capacitance versus expansion force for example 2;
FIG. 8 is a diagram of a plurality of sub-regions into which the cell test locations of example 2 are divided;
FIG. 9 is a graph showing the variation of the subunits in the cell test pattern during the charging process of example 2;
fig. 10 is a schematic diagram of an electronic device of embodiment 3;
fig. 11 is a schematic diagram of a computer storage medium according to embodiment 4.
Detailed Description
The present application will be further described with reference to the accompanying drawings and detailed description, wherein it is to be understood that, on the premise of no conflict, the following embodiments or technical features may be arbitrarily combined to form new embodiments.
In the following description, suffixes such as "module", "component", or "unit" for representing elements are used only for facilitating the description of the present application, and have no specific meaning per se. Thus, "module," "component," or "unit" may be used in combination.
Experiments prove that under different working conditions of the battery, the expansion force is more sensitive to the change of the SOC and the SOH than the voltage. Therefore, the application provides a method for estimating the SOC and the SOH according to the expansion force of the battery and carrying out the battery thermal runaway early warning, and the problem of non-monotonic change of the input parameters of the battery core and the SOC and the SOH can be solved by constructing the battery core SOC-SOH prediction model, so that the estimation of the SOC and the SOH of the battery under different environment temperatures, voltages and expansion forces is realized; by constructing the expansion force prediction model, the risk can be perceived in time before the battery fails, the dynamic adjustment is quick, the acquisition range is wide, the adaptability is strong, and the accuracy and the reliability are high.
Example 1
The thermal runaway early warning system of the large-scale energy storage battery comprises a main controller, a voltage detection unit, a temperature detection unit, a pressure detection unit, a communication isolation unit, a voltage regulation circuit and a signal processing module, wherein the voltage regulation circuit is used for adapting different input voltages to make corresponding design adjustment and outputting power supply system components, the communication isolation unit adopts communication isolation and IO operation isolation devices including, but not limited to UART, SPI, IIC and the like, the main controller is communicated with external equipment through the communication isolation unit, the voltage detection unit is used for detecting the voltage of a battery core, the temperature detection unit is used for detecting the temperature of the battery core, the pressure detection unit is used for detecting the expansion force of the battery core, the signal processing module uses a basic element diode, an operational amplifier and a resistance-capacitance parameter to realize the functions of filtering and protecting signals, a resistance voltage division circuit, a differential acquisition function, a program-controlled amplifier and the like, the main controller is used for storing an expansion force prediction model and a battery core SOC-SOH prediction model, processing acquired data, and updating and alarming the current SOC and SOH of the battery core through the acquired data, the expansion force prediction model and the battery core SOH prediction model.
The large-scale energy storage battery adopts a multi-cell serial-parallel connection mode, and 100% equalization of charging and discharging is impossible due to individual differences of cells. Aiming at the expansion force monitoring requirement of the battery cell, the embodiment performs real-time pressure distribution measurement on the expansion force of the battery cell through a high-precision pressure sensor. And (3) placing the film sensor with the corresponding size on the soft package battery core, then fixing the clamp, and performing charge-discharge cycle test on the battery core to obtain the expansion force value and expansion pressure distribution condition of the battery core on the fixed clamp each time, thereby providing analysis data for the updating of the SOC and SOH of the energy storage battery and the early warning of thermal runaway. The specific thermal runaway early warning method of the large energy storage battery can refer to the corresponding description in the following method embodiments, and will not be described in detail herein.
And the expansion force generated when the battery cell expands is unevenly distributed due to uneven concentration distribution in the battery. In this embodiment, the test positions of the cells are divided into a plurality of sub-areas, as shown in fig. 8. The film pressure sensor can be used for realizing expansion force change of different subareas of the test position of the battery cell in the charging and discharging process. By monitoring the expansion force changes of different positions of the surface of the battery cell in the charge and discharge process and further analyzing the expansion force change trend of each sub-region, the expansion force distribution difference of the surface of the battery cell can be quantitatively represented, the expansion force change degree of the battery cell can be more accurately and timely found, the occurrence of thermal runaway can be timely and accurately early-warned, the safety protection can be carried out in advance, and the occurrence of large-scale thermal failure can be prevented.
According to the application, the acquisition dimension is newly increased, the system safety is improved, and the full life cycle failure cost is reduced; the multidimensional data is checked, and the precision and the accuracy of the system state parameters are improved; the self-learning parameters provide failure early warning and thermal runaway early warning, intervene security protection in advance, prevent large-scale failure from happening, solve traditional detection scheme unable timely perception risk before the inefficacy, have obvious time difference and detect the error problem. The application has the advantages of rapid dynamic adjustment, wide acquisition range, strong adaptability, high precision and high reliability, is very suitable for the application and popularization of the energy storage large-scale battery system, and improves the safety of products.
Example 2
The thermal runaway warning method of the thermal runaway warning system of the large-sized energy storage battery can refer to corresponding description in the embodiment of the system for the detailed description of the system, and the detailed description is omitted herein. As shown in fig. 4, the method comprises the following steps:
s1, a main controller receives the temperature of a battery cell in an energy storage battery detected by a temperature detection unit and the voltage of the battery cell in the energy storage battery detected by a voltage detection unit in real time; in this embodiment, the main controller sends an acquisition instruction to the detection unit; the detection unit reports the acquired voltage and temperature data to the main controller.
S2, acquiring the current running state of a battery core in the energy storage battery, wherein the running state comprises a charging state and a discharging state;
s3, taking the running state, the received voltage and the received temperature as input of an expansion force prediction model to obtain a corresponding expansion force value in the current state; as shown in fig. 5, the construction of the expansion force prediction model includes the following steps:
s31, newly-built battery cell data;
s32, preparing a plurality of groups of SOC and SOH circuit core samples;
s33, controlling the temperatures of a plurality of groups of SOC and SOH cell samples;
s34, performing charge and discharge tests on the electric core sample in various preset states;
s35, simultaneously recording the expansion force of the battery cell, wherein the preset state comprises a preset temperature and a preset charge-discharge multiplying power;
s36, processing the temperature, voltage and expansion force of the battery cell to generate a database;
s37, in a database, counting the distribution of the expansion force of the battery cell in a temperature and voltage two-dimensional space;
and S38, fitting the distribution trend of the expansion force of the battery core in a temperature and voltage two-dimensional space, for example, fitting by adopting a least square method, so as to obtain an expansion force prediction model.
S4, receiving the expansion force value of the battery core in the energy storage battery detected by the pressure detection unit in real time; specifically, the step S4 includes the steps of:
as shown in fig. 8, the testing position of the battery cell is divided into a plurality of sub-areas, and each sub-area corresponds to one pressure sensor and is used for realizing expansion force detection of different sub-areas of the testing position of the battery cell in the charging and discharging processes.
Establishing a corresponding relation between each sub-area of the battery cell and a sub-unit in a test pattern of the battery cell; the test pattern subunits of the cell are shown in FIG. 9; FIG. 9 is a schematic diagram showing the change in expansion force of each sub-region during charging, and the discharging process is opposite to that shown in FIG. 9; in fig. 9, the darker the color of the sub-cell represents a greater expansion force, and the expansion force of the cell middle region increases significantly with an increase in SOC; during discharge, the SOC gradually decreases, and the expansion force corresponding to each sub-region gradually decreases.
Receiving expansion force values acquired by pressure sensors corresponding to each sub-area of the battery cell in real time;
displaying the expansion force corresponding to the subarea of the battery cell in a color mode in a subunit in a test pattern of the battery cell; wherein the expansion force is displayed in the same color, and the magnitude of the expansion force has a positive correlation with the shade of the color. Through the steps, the distribution condition and the difference of the expansion force on the surface of the battery cell can be quantitatively represented.
S5, comparing the received expansion force value with the corresponding expansion force value in the current state, and calculating to obtain an expansion force difference value; because SOH has relativity with expansion force and predictable change range/speed, when the expansion force exceeds reference data learned by the battery cell, the safety exception handling state is entered. Specifically, the expansion force value corresponding to each sub-area of the received battery cell is compared with the expansion force value corresponding to the current state, the expansion force difference value of each sub-area of the battery cell is calculated, and the maximum expansion force difference value is used as the final expansion force difference value.
S6, judging whether the expansion force difference value is larger than a first threshold value, if so, setting the first threshold value to be 10%;
s7, otherwise, taking the running state, the acquired voltage, temperature and expansion force data as input of a battery cell SOC-SOH prediction model, obtaining corresponding SOC and SOH, and updating the current SOC and SOH of the battery cell; the construction of the battery cell SOC-SOH model comprises the following steps:
judging whether the temperature, the voltage and the expansion force of the power core are effective or not, wherein some acquired data are not in a normal range due to sampling reasons, eliminating the data in order to avoid the influence of the data on the accuracy of a subsequent calculation result, and continuously acquiring new data to be provided for a subsequent step for processing;
otherwise, jumping to control the temperature steps of a plurality of groups of SOC and SOH cell samples to continue to execute;
if yes, in a database, counting the distribution of the SOC and the SOH in the three-dimensional space of temperature, voltage and expansion force; wherein the voltage/SOC versus expansion force is shown in fig. 6. The change in cell expansion force is correlated to capacity, i.e., SOH has a correlation to expansion force, as shown in fig. 7, from the relationship between expansion force versus capacity decay for square cells, it can be seen that at capacities below 80%, the rate of increase in cell expansion force increases rapidly, and before it increases more slowly.
Calculating partial differentiation of the temperature, the voltage and the expansion force on the SOC and the SOH respectively;
and fitting the SOC and the SOH with the corresponding partial differential calculation results to obtain a battery cell SOC-SOH prediction model, and if fitting is performed by adopting a polynomial fitting algorithm, obtaining the battery cell SOC-SOH prediction model.
In order to obtain the expansion force data of all the subareas of the battery cell, the step of counting the distribution of the SOC and the SOH in the three-dimensional space of temperature, voltage and expansion force further comprises the following steps:
if the battery cell is at the charge and discharge end, collecting expansion force data and judging whether the graphic data is complete;
if yes, jumping to a step of counting the distribution of the SOH and the SOH in the temperature, voltage and expansion force three-dimensional space;
otherwise, jumping to the step of collecting the expansion force data and judging whether the graphic data is complete.
As the expansion force of the battery cell has the same change trend along with the charge and discharge process, the change trend sequentially comprises a rapid growth stage, a slow growth stage, a rapid growth stage and a slow fluctuation stage, and the rising amplitude values of the expansion force are obviously different. Therefore, the step of judging whether the battery cell is at the end of charge and discharge specifically includes the following steps:
calculating the expansion force change rate of each sub-area of the battery cell, wherein the expansion force change rate of each sub-area of the battery cell is the differential of the acquired expansion force and capacity of each sub-area of the battery cell;
displaying the calculated expansion force change rate of each sub-area of the battery cell in a corresponding sub-unit in a test pattern of the battery cell;
acquiring the running state of the battery cell;
judging whether the expansion force change rate in the charged state of the power core is less than 0;
if yes, determining that the battery cell is at the charging end;
otherwise, judging that the battery cell is not at the charging end;
judging whether the change rate of the expansion force in the discharging state of the power core is larger than a preset value, if so, setting to be 2;
if yes, determining that the battery cell is at the discharge end;
otherwise, determining that the battery cell is not at the discharge end
Wherein, judge whether the figure data is complete including the following steps:
judging whether all the subunits displayed in color form a complete test pattern of the battery cell;
if yes, judging that the graphic data is complete;
otherwise, judging that the graphic data is incomplete.
S8, judging whether the expansion force difference value is larger than a second threshold value, if so, setting the second threshold value to 20%;
s9, otherwise, counting the abnormal times, jumping to the step S1, repeatedly executing until the accumulated abnormal times exceed a preset value, and carrying out battery cell abnormality alarm;
and S10, if yes, alarming for abnormal battery cell.
Example 3
An electronic device, as shown in fig. 10, comprising: a memory having program code stored thereon; a processor coupled to the memory and which when executed by the processor, implements the method described above. For detailed description of the method, reference may be made to corresponding descriptions in the above method embodiments, and details are not repeated here.
Example 4
A computer readable storage medium having stored thereon program instructions that when executed implement the above method as shown in fig. 11. For detailed description of the method, reference may be made to corresponding descriptions in the above method embodiments, and details are not repeated here.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description is illustrative of embodiments of the present disclosure and is not to be construed as limiting one or more embodiments of the present disclosure. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, or the like, which are within the spirit and principles of one or more embodiments of the present disclosure, are intended to be included within the scope of the claims of one or more embodiments of the present disclosure.
Claims (8)
1. The thermal runaway early warning method of the large energy storage battery is characterized by comprising the following steps of:
s1, receiving the temperature of the battery cell in the energy storage battery detected by the temperature detection unit and the voltage of the battery cell in the energy storage battery detected by the voltage detection unit in real time;
s2, acquiring the current running state of the battery cell in the energy storage battery;
s3, taking the running state, the received voltage and the received temperature as input of an expansion force prediction model to obtain a corresponding expansion force value in the current state;
s4, receiving the expansion force value of the battery core in the energy storage battery detected by the pressure detection unit in real time;
s5, comparing the received expansion force value with the corresponding expansion force value in the current state, and calculating to obtain an expansion force difference value;
s6, judging whether the expansion force difference value is larger than a first threshold value;
s7, otherwise, taking the running state, the received voltage, the temperature and the expansion force value as input of a battery cell SOC-SOH prediction model, obtaining corresponding SOC and SOH, and updating the current SOC and SOH of the battery cell;
s8, judging whether the expansion force difference value is larger than a second threshold value or not;
s9, otherwise counting the abnormal times, jumping to the step S1, repeatedly executing until the accumulated abnormal times exceed a preset value, and carrying out battery cell abnormality alarm;
s10, if yes, alarming for abnormality of the battery cell;
the construction of the expansion force prediction model in the step S3 comprises the following steps:
controlling the temperatures of a plurality of groups of SOC and SOH cell samples;
performing charge and discharge tests on the electric core sample in various preset states, and recording the expansion force of the electric core;
processing the temperature, voltage and expansion force of the battery cell to generate a database;
in a database, counting the distribution of the expansion force of the battery cell in a temperature and voltage two-dimensional space;
fitting the distribution trend of the expansion force of the battery cell in a temperature and voltage two-dimensional space to obtain an expansion force prediction model;
the construction of the battery cell SOC-SOH model in the step S7 comprises the following steps:
judging whether the temperature, voltage and expansion force of the power core are effective or not;
otherwise, jumping to the temperature step of controlling a plurality of groups of SOC and SOH cell samples to continue to be executed;
if yes, in a database, counting the distribution of the SOC and the SOH in the three-dimensional space of temperature, voltage and expansion force;
calculating partial differentiation of the temperature, the voltage and the expansion force on the SOC and the SOH respectively;
and fitting the SOC and the SOH with the corresponding partial differential calculation results to obtain a battery cell SOC-SOH prediction model.
2. The thermal runaway warning method of a large energy storage battery according to claim 1, wherein the step S4 includes the steps of:
dividing the test position of the battery cell into a plurality of subareas;
establishing a corresponding relation between each sub-area of the battery cell and a sub-unit in a test pattern of the battery cell;
and receiving the expansion force values acquired by the pressure sensors corresponding to each sub-area of the battery cell in real time.
3. The thermal runaway warning method of a large energy storage battery according to claim 2, wherein the step S4 further comprises the steps of:
displaying the expansion force corresponding to the subarea of the battery cell in a color mode in a subunit in a test pattern of the battery cell; wherein the expansion force is displayed in the same color, and the magnitude of the expansion force has a positive correlation with the shade of the color.
4. The thermal runaway warning method of a large energy storage battery according to claim 2, wherein the step S5 further comprises the steps of:
comparing the received expansion force value corresponding to each sub-area of the battery cell with the expansion force value corresponding to the current state, calculating to obtain the expansion force difference value of each sub-area of the battery cell, and taking the maximum expansion force difference value as the final expansion force difference value.
5. The thermal runaway warning method of a large energy storage battery according to claim 1, further comprising the following steps before the step of distributing the statistical SOC, SOH in a three-dimensional space of temperature, voltage, expansion force:
if the battery cell is at the charge and discharge end, collecting expansion force data and judging whether the graphic data is complete;
if yes, jumping to the step of counting the distribution of the SOH and the SOC in the three-dimensional space of temperature, voltage and expansion force;
otherwise, jumping to the step of collecting the expansion force data and judging whether the graphic data is complete;
the judging process of the battery cell at the charge and discharge terminal comprises the following steps:
calculating the expansion force change rate of each sub-area of the battery cell, wherein the expansion force change rate of each sub-area of the battery cell is the differential of the acquired expansion force and capacity of each sub-area of the battery cell;
displaying the calculated expansion force change rate of each sub-area of the battery cell in a corresponding sub-unit in a test pattern of the battery cell;
acquiring the running state of the battery cell;
judging whether the expansion force change rate in the charged state of the power core is less than 0;
if yes, determining that the battery cell is at the charging end;
otherwise, judging that the battery cell is not at the charging end;
judging whether the change rate of the expansion force in the discharging state of the power core is larger than a preset value or not;
if yes, determining that the battery cell is at the discharge end;
otherwise, judging that the battery cell is not at the discharge end;
the step of judging whether the graphic data is complete comprises the following steps:
judging whether all the subunits displayed in color form a complete test pattern of the battery cell;
if yes, judging that the graphic data is complete;
otherwise, judging that the graphic data is incomplete.
6. An electronic device, comprising: a memory having program code stored thereon; a processor coupled to the memory and which, when executed by the processor, implements the method of any of claims 1-5.
7. A computer readable storage medium, having stored thereon program instructions which, when executed, implement the method of any of claims 1-5.
8. A thermal runaway warning system for a large energy storage battery implementing the method of any one of claims 1-5, characterized by: the battery cell monitoring device comprises a main controller, a voltage detection unit, a temperature detection unit, a pressure detection unit and a communication isolation unit, wherein the main controller is communicated with external equipment through the communication isolation unit, the voltage detection unit is used for detecting the voltage of a battery cell, the temperature detection unit is used for detecting the temperature of the battery cell, the pressure detection unit is used for detecting the expansion force of the battery cell, the main controller is used for storing an expansion force prediction model and a battery cell SOC-SOH prediction model, processing collected data, and updating and abnormal battery cell alarming are carried out on the current SOC and SOH of the battery cell through the collected data, the expansion force prediction model and the battery cell SOC-SOH prediction model.
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