Lithium iron phosphate battery thermal runaway trend prediction method and system

Abstract

The invention provides a lithium iron phosphate battery thermal runaway trend prediction method and system. The method comprises the steps that real-time operation data of a to-be-predicted lithium iron phosphate battery, initial estimated values of all parameters of an equivalent circuit model, a basic process noise covariance matrix and a basic measurement noise covariance matrix within a preset duration are acquired; determining an adjustment factor, and determining an adjusted process noise covariance matrix and an adjusted basic measurement noise covariance matrix based on the adjustment factor; optimizing the initial estimated value of each parameter of the equivalent circuit model corresponding to the lithium iron phosphate battery by using an extended Kalman filtering algorithm to obtain optimized ohm internal resistance, first-stage polarization internal resistance and second-stage polarization internal resistance; determining the temperature rise rate of the to-be-predicted lithium iron phosphate battery at the current moment t and the predicted temperature of the to-be-predicted lithium iron phosphate battery at the moment t + 1; and performing thermal runaway trend prediction on the lithium iron phosphate battery. According to the technical scheme provided by the invention, the reliability, the sensitivity and the timeliness of the prediction result are improved.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a method and system for predicting the thermal runaway trend of a lithium iron phosphate battery. Background Art

[0002] Lithium iron phosphate (LiFePO4) batteries are widely used in electric vehicles, energy storage systems, and other fields due to their superior safety, long cycle life, and low cost. However, under conditions such as overcharge, over-discharge, internal short circuits, or high external temperatures, LiFePO4 batteries can still experience thermal runaway, leading to serious safety incidents such as combustion and explosion. Therefore, accurately predicting the thermal runaway trends of LiFePO4 batteries is crucial to ensuring the safe operation of battery systems.

[0003] The existing method of using the extended Kalman filter algorithm to online identify equivalent circuit model parameters and predict thermal runaway trends based on this identification reveals inherent flaws when the battery undergoes specific and common operating condition transitions. Specifically, when a lithium iron phosphate battery undergoes a period of dynamic operating conditions with sustained current fluctuations (e.g., frequent acceleration, deceleration, and start-stopping of vehicles in urban congestion, or rapid power throughput of energy storage systems in response to grid commands), followed by a transition to low-current continuous operation or near-stationary low-power output conditions (e.g., a vehicle entering a stable low-speed cruise state, or an energy storage system entering standby or low-power maintenance mode), the standard extended Kalman filter algorithm, due to its typically static process noise covariance matrix Q and measurement noise covariance matrix R, cannot immediately adapt to the model prediction uncertainty and significant changes in the voltage measurement signal-to-noise ratio caused by this shift from a drastic to a gentle operating condition. This inadaptability directly results in: in the initial stage of operating condition conversion and the subsequent low-power operation stage, the extended Kalman filter algorithm's identification accuracy of the battery internal resistance parameters is reduced and the convergence speed is slow. The internal resistance identification deviation introduced by model simplification and incentive changes under previous dynamic conditions is difficult to be quickly and effectively corrected at this stage. Since internal resistance is a key physical quantity for evaluating battery thermal stability and early thermal runaway risk, this internal resistance identification inaccuracy problem that occurs under common operating condition conversion significantly weakens the accuracy and timeliness of the thermal runaway warning system that relies on this internal resistance, resulting in deviations in the assessment of the potential thermal runaway risk of the battery, increasing the probability of missed or delayed reporting of thermal runaway events, and posing a threat to the safe operation of the battery system. Therefore, there is an urgent need to propose a solution that can accurately predict battery thermal runaway. Summary of the Invention

[0004] The present application provides a method and system for predicting the thermal runaway trend of a lithium iron phosphate battery, so as to at least solve the technical problem that deviations occur in the assessment of the potential thermal runaway risk of the battery, which increases the probability of missing or late reporting of thermal runaway events and poses a threat to the safe operation of the battery system.

[0005] The first embodiment of the present application provides a method for predicting thermal runaway trend of a lithium iron phosphate battery, the method comprising: Acquire real-time operating data of a lithium iron phosphate battery to be predicted within a preset time period, initial estimated values ​​of various parameters of an equivalent circuit model corresponding to the lithium iron phosphate battery, a basic process noise covariance matrix, and a basic measurement noise covariance matrix of the lithium iron phosphate battery, wherein the real-time operating data includes: an operating current and a key point temperature of the battery, and the initial estimated values ​​of various parameters include: an initial estimated value of ohmic internal resistance, an initial estimated value of first-stage polarization internal resistance, an initial estimated value of second-stage polarization internal resistance, and an initial estimated value of state of charge; Determining the adjustment factor of the basic process noise covariance matrix and the voltage noise adjustment factor of the basic measurement noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within a preset time length, and determining the adjusted process noise covariance matrix based on the adjustment factor of the basic process noise covariance matrix, and determining the adjusted basic measurement noise covariance matrix based on the voltage noise adjustment factor of the basic measurement noise covariance matrix; Based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix, and using an extended Kalman filter algorithm, initial estimated values ​​of parameters of the equivalent circuit model corresponding to the lithium iron phosphate battery are optimized to obtain optimized ohmic internal resistance, first-stage polarization internal resistance, second-stage polarization internal resistance, and state of charge; Determine the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t and the predicted temperature at time t+1 according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-level polarization internal resistance, the second-level polarization internal resistance, and the state of charge; The thermal runaway trend of the lithium iron phosphate battery is predicted based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at the time t+1, and the key point temperature.

[0006] Preferably, the method further comprises: Determining an average operating current absolute value of the lithium iron phosphate battery to be predicted within the preset time period based on the operating current of the lithium iron phosphate battery to be predicted within the preset time period, and judging whether the basic measurement noise covariance matrix needs to be adjusted based on the average operating current absolute value; If the basic measurement noise covariance matrix needs to be adjusted, a voltage noise adjustment factor of the basic measurement noise covariance matrix is ​​determined; otherwise, the basic measurement noise covariance matrix is ​​used as the adjusted measurement noise covariance matrix.

[0007] Furthermore, the equivalent circuit model of the lithium iron phosphate battery is a second-order Model.

[0008] Furthermore, the step of determining the adjustment factor of the basic process noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within a preset time period includes: Determining the average value of the current standard deviation and the current change rate of the lithium iron phosphate battery to be predicted within the preset time period based on the operating current of the lithium iron phosphate battery to be predicted within the preset time period; The comprehensive dynamic index within the preset time length is determined according to the current standard deviation and current change rate of the lithium iron phosphate battery to be predicted within the preset time length, and then the adjustment factor of the basic process noise covariance matrix is ​​determined according to the comprehensive dynamic index.

[0009] Furthermore, the calculation formula of the comprehensive dynamic index is as follows:

[0010] Where, is a comprehensive dynamic index, is the current standard deviation, is the average value of the current change rate, is the rated current of the lithium iron phosphate battery, is the sampling time interval of the extended Kalman filter algorithm; The calculation formula of the adjustment factor of the basic process noise covariance matrix is ​​as follows:

[0011] Where, is the adjustment factor for the underlying process noise covariance matrix, is the sensitivity parameter, It is the reference point of dynamic saturation of working condition.

[0012] Furthermore, the voltage noise adjustment factor of the basic measurement noise covariance matrix is ​​calculated as follows:

[0013] Where, is the voltage noise adjustment factor for the underlying measurement noise covariance matrix, is the preset current judgment threshold, is the absolute value of the average operating current, is the adjustment intensity factor for the underlying measurement noise covariance matrix, is a function whose value is , otherwise .

[0014] Furthermore, the calculation formula of the adjusted process noise covariance matrix is ​​as follows:

[0015] Where, is the adjusted process noise covariance matrix, is the basic process noise covariance matrix; The calculation formula of the adjusted basic measurement noise covariance matrix is ​​as follows:

[0016] Where, is the adjusted basic measurement noise covariance matrix, is the basic measurement noise variance of the adjusted terminal voltage, , is the basic measurement noise variance of the terminal voltage.

[0017] Furthermore, the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t and the predicted temperature at time t+1 are determined based on the operating current, the key point temperature, the optimized ohmic internal resistance, the first-level polarization internal resistance, the second-level polarization internal resistance, and the state of charge, including: Determine the total heat generation rate at the current time t according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-stage polarization internal resistance, the second-stage polarization internal resistance, and the state of charge; The total heat generation rate at the current time t is input into a first-order lumped parameter thermal model, and the first-order lumped parameter thermal model is solved to obtain the temperature rise rate at the current time t and the predicted temperature at time t+1.

[0018] Preferably, the thermal runaway trend prediction of the lithium iron phosphate battery based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at time t+1, and the key point temperature includes: Determine whether the temperature rise rate at the current moment t is greater than a preset temperature rise rate threshold, and / or whether the predicted temperature at the moment t+1 is greater than or equal to a preset thermal runaway critical point threshold, and / or whether the key point temperature is greater than a preset temperature threshold. If so, determine that the lithium iron phosphate battery has a thermal runaway trend; otherwise, the lithium iron phosphate battery does not have a thermal runaway trend.

[0019] The second embodiment of the present application provides a system for predicting thermal runaway trends of lithium iron phosphate batteries, including: An acquisition module is used to obtain real-time operating data of the lithium iron phosphate battery to be predicted within a preset time period, initial estimated values ​​of various parameters of the equivalent circuit model corresponding to the lithium iron phosphate battery, and the basic process noise covariance matrix and basic measurement noise covariance matrix of the lithium iron phosphate battery, wherein the real-time operating data includes: operating current and key point temperature of the battery, and the initial estimated values ​​of various parameters include: initial estimated value of ohmic internal resistance, initial estimated value of first-stage polarization internal resistance, initial estimated value of second-stage polarization internal resistance, and initial estimated value of state of charge; A first determination module is used to determine the adjustment factor of the basic process noise covariance matrix and the voltage noise adjustment factor of the basic measurement noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within a preset time length, and determine the adjusted process noise covariance matrix based on the adjustment factor of the basic process noise covariance matrix, and determine the adjusted basic measurement noise covariance matrix based on the voltage noise adjustment factor of the basic measurement noise covariance matrix; An optimization module is used to optimize the initial estimated values ​​of each parameter of the equivalent circuit model corresponding to the lithium iron phosphate battery based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix, and to obtain optimized ohmic internal resistance, first-stage polarization internal resistance, second-stage polarization internal resistance, and state of charge using an extended Kalman filter algorithm; A second determination module is used to determine the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t and the predicted temperature at time t+1 according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-level polarization internal resistance, the second-level polarization internal resistance, and the state of charge; A prediction module is used to predict the thermal runaway trend of the lithium iron phosphate battery based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at the time t+1 and the key point temperature.

[0020] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: The present application proposes a method and system for predicting the thermal runaway trend of a lithium iron phosphate battery, the method comprising: obtaining real-time operating data of the lithium iron phosphate battery to be predicted within a preset time length, initial estimated values ​​of each parameter of the equivalent circuit model corresponding to the lithium iron phosphate battery, the basic process noise covariance matrix and the basic measurement noise covariance matrix of the lithium iron phosphate battery, wherein the real-time operating data comprises: the operating current and the key point temperature of the battery; the initial estimated values ​​of each parameter comprise: the initial estimated value of the ohmic internal resistance, the initial estimated value of the first-stage polarization internal resistance, and the initial estimated value of the second-stage polarization internal resistance; determining the adjustment factor of the basic process noise covariance matrix and the voltage noise adjustment factor of the basic measurement noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within the preset time length, and determining the adjusted process noise covariance matrix based on the adjustment factor of the basic process noise covariance matrix , based on the voltage noise adjustment factor of the basic measurement noise covariance matrix, an adjusted basic measurement noise covariance matrix is ​​determined; based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix, the initial estimated values ​​of the parameters of the equivalent circuit model corresponding to the lithium iron phosphate battery are optimized using the extended Kalman filter algorithm to obtain the optimized ohmic internal resistance, first-level polarization internal resistance, second-level polarization internal resistance, and state of charge; based on the operating current, the key point temperature, the optimized ohmic internal resistance, first-level polarization internal resistance, second-level polarization internal resistance, and state of charge, the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t and the predicted temperature at time t+1 are determined; based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at time t+1, and the key point temperature, the thermal runaway trend of the lithium iron phosphate battery is predicted. The technical solution proposed in this application improves the accuracy and real-time performance of battery internal resistance identification, thereby improving the reliability and accuracy of thermal runaway prediction.

[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a flow chart of a method for predicting thermal runaway trend of a lithium iron phosphate battery according to one embodiment of the present application; Figure 2 This is a structural diagram of a lithium iron phosphate battery thermal runaway trend prediction system provided according to one embodiment of the present application. DETAILED DESCRIPTION

[0023] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0024] The present application proposes a method and system for predicting the thermal runaway trend of a lithium iron phosphate battery, the method comprising: obtaining real-time operating data of the lithium iron phosphate battery to be predicted within a preset time length, initial estimated values ​​of each parameter of the equivalent circuit model corresponding to the lithium iron phosphate battery, the basic process noise covariance matrix and the basic measurement noise covariance matrix of the lithium iron phosphate battery, wherein the real-time operating data include: operating current and key point temperature of the battery; the initial estimated values ​​of each parameter include: initial estimated value of ohmic internal resistance, initial estimated value of first-stage polarization internal resistance, initial estimated value of second-stage polarization internal resistance; determining the adjustment factor of the basic process noise covariance matrix and the voltage noise adjustment factor of the basic measurement noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within the preset time length, and determining the adjusted process noise covariance based on the adjustment factor of the basic process noise covariance matrix. Matrix, based on the voltage noise adjustment factor of the basic measurement noise covariance matrix, an adjusted basic measurement noise covariance matrix is ​​determined; based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix, the initial estimated values ​​of the parameters of the equivalent circuit model corresponding to the lithium iron phosphate battery are optimized using the extended Kalman filter algorithm to obtain the optimized ohmic internal resistance, first-level polarization internal resistance, and second-level polarization internal resistance; based on the operating current, the key point temperature, the optimized ohmic internal resistance, first-level polarization internal resistance, second-level polarization internal resistance, and state of charge, the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t and the predicted temperature at time t+1 are determined; based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at time t+1, and the key point temperature, the thermal runaway trend of the lithium iron phosphate battery is predicted. The technical solution proposed in this application improves the accuracy and real-time performance of battery internal resistance identification, thereby improving the reliability and accuracy of thermal runaway prediction.

[0025] The following describes a method and system for predicting thermal runaway trend of a lithium iron phosphate battery according to an embodiment of the present application with reference to the accompanying drawings.

[0026] Example 1 Figure 1 This is a flow chart of a method for predicting thermal runaway trend of a lithium iron phosphate battery according to one embodiment of the present application. Figure 1 As shown, the method includes: Step 1: Acquire real-time operating data of the lithium iron phosphate battery to be predicted within a preset time, initial estimated values ​​of various parameters of the equivalent circuit model corresponding to the lithium iron phosphate battery, and the basic process noise covariance matrix and basic measurement noise covariance matrix of the lithium iron phosphate battery, wherein the real-time operating data includes: operating current and key point temperature of the battery, and the initial estimated values ​​of various parameters include: initial estimated value of ohmic internal resistance, initial estimated value of first-stage polarization internal resistance, initial estimated value of second-stage polarization internal resistance, and initial estimated value of state of charge; It should be noted that the equivalent circuit model of the lithium iron phosphate battery is a second-order Model.

[0027] Specifically, the sensor unit in the battery management system (BMS) that is tightly integrated with the lithium iron phosphate battery is used to collect data at preset system sampling intervals. (In this embodiment, the interval can be 0.1 seconds, corresponding to a sampling frequency of 10 Hz) Continuously and in real time collect the terminal voltage of the battery , working current (The direction of the current is positive during charging and negative during discharging) and the temperature of one or more key points that can reflect the internal thermal state of the battery At the same time, the inherent specification parameters of the lithium iron phosphate battery, especially its 1C rated current value, are obtained or pre-stored. This embodiment refers to an IFR18650 battery, wherein For 2A, this It will serve as the benchmark for normalizing the working condition dynamics index in subsequent steps.

[0028] At the same time, the subsequent parameter identification and optimization is based on an online parameter identification method based on an equivalent circuit model (second-order RC model) and an extended Kalman filter (EKF) algorithm. Therefore, the second-order RC equivalent circuit model that can better balance the model accuracy and computational complexity in this field is selected as the basic model to describe the dynamic characteristics of the battery. The standard EKF algorithm has been routinely initialized, including setting the initial estimated values ​​of various parameters (such as ohmic internal resistance, resistance and capacitance of the two-stage RC network, and initial state of charge SOC) for the selected second-order RC model, and setting the basic process noise covariance moment of the lithium iron phosphate battery required by the EKF algorithm. and the underlying measurement noise covariance matrix , which contains the fundamental voltage measurement noise variance .

[0029] In this embodiment, the state vector of the second-order RC model is Defined as (represents state of charge , the first RC network voltage , the second RC network voltage ), and the current As the input of the system, the terminal voltage As the output of the system, then: the basic process noise covariance matrix is a diagonal matrix, and its expression is: , represents a diagonal matrix. In this embodiment, Considering the selected second-order RC model (the state vector is ) engineering practice. The specific settings and acquisition methods are as follows:

[0030] The methods for obtaining each component are as follows: :This value reflects the The uncertainty in the prediction by the Coulomb integration method is mainly due to the cumulative effect of the measurement noise of the current sensor and the slight deviation of the coulomb efficiency. By analyzing the noise specifications of the current sensor and the coulomb efficiency characteristics of the battery, the The error variance that may occur within one sampling step.

[0031] is set to a very small value, since it allows that the Coulomb integral is highly accurate in the short term, It is a typical order of magnitude.

[0032] and : This value reflects the second order The uncertainty of the model in describing the complex electrochemical polarization and concentration polarization dynamics of the battery. The network is a simplification of the real electrochemical process, and its parameters (resistance, capacitance) will vary with , temperature and current change nonlinearly, and the model cannot fully capture these changes. In the existing technology, the offline test ( The battery response data under different conditions is obtained by testing (residual analysis) and the difference (residual) between the model prediction and the actual response is analyzed to statistically estimate the error variance of the polarization voltage prediction.

[0033] and Usually set to It is several orders of magnitude larger because it acknowledges that modeling polarization processes has larger uncertainties than modeling charge integrals. This represents a typical value for moderate model uncertainty, which allows the extended Kalman filter algorithm to have sufficient room for adjustment in estimating the polarization voltage under dynamic conditions.

[0034] Basic measurement noise covariance matrix Under this input and output definition, is a scalar whose expression is:

[0035] in, is the terminal voltage The basic measurement noise variance is mainly set according to the accuracy characteristics of the voltage sensor.

[0036] Step 2: determining the adjustment factor of the basic process noise covariance matrix and the voltage noise adjustment factor of the basic measurement noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within a preset time length, and determining the adjusted process noise covariance matrix based on the adjustment factor of the basic process noise covariance matrix, and determining the adjusted basic measurement noise covariance matrix based on the voltage noise adjustment factor of the basic measurement noise covariance matrix; Furthermore, the step of determining the adjustment factor of the basic process noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within a preset time period includes: Determining the average value of the current standard deviation and the current change rate of the lithium iron phosphate battery to be predicted within the preset time period based on the operating current of the lithium iron phosphate battery to be predicted within the preset time period; The comprehensive dynamic index within the preset time length is determined according to the current standard deviation and current change rate of the lithium iron phosphate battery to be predicted within the preset time length, and then the adjustment factor of the basic process noise covariance matrix is ​​determined according to the comprehensive dynamic index.

[0037] The calculation formula of the comprehensive dynamic index is as follows:

[0038] Where, is a comprehensive dynamic index, is the current standard deviation, is the average value of the current change rate, is the rated current of the lithium iron phosphate battery, which can be 2A in this embodiment. To extend the sampling time interval of the Kalman filter algorithm, this embodiment is 0.1s, that is, 10Hz; The calculation formula of the adjustment factor of the basic process noise covariance matrix is ​​as follows:

[0039] Where, is the adjustment factor of the basic process noise covariance matrix, and its value range is , Is the sensitivity parameter, which is used to control From close (Stable condition) Transition to close (dynamic conditions), the steepness of the response curve. The larger the value, the more drastic the transition. The value of is set to This value is chosen to make the adjustment factor In the comprehensive dynamic index Specifically, the working condition dynamic degree saturation reference point set in this embodiment is for Expectations exist A reasonable range nearby (e.g. ,Right now arrive When the range of Able to complete its main transition process (from close to Change to close ), The reference point of the working condition dynamic degree saturation. This parameter defines when the comprehensive dynamic degree index is equal to this value, The value of , that is, the adjustment effect reaches half the level. Type battery, the selected value is .

[0040] The calculation formula of the adjusted process noise covariance matrix is ​​as follows:

[0041] Where, is the adjusted process noise covariance matrix, is the basic process noise covariance matrix; It should be noted that in order to realize the process noise covariance matrix Dynamic adjustment to adapt to the transition of battery operating conditions from high dynamic to stable state. This step constructs Matrix adjustment factor This factor acts directly on the basic process noise covariance matrix The calculation is based on the battery operation data obtained online and two core adjustment parameters, and ensures that the adjustment factor is Changes within the range. The calculation formula is as follows:

[0042] First, the core part of the formula A comprehensive dynamic index is calculated. This index takes the current standard deviation and the average current change within a sampling period (given by Represents) relative to the rated current of the battery Normalized and fused into a dimensionless comprehensive metric in the form of Euclidean norm. The larger the value, the more the current working condition deviates from the absolute stable state and the stronger the dynamics.

[0043] Secondly, the exponential term in the denominator Its behavioral characteristics are as follows: When the comprehensive dynamic Much smaller than the saturation reference point When (the working condition is very stable), is a large positive number, resulting in The term is very large, so the entire The value of (i.e. 1 / (1+large number)) approaches .

[0044] When the comprehensive dynamic Much larger than the saturation reference point When (i.e. the working condition is very dynamic), is a large negative number, resulting in Item is very small close , so the whole The value of (i.e. 1 / (1+decimal)) approaches .

[0045] When the comprehensive dynamic Exactly equal to the saturation reference point hour, for , Item is ,entire The value of .

[0046] parameter Controls the curve at the center point The steepness of the transition nearby. The bigger it is, the steeper the curve is. From close to close The faster the transition, the Deviation The more sensitive the response.

[0047] therefore, The value of Increases smoothly to approximately This means: When the working condition is very stable ( Approaching ), will approach its lower limit (a very small positive number, but not 0). At this point, the adjusted process noise covariance matrix will be significantly reduced, which is consistent with the logic that the reduction of model uncertainty under stable conditions should greatly increase the trust in model predictions.

[0048] When the working conditions are very dynamic larger value), will approach its upper limit .at this time, Close to or equal to the basic , which allows Maintain a high estimate of model uncertainty under dynamic conditions.

[0049] The final adjusted matrix:

[0050] The calculated Matrix adjustment factor Multiply by the underlying process noise covariance matrix , we can get the process noise covariance matrix optimized in this step ,Should Will be used for subsequent Iterative calculation of the algorithm.

[0051] In an embodiment of the present disclosure, the method further includes: Determining an average operating current absolute value of the lithium iron phosphate battery to be predicted within the preset time period based on the operating current of the lithium iron phosphate battery to be predicted within the preset time period, and judging whether the basic measurement noise covariance matrix needs to be adjusted based on the average operating current absolute value; If the basic measurement noise covariance matrix needs to be adjusted, a voltage noise adjustment factor of the basic measurement noise covariance matrix is ​​determined; otherwise, the basic measurement noise covariance matrix is ​​used as the adjusted measurement noise covariance matrix.

[0052] It should be noted that it is necessary to determine whether the battery is in a low current operation state. If so, the measurement noise covariance matrix is ​​dynamically increased according to the actual size of the current. The noise variance term in the voltage measurement corresponds to the voltage measurement. Specifically, when the monitored average current amplitude is lower than a preset low current threshold, the adjustment of the voltage measurement noise variance is initiated. The smaller the current amplitude (i.e., the worse the signal-to-noise ratio), the greater the increase in the voltage measurement noise variance. In this way, The algorithm can automatically reduce the trust in voltage measurement data under low current and low signal-to-noise ratio conditions and rely more on the steps Optimized model predictions prevent them from being contaminated by noise and help avoid bias introduced by previous operating conditions.

[0053] Furthermore, the voltage noise adjustment factor of the basic measurement noise covariance matrix is ​​calculated as follows:

[0054] Where, is the voltage noise adjustment factor for the underlying measurement noise covariance matrix, The preset current judgment threshold is the low current judgment threshold. When the absolute value of the average current of the battery is When the current is lower than the threshold, the voltage measurement noise variance is adjusted. Set to This value is selected for the lithium iron phosphate battery used in this embodiment (a battery with a nominal capacity of , in new battery state The AC internal resistance is approximately of The typical noise standard deviation of the voltage measurement system used in this embodiment is approximately To ensure that the voltage response signal generated by the battery under low current excitation can be effectively identified and distinguished from the measurement noise, it is usually required that the response signal is at least several times the noise standard deviation. The battery can stably produce the equivalent internal resistance component of the response under small current excitation. If it is to produce at least The voltage response (i.e. times The required excitation current is approximately Furthermore, considering that the internal resistance of batteries increases due to factors such as aging and low temperature in actual applications, as well as The algorithm provides more sufficient incentives to ensure the effectiveness of parameter identification. Set in , which can effectively define the low current working region where the signal-to-noise ratio of the voltage signal is significantly reduced when the working current is lower than this value, and measurement noise covariance adjustment needs to be activated, thereby ensuring parameter identification in this region; is the absolute value of the average operating current, The adjustment intensity factor of the basic measurement noise covariance matrix controls the maximum relative intensity of the voltage measurement noise variance that can be amplified under low current conditions. In this embodiment, Determined When the signal-to-noise ratio of voltage measurement deteriorates significantly due to low current, the noise covariance can be Provide adequate but not excessive compensation to ensure The performance of the algorithm; is a function whose value is , otherwise .

[0055] The calculation formula of the adjusted basic measurement noise covariance matrix is ​​as follows:

[0056] Where, is the adjusted basic measurement noise covariance matrix, is the basic measurement noise variance of the adjusted terminal voltage, , is the basic measurement noise variance of the terminal voltage.

[0057] It should be noted that in order to achieve the above measurement noise covariance matrix Dynamic adjustment of the medium voltage measurement noise term. This step constructs the optimization factor The manifestation of -- Matrix voltage noise adjustment factor , which directly affects the basic voltage measurement noise variance . The calculation formula is as follows:

[0058] R matrix voltage noise adjustment factor , which dynamically adjusts the reliability of voltage measurement according to whether the battery is in a low current operating state and the specific size of the current, thereby solving the problem of fixed Matrix leads to The problem of over-trusting voltage measurement data contaminated by noise.

[0059] Only when the average current absolute value calculated in real time Less than the threshold set by the core When , the value of the indicator function is , the subsequent adjustment items will take effect; otherwise, the indicator function value is , so that the entire product term is ,at this time =1, meaning no adjustment is made and the voltage measurement noise variance remains at its base value. This design ensures that adjustment is only activated when small current effects are of real concern.

[0060] When the adjustment is activated, This item plays a role of gradual adjustment. Its value changes with From close Change to close , and from close Increases linearly to approximately This means that when the current is just below the threshold When the value of this item is small, The contribution of this item is also small, and the adjustment range is slight; when the current is very small, far below the threshold, the value of this item is close to ,right This linear gradient design makes the adjustment intensity inversely proportional to the current size (i.e., the degree of signal-to-noise ratio degradation), which is consistent with actual physical conditions.

[0061] parameter Determines the maximum strength of the adjustment. The entire product term The maximum value of .therefore, The maximum value of This means that the voltage measurement noise variance can be amplified to at most times. In this embodiment, Set to This value is selected to be the average current Approaching zero, the signal-to-noise ratio of the voltage signal is extremely low (for this embodiment, the reference Battery, in The voltage signal amplitude generated by the current is The standard deviation of the measurement noise is comparable, and the signal-to-noise ratio is only The equivalent noise standard deviation of voltage measurement can be improved by about times. This level of noise amplification is sufficient to The algorithm significantly reduces the trust in such low-quality measurement data, thus giving priority to model prediction and effectively suppressing the interference of measurement noise on the identification results of parameters such as internal resistance. But there is still a certain amplitude, The value of Towards The adjustment range is adapted to the degree of deterioration of the signal-to-noise ratio.

[0062] The final adjusted Voltage measurement noise variance in the matrix:

[0063] The calculated Matrix voltage noise adjustment factor Multiply the voltage measurement noise variance by the base , the voltage measurement noise variance optimized in this step can be obtained .

[0064] Update the measurement noise covariance matrix : The final measurement noise covariance matrix By the basic matrix (usually a diagonal matrix containing the basic noise variance of all measurements such as voltage and current) is updated. The elements on its diagonal corresponding to voltage measurements are replaced with the newly calculated The noise variance terms of other measured quantities remain unchanged in this step. will with Used together for subsequent Iterative calculation of the algorithm.

[0065] Step 3: Based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix, the initial estimated values ​​of the parameters of the equivalent circuit model corresponding to the lithium iron phosphate battery are optimized using an extended Kalman filter algorithm to obtain optimized ohmic internal resistance, first-stage polarization internal resistance, second-stage polarization internal resistance, and state of charge; It should be noted that the dynamically adjusted process noise covariance matrix is ​​obtained and the measurement noise covariance matrix Afterwards, this step applies these two optimized covariance matrices to the extended Kalman filter algorithm to replace the fixed basic covariance matrix used in the standard application of the algorithm. and (yes The part related to voltage measurement is ). The algorithm itself is based on the selected second-order The equivalent circuit model and its preset initial parameter state are iteratively calculated. In the calculation cycle, the algorithm first performs the prediction step, using the battery model and the state estimate at the previous moment to predict the current state; then, in the update step, the residual between the actual measurement value and the predicted value at the current moment is used, combined with the Kalman gain, to correct the state estimate. During the iterative process, the algorithm can identify and update the various parameters in the equivalent circuit model online, especially the resistance parameters that are closely related to the energy loss and heat generation inside the battery. The final output of this step is the battery internal resistance parameter obtained after the optimization method of the present invention, which can more accurately reflect the actual state of the battery when switching from high dynamic to low current stable working conditions. The parameters include the optimized ohmic internal resistance And the first-stage polarization internal resistance , the second-stage polarization internal resistance Etc. These optimized internal resistance parameters will serve as key input data for subsequent thermal runaway trend prediction.

[0066] Among them, in one calculation cycle, The iterative calculation process mainly includes the following steps: 1. Prediction steps: State prediction: using the previous moment The optimal estimate of the state and the second-order The state equation of the model predicts the prior estimate of the state at the current time t.

[0067] Error covariance prediction: using the previous moment The error covariance matrix , the Jacobian matrix of the state transfer matrix , and the optimized process noise covariance matrix , to predict the error covariance prior estimate at the current time t .

[0068] Here, Instead of a fixed . The value is determined dynamically by the real-time working conditions.

[0069] 2. Update steps: Kalman gain calculation: using the predicted error covariance , the Jacobian matrix of the observation matrix , the optimized measurement noise covariance matrix , to calculate the Kalman gain at the current moment .

[0070] Here, (This includes Adjusted voltage noise variance ) instead of a fixed . The value is determined by the real-time average current.

[0071] State update: Utilize the residual between the actual voltage measurement value at the current moment and the voltage value predicted by the model, combined with the Kalman gain , correct the prior estimate of the state and get the current moment The optimal estimated value of the state (which includes parameters such as optimized internal resistance and state of charge).

[0072] Error covariance update: using Kalman gain and the predicted error covariance , update the error covariance matrix and get , prepare for the next iteration.

[0073] Step 4: Determine the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t and the predicted temperature at time t+1 according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-level polarization internal resistance, the second-level polarization internal resistance, and the state of charge; In the embodiment of the present disclosure, step 4 specifically includes: Determine the total heat generation rate at the current time t according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-stage polarization internal resistance, the second-stage polarization internal resistance, and the state of charge; The total heat generation rate at the current time t is input into a first-order lumped parameter thermal model, and the first-order lumped parameter thermal model is solved to obtain the temperature rise rate at the current time t and the predicted temperature at time t+1.

[0074] It should be noted that the total heat generation rate of the battery at the current time t is first calculated.

[0075] Subsequently, the calculated total heat generation rate is used as heat input and processed using a first-order lumped parameter thermal model. The first-order lumped parameter thermal model is discretized and solved using the forward Euler method, which can estimate the current temperature rise rate of the battery in real time and predict the temperature change trajectory of the battery in a short period of time in the future.

[0076] The total heat generation rate By Joule heat , polarization heat and reaction heat Superposition gives:

[0077] Where, Joule heat The calculation formula is: ; Polarization heat The calculation formula is: ; Reaction heat The calculation formula is: ; in, is the open circuit voltage temperature coefficient.

[0078] The mathematical expression of the first-order lumped parameter thermal model is as follows:

[0079] Where, For the quality of the battery, is the specific heat capacity of the battery , is the comprehensive convection heat transfer coefficient between the battery surface and the environment, is the surface heat exchange area of ​​the battery, is the real-time measured battery ambient temperature, is the temperature rise rate of the battery.

[0080] Step 5: Predicting the thermal runaway trend of the lithium iron phosphate battery based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at the time t+1, and the key point temperature; In the embodiment of the present disclosure, step 5 specifically includes: Determine whether the temperature rise rate at the current moment t is greater than a preset temperature rise rate threshold, and / or whether the predicted temperature at the moment t+1 is greater than or equal to a preset thermal runaway critical point threshold, and / or whether the key point temperature is greater than a preset temperature threshold. If so, determine that the lithium iron phosphate battery has a thermal runaway trend; otherwise, the lithium iron phosphate battery does not have a thermal runaway trend.

[0081] It should be noted that the currently calculated battery temperature, temperature rise rate, and predicted future temperature are compared with a set of pre-set, multi-dimensional thermal runaway criteria developed for the safe operating characteristics of lithium iron phosphate batteries. These criteria typically include, but are not limited to: whether the battery temperature exceeds the maximum allowable safe operating temperature, whether the temperature rise rate exceeds a set danger threshold, and whether the predicted future temperature reaches a known thermal runaway critical point. Once any or a combination of these criteria is triggered, the system determines that the battery is at risk of thermal runaway.

[0082] In the embodiment of the present disclosure, when the lithium iron phosphate battery has a tendency of thermal runaway, an early warning is issued.

[0083] To sum up, the method for predicting the thermal runaway trend of a lithium iron phosphate battery proposed in this embodiment significantly improves the accuracy of internal resistance identification under critical operating conditions, so that the heat generation rate calculated based on this optimized internal resistance can more realistically reflect the internal thermal state of the battery, thereby ensuring the input data quality of the entire thermal runaway trend prediction process, and ultimately improving the reliability, sensitivity and timeliness of the prediction results, effectively enhancing the operational safety of the lithium iron phosphate battery system.

[0084] Example 2 Figure 2 This is a structural diagram of a lithium iron phosphate battery thermal runaway trend prediction system provided according to one embodiment of the present application, such as Figure 2 As shown, the system includes: An acquisition module 100 is configured to acquire real-time operating data of a lithium iron phosphate battery to be predicted within a preset time period, initial estimated values ​​of various parameters of an equivalent circuit model corresponding to the lithium iron phosphate battery, and a basic process noise covariance matrix and a basic measurement noise covariance matrix of the lithium iron phosphate battery, wherein the real-time operating data includes: an operating current and a key point temperature of the battery; and the initial estimated values ​​of various parameters include: an initial estimated value of an ohmic internal resistance, an initial estimated value of a first-stage polarization internal resistance, an initial estimated value of a second-stage polarization internal resistance, and an initial estimated value of a state of charge. The equivalent circuit model corresponding to the lithium iron phosphate battery is a second-order Model; A first determination module 200 is configured to determine an adjustment factor of the basic process noise covariance matrix and a voltage noise adjustment factor of the basic measurement noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within a preset time period, and determine an adjusted process noise covariance matrix based on the adjustment factor of the basic process noise covariance matrix, and determine an adjusted basic measurement noise covariance matrix based on the voltage noise adjustment factor of the basic measurement noise covariance matrix; An optimization module 300 is configured to optimize the initial estimated values ​​of the parameters of the equivalent circuit model corresponding to the lithium iron phosphate battery based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix, and to obtain optimized ohmic internal resistance, first-stage polarization internal resistance, second-stage polarization internal resistance, and state of charge using an extended Kalman filter algorithm; A second determination module 400 is configured to determine the temperature rise rate of the lithium iron phosphate battery to be predicted at a current time t and a predicted temperature at time t+1 based on the operating current, the key point temperature, the optimized ohmic internal resistance, the first-stage polarization internal resistance, the second-stage polarization internal resistance, and the state of charge; The prediction module 500 is used to predict the thermal runaway trend of the lithium iron phosphate battery based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at the time t+1 and the key point temperature.

[0085] In the embodiment of the present disclosure, the first determining module 200 is further configured to: Determining an average operating current absolute value of the lithium iron phosphate battery to be predicted within the preset time period based on the operating current of the lithium iron phosphate battery to be predicted within the preset time period, and judging whether the basic measurement noise covariance matrix needs to be adjusted based on the average operating current absolute value; If the basic measurement noise covariance matrix needs to be adjusted, a voltage noise adjustment factor of the basic measurement noise covariance matrix is ​​determined; otherwise, the basic measurement noise covariance matrix is ​​used as the adjusted measurement noise covariance matrix.

[0086] In the embodiment of the present disclosure, the first determining module 200 is further configured to: Determining the average value of the current standard deviation and the current change rate of the lithium iron phosphate battery to be predicted within the preset time period based on the operating current of the lithium iron phosphate battery to be predicted within the preset time period; The comprehensive dynamic index within the preset time length is determined according to the current standard deviation and current change rate of the lithium iron phosphate battery to be predicted within the preset time length, and then the adjustment factor of the basic process noise covariance matrix is ​​determined according to the comprehensive dynamic index.

[0087] The calculation formula of the comprehensive dynamic index is as follows:

[0088] Where, is a comprehensive dynamic index, is the current standard deviation, is the average value of the current change rate, is the rated current of the lithium iron phosphate battery, is the sampling time interval of the extended Kalman filter algorithm; The calculation formula of the adjustment factor of the basic process noise covariance matrix is ​​as follows:

[0089] Where, is the adjustment factor for the underlying process noise covariance matrix, is the sensitivity parameter, It is the reference point of dynamic saturation of working condition.

[0090] The voltage noise adjustment factor of the basic measurement noise covariance matrix is ​​calculated as follows:

[0091] Where, is the voltage noise adjustment factor for the underlying measurement noise covariance matrix, is the preset current judgment threshold, is the absolute value of the average operating current, is the adjustment intensity factor for the underlying measurement noise covariance matrix, is a function whose value is , otherwise .

[0092] The calculation formula of the adjusted process noise covariance matrix is ​​as follows:

[0093] Where, is the adjusted process noise covariance matrix, is the basic process noise covariance matrix; The calculation formula of the adjusted basic measurement noise covariance matrix is ​​as follows:

[0094] Where, is the adjusted basic measurement noise covariance matrix, is the basic measurement noise variance of the adjusted terminal voltage, , is the basic measurement noise variance of the terminal voltage.

[0095] In the embodiment of the present disclosure, the second determining module 400 is further configured to: Determine the total heat generation rate at the current time t according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-stage polarization internal resistance, the second-stage polarization internal resistance, and the state of charge; The total heat generation rate at the current time t is input into a first-order lumped parameter thermal model, and the first-order lumped parameter thermal model is solved to obtain the temperature rise rate at the current time t and the predicted temperature at time t+1.

[0096] In the embodiment of the present disclosure, the prediction module 500 is further configured to: Determine whether the temperature rise rate at the current moment t is greater than a preset temperature rise rate threshold, and / or whether the predicted temperature at the moment t+1 is greater than or equal to a preset thermal runaway critical point threshold, and / or whether the key point temperature is greater than a preset temperature threshold. If so, determine that the lithium iron phosphate battery has a thermal runaway trend; otherwise, the lithium iron phosphate battery does not have a thermal runaway trend.

[0097] To sum up, the thermal runaway trend prediction system for lithium iron phosphate batteries proposed in this embodiment significantly improves the accuracy of internal resistance identification under critical working conditions, so that the heat generation rate calculated based on this optimized internal resistance can more realistically reflect the internal thermal state of the battery, thereby ensuring the input data quality of the entire thermal runaway trend prediction process, and ultimately improving the reliability, sensitivity and timeliness of the prediction results, effectively enhancing the operational safety of the lithium iron phosphate battery system.

[0098] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0099] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0100] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for predicting thermal runaway trend of lithium iron phosphate batteries, characterized in that: The method comprises: Acquire real-time operating data of a lithium iron phosphate battery to be predicted within a preset time period, initial estimated values ​​of various parameters of an equivalent circuit model corresponding to the lithium iron phosphate battery, a basic process noise covariance matrix, and a basic measurement noise covariance matrix of the lithium iron phosphate battery, wherein the real-time operating data includes: an operating current and a key point temperature of the battery, and the initial estimated values ​​of various parameters include: an initial estimated value of ohmic internal resistance, an initial estimated value of first-stage polarization internal resistance, an initial estimated value of second-stage polarization internal resistance, and an initial estimated value of state of charge; Determining the adjustment factor of the basic process noise covariance matrix and the voltage noise adjustment factor of the basic measurement noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within a preset time length, and determining the adjusted process noise covariance matrix based on the adjustment factor of the basic process noise covariance matrix, and determining the adjusted basic measurement noise covariance matrix based on the voltage noise adjustment factor of the basic measurement noise covariance matrix; Based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix, and using an extended Kalman filter algorithm, initial estimated values ​​of parameters of the equivalent circuit model corresponding to the lithium iron phosphate battery are optimized to obtain optimized ohmic internal resistance, first-stage polarization internal resistance, second-stage polarization internal resistance, and state of charge; Determine the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t and the predicted temperature at time t+1 according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-level polarization internal resistance, the second-level polarization internal resistance, and the state of charge; The thermal runaway trend of the lithium iron phosphate battery is predicted based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at the time t+1, and the key point temperature.

2. The method according to claim 1, wherein The method further comprises: Determining an average operating current absolute value of the lithium iron phosphate battery to be predicted within the preset time period based on the operating current of the lithium iron phosphate battery to be predicted within the preset time period, and judging whether the basic measurement noise covariance matrix needs to be adjusted based on the average operating current absolute value; If the basic measurement noise covariance matrix needs to be adjusted, a voltage noise adjustment factor of the basic measurement noise covariance matrix is ​​determined; otherwise, the basic measurement noise covariance matrix is ​​used as the adjusted measurement noise covariance matrix.

3. The method according to claim 2, wherein The equivalent circuit model corresponding to the lithium iron phosphate battery is a second-order Model.

4. The method according to claim 3, wherein The step of determining the adjustment factor of the basic process noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within a preset time period includes: Determining the average value of the current standard deviation and the current change rate of the lithium iron phosphate battery to be predicted within the preset time period based on the operating current of the lithium iron phosphate battery to be predicted within the preset time period; The comprehensive dynamic index within the preset time length is determined according to the current standard deviation and current change rate of the lithium iron phosphate battery to be predicted within the preset time length, and then the adjustment factor of the basic process noise covariance matrix is ​​determined according to the comprehensive dynamic index.

5. The method according to claim 4, wherein The calculation formula of the comprehensive dynamic index is as follows: Where, is a comprehensive dynamic index, is the current standard deviation, is the average value of the current change rate, is the rated current of the lithium iron phosphate battery, is the sampling time interval of the extended Kalman filter algorithm; The calculation formula of the adjustment factor of the basic process noise covariance matrix is ​​as follows: Where, is the adjustment factor for the underlying process noise covariance matrix, is the sensitivity parameter, It is the reference point of dynamic saturation of working condition.

6. The method according to claim 5, wherein The voltage noise adjustment factor of the basic measurement noise covariance matrix is ​​calculated as follows: Where, is the voltage noise adjustment factor for the underlying measurement noise covariance matrix, is the preset current judgment threshold, is the absolute value of the average operating current, is the adjustment intensity factor for the underlying measurement noise covariance matrix, is a function whose value is , otherwise .

7. The method according to claim 6, wherein The calculation formula of the adjusted process noise covariance matrix is ​​as follows: Where, is the adjusted process noise covariance matrix, is the basic process noise covariance matrix; The calculation formula of the adjusted basic measurement noise covariance matrix is ​​as follows: Where, is the adjusted basic measurement noise covariance matrix, is the basic measurement noise variance of the adjusted terminal voltage, , is the basic measurement noise variance of the terminal voltage.

8. The method according to claim 7, wherein The step of determining the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t and the predicted temperature at the time t+1 according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-level polarization internal resistance, the second-level polarization internal resistance, and the state of charge includes: Determine the total heat generation rate at the current time t according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-stage polarization internal resistance, the second-stage polarization internal resistance, and the state of charge; The total heat generation rate at the current time t is input into a first-order lumped parameter thermal model, and the first-order lumped parameter thermal model is solved to obtain the temperature rise rate at the current time t and the predicted temperature at time t+1.

9. The method according to claim 1, wherein The predicting of the thermal runaway trend of the lithium iron phosphate battery based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at the time t+1, and the key point temperature includes: Determine whether the temperature rise rate at the current moment t is greater than a preset temperature rise rate threshold, and / or whether the predicted temperature at the moment t+1 is greater than or equal to a preset thermal runaway critical point threshold, and / or whether the key point temperature is greater than a preset temperature threshold. If so, determine that the lithium iron phosphate battery has a thermal runaway trend; otherwise, the lithium iron phosphate battery does not have a thermal runaway trend.

10. A lithium iron phosphate battery thermal runaway trend prediction system, characterized in that: The system comprises: An acquisition module is used to obtain real-time operating data of the lithium iron phosphate battery to be predicted within a preset time period, initial estimated values ​​of various parameters of the equivalent circuit model corresponding to the lithium iron phosphate battery, and the basic process noise covariance matrix and basic measurement noise covariance matrix of the lithium iron phosphate battery, wherein the real-time operating data includes: operating current and key point temperature of the battery, and the initial estimated values ​​of various parameters include: initial estimated value of ohmic internal resistance, initial estimated value of first-stage polarization internal resistance, initial estimated value of second-stage polarization internal resistance, and initial estimated value of state of charge; A first determination module is used to determine the adjustment factor of the basic process noise covariance matrix and the voltage noise adjustment factor of the basic measurement noise covariance matrix according to the operating current of the lithium iron phosphate battery to be predicted within a preset time length, and determine the adjusted process noise covariance matrix based on the adjustment factor of the basic process noise covariance matrix, and determine the adjusted basic measurement noise covariance matrix based on the voltage noise adjustment factor of the basic measurement noise covariance matrix; An optimization module is used to optimize the initial estimated values ​​of each parameter of the equivalent circuit model corresponding to the lithium iron phosphate battery based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix, and to obtain optimized ohmic internal resistance, first-stage polarization internal resistance, and second-stage polarization internal resistance using an extended Kalman filter algorithm; A second determination module is used to determine the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t and the predicted temperature at time t+1 according to the operating current, the key point temperature, the optimized ohmic internal resistance, the first-level polarization internal resistance, the second-level polarization internal resistance, and the state of charge; A prediction module is used to predict the thermal runaway trend of the lithium iron phosphate battery based on the temperature rise rate of the lithium iron phosphate battery to be predicted at the current time t, the predicted temperature at the time t+1 and the key point temperature.

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