A battery detection system, a detection method and a battery structure

Abstract

The application relates to the technical field of battery detection, and discloses a battery detection system, a detection method and a battery structure. First, the application collects the working current and the environmental temperature outside the battery cell, collects the state vector of the precursor quantity of thermal runaway inside the battery cell, collects the terminal voltage, the surface temperature and the air pressure in the battery pack outside the battery cell, and obtains a discrete state transition function and a discrete observation function through discretization processing; according to a spatial model of the discrete state coupling of electrochemistry, heat and gas production, input quantities and observation quantities are collected in real time, Sigma point sampling, time updating and observation updating are sequentially performed, and the state estimation value at the current moment is obtained; and based on the sliding window statistics of the innovation sequence; according to the internal precursor state quantity, the preset three-level early warning threshold is matched, and a thermal runaway early warning signal of the corresponding level is output. The application solves the technical problems that the early internal precursor signal of thermal runaway cannot be captured and the kinetic model simplification error is large in the prior art.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, specifically to a battery testing system, testing method, and battery structure. Background Technology

[0002] With the rapid growth in the number of new energy vehicles, safety accidents caused by thermal runaway of power batteries are occurring frequently. Thermal runaway warning has become a core safety function of battery management systems (BMS). Existing power battery thermal runaway detection and warning technologies mainly suffer from three core defects:

[0003] First, the early warning is significantly delayed. Most existing algorithms rely solely on three types of external macroscopic parameters—voltage, current, and cell surface temperature—for threshold judgment. They cannot capture early internal precursor signals of thermal runaway, such as SEI film decomposition, electrolyte gas generation, and micro-internal short circuits. They can only trigger alarms after thermal runaway has entered the stage of rapid temperature rise, which is essentially a "post-event alarm," leaving insufficient time for drivers and passengers to escape and take action.

[0004] Second, the accuracy of the kinetic model is insufficient. Existing thermal runaway models are generally oversimplified: they mostly use a single-step exothermic reaction to approximate the whole package thermal process, ignore the cascade triggering mechanism and temperature dependence of multi-step side reactions, and do not consider the temperature difference between the core hot spot and the surface inside the cell. This results in large deviations in the calculation of the thermal runaway critical temperature and the exothermic rate. The simulation error of the whole package-level heat propagation can reach more than 35%, which cannot support accurate early warning.

[0005] Therefore, to address the above issues, a battery testing system, testing method, and battery structure are needed. Summary of the Invention

[0006] The purpose of this invention is to provide a battery detection system, detection method, and battery structure. This invention solves the technical problems of existing technologies, such as the inability to capture early internal precursor signals of thermal runaway and large simplification errors in dynamic models.

[0007] This invention is implemented as follows:

[0008] This invention provides a battery testing method, specifically performed according to the following steps:

[0009] The system collects the operating current and ambient temperature outside the battery cell, the state vector of the precursor to thermal runaway inside the battery cell, and the terminal voltage, surface temperature, and gas pressure inside the battery pack outside the battery cell. It establishes continuous state equations and observation equations, including the four-step cascaded exothermic side reaction kinetics, the two-stage thermal equilibrium of the core and surface, and the gas generation kinetics. Discrete state transition functions and discrete observation functions are obtained through discretization, and a spatial model coupling the discrete states of electrochemistry, thermal, and gas generation is constructed.

[0010] Based on the spatial model of discrete state coupling of electrochemistry, thermal and gas production, input quantities and observations are collected in real time, and Sigma point sampling, time update and observation update are performed in sequence to obtain the state estimate at the current moment; and based on the sliding window statistics of the innovation sequence, the process noise covariance and observation noise covariance are adaptively adjusted; the output includes the internal precursor state quantities including core temperature, remaining mass of SEI film, depth of each side reaction and cumulative gas production volume.

[0011] Based on the internal precursor state variables, three types of precursor characteristic indicators—SEI decomposition rate, core temperature rise rate, and gas production rate—are calculated. These are then matched with preset three-level early warning thresholds, and corresponding level thermal runaway early warning signals are output.

[0012] Furthermore, the state vector is a 9-dimensional vector, including the cell's state of charge, cell polarization voltage, cell core temperature, remaining mass of the SEI film, reaction depth of the SEI film decomposition reaction, reaction depth of the reaction between the negative electrode lithium intercalation carbon and the electrolyte, reaction depth of the electrolyte oxidation decomposition reaction, reaction depth of the positive electrode active material decomposition reaction, and the cumulative gas production volume of the cell, expressed as:

[0013] in, For the first The state of charge of the battery cell at any given time; For the first Cell polarization voltage at any given time; For the first Constant temperature of the battery cell core; For the first Remaining mass of the SEI membrane at any given time; The reaction depth of the SEI membrane decomposition reaction; The reaction depth of the lithium-intercalated carbon anode with the electrolyte; The reaction depth of the electrolyte oxidation decomposition reaction; The reaction depth of the positive electrode active material decomposition reaction; For the first The cumulative gas production volume of the battery cell at any given time.

[0014] Furthermore, the four-step cascaded exothermic side reaction kinetics, constructed based on the Arrhenius equation, are expressed in continuous form as follows:

[0015]

[0016] in, For the first Pre-exponential factors of class reactions; For the first Activation energy of a reaction; This is the universal gas constant, with a value of 8.314 J / mol·K; For the first The reaction order of a class of reactions. Furthermore, the continuous state expression for the two-level thermal equilibrium of the core surface is as follows:

[0017]

[0018] Among them, the total heat release rate It comprises three parts: Joule heating, polarization heat, and exothermic reactions in four steps. The expression is:

[0019]

[0020] in, The internal resistance of the battery cell is ohms. For the first The heat released per unit depth of reaction of a side reaction; The equivalent heat transfer coefficient from the core to the surface of the battery cell; The heat exchange surface area of ​​the battery cell; The average density of the battery cells; The specific heat capacity of the battery cell at constant voltage; This refers to the volume of the battery cell.

[0021] Furthermore, based on the continuous state of the two-stage thermal equilibrium at the core surface, the continuous state equation for the two-stage thermal equilibrium at the core surface is constructed as follows:

[0022]

[0023] The total heat release rate It comprises three parts: Joule heating, polarization heat, and exothermic reactions in four steps. The expression is:

[0024]

[0025] In the formula, The internal resistance of the battery cell is in ohms. The heat released per unit reaction depth for the i-th type of side reaction; The equivalent heat transfer coefficient from the core to the surface of the battery cell; The heat exchange surface area of ​​the battery cell; The average density of the battery cells; The specific heat capacity of the battery cell at constant voltage; This refers to the volume of the battery cell.

[0026] Furthermore, the adaptive update expression for the noise covariance is:

[0027]

[0028] in, The length of the sliding window; For the first The information sequence at time point, the difference between the actual observed value and the observed predicted value; For the first Kalman gain at time step.

[0029] Furthermore, the expressions for the three types of precursor characteristic indicators are as follows:

[0030] The expression for the SEI decomposition rate is as follows:

[0031]

[0032] in This represents the mass consumption coefficient per unit reaction depth in the SEI decomposition reaction.

[0033] The expression for the core temperature rise rate is as follows:

[0034] ;

[0035] The gas production rate is expressed as follows:

[0036] ;

[0037] The three-level early warning thresholds were calibrated using an accelerated calorimeter experiment, and the specific determination rules are as follows:

[0038] Level 1 warning: If either the SEI decomposition rate is greater than the first SEI rate threshold or the gas production rate is greater than the first gas production rate threshold, it is determined to be an early hidden risk.

[0039] Level 2 warning: If either the core temperature is greater than the second core temperature threshold or the core temperature rise rate is greater than the second temperature rise rate threshold, it is judged as a medium to high risk of thermal runaway.

[0040] Level 3 warning: If either the core temperature exceeds the third core temperature threshold, or the reaction depth between the lithium-intercalated carbon electrode and the electrolyte exceeds the third reaction depth threshold, it is determined to be a risk of irreversible thermal runaway.

[0041] Furthermore, a battery testing system includes a data acquisition module for real-time acquisition of data on cell operating current, terminal voltage, surface temperature, ambient temperature, and internal air pressure of the battery pack.

[0042] The coupled modeling module is used to construct a discrete state-space model of electrochemical-thermal-gas production coupled with multi-step side reactions, store the intrinsic parameters of the battery cell and model coefficients, and output discrete state transition functions and discrete observation functions.

[0043] An adaptive state estimation module is used to perform adaptive unscented Kalman filtering operations. It takes the data collected by the data acquisition module and the model parameters of the coupled modeling module as inputs and outputs a state estimate containing internal precursors.

[0044] The graded early warning module is used to calculate precursor characteristic indicators based on internal precursor quantities, match preset early warning thresholds, and output corresponding early warning signals and handling instructions.

[0045] Furthermore, the present invention provides a battery structure comprising a battery pack assembled from at least one cylindrical battery, the battery pack being applicable to a battery testing method as described in any one of the above.

[0046] Furthermore, the present invention provides a computer-storable medium storing a computer program, wherein when the program is executed, it sequentially executes any one of the battery detection methods described above.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] 1. This invention constructs a coupled mechanism model that includes SEI decomposition, multi-step side reactions, and gas production processes. It transforms internal precursors that cannot be directly measured into hidden states in the state space. By combining multi-source observation data with AUKF online inversion, it can capture early thermal runaway signals that cannot be identified by traditional external parameters. The warning time is 10-15 minutes earlier than the traditional surface temperature threshold method, realizing a technological upgrade from "post-event alarm" to "pre-event warning".

[0049] 2. This invention couples the Arrhenius kinetics of four-step core exothermic side reactions, introduces a core-surface two-level heat conduction model to consider local hot spot effects, fully considers the temperature dependence of thermal parameters and the reaction cascade characteristics, reduces the calculation error of thermal runaway critical temperature, and greatly improves the accuracy of early warning. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0052] Figure 1 This is a flowchart of the method of the present invention.

[0053] Figure 2 This is a system structure diagram of the present invention;

[0054] Figure 3 This is a structural diagram of the storage medium of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to describe selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1

[0058] Please see Figure 1 This invention provides a battery testing method, specifically performed according to the following steps:

[0059] The system collects the operating current and ambient temperature outside the battery cell, the state vector of the precursor to thermal runaway inside the battery cell, and the terminal voltage, surface temperature, and gas pressure inside the battery pack outside the battery cell. It establishes continuous state equations and observation equations, including the four-step cascaded exothermic side reaction kinetics, the two-stage thermal equilibrium of the core and surface, and the gas generation kinetics. Discrete state transition functions and discrete observation functions are obtained through discretization, and a spatial model coupling the discrete states of electrochemistry, thermal, and gas generation is constructed.

[0060] Based on the spatial model of discrete state coupling of electrochemistry, thermal and gas production, input quantities and observations are collected in real time, and Sigma point sampling, time update and observation update are performed in sequence to obtain the state estimate at the current moment; and based on the sliding window statistics of the innovation sequence, the process noise covariance and observation noise covariance are adaptively adjusted; the output includes the internal precursor state quantities including core temperature, remaining mass of SEI film, depth of each side reaction and cumulative gas production volume.

[0061] Based on the internal precursor state variables, three types of precursor characteristic indicators—SEI decomposition rate, core temperature rise rate, and gas production rate—are calculated. These are then matched with preset three-level early warning thresholds, and corresponding level thermal runaway early warning signals are output.

[0062] In this embodiment, the state vector is a 9-dimensional vector, including the cell's state of charge, cell polarization voltage, cell core temperature, remaining mass of the SEI film, reaction depth of the SEI film decomposition reaction, reaction depth of the reaction between the negative electrode lithium intercalation carbon and the electrolyte, reaction depth of the electrolyte oxidation decomposition reaction, reaction depth of the positive electrode active material decomposition reaction, and the cumulative gas production volume of the cell, expressed as:

[0063]

[0064] in, For the first The state of charge of the battery cell at any given time; For the first Cell polarization voltage at any given time; For the first Constant temperature of the battery cell core; For the first Remaining mass of the SEI membrane at any given time; The reaction depth of the SEI membrane decomposition reaction; The reaction depth of the lithium-intercalated carbon anode with the electrolyte; The reaction depth of the electrolyte oxidation decomposition reaction; The reaction depth of the positive electrode active material decomposition reaction; For the first The cumulative gas production volume of the battery cell at any given time.

[0065] In this embodiment, the four-step cascaded exothermic side reaction kinetics are constructed based on the Arrhenius equation, and the continuous form expression is as follows:

[0066]

[0067] in, For the first Pre-exponential factors of class reactions; For the first Activation energy of a reaction; This is the universal gas constant, with a value of 8.314 J / mol·K; For the first The reaction order of a class of reactions.

[0068] In this embodiment, the continuous state expression of the two-level thermal equilibrium on the core surface is:

[0069]

[0070] Among them, the total heat release rate It comprises three parts: Joule heating, polarization heat, and exothermic reactions in four steps. The expression is:

[0071]

[0072] in, The internal resistance of the battery cell is in ohms. For the first The heat released per unit depth of reaction of a side reaction; The equivalent heat transfer coefficient from the core to the surface of the battery cell; The heat exchange surface area of ​​the battery cell; The average density of the battery cells; The specific heat capacity of the battery cell at constant voltage; This refers to the volume of the battery cell.

[0073] In this embodiment, based on the continuous state of the two-level thermal equilibrium of the core surface, the continuous state equation of the two-level thermal equilibrium of the core surface is constructed as follows:

[0074]

[0075] The total heat release rate It comprises three parts: Joule heating, polarization heat, and exothermic reactions in four steps. The expression is:

[0076]

[0077] In the formula, The internal resistance of the battery cell is in ohms. The heat released per unit reaction depth for the i-th type of side reaction; The equivalent heat transfer coefficient from the core to the surface of the battery cell; The heat exchange surface area of ​​the battery cell; The average density of the battery cells; The specific heat capacity of the battery cell at constant voltage; This refers to the volume of the battery cell.

[0078] In this embodiment, the adaptive update expression for the noise covariance is:

[0079]

[0080] in, The length of the sliding window; For the first The information sequence at time point, the difference between the actual observed value and the observed predicted value; For the first Kalman gain at time step.

[0081] In this embodiment, the expressions for the three types of precursor characteristic indicators are as follows:

[0082] The expression for the SEI decomposition rate is as follows:

[0083]

[0084] in This represents the mass consumption coefficient per unit reaction depth in the SEI decomposition reaction.

[0085] The expression for the core temperature rise rate is as follows:

[0086] ;

[0087] The gas production rate is expressed as follows:

[0088] ;

[0089] The three-level early warning thresholds were calibrated using an accelerated calorimeter experiment, and the specific determination rules are as follows:

[0090] Level 1 warning: If either the SEI decomposition rate is greater than the first SEI rate threshold or the gas production rate is greater than the first gas production rate threshold, it is determined to be an early hidden risk.

[0091] Level 2 warning: If either the core temperature is greater than the second core temperature threshold or the core temperature rise rate is greater than the second temperature rise rate threshold, it is judged as a medium to high risk of thermal runaway.

[0092] Level 3 warning: If either the core temperature exceeds the third core temperature threshold, or the reaction depth between the lithium-intercalated carbon electrode and the electrolyte exceeds the third reaction depth threshold, it is determined to be a risk of irreversible thermal runaway.

[0093] In this embodiment, the specific details are performed according to the following steps: Step S1: Construct a multi-step side-reaction coupled electrochemical-thermal-gas-production discrete state-space model. The core of this step is to transform the internal physicochemical processes of thermal runaway evolution into a standard discrete state-space model that can be used for state observation, providing a mechanistic basis for subsequent filtered observations. The discrete state transition function and discrete observation function output in this step will be directly used as the input to step S2.

[0094] A unified variable system defines consistent data naming and symbol standards throughout the process: System input quantities (externally collectable in real time).

[0095]

[0096] The cell's operating current at time k is positive when charging and negative when discharging, in units of: Ambient temperature at time k, unit: K. System state variables (including internal unmeasurable precursors, to be estimated online):

[0097] (Total 9 dimensions)

[0098] State of charge of the battery cell at time k, dimensionless, range of values. Cell polarization voltage at time k, unit: : Core temperature of the cell at time k, corresponding to the local hot spot temperature where the side reaction actually occurs, unit: K4. :

[0099] The remaining mass of the SEI membrane at time k, in kg. : The reaction depth of the SEI membrane decomposition reaction at time k, dimensionless, 0 = not started, 1 = complete reaction.

[0100] : The reaction depth of the lithium-intercalated carbon at the negative electrode and the electrolyte at time k, dimensionless : The depth of the electrolyte oxidation-decomposition reaction at time k, dimensionless : The depth of the decomposition reaction of the positive electrode active material at time k, dimensionless

[0101] : Cumulative gas production volume of the battery cell at time k, unit: m³

[0102] System observations (measurable by external sensors, used for filter calibration):

[0103]

[0104] Cell terminal voltage at time k, unit: Cell surface temperature at time k, unit: : Internal gas pressure of the battery pack at time k, unit: Pa. 1.2 Continuous-time equation of state construction: Based on electrochemical kinetics, heat conduction, and gas generation laws, differential equations for 9 state variables are established, with a sampling period of... 1. Dynamics of the state of charge (Coulomb counting principle)

[0105] In the formula, Q_n is the rated capacity of the battery cell, in Ah; the negative sign indicates that the SOC decreases during discharge.

[0106] Polarization voltage dynamics (first-order RC polarization circuit)

[0107]

[0108] In the formula, Cell polarization resistance, unit: ; This refers to the cell polarization capacitance, measured in F.

[0109] Core temperature dynamics (core-surface two-level thermal equilibrium)

[0110]

[0111] The total heat release rate It consists of three parts: Joule heating, polarization heat, and four-step exothermic side reactions.

[0112]

[0113] In the formula, The internal resistance of the battery cell in ohms, unit: ; is the heat release per unit reaction depth for the i-th type of side reaction, in J; h is the equivalent heat transfer coefficient from the cell core to the surface, in J. ; The heat exchange surface area of ​​the battery cell is expressed in m². Average cell density, unit: kg / m³; Specific heat capacity at constant voltage of battery cell, unit: Cell volume, unit: m³. 4. SEI membrane mass kinetics

[0114] In the formula, This is the mass consumption coefficient per unit reaction depth of the SEI decomposition reaction, in kg; the negative sign indicates that the mass of SEI decreases during the reaction.

[0115] Four-step side reaction kinetics (Arrhenius equation, coupled with temperature dependence)

[0116] In the formula, The pre-exponential factor for the i-th type of reaction, in units of: ; The activation energy of the i-th type reaction, in units of: ; This is a universal gas constant with a fixed value. i Let be the reaction order of the i-th type of reaction, which is dimensionless.

[0117] Cumulative gas production volume dynamics

[0118]

[0119] In the formula, Let be the gas production volume factor per unit reaction depth for the i-th type of side reaction, in m³. The continuous-time observation equations establish a mapping relationship between state variables and externally measurable quantities: 1. Terminal voltage observation equations

[0120]

[0121] In the formula, Let be the cell open-circuit voltage, and let be a two-dimensional function of SOC and core temperature, which is experimentally calibrated to a polynomial form.

[0122] Surface temperature observation equation (core-surface heat conduction mapping)

[0123]

[0124] In the formula, 3. The convective heat transfer coefficient from the cell surface to the environment, in W / (m²·K). 4. In-bag pressure observation equation (derivation of the ideal gas law).

[0125]

[0126] In the formula, This is the calibration coefficient for gas production-pressure conversion, in Pa / m³. For reference temperature, the value is 298.15K, unit: K.

[0127] The model discretization process uses the forward Euler method to discretize the continuous model, with a sampling period of [period missing]. This yields the standard discrete state-space model:

[0128] In the formula, The discrete state transition function is obtained by discretizing the differential equations in Section 1.2, for example... The discrete observation function is directly obtained from the observation equation in Section 1.3; Let be the process noise vector, corresponding to the model uncertainty, and its covariance matrix be . ; Let be the observation noise vector, corresponding to the sensor measurement error, and its covariance matrix be . .

[0129] Step S2: Online estimation of internal state based on adaptive unscented Kalman filter. This step uses the discrete state-space model output from step S1 as the basis for calculation, and inputs the real-time acquired input quantities. With observation The output includes state estimates of internal precursors. It is directly used as the input for step S3.

[0130] Filter parameter initialization settings: Initial filter values ​​and hyperparameters; Initial state estimation: Calibration based on the cell's factory parameters

[0131] Initial state covariance: , is a diagonal matrix, set according to parameter uncertainty.

[0132] Initial process noise covariance: , is a diagonal matrix, set according to the model accuracy.

[0133] Initial observation noise covariance: It is a diagonal matrix, set according to the sensor accuracy.

[0134] UKF hyperparameters: , , Used for control Point distribution range

[0135] Point sampling is based on the state estimation at time k-1. Covariance Generate 2n+1 Points, where n=9 is the state dimension:

[0136] Corresponding weighting coefficients:

[0137] In the formula, For scale parameters; For the i-th point; Calculate the weights for the mean; Calculate the weights for the covariance.

[0138] In this embodiment, the time update (prediction step) involves all... Substitute the discrete state transition function output in step S1 into the input. The prediction was obtained. point: For prediction Point-weighted summation yields the state prediction value. - and predicted covariance

[0139]

[0140] Observation update (correction step) will predict Substitute the discrete observation function output in step S1 into the input. , to obtain observation and prediction point:

[0141]

[0142] Weighted calculation of observed predicted values Observation covariance State-observation cross-covariance :

[0143]

[0144] Calculate Kalman gain And update the state estimate at time k. Covariance

[0145] In the formula, Let k be the actual observation vector collected at time k; This is a new sequence that reflects the deviation between actual measurements and model predictions.

[0146] Noise covariance adaptive adjustment is achieved by using sliding window statistics based on the innovation sequence to update the process noise covariance in real time. and observation noise covariance To address the issues of noise statistical mismatch and filter divergence caused by changes in operating conditions and battery aging:

[0147] In the formula, The sliding window length is typically 10-20. This step ultimately outputs the complete state vector estimated in real time for each sampling moment. This includes core temperature. Remaining mass of SEI membrane Depth of each side reaction Cumulative gas production Once the internal precursor quantity is reached, directly input it into step S3.

[0148] Step S3: Thermal runaway graded early warning based on internal precursors. This step takes the internal state estimate output from step S2 as input, extracts precursor features and matches thresholds, and outputs an early warning signal.

[0149] Precursor feature extraction calculates three core precursor indices from the estimated state vector: 1. SEI decomposition rate:

[0150]

[0151] Reflects the severity of abnormal SEI membrane decomposition.

[0152] Core temperature rise rate:

[0153]

[0154] The rate of internal heat accumulation precedes the rise in surface temperature.

[0155] Gas production rate:

[0156]

[0157] It reflects the intensity of gas production from the decomposition of the internal electrolyte.

[0158] The graded warning thresholds and judgment rules are based on the three-level thresholds determined by the accelerated calorimeter (ARC) test of the battery cell. These thresholds correspond to different stages of thermal runaway evolution, and the corresponding level of warning is triggered when any one of the triggering conditions is met.

[0159] Example 2

[0160] In this embodiment, as Figure 2The present invention provides a battery testing system, including a data acquisition module for real-time acquisition of data on cell operating current, terminal voltage, surface temperature, ambient temperature, and internal air pressure of the battery pack.

[0161] The coupled modeling module is used to construct a discrete state-space model of electrochemical-thermal-gas production coupled with multi-step side reactions, store the intrinsic parameters of the battery cell and model coefficients, and output discrete state transition functions and discrete observation functions.

[0162] An adaptive state estimation module is used to perform adaptive unscented Kalman filtering operations. It takes the data collected by the data acquisition module and the model parameters of the coupled modeling module as inputs and outputs a state estimate containing internal precursors.

[0163] The graded early warning module is used to calculate precursor characteristic indicators based on internal precursor quantities, match preset early warning thresholds, and output corresponding early warning signals and handling instructions.

[0164] In this embodiment, the present invention provides a battery structure comprising a battery pack assembled from at least one cylindrical battery, which is applicable to a battery testing method as described above.

[0165] Example 3

[0166] like Figure 3 The present invention provides a computer-storable medium storing a computer program, wherein when the program is executed, it sequentially executes any one of the above-described battery detection methods.

[0167] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations will be apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A battery testing method, characterized in that: include: The system collects the operating current and ambient temperature outside the battery cell, the state vector of the precursor to thermal runaway inside the battery cell, and the terminal voltage, surface temperature, and gas pressure inside the battery pack outside the battery cell. It establishes continuous state equations and observation equations, including the four-step cascaded exothermic side reaction kinetics, the two-stage thermal equilibrium of the core and surface, and the gas generation kinetics. Discrete state transition functions and discrete observation functions are obtained through discretization, and a spatial model coupling the discrete states of electrochemistry, thermal, and gas generation is constructed. Based on the spatial model of discrete state coupling of electrochemistry, thermal and gas production, the input and observation quantities are collected in real time, and Sigma point sampling, time update and observation update are performed in sequence to obtain the state estimate at the current time. Based on the sliding window statistics of the innovation sequence, the process noise covariance and observation noise covariance are adaptively adjusted. The output includes internal precursor state variables such as core temperature, remaining mass of SEI membrane, depth of each side reaction, and cumulative gas production volume. Based on the internal precursor state variables, three types of precursor characteristic indicators—SEI decomposition rate, core temperature rise rate, and gas production rate—are calculated. These are then matched with preset three-level early warning thresholds, and corresponding level thermal runaway early warning signals are output.

2. The battery testing method according to claim 1, characterized in that: The state vector is a 9-dimensional vector, including the cell's state of charge, cell polarization voltage, cell core temperature, remaining SEI film mass, reaction depth of the SEI film decomposition reaction, reaction depth of the reaction between the negative electrode lithium intercalation carbon and the electrolyte, reaction depth of the electrolyte oxidation decomposition reaction, reaction depth of the positive electrode active material decomposition reaction, and the cumulative gas production volume of the cell, expressed as: in, For the first The state of charge of the battery cell at any given time; For the first Cell polarization voltage at any given time; For the first Constant temperature of the battery cell core; For the first Remaining mass of the SEI membrane at any given time; The reaction depth of the SEI membrane decomposition reaction; The reaction depth of the lithium-intercalated carbon anode with the electrolyte; The reaction depth of the electrolyte oxidation decomposition reaction; The reaction depth of the positive electrode active material decomposition reaction; For the first The cumulative gas production volume of the battery cell at any given time.

3. The battery testing method according to claim 1, characterized in that: The four-step cascade exothermic side reaction kinetics described above are constructed based on the Arrhenius equation, and the continuous form expression is as follows: in, For the first Pre-exponential factors of class reactions; For the first Activation energy of a class of reactions; This is the universal gas constant, with a value of 8.314 J / mol·K; For the first The reaction order of a class of reactions.

4. The battery testing method according to claim 1, characterized in that: The continuous state expression for the two-stage thermal equilibrium of the core surface is as follows: Among them, the total heat release rate It comprises three parts: Joule heating, polarization heat, and exothermic reactions in four steps. The expression is: in, The internal resistance of the battery cell is in ohms. For the first The heat released per unit depth of reaction of a side reaction; The equivalent heat transfer coefficient from the core to the surface of the battery cell; The heat exchange surface area of ​​the battery cell; The average density of the battery cells; The specific heat capacity of the battery cell at constant voltage; This refers to the volume of the battery cell.

5. The battery testing method according to claim 4, characterized in that: Based on the continuous state of the two-level thermal equilibrium at the core surface, the continuous state equation for the two-level thermal equilibrium at the core surface is constructed as follows: The total heat release rate It comprises three parts: Joule heating, polarization heat, and exothermic reactions in four steps. The expression is: In the formula, The internal resistance of the battery cell is in ohms. The heat released per unit reaction depth for the i-th type of side reaction; The equivalent heat transfer coefficient from the core to the surface of the battery cell; The heat exchange surface area of ​​the battery cell; The average density of the battery cells; The specific heat capacity of the battery cell at constant voltage; This refers to the volume of the battery cell.

6. The battery testing method according to claim 1, characterized in that: The adaptive update expression for the noise covariance is: in, The length of the sliding window; For the first The information sequence at time point, the difference between the actual observed value and the observed predicted value; For the first Kalman gain at time step.

7. The battery testing method according to claim 1, characterized in that: The expressions for the three types of precursor characteristic indicators are as follows: The expression for the SEI decomposition rate is as follows: in This represents the mass consumption coefficient per unit reaction depth in the SEI decomposition reaction. The expression for the core temperature rise rate is as follows: ； The gas production rate is expressed as follows: ； The three-level early warning thresholds were calibrated using an accelerated calorimeter experiment, and the specific determination rules are as follows: Level 1 warning: If either the SEI decomposition rate is greater than the first SEI rate threshold or the gas production rate is greater than the first gas production rate threshold, it is determined to be an early hidden risk. Level 2 warning: If either the core temperature is greater than the second core temperature threshold or the core temperature rise rate is greater than the second temperature rise rate threshold, it is judged as a medium to high risk of thermal runaway. Level 3 warning: If either the core temperature exceeds the third core temperature threshold, or the reaction depth between the lithium-intercalated carbon electrode and the electrolyte exceeds the third reaction depth threshold, it is determined to be a risk of irreversible thermal runaway.

8. A battery testing system, characterized in that: Includes a data acquisition module for real-time acquisition of data on cell operating current, terminal voltage, surface temperature, ambient temperature, and internal air pressure of the battery pack; The coupled modeling module is used to construct a discrete state-space model of electrochemical-thermal-gas production coupled with multi-step side reactions, store the intrinsic parameters of the battery cell and model coefficients, and output discrete state transition functions and discrete observation functions. An adaptive state estimation module is used to perform adaptive unscented Kalman filtering operations. It takes the data collected by the data acquisition module and the model parameters of the coupled modeling module as inputs and outputs a state estimate containing internal precursors. The graded early warning module is used to calculate the precursor characteristic indicators based on the internal precursor quantities, match the preset early warning thresholds, and output the corresponding level of early warning signals and handling instructions.

9. A battery structure comprising a battery pack assembled from at least one cylindrical battery, characterized in that: This battery pack is applicable to a battery testing method according to any one of claims 1-7.

10. A computer-storable medium storing a computer program therein, characterized in that: When the program is executed, it sequentially executes any one of the battery detection methods described in claims 1-7.

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