Battery thermal runaway and aging risk early warning method based on battery digital twinborn model

Abstract

The embodiment of the invention discloses a battery thermal runaway and aging risk early warning method based on a battery digital twin model. One specific embodiment of the method comprises the following steps: acquiring operation state parameter information and environment parameter information of an energy storage battery; constructing and calibrating a battery digital twinborn model; based on the electrochemical sub-model, simulating electrochemical reaction and side reaction processes in the charging and discharging process of the battery; simulating an internal heat transfer process of the battery based on the operation state parameter information, the environment parameter information, the heat production quantity information and the heat conduction sub-model; based on the side reaction rate information, the battery internal temperature distribution information, the environmental parameter information and the gas generation and transmission sub-model, battery internal air pressure simulation processing is carried out; generating residual service life information of the battery; and executing battery thermal runaway and aging risk early warning based on the internal pressure distribution information, the battery internal temperature distribution information, the potential distribution information and the battery use residual life information. According to the embodiment, waste of early warning resources is reduced.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of computer technology, and particularly to a battery thermal runaway and aging risk early warning method based on a battery digital twin model. BACKGROUND

[0002] The battery thermal runaway and aging risk early warning based on the battery digital twin model is a technology for predicting and early warning the battery thermal runaway and aging risk. At present, when detecting and early warning the battery thermal runaway and aging risk, a commonly used method is to rely on single parameter monitoring (such as voltage, temperature, etc.) to predict and early warn the battery thermal runaway or aging.

[0003] However, when detecting and early warning the battery thermal runaway and aging risk by using the above method, the following technical problems often exist:

[0004] Relying on single parameter monitoring (such as voltage, temperature, etc.) to predict and early warn the battery thermal runaway or aging lacks global monitoring of the complex physical and chemical processes inside the battery. The battery thermal runaway and aging are not only affected by a single factor (such as temperature or voltage), but also the result of the joint action of multiple factors (such as environmental temperature, the charging and discharging state of the battery, chemical reactions inside the battery, etc.). Single parameter often cannot accurately simulate the mutual influence and feedback between these factors, resulting in low accuracy of the battery thermal runaway and aging risk prediction and waste of early warning resources.

[0005] The above information disclosed in this BACKGROUND section is only for the purpose of enhancing the understanding of the background of the present inventive concepts, and therefore, it can contain information that is not prior art known to those of ordinary skill in the art. SUMMARY

[0006] The summary of the present disclosure is intended to introduce the concepts in a simplified form, which will be described in detail in the following detailed description. The summary of the present disclosure is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0007] Some embodiments of the present disclosure propose a battery thermal runaway and aging risk early warning method, device, electronic equipment and computer readable medium based on a battery digital twin model to solve one or more of the technical problems mentioned in the above BACKGROUND section.

[0008] In a first aspect, some embodiments of the present disclosure provide a battery thermal runaway and aging risk early warning method based on a battery digital twin model, the method comprising: collecting operating state parameter information of a battery and environmental parameter information of an extreme environment in which the battery is located through a sensor group; constructing and calibrating a battery digital twin model based on the operating state parameters, the environmental parameter information, and preset battery physical property parameter information, wherein the battery digital twin model comprises a heat conduction sub-model, an electrochemical sub-model, a gas generation and transmission sub-model, and a material aging sub-model; simulating electrochemical reactions and side reaction processes during battery charging and discharging based on the operating state parameter information and the electrochemical sub-model, to obtain heat generation information, side reaction rate information, potential distribution information, and electrochemical performance information; simulating battery internal heat transfer processes based on the operating state parameter information, the environmental parameter information, the heat generation information, and the heat conduction sub-model, to obtain battery internal temperature distribution information; performing battery internal pressure simulation processing based on the side reaction rate information, the battery internal temperature distribution information, the environmental parameter information, and the gas generation and transmission sub-model, to obtain internal pressure distribution information; generating battery use remaining life information based on the electrochemical performance information, the environmental parameter information, the battery internal temperature distribution information, and the material aging sub-model; and performing battery thermal runaway and aging risk early warning based on the internal pressure distribution information, the battery internal temperature distribution information, the potential distribution information, and the battery use remaining life information.

[0009] In a second aspect, some embodiments of the present disclosure provide a battery thermal runaway and aging risk early warning device based on a battery digital twin model. The device comprises: a collection unit configured to collect operating state parameter information of an energy storage battery and environmental parameter information of an extreme environment in which the energy storage battery is located through a sensor group; a construction unit configured to construct and calibrate a battery digital twin model based on the operating state parameters, the environmental parameter information, and preset battery physical property parameter information, wherein the battery digital twin model comprises a heat conduction sub-model, an electrochemical sub-model, a gas generation and transmission sub-model, and a material aging sub-model; a first simulation unit configured to simulate electrochemical reaction and side reaction processes in a battery charging and discharging process based on the operating state parameter information and the electrochemical sub-model to obtain heat generation information, side reaction rate information, potential distribution information, and electrochemical performance information; a second simulation unit configured to simulate an internal heat transfer process of the battery based on the operating state parameter information, the environmental parameter information, the heat generation information, and the heat conduction sub-model to obtain internal temperature distribution information of the battery; a gas pressure simulation processing unit configured to perform internal gas pressure simulation processing of the battery based on the side reaction rate information, the internal temperature distribution information of the battery, the environmental parameter information, and the gas generation and transmission sub-model to obtain internal pressure distribution information; a generation unit configured to generate battery use remaining life information based on the electrochemical performance information, the environmental parameter information, the internal temperature distribution information of the battery, and the material aging sub-model; and an early warning unit configured to perform battery thermal runaway and aging risk early warning based on the internal pressure distribution information, the internal temperature distribution information of the battery, the potential distribution information, and the battery use remaining life information.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the implementations of the first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method described in any of the implementations of the first aspect.

[0012] The above various embodiments of the present disclosure have the following beneficial effects: by the battery thermal runaway and aging risk early warning method based on the battery digital twin model of some embodiments of the present disclosure, the waste of early warning resources is reduced. Specifically, the reason for the waste of early warning resources is that: relying on single parameter monitoring (such as voltage, temperature, etc.), the prediction and early warning of battery thermal runaway or aging lack global monitoring of the complex physical and chemical processes inside the battery, and the thermal runaway and aging of the battery are not only affected by a single factor (such as temperature or voltage), but also the result of the joint action of multiple factors (for example, environmental temperature, state of charge of the battery, chemical reaction in the battery, etc.). Single parameter often cannot accurately simulate the mutual influence and feedback between these factors, resulting in low accuracy of battery thermal runaway and aging risk prediction, and waste of early warning resources. Based on this, the battery thermal runaway and aging risk early warning method based on the battery digital twin model of some embodiments of the present disclosure, first, the sensor group collects the operating state parameter information of the energy storage battery and the environmental parameter information of the extreme environment where the above energy storage battery is located; based on the above operating state parameters, the above environmental parameter information and the preset battery physical property parameter information, a battery digital twin model is constructed and calibrated, wherein the battery digital twin model includes a thermal conduction sub-model, an electrochemical sub-model, a gas generation and transmission sub-model and a material aging sub-model. Thus, a battery digital twin model capable of comprehensively reflecting the internal multi-physical field coupling state of the battery can be established. Then, based on the above operating state parameter information and the above electrochemical sub-model, the electrochemical reaction and side reaction process in the battery charging and discharging process is simulated to obtain heat generation information, side reaction rate information, potential distribution information and electrochemical performance information. Thus, the micro-electrochemical state inside the battery can be revealed, and the heat generation information, side reaction rate information, potential distribution information and electrochemical performance information can be obtained. Next, based on the above operating state parameter information, the above environmental parameter information, the above heat generation information and the above thermal conduction sub-model, the heat transfer process inside the battery is simulated to obtain the internal temperature distribution information of the battery. Thus, the internal temperature distribution information of the battery can be obtained, which characterizes the real three-dimensional temperature field inside the battery, rather than only the surface temperature. Then, based on the above side reaction rate information, the above internal temperature distribution information of the battery, the above environmental parameter information and the above gas generation and transmission sub-model, the internal pressure simulation processing of the battery is performed to obtain the internal pressure distribution information. The generation and pressure change of the gas inside the battery are the key factors affecting the safety of the battery, especially in the case of thermal runaway. Through pressure simulation, the pressure change inside the battery can be better simulated, and the internal pressure distribution information of the battery can be obtained. Then, based on the above electrochemical performance information, the above environmental parameter information, the above internal temperature distribution information of the battery and the above material aging sub-model, the battery use remaining life information is generated.Therefore, instead of looking at voltage or temperature in isolation, by combining electrochemical performance, environmental parameters, temperature distribution information and material aging sub-models, the battery life prediction can take into account the influence of different factors, thereby obtaining more accurate remaining useful life information. Then, based on the above internal pressure distribution information, the above battery internal temperature distribution information, the above potential distribution information and the above battery remaining useful life information, the battery thermal runaway and aging risk warning is performed. Therefore, the battery thermal runaway and aging risk warning can be performed with the support of multi-dimensional data (internal pressure distribution information, battery internal temperature distribution information, potential distribution information and more accurate battery remaining useful life information). Also because of the comprehensive application of digital twin model and multi-dimensional data (operation state parameter information and environmental parameter information), the shortcomings of single parameter monitoring are made up, not only the electrochemical process, heat conduction, gas generation, material aging and other factors of the battery can be considered comprehensively, but also the dynamic behavior of the battery under different environments can be simulated, thereby providing more accurate thermal runaway and aging prediction, making the accuracy of the battery thermal runaway and aging risk warning higher, reducing the number of missed warnings and false warnings, and reducing the waste of warning resources. BRIEF DESCRIPTION OF DRAWINGS

[0013] The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by describing in detail some embodiments thereof with reference to the attached drawings in which:

[0014] Figure 1 is a flow chart of some embodiments of a battery thermal runaway and aging risk warning method based on a battery digital twin model according to the present disclosure;

[0015] Figure 2 is a structural schematic diagram of some embodiments of a battery thermal runaway and aging risk warning device based on a battery digital twin model according to the present disclosure;

[0016] Figure 3 is a structural schematic diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described below in more detail with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes, and are not intended to limit the scope of protection of the present disclosure.

[0018] In addition, it needs to be noted that only parts related to the present application are shown in the drawings for the convenience of description. The embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0019] It should be noted that the concepts of "first", "second", etc. mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the modification of "one", "multiple" mentioned in the present disclosure is illustrative and not restrictive, and those skilled in the art should understand that unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0021] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present disclosure are only for illustrative purposes, and are not used to limit the scope of the messages or information.

[0022] The present disclosure will be described in detail below with reference to the drawings and in conjunction with embodiments.

[0023] Figure 1 Flow 100 of some embodiments of the battery thermal runaway and aging risk early warning method based on the battery digital twin model according to the present disclosure is shown. The battery thermal runaway and aging risk early warning method based on the battery digital twin model includes the following steps:

[0024] Step 101: Collecting operating state parameter information of the energy storage battery and environment parameter information of the extreme environment in which the energy storage battery is located through a sensor group.

[0025] In some embodiments, the execution subject (e.g., a computing device) of the battery thermal runaway and aging risk early warning method based on the battery digital twin model can collect the operating state parameter information of the energy storage battery and the environmental parameter information of the extreme environment in which the energy storage battery is located through a sensor group. The operating state parameter information can be information reflecting the current working condition and internal state of the battery during charging and discharging. The operating state parameter information can include charging and discharging current values, voltage values, and surface temperatures. The environmental parameter information can be environmental parameters of the external environment surrounding the battery. The environmental parameter information can include, but is not limited to, the following: environmental air pressure, environmental temperature, ultraviolet radiation intensity, sand concentration, and wind speed. The sensor group can be various sensors associated with the energy storage battery. The sensor group can include, but is not limited to, the following: a current sensor, a voltage sensor, a first temperature sensor, an air pressure sensor, a second temperature sensor, an ultraviolet radiation sensor, and a sand concentration sensor. The first temperature sensor can be a temperature sensor installed at a point on the outer surface of the energy storage battery for collecting the surface temperature of the energy storage battery. The second temperature sensor can be a temperature sensor for collecting the temperature of the environment in which the energy storage battery is located. It should be noted that the operating state parameter information of the energy storage battery and the environmental parameter information of the extreme environment in which the energy storage battery is located can be collected in real time through the sensor group. The extreme environment can be a highland desert environment.

[0026] In some optional implementations of some embodiments, the execution subject can collect the operating state parameter information of the energy storage battery and the environmental parameter information of the extreme environment in which the energy storage battery is located through a sensor group by the following steps:

[0027] First, the charging and discharging current values are collected by the current sensor.

[0028] Second, the voltage values of the energy storage battery are collected by the voltage sensor.

[0029] Third, the surface temperature of the energy storage battery is collected by the first temperature sensor.

[0030] Fourth, the charging and discharging current values, the voltage values, and the surface temperature are determined as the operating state parameter information.

[0031] Fifth, the environmental air pressure of the extreme environment in which the energy storage battery is located is collected by the air pressure sensor.

[0032] Sixth, the environmental temperature of the extreme environment is collected by the second temperature sensor.

[0033] Seventh, the ultraviolet radiation intensity of the extreme environment is collected by the ultraviolet radiation sensor.

[0034] In the eighth step, the sand concentration of the extreme environment is collected by the sand concentration sensor.

[0035] In the ninth step, the wind speed of the extreme environment is collected by the wind speed sensor.

[0036] In the tenth step, the collected environmental pressure, environmental temperature, ultraviolet radiation intensity, sand concentration, and wind speed are determined as the environmental parameter information.

[0037] In step 102, a battery digital twin model is constructed and calibrated based on the operating state parameters, the environmental parameter information, and the preset battery physical property parameter information.

[0038] In some embodiments, the execution subject can construct and calibrate a battery digital twin model based on the operating state parameters, the environmental parameter information, and the preset battery physical property parameter information. The battery digital twin model includes a heat conduction sub-model, an electrochemical sub-model, a gas generation and transmission sub-model, and a material aging sub-model. The battery digital twin model is a virtual model that simulates and predicts the behavior of the battery through digital means, and is designed to accurately reflect the performance of the battery under different use environments and working conditions.

[0039] In some optional implementations of some embodiments, the execution subject can construct and calibrate a battery digital twin model based on the operating state parameters, the environmental parameter information, and the preset battery physical property parameter information by the following steps:

[0040] In a first step, a battery digital twin model including a heat conduction sub-model, an electrochemical sub-model, a gas generation and transport sub-model, and a material aging sub-model is created based on preset battery physical property parameter information. The preset battery physical property parameter information can be a set of physical quantity parameters used to construct the digital twin model. The preset battery physical property parameter information can include, but is not limited to, each of the following: geometric structure parameter information, material thermal physical property parameter information, electrochemical property parameter information, side reaction and gas generation parameter information, and material aging mechanism parameter information. The geometric structure parameter information can include, but is not limited to, each of the following: three-dimensional size, electrode thickness, separator thickness, current collector size, shell shape and thickness. The material thermal physical property parameter information can be used to calculate the heat generation, heat transfer and heat dissipation processes inside the battery. The material thermal physical property parameters can include the density, specific heat capacity and thermal conductivity of the materials of each component of the battery. The electrochemical property parameter information can be used to simulate the voltage response, current distribution and reaction rate of the battery. The electrochemical property parameter information can include, but is not limited to, each of the following: open-circuit voltage curve, lithium ion diffusion coefficient, electrolyte conductivity and battery internal resistance. The side reaction and gas generation parameter information can be used to describe the parameters of the side reaction kinetics and its gas products inside the battery. The side reaction and gas generation parameter information can include kinetic parameter information (e.g., Arrhenius formula) and stoichiometric coefficient information (e.g., the type (such as H2, CO, CO2) and molar amount of gas generated per unit side reaction) of the side reaction. The material aging mechanism parameter information can be parameters used to construct the material aging sub-model. The material aging sub-model can be used to simulate the active lithium loss and active material failure processes. The heat conduction sub-model can be a three-dimensional non-steady-state heat conduction model based on the law of conservation of energy and heat transfer theory, used to simulate the heat transfer process inside the battery. The heat conduction sub-model can take the operating state parameter information, the environmental parameter information, and the heat generation quantity information as inputs, and output the battery internal temperature distribution information. The battery internal temperature distribution information can be a three-dimensional temperature field distribution of the energy storage battery. The electrochemical sub-model can be a battery quasi-two-dimensional model that takes the charge and discharge current value or voltage value and surface temperature included in the environmental parameter information as input, and outputs heat generation quantity information, side reaction rate information, potential distribution information, and electrochemical performance information, to simulate the electrochemical reaction and side reaction process during the battery charging and discharging process (for example, the side reaction can be the growth reaction of the solid electrolyte interface (SEI) film). The electrochemical sub-model can feed back the generated heat generation quantity information to the electrochemical sub-model to update its reaction rate parameters, thereby realizing bidirectional coupling between the two sub-models.The above gas generation and transmission sub-model can take the side reaction rate information, the above battery internal temperature distribution information, and the above environmental parameter information as inputs, and take the internal pressure distribution information as an output, to simulate a CFD-DEM coupling model of the battery when the side reaction occurs, the gas generation in the battery, and the pressure change process. The above material aging sub-model can be a mathematical or physical model that can predict the battery use remaining life information based on the electrochemical performance information, the above environmental parameter information, and the above battery internal temperature distribution information.

[0041] In the second step, the created battery digital twin model is calibrated. In practice, the created battery digital twin model can be calibrated by a real-time calibration technology of the digital twin model. As an example, real-world data (i.e., data received in real time during the actual operation of the battery, such as voltage, current, temperature, etc.) can be used to automatically and inversely adjust unknown or variable parameters inside the digital twin model based on a filter-based algorithm, so that the output prediction (such as voltage) of the model infinitely approaches the actual measurement of the sensor.

[0042] In step 103, based on the operating state parameter information and the electrochemical sub-model, the electrochemical reaction and the side reaction process during the charging and discharging process of the battery are simulated to obtain heat generation information, side reaction rate information, potential distribution information, and electrochemical performance information.

[0043] In some embodiments, the above execution subject can simulate the electrochemical reaction and the side reaction process during the charging and discharging process of the battery based on the above operating state parameter information and the above electrochemical sub-model to obtain heat generation information, side reaction rate information, potential distribution information, and electrochemical performance information. The above heat generation information can represent the total heat generated by the battery during operation. The above side reaction rate information can represent the side reaction rate of the battery. The above potential distribution information can represent the potential (voltage) value of the electrode material at different positions inside the energy storage battery relative to the reference electrode (usually metal lithium), which can be represented by three-dimensional spatial distribution data. For example, the above potential distribution information can be "the potential of the point with coordinates (20, 20, 10) in the negative electrode region of the battery (assuming the positive electrode material is lithium cobaltate) relative to the reference electrode is 3.7V; the potential of the point with coordinates (50, 30, 8) is 3.68V; the potential of the point with coordinates (80, 40, 12) is 3.72V." The above electrochemical performance information can include state of charge information, overpotential, and side reaction current. The above state of charge information can be the SOC value of the battery. The above side reaction current can be the equivalent current of the side reaction (for example, the SEI film growth current, which is the current consumed by the reduction of the electrolyte to form a passivation film (SEI film) on the negative electrode surface).

[0044] In some optional implementations of some embodiments, the execution subject can simulate the electrochemical reaction and side reaction process in the battery charging and discharging process based on the running state parameter information and the electrochemical sub-model to obtain the heat generation information, the side reaction rate information, the potential distribution information and the electrochemical performance information:

[0045] In the first step, the charging and discharging current value or the voltage value and the surface temperature included in the environmental parameter information are input into the electrochemical sub-model to simulate the electrochemical reaction and side reaction process in the battery charging and discharging process to obtain the heat generation information, the side reaction rate information, the potential distribution information and the electrochemical performance information, wherein the electrochemical performance information includes the state of charge information, the overpotential and the side reaction current.

[0046] In step 104, the battery internal heat transfer process is simulated based on the running state parameter information, the environmental parameter information, the heat generation information and the heat conduction sub-model to obtain the battery internal temperature distribution information.

[0047] In some embodiments, the execution subject can simulate the battery internal heat transfer process based on the running state parameter information, the environmental parameter information, the heat generation information and the heat conduction sub-model to obtain the battery internal temperature distribution information.

[0048] In some optional implementations of some embodiments, the execution subject can simulate the battery internal heat transfer process based on the running state parameter information, the environmental parameter information, the heat generation information and the heat conduction sub-model to obtain the battery internal temperature distribution information by the following steps:

[0049] In the first step, the surface temperature included in the running state parameter information is determined as a target surface temperature.

[0050] In the second step, the environmental temperature and the wind speed included in the environmental parameter information are determined as a target environmental temperature and a target wind speed, respectively.

[0051] In the third step, the target surface temperature, the target environmental temperature and the target wind speed are input into the heat conduction sub-model to simulate the battery internal heat transfer process to obtain the battery internal temperature distribution information. The battery internal temperature distribution information can represent the temperature at different positions inside the energy storage battery. The battery internal temperature distribution information includes various distribution point temperature information, and each distribution point temperature information in the various distribution point temperature information includes position information and a temperature value. The position information can represent a position inside the battery. The temperature value can be the temperature of the position represented by the position information.

[0052] In step 105, based on the side reaction rate information, the battery internal temperature distribution information, the environmental parameter information, and the gas generation and transport sub-model, a battery internal gas pressure simulation process is performed to obtain internal pressure distribution information.

[0053] In some embodiments, the execution subject can perform a battery internal gas pressure simulation process based on the side reaction rate information, the battery internal temperature distribution information, the environmental parameter information, and the gas generation and transport sub-model to obtain internal pressure distribution information. The internal pressure distribution information can represent the gas pressure at different spatial positions in the battery.

[0054] In some optional implementations of some embodiments, the execution subject can perform a battery internal gas pressure simulation process based on the side reaction rate information, the battery internal temperature distribution information, the environmental parameter information, and the gas generation and transport sub-model to obtain internal pressure distribution information by the following steps:

[0055] First, the gas generation and transport sub-model is configured to perform the following steps:

[0056] In a first sub-step, a set of gas generation coefficient information is obtained from a preset database, wherein each gas generation coefficient information in the set of gas generation coefficient information corresponds to a gas identifier. The preset database can be a relational database. The set of gas generation coefficient information can represent the respective gas generation coefficients corresponding to the respective gases generated by chemical reactions in the battery. Each gas generation coefficient information in the set of gas generation coefficient information represents a gas generation coefficient. The gas generation coefficient represents a constant relationship between the gas product generation rate and the side reaction rate. Under the same side reaction rate condition, the faster the gas generation rate.

[0057] In a second sub-step, for each gas generation coefficient information in the set of gas generation coefficient information, gas molar generation rate information is generated based on the gas generation coefficient information and the side reaction rate information. In practice, the execution subject can determine the product of the gas generation coefficient represented by the gas generation coefficient information and the side reaction rate represented by the side reaction rate information as the gas molar generation rate to be the gas molar generation rate information. As an example, the gas identifier corresponding to the gas generation coefficient can be carbon monoxide (CO), the gas generation coefficient of carbon monoxide (CO) can be 0.3, and the side reaction rate information can be 0.01 mol / (L·s). Then the gas molar generation rate information can be 0.3 x 0.01 mol / (L·s) = 0.003 mol / (L·s).

[0058] In a third sub-step, the generated respective gas molar generation rate information is determined as a set of gas molar generation rate information.

[0059] A fourth sub-step, for each gas molar generation rate information in the set of gas molar generation rate information, the following steps are performed:

[0060] A first sub-step, determining the gas generation coefficient information corresponding to the gas molar generation rate information in the set of gas generation coefficient information as target gas generation coefficient information.

[0061] A second sub-step, determining the gas identifier corresponding to the target gas generation coefficient information as target gas identifier.

[0062] A third sub-step, generating the gas moles of the gas corresponding to the target gas identifier within the time step represented by the preset time step information based on the preset time step information and the gas molar generation rate information. In practice, the execution subject can determine the product of the time length represented by the preset time step information and the gas molar generation rate represented by the gas molar generation rate information as the gas moles within the time step represented by the preset time step information.

[0063] A fourth sub-step, generating the gas pressure distribution information of the gas inside the energy storage battery based on the gas moles, the gas constant, the preset battery volume information and the battery internal temperature distribution information. In practice, for each distribution point temperature information in the battery internal temperature distribution information, the execution subject can determine the temperature value included in the distribution point temperature information as a target temperature value. The location information included in the distribution point temperature information is determined as target location information. Then, the execution subject can determine the product of the gas moles and the gas constant as a first value. Next, the execution subject can determine the product of the first value and the target temperature value as a second value. Then the ratio of the second value to the volume represented by the preset battery volume information is determined as a gas pressure value. Next, the execution subject can determine the target location information and the gas pressure value as distribution point gas pressure information. Finally, the execution subject can determine the determined distribution point gas pressure information as the gas pressure distribution information of the gas inside the energy storage battery.

[0064] In the second step, the internal pressure distribution information is generated based on the ambient pressure included in the environmental parameter information and the generated gas pressure distribution information of each gas in the energy storage battery. In practice, the execution subject can determine at least one distribution point pressure information with the same target position information in each gas pressure distribution information as the distribution point pressure information set. For each distribution point pressure information set determined in each distribution point pressure information set, the execution subject can determine the target position information included in one distribution point pressure information in the distribution point pressure information set as the reference position information. Then, the sum of at least one pressure value included in the distribution point pressure information set can be determined as the total pressure value. After that, the execution subject can determine the reference position information and the total pressure value as the distribution point comprehensive pressure information. Finally, the execution subject can determine each distribution point comprehensive pressure information as the internal pressure distribution information.

[0065] In step 106, the battery use remaining life information is generated based on the electrochemical performance information, the environmental parameter information, the battery internal temperature distribution information, and the material aging sub-model.

[0066] In some embodiments, the execution subject can generate the battery use remaining life information based on the electrochemical performance information, the environmental parameter information, the battery internal temperature distribution information, and the material aging sub-model. In practice, the execution subject can input the electrochemical performance information, the environmental parameter information, and the battery internal temperature distribution information into the material aging sub-model to obtain the battery use remaining life information. The battery use remaining life information includes a battery use remaining life value and a battery life decay curve. The battery use remaining life value can be the time or cycle number that the battery can continue to work effectively until complete failure or performance degradation to a certain extent under the current use state. The material aging sub-model can be a data-driven battery life prediction model that establishes a mapping relationship from the multi-dimensional feature vector (a multi-dimensional vector representing the electrochemical performance information, the environmental parameter information, and the battery internal temperature distribution information) to the battery use remaining life information through a machine learning algorithm. The machine learning algorithm can use a random forest regressor or a neural network regression model.

[0067] In step 107, the battery thermal runaway and aging risk warning is performed based on the internal pressure distribution information, the battery internal temperature distribution information, the potential distribution information, and the battery use remaining life information.

[0068] In some embodiments, the execution subject can perform the battery thermal runaway and aging risk warning based on the internal pressure distribution information, the battery internal temperature distribution information, the potential distribution information, and the battery use remaining life information.

[0069] In the process of solving the problems mentioned in the background by adopting technical solutions, the following problems are often accompanied:

[0070] In the process of battery thermal runaway and aging risk warning, a fixed pressure and temperature threshold (such as pressure > 1Bar alarm, temperature > 60℃ alarm) is often used to perform battery thermal runaway warning. However, the pressure and temperature of the energy storage battery may fluctuate greatly under different working conditions (such as different loads, different environmental temperatures, different charging and discharging modes, etc.). Using a fixed threshold (such as pressure > 1Bar alarm, temperature > 60℃ alarm) may result in false positives or false negatives, wasting warning resources (such as wasting resources consumed by sound and light warnings). (For example, when the battery is in normal high-load operation, the pressure and temperature may exceed the fixed threshold, but this does not mean that the battery has a thermal runaway or aging problem). Moreover, battery thermal runaway is a nonlinear process from the incubation period to the acceleration period, and the heat and gas generated in the early stage (such as SEI film decomposition, electrolyte decomposition) are less, and the pressure and temperature rise slowly. Using a fixed pressure and temperature threshold for battery thermal runaway warning means that when the threshold is reached, the battery has entered an irreversible thermal runaway stage, resulting in a lag in thermal runaway warning.

[0071] In the face of the above technical problems, the inventors decided to adopt the following solutions:

[0072] In some optional implementations of some embodiments, the above execution subject can perform battery thermal runaway and aging risk warning based on the above internal pressure distribution information, the above battery internal temperature distribution information, the above potential distribution information, and the above battery remaining service life information by the following steps:

[0073] First, obtain historical internal pressure distribution information and historical battery internal temperature distribution information from a pre-set database. The time at which the sensor group collects the operating state parameter information and environmental parameter information of the energy storage battery can be determined as the collection time. The historical internal pressure distribution information can be the internal pressure distribution information of the energy storage battery produced before the collection time. The historical battery internal temperature distribution information can be the internal temperature distribution information of the energy storage battery generated before the collection time.

[0074] Second, determine the maximum pressure value in the historical internal pressure distribution information as the historical target pressure value.

[0075] Third, determine the highest temperature value in the historical battery internal temperature distribution information as the historical hot spot temperature value.

[0076] Fourth, determine the maximum pressure value in the internal pressure distribution information as the target pressure value.

[0077] Fifthly, the highest temperature value in the temperature distribution information of the battery is determined as the hotspot temperature value.

[0078] Sixthly, the pressure rise rate information is generated based on the historical target pressure value, the target pressure value and the preset time step information. In practice, in response to determining that the target pressure value is greater than the historical target pressure value, the difference between the target pressure value and the historical target pressure value is determined as a target difference value. Then, the ratio of the target difference value to the time length represented by the preset time step is determined as the pressure rise rate as the pressure rise rate information.

[0079] Seventhly, the temperature rise rate information is generated based on the historical hotspot temperature value, the hotspot temperature value and the preset time step information. In practice, in response to determining that the hotspot temperature value is greater than the historical hotspot temperature value, the difference between the hotspot temperature value and the historical hotspot temperature value is determined as a hotspot difference value. Then, the ratio of the hotspot difference value to the time length represented by the preset time step information is determined as the temperature rise rate as the temperature rise rate information.

[0080] Eighthly, the negative electrode potential corresponding to the preset negative electrode position information in the potential distribution information is determined. The preset negative electrode position information can be a preset position of the negative electrode in the energy storage battery, which can be represented by a coordinate point, for example, the preset negative electrode position information can be (20, 20, 10).

[0081] In the ninth step, based on the hotspot temperature value, the pressure rise rate information, the temperature rise rate information, and the negative electrode potential, the thermal runaway risk level information is generated. In practice, in response to determining that the negative electrode potential is greater than zero, the hotspot temperature value is less than or equal to a preset reference hotspot temperature value, the pressure rise rate represented by the pressure rise rate information is less than or equal to a preset pressure rise rate, and the temperature rise rate represented by the temperature rise rate information is less than or equal to a preset temperature rise rate, a preset normal level is determined as the thermal runaway risk level information. For example, the preset normal level can be "1 level". In response to determining that the negative electrode potential is greater than zero, the hotspot temperature value is greater than the preset reference hotspot temperature value, the pressure rise rate represented by the pressure rise rate information is less than or equal to the preset pressure rise rate, and the temperature rise rate represented by the temperature rise rate information is less than or equal to the preset temperature rise rate, a first preset level is determined as the thermal runaway risk level information. The first preset level can be "2 level". In response to determining that at least two of the negative electrode potential is less than or equal to zero, the hotspot temperature value is greater than the preset reference hotspot temperature value, the pressure rise rate represented by the pressure rise rate information is greater than the preset pressure rise rate, and the temperature rise rate represented by the temperature rise rate information is greater than the preset temperature rise rate, a second preset level is determined as the thermal runaway risk level information. In response to determining that the negative electrode potential is less than or equal to zero, the hotspot temperature value is greater than the preset reference hotspot temperature value, the pressure rise rate represented by the pressure rise rate information is greater than the preset pressure rise rate, and the temperature rise rate represented by the temperature rise rate information is greater than the preset temperature rise rate, a third preset level is determined as the thermal runaway risk level information. It should be noted that the third preset level is greater than the second preset level, and the second preset level is greater than the first preset level.

[0082] In the tenth step, in response to determining that the thermal runaway risk level represented by the thermal runaway risk level information is the first preset level, information representing a thermal runaway risk is sent to a preset maintenance terminal. The information representing a thermal runaway risk can be text information.

[0083] In the eleventh step, in response to determining that the thermal runaway risk level represented by the thermal runaway risk level information is greater than the first preset level, an alarm device corresponding to the energy storage battery is controlled to issue an audible and visual alarm, a cooling device of the energy storage battery is controlled to be turned on, and a fire extinguishing system is triggered to issue a warning. The alarm device can be an audible and visual alarm. The cooling device can be a liquid-cooled energy storage battery cabinet.

[0084] In the twelfth step, in response to determining that the battery use remaining life value included in the battery use remaining life information is less than or equal to a preset life value, warning information representing a battery replacement is sent to a preset maintenance terminal. The preset maintenance terminal can be a terminal device used by a maintenance personnel of the energy storage battery.

[0085] The technical scheme and related content thereof serve as one of the invention points of the embodiments of the present disclosure, and solve the technical problem of "delayed thermal runaway early warning and wasted early warning resources". Factors leading to delayed thermal runaway early warning and wasted early warning resources are often as follows: in the process of battery thermal runaway and aging risk early warning, a fixed pressure and temperature threshold (such as pressure > 1Bar alarm, temperature > 60℃ alarm) is often set to perform battery thermal runaway early warning. However, the pressure and temperature of the energy storage battery may fluctuate greatly under different working conditions (such as different loads, different ambient temperatures, different charging and discharging modes, etc.). The use of a fixed threshold (such as pressure > 1Bar alarm, temperature > 60℃ alarm) may lead to false positives or false negatives, wasting early warning resources (for example, wasting resources consumed by sound and light early warning). (For example, when the battery is in normal high-load operation, the pressure and temperature may exceed the fixed threshold, but this does not mean that the battery has a thermal runaway or aging problem). Moreover, battery thermal runaway is a nonlinear process from the incubation period to the acceleration period, and the heat and gas generated in the early stage (such as SEI film decomposition, electrolyte decomposition) are less, and the pressure and temperature rise slowly. Using a fixed pressure and temperature threshold to perform battery thermal runaway early warning, when the threshold is reached, it means that the battery has entered an irreversible thermal runaway stage, leading to delayed thermal runaway early warning. If the above factors are solved, the timeliness of thermal runaway early warning and the reduction of early warning resource waste can be achieved. In order to achieve this effect, first, the historical internal pressure distribution information and the historical battery internal temperature distribution information corresponding to the above energy storage battery are obtained from the preset database. Thus, the historical internal pressure distribution information and the historical battery internal temperature distribution information of the energy storage battery can be obtained for trend analysis. Then, the maximum pressure value in the historical internal pressure distribution information is determined as the historical target pressure value. After that, the highest temperature value in the historical battery internal temperature distribution information is determined as the historical hotspot temperature value. Thus, the historical target pressure value and the historical hotspot temperature value reflecting the pressure and temperature level of the battery under normal load or specific working conditions can be obtained. Next, the maximum pressure value in the internal pressure distribution information is determined as the target pressure value. The highest temperature value in the battery internal temperature distribution information is determined as the hotspot temperature value. Then, based on the historical target pressure value, the target pressure value, and the preset time step information, pressure rise rate information is generated. Based on the historical hotspot temperature value, the hotspot temperature value, and the preset time step information, temperature rise rate information is generated. Thus, the speed of temperature and pressure rise can be calculated through the above steps to predict whether the battery is likely to enter the thermal runaway stage in advance. Next, the negative electrode potential corresponding to the preset negative electrode position information in the potential distribution information is determined. The negative electrode potential of the battery is closely related to the thermal runaway process of the battery, and thus the negative electrode potential for judging whether the battery has a thermal runaway risk can be obtained.Then, based on the hotspot temperature value, the pressure rise rate information, the temperature rise rate information, and the negative electrode potential, generate thermal runaway risk level information. In this way, according to the change rate of the battery pressure and temperature, i.e., the pressure rise rate information and the temperature rise rate information, the abnormal change can be captured in advance through the change of the rise rate, and the risk level is judged in combination with the multi-parameter fusion (hotspot temperature, negative electrode potential), to obtain thermal runaway risk level information with higher accuracy and earlier thermal runaway risk identification timeliness. Then, in response to determining that the thermal runaway risk level represented by the thermal runaway risk level information is a first preset level, information representing attention to the thermal runaway risk is sent to a preset maintenance terminal. Then, in response to determining that the thermal runaway risk level represented by the thermal runaway risk level information is greater than the first preset level, control the alarm device corresponding to the energy storage battery to issue an audible and visual alarm, control the cooling device of the energy storage battery to start, and trigger the fire fighting system warning. Then, in response to determining that the battery use remaining life value included in the battery use remaining life information is less than or equal to a preset life value, warning information representing replacing the battery is sent to a preset maintenance terminal. In this way, the thermal runaway risk warning (i.e., audible and visual alarm, triggering the fire fighting system warning) can be performed according to the thermal runaway risk level information with higher accuracy and earlier thermal runaway risk identification timeliness. At the same time, when the battery use remaining life value included in the battery use remaining life information is less than or equal to a preset life value, the battery replacement warning is performed. Because the thermal runaway risk level is judged based on the change trend (pressure rise rate, temperature rise rate) and multi-parameter fusion (hotspot temperature, negative electrode potential), rather than relying on a fixed threshold, the early signs of battery thermal runaway can be identified earlier, the normal working condition fluctuations can be distinguished from the real risks, and the false positives and false negatives can be reduced. Furthermore, the accuracy and timeliness of the thermal runaway warning can be improved, and the waste of warning resources can be avoided.

[0086] The above various embodiments of the present disclosure have the following beneficial effects: by the battery thermal runaway and aging risk early warning method based on the battery digital twin model of some embodiments of the present disclosure, the waste of early warning resources is reduced. Specifically, the reason for the waste of early warning resources is that: relying on single parameter monitoring (such as voltage, temperature, etc.), the prediction and early warning of battery thermal runaway or aging lack global monitoring of the complex physical and chemical processes inside the battery, and the thermal runaway and aging of the battery are not only affected by a single factor (such as temperature or voltage), but also the result of the joint action of multiple factors (for example, environmental temperature, state of charge of the battery, chemical reaction in the battery, etc.). Single parameter often cannot accurately simulate the mutual influence and feedback between these factors, resulting in low accuracy of battery thermal runaway and aging risk prediction, and waste of early warning resources. Based on this, the battery thermal runaway and aging risk early warning method based on the battery digital twin model of some embodiments of the present disclosure, first, the sensor group collects the operating state parameter information of the energy storage battery and the environmental parameter information of the extreme environment where the above energy storage battery is located; based on the above operating state parameters, the above environmental parameter information and the preset battery physical property parameter information, a battery digital twin model is constructed and calibrated, wherein the battery digital twin model includes a thermal conduction sub-model, an electrochemical sub-model, a gas generation and transmission sub-model and a material aging sub-model. Thus, a battery digital twin model capable of comprehensively reflecting the internal multi-physical field coupling state of the battery can be established. Then, based on the above operating state parameter information and the above electrochemical sub-model, the electrochemical reaction and side reaction process in the battery charging and discharging process is simulated to obtain heat generation information, side reaction rate information, potential distribution information and electrochemical performance information. Thus, the micro-electrochemical state inside the battery can be revealed, and the heat generation information, side reaction rate information, potential distribution information and electrochemical performance information can be obtained. Next, based on the above operating state parameter information, the above environmental parameter information, the above heat generation information and the above thermal conduction sub-model, the heat transfer process inside the battery is simulated to obtain the internal temperature distribution information of the battery. Thus, the internal temperature distribution information of the battery can be obtained, which characterizes the real three-dimensional temperature field inside the battery, rather than only the surface temperature. Then, based on the above side reaction rate information, the above internal temperature distribution information of the battery, the above environmental parameter information and the above gas generation and transmission sub-model, the internal pressure simulation processing of the battery is performed to obtain the internal pressure distribution information. The generation and pressure change of the gas inside the battery are the key factors affecting the safety of the battery, especially in the case of thermal runaway. Through pressure simulation, the pressure change inside the battery can be better simulated, and the internal pressure distribution information of the battery can be obtained. Then, based on the above electrochemical performance information, the above environmental parameter information, the above internal temperature distribution information of the battery and the above material aging sub-model, the battery use remaining life information is generated.Thus, instead of looking at voltage or temperature in isolation, by combining electrochemical performance, environmental parameters, temperature distribution information, and material aging sub-models, the battery life prediction can take into account the influence of different factors, thereby obtaining more accurate remaining useful life information. Then, based on the above internal pressure distribution information, the above battery internal temperature distribution information, the above potential distribution information, and the above battery remaining useful life information, a battery thermal runaway and aging risk warning is performed. Thus, the battery thermal runaway and aging risk warning can be performed with the support of multi-dimensional data (internal pressure distribution information, battery internal temperature distribution information, potential distribution information, and more accurate battery remaining useful life information). Also because of the comprehensive application of digital twin models and multi-dimensional data (operating state parameter information and environmental parameter information), the shortcomings of single parameter monitoring are made up, not only can the electrochemical process, heat conduction, gas generation, material aging, and other factors of the battery be considered comprehensively, but also the dynamic behavior of the battery under different environments can be simulated, thereby providing more accurate thermal runaway and aging prediction, making the battery thermal runaway and aging risk warning more accurate, reducing the number of missed warnings and false warnings, and reducing the waste of warning resources.

[0087] Further reference Figure 2 , as an implementation of the method shown in each figure, the present disclosure provides some embodiments of a battery thermal runaway and aging risk warning device based on a battery digital twin model, which device embodiments correspond to those method embodiments shown in Figure 1 , and the device can be applied in various electronic devices.

[0088] As Figure 2As shown, the battery thermal runaway and aging risk early warning device 200 of some embodiments based on the battery digital twin model comprises: a collection unit 201, a construction unit 202, a first simulation unit 203, a second simulation unit 204, a gas pressure simulation processing unit 205, a generation unit 206, and an early warning unit 207. Among them, the collection unit 201 is configured to collect the operating state parameter information of the energy storage battery and the environmental parameter information of the extreme environment where the energy storage battery is located through a sensor group; the construction unit 202 is configured to construct and calibrate a battery digital twin model based on the operating state parameters, the environmental parameter information and the preset battery physical property parameter information, wherein the battery digital twin model comprises a heat conduction sub-model, an electrochemical sub-model, a gas generation and transmission sub-model and a material aging sub-model; the first simulation unit 203 is configured to simulate the electrochemical reaction and side reaction process in the battery charging and discharging process based on the operating state parameter information and the electrochemical sub-model, to obtain heat generation information, side reaction rate information, potential distribution information and electrochemical performance information; the second simulation unit 204 is configured to simulate the internal heat transfer process of the battery based on the operating state parameter information, the environmental parameter information, the heat generation information and the heat conduction sub-model, to obtain the internal temperature distribution information of the battery; the gas pressure simulation processing unit 205 is configured to perform internal gas pressure simulation processing based on the side reaction rate information, the internal temperature distribution information of the battery, the environmental parameter information and the gas generation and transmission sub-model, to obtain the internal pressure distribution information; the generation unit 206 is configured to generate battery use remaining life information based on the electrochemical performance information, the environmental parameter information, the internal temperature distribution information of the battery and the material aging sub-model; the early warning unit 207 is configured to perform battery thermal runaway and aging risk early warning based on the internal pressure distribution information, the internal temperature distribution information of the battery, the potential distribution information and the battery use remaining life information.

[0089] It can be understood that the units recorded in the device 200 correspond to the respective steps in the method described above. Figure 1 Therefore, the operations, features and beneficial effects described above for the method also apply to the device 200 and the units contained therein, which will not be described here again.

[0090] Reference is made below to Figure 3 which shows a structural schematic diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.

[0091] As Figure 3As shown, the electronic device 300 can include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301 that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 302 or loaded into a random access memory (RAM) 303 from a storage device 308. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0092] In general, the following devices can be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 can allow the electronic device 300 to communicate wirelessly or wired with other devices to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that not all of the shown devices are required to be implemented or present. More or fewer devices can alternatively be implemented or present. Figure 3 Each block shown in the middle can represent a device or, as desired, multiple devices.

[0093] In particular, processes described above with reference to the flowcharts can be implemented as a computer software program according to some embodiments of the present disclosure. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In some such embodiments, the computer program can be downloaded and installed from a network through the communication devices 309, or installed from the storage devices 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the functions defined in the methods of some embodiments of the present disclosure are performed.

[0094] Note that the computer-readable medium in some embodiments of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example and without limitation, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, the computer-readable signal medium can include a data signal propagated in baseband or propagated as a carrier wave in a propagated data signal, in which the computer-readable program code is contained. Such a propagated data signal can take a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium that is not a storage medium and that can communicate, propagate, or transport program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained in the computer-readable medium can be transmitted using any suitable medium, including but not limited to wire, cable, RF, etc., or any suitable combination of the foregoing.

[0095] In some embodiments, the client, server, or both can communicate using any current known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet, and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any current known or future developed networks.

[0096] The computer readable medium can be contained in the electronic device or exist separately from the electronic device. The computer readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: collect operating state parameter information of the energy storage battery and environmental parameter information of an extreme environment in which the energy storage battery is located through a sensor group; construct and calibrate a battery digital twin model based on the operating state parameter information, the environmental parameter information, and preset battery physical attribute parameter information, wherein the battery digital twin model includes a heat conduction sub-model, an electrochemical sub-model, a gas generation and transmission sub-model, and a material aging sub-model; simulate electrochemical reactions and side reaction processes in a battery charging and discharging process based on the operating state parameter information and the electrochemical sub-model to obtain heat generation information, side reaction rate information, potential distribution information, and electrochemical performance information; simulate an internal heat transfer process of the battery based on the operating state parameter information, the environmental parameter information, the heat generation information, and the heat conduction sub-model to obtain internal temperature distribution information of the battery; perform internal pressure simulation processing of the battery based on the side reaction rate information, the internal temperature distribution information of the battery, the environmental parameter information, and the gas generation and transmission sub-model to obtain internal pressure distribution information; generate battery use remaining life information based on the electrochemical performance information, the environmental parameter information, the internal temperature distribution information of the battery, and the material aging sub-model; and perform battery thermal runaway and aging risk early warning based on the internal pressure distribution information, the internal temperature distribution information of the battery, the potential distribution information, and the battery use remaining life information.

[0097] Computer program code for carrying out operations of some embodiments of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0098] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0099] The units described in some embodiments of the present disclosure can be implemented by means of software, or by means of hardware. The described units can also be arranged in a processor, for example, it can be described that: a processor includes an acquisition unit, a construction unit, a first simulation unit, a second simulation unit, a gas pressure simulation processing unit, a generation unit and a warning unit. Among them, the name of these units does not constitute a limitation to the unit itself in some cases, for example, the acquisition unit can also be described as: "a unit for acquiring the operating state parameter information of the energy storage battery and the environmental parameter information of the extreme environment in which the above-mentioned energy storage battery is located through the sensor group".

[0100] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, example types of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), etc.

[0101] The above description is merely some of the preferred embodiments of the present disclosure and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combinations of technical features, and should also cover other technical solutions formed by any combination of technical features or their equivalent features without departing from the inventive concept. For example, the features are replaced with the technical features disclosed in the embodiments of the present disclosure (but not limited to) having similar functions to form technical solutions.

Claims

1. A method for early warning of battery thermal runaway and aging risks based on a battery digital twin model, comprising: The sensor array collects the operating status parameters of the energy storage battery and the environmental parameters of the extreme environment in which the energy storage battery is located. Based on the operating status parameters, the environmental parameter information, and the preset battery physical property parameter information, a battery digital twin model is constructed and calibrated. The battery digital twin model includes a thermal conduction sub-model, an electrochemical sub-model, a gas generation and transport sub-model, and a material aging sub-model. Based on the operating state parameter information and the electrochemical sub-model, the electrochemical reactions and side reactions during the battery charging and discharging process are simulated to obtain heat generation information, side reaction rate information, potential distribution information and electrochemical performance information; Based on the operating status parameter information, the environmental parameter information, the heat generation information, and the heat conduction sub-model, the heat transfer process inside the battery is simulated to obtain the internal temperature distribution information of the battery. Based on the side reaction rate information, the battery internal temperature distribution information, the environmental parameter information, and the gas generation and transport sub-model, the battery internal pressure simulation is performed to obtain the internal pressure distribution information. Based on the electrochemical performance information, the environmental parameter information, the battery internal temperature distribution information, and the material aging sub-model, the remaining battery life information is generated. Based on the internal pressure distribution information, the internal temperature distribution information of the battery, the potential distribution information, and the remaining lifespan information of the battery, a battery thermal runaway and aging risk warning is performed.

2. The method according to claim 1, wherein, The sensor group includes a current sensor, a voltage sensor, a first temperature sensor, a barometric pressure sensor, a second temperature sensor, an ultraviolet radiation sensor, and a dust concentration sensor. It also collects operational status parameters of the energy storage battery and environmental parameters of the extreme environment in which the energy storage battery is located, including: The charging and discharging current values ​​are acquired through the current sensor; The voltage value of the energy storage battery is acquired by the voltage sensor; The surface temperature of the energy storage battery is collected using the first temperature sensor; The charging / discharging current value, the voltage value, and the surface temperature are determined as operating status parameter information; The atmospheric pressure of the extreme environment in which the energy storage battery is located is collected by the air pressure sensor. The ambient temperature of the extreme environment is collected by the second temperature sensor; The intensity of ultraviolet radiation in the extreme environment is collected by the ultraviolet radiation sensor. The dust concentration in the extreme environment is collected by the dust concentration sensor. The wind speed in the extreme environment is collected by the wind speed sensor. The collected ambient air pressure, ambient temperature, ultraviolet radiation intensity, dust concentration, and wind speed are determined as environmental parameter information.

3. The method according to claim 1, wherein, The process of constructing and calibrating a battery digital twin model based on the operating status parameters, environmental parameter information, and preset battery physical property parameter information includes: Based on preset battery physical property parameters, a digital twin model of the battery is created, which includes a thermal conduction sub-model, an electrochemical sub-model, a gas generation and transport sub-model, and a material aging sub-model. The created battery digital twin model is calibrated.

4. The method according to claim 1, wherein, Based on the operating state parameter information and the electrochemical sub-model, the electrochemical reactions and side reactions during the battery charging and discharging process are simulated to obtain heat generation information, side reaction rate information, potential distribution information, and electrochemical performance information, including: The charging / discharging current or voltage values ​​and surface temperature, which are included in the environmental parameter information, are input into the electrochemical sub-model to simulate the electrochemical reactions and side reactions during the battery charging and discharging process, so as to obtain heat generation information, side reaction rate information, potential distribution information and electrochemical performance information. The electrochemical performance information includes state of charge information, overpotential and side reaction current.

5. The method according to claim 1, wherein, The process of simulating the internal heat transfer process of the battery based on the operating status parameter information, the environmental parameter information, the heat generation information, and the heat conduction sub-model, to obtain the internal temperature distribution information of the battery, includes: The surface temperature included in the operating status parameter information is determined as the target surface temperature; The ambient temperature and wind speed included in the environmental parameter information are respectively determined as the target ambient temperature and the target wind speed; The target surface temperature, the target ambient temperature, and the target wind speed are input into the heat conduction sub-model to simulate the heat transfer process inside the battery and obtain the internal temperature distribution information of the battery.

6. The method according to claim 1, wherein, The process of simulating the internal gas pressure of the battery based on the side reaction rate information, the internal temperature distribution information of the battery, the environmental parameter information, and the gas generation and transport sub-model, to obtain internal pressure distribution information, includes: The gas generation and transport sub-model is configured to perform the following steps: A set of gas generation coefficient information is obtained from a preset database, wherein each gas generation coefficient in the set of gas generation coefficient information corresponds to a gas identifier; For each gas generation coefficient in the gas generation coefficient information set, gas molar generation rate information is generated based on the gas generation coefficient information and the side reaction rate information; The generated gas molar generation rate information is defined as a gas molar generation rate information set; For each gas molar generation rate information in the gas molar generation rate information set, perform the following steps: The gas generation coefficient information corresponding to the gas molar generation rate information in the gas generation coefficient information set is determined as the target gas generation coefficient information; The gas identifier corresponding to the target gas generation coefficient information is determined as the target gas identifier; Based on the preset time step information and the gas mole generation rate information, the number of gas moles corresponding to the target gas identifier within the time step represented by the preset time step information is generated. Based on the number of gas moles, gas constant, preset battery volume information, and battery internal temperature distribution information, the gas pressure distribution information inside the energy storage battery is generated. Based on the environmental parameters, including the ambient air pressure and the pressure distribution information of each gas generated inside the energy storage battery, internal pressure distribution information is generated.

7. A battery thermal runaway and aging risk early warning device based on a battery digital twin model, comprising: The acquisition unit is configured to acquire the operating status parameters of the energy storage battery and the environmental parameters of the extreme environment in which the energy storage battery is located through a sensor array. The construction unit is configured to construct and calibrate a battery digital twin model based on the operating state parameters, the environmental parameter information, and the preset battery physical property parameter information. The battery digital twin model includes a thermal conduction sub-model, an electrochemical sub-model, a gas generation and transport sub-model, and a material aging sub-model. The first simulation unit is configured to simulate the electrochemical reactions and side reactions during the charging and discharging process of the battery based on the operating state parameter information and the electrochemical sub-model, and obtain heat generation information, side reaction rate information, potential distribution information and electrochemical performance information. The second simulation unit is configured to simulate the heat transfer process inside the battery based on the operating status parameter information, the environmental parameter information, the heat generation information, and the heat conduction sub-model, and obtain the internal temperature distribution information of the battery. The gas pressure simulation processing unit is configured to perform internal gas pressure simulation processing of the battery based on the side reaction rate information, the internal temperature distribution information of the battery, the environmental parameter information, and the gas generation and transport sub-model, so as to obtain internal pressure distribution information. The generation unit is configured to generate battery remaining lifespan information based on the electrochemical performance information, the environmental parameter information, the battery internal temperature distribution information, and the material aging sub-model. The early warning unit is configured to perform early warning of battery thermal runaway and aging risks based on the internal pressure distribution information, the battery internal temperature distribution information, the potential distribution information, and the battery remaining service life information.

8. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 6.

9. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 6.

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