Low-temperature discharge performance prediction method, device and equipment and storage medium

Abstract

The invention provides a low-temperature discharge performance prediction method, device and equipment and a storage medium, and relates to the technical field of electrochemical energy storage, and the method comprises the steps: obtaining the instantaneous temperature of a lithium ion battery; determining a theoretical equilibrium conductivity and a theoretical equilibrium diffusion coefficient based on the instantaneous temperature; calculating the effective conductivity and the effective diffusion coefficient of the electrolyte; calculating liquid phase diffusion polarization and ohmic polarization; and determining the discharge voltage of the lithium ion battery. According to the method, the electrolyte low-temperature hysteresis effect is simulated by calculating the effective conductivity and the effective diffusion coefficient, and the defects of an existing model are corrected. When the method is applied to key liquid phase and ohmic polarization calculation, the low-temperature discharge voltage can be accurately predicted, and the prediction average error is obviously reduced from more than 50mV to less than 15mV.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrochemical energy storage, and in particular to a low-temperature discharge performance prediction method and device, equipment and a storage medium. BACKGROUND

[0002] In the field of electrochemical energy storage, lithium-ion batteries have become the mainstream energy storage solution due to their high energy density and long cycle life. In order to ensure the safe, efficient and reliable operation of lithium-ion batteries in various application scenarios (such as electric vehicles, grid energy storage, consumer electronics, etc.), the battery management system (BMS) plays a crucial role. One of the core functions of the battery management system is to rely on accurate battery models to detect, estimate and predict the internal state and external performance of the battery in real time, such as estimating the remaining capacity (SOC), health status (SOH) of the battery, and predicting its power output and voltage response under future working conditions. Therefore, establishing a model that can accurately reflect the electrochemical characteristics of the battery is the basis for realizing high-performance prediction and precise detection technology.

[0003] Currently, mathematical models for predicting battery performance are mainly divided into equivalent circuit models and electrochemical models. Equivalent circuit models can better simulate the voltage dynamic changes of the battery during charging and discharging, especially under medium and low rate working conditions, and have lower computational complexity, making them suitable for real-time applications. Electrochemical models, on the other hand, can directly reflect the electrochemical processes occurring inside the battery and reveal the fundamental reasons for battery performance changes. Such models can provide high prediction accuracy under various working conditions, including high-rate charging and discharging, and their application advantages are increasingly prominent with the increasing computing power of modern chips. Currently, the electrochemical model of lithium-ion batteries is mainly based on the pseudo two-dimensional (P2D) model, which is widely used in state estimation and performance prediction of battery management systems.

[0004] However, existing battery models have significant technical defects under certain working conditions. For equivalent circuit models, the model parameters have weak correlation with the physical and chemical processes inside the battery, making it difficult to accurately reflect the performance of the battery when it ages or the internal mechanism changes, and the model accuracy decreases when dealing with high-rate charging or complex working conditions, making it unable to capture nonlinear behavior. For more accurate electrochemical models, their application in low-temperature environments also has serious problems. Traditional P2D models usually use the Arrhenius formula to describe the relationship between the temperature and the conductivity and diffusion coefficient of the electrolyte. This assumption will produce a large prediction error when predicting the voltage change of the battery after long-term static placement at low temperature and then discharging at high current, especially for large-size batteries.

[0005] The root cause of this prediction error is that existing electrochemical models ignore the dynamic hysteresis characteristics of the electrolyte. In a low-temperature environment (e.g., -30°C to 0°C), when the battery self-heats due to large current discharge, the internal temperature changes rapidly, and the microstructure adjustment of the electrolyte takes time, and the recovery of its conductivity and diffusion coefficient cannot immediately respond to the change in instantaneous temperature, showing a significant hysteresis effect, which is similar to "thawing". The larger the size of the battery cell and the more the amount of electrolyte, the more obvious the hysteresis effect. The Arrhenius equation cannot accurately describe this behavior, resulting in a serious lack of prediction accuracy in the low-temperature region of the model, and the discharge voltage curve predicted by the traditional model deviates significantly from the experimental results, with an error of up to 15% to 20%. This inaccurate voltage prediction will further lead to inaccurate predictions of power and SOC, severely limiting the application range of the model. Even some improved electrochemical models in the industry for low-temperature applications or related literature on heat generation have failed to consider or analyze the dynamic hysteresis effect of the electrolyte, but continue to use the Arrhenius equation or its variants to update the electrolyte parameters. Therefore, under low-temperature conditions, the existing technology lacks a prediction method that can accurately simulate battery performance. SUMMARY

[0006] The purpose of the present application is to provide a low-temperature discharge performance prediction method, device, equipment and storage medium, which simulates the low-temperature hysteresis effect of the electrolyte by calculating the "effective conductivity" and "effective diffusion coefficient", and corrects the existing model. When applied to key liquid phase and ohmic polarization calculations, it can accurately predict the low-temperature discharge voltage, significantly reducing the prediction average error from more than 50mV to less than 15mV.

[0007] In order to achieve the above-mentioned purpose of the present application, the following technical solutions are adopted: In a first aspect, the present application provides a low-temperature discharge performance prediction method, comprising: obtaining the instantaneous temperature of a lithium ion battery; determining the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient based on the instantaneous temperature; calculating the effective conductivity and the effective diffusion coefficient of the electrolyte according to the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient; calculating the liquid phase diffusion polarization and the ohmic polarization of the lithium ion battery during the discharge process using the effective conductivity and the effective diffusion coefficient; determining the discharge voltage of the lithium ion battery based on the liquid phase diffusion polarization, the ohmic polarization, the electrode equilibrium potential, the electrochemical polarization, and the average solid phase diffusion polarization.

[0008] In an optional embodiment, the determination of the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient based on the instantaneous temperature comprises: The theoretical equilibrium conductivity was determined using the Arrhenius formula. and the theoretical equilibrium diffusion coefficient : ; ; Wherein, T represents the instantaneous temperature; Represents reference temperature; The theoretical equilibrium conductivity represents the instantaneous temperature T. The theoretical equilibrium diffusion coefficient represents the instantaneous temperature T. Represents reference temperature Electrolyte conductivity at the specified levels; Represents reference temperature Electrolyte diffusion coefficient; and It is the activation energy.

[0009] In an optional implementation, the effective conductivity The calculation expression is: / =( ( )- ) / ; in, ( ) represents the theoretical equilibrium conductivity; Represents the conductivity hysteresis time constant; and / or, The effective diffusion coefficient The calculation expression is: / =( - ) / ; in, This represents the theoretical equilibrium diffusion coefficient; This represents the diffusion coefficient lag time constant.

[0010] In an optional implementation, the calibration method for the conductivity hysteresis time constant includes: Receive conductivity calibration data; the conductivity calibration data includes: electrolyte temperature from low temperature Move to room temperature Data on conductivity changes over time t obtained in the environment and the aforementioned and steady-state conductivity and The conductivity calibration data is fitted to the following by the processor: The conductivity hysteresis time constant is obtained by solving the problem. ; and / or, The calibration method for the diffusion coefficient hysteresis time constant includes: Receive diffusion coefficient calibration data; the diffusion coefficient calibration data includes: electrolyte from low temperature Move to room temperature Data on the diffusion coefficient as a function of time t obtained in the environment and the aforementioned and steady-state diffusion coefficient of the following and The processor fits the diffusion coefficient calibration data to: The diffusion coefficient lag time constant is obtained by solving the problem. .

[0011] In an optional embodiment, calculating the liquid-phase diffusion polarization and ohmic polarization of the lithium-ion battery during discharge using the effective conductivity and the effective diffusion coefficient includes: Based on the calculation expression respectively Calculate the effective ionic conductivity of the electrolyte in the positive electrode, negative electrode, and membrane regions. ;as well as, Based on the calculation expression respectively Calculate the effective diffusion coefficient of the electrolyte in the positive and negative electrode regions. Furthermore, based on the aforementioned Calculate the liquid phase diffusion polarization; Where i represents the positive electrode, negative electrode, or membrane region; This represents the porosity of the corresponding region; This represents the Bruggeman constant for the corresponding region.

[0012] In an optional implementation, the method for calculating the liquid-phase diffusion polarization includes: The liquid phase diffusion time constants of the positive and negative electrodes are calculated based on the effective diffusion coefficient of the electrolyte. The change in liquid phase lithium ion concentration is determined based on the liquid phase diffusion time constant, thereby determining the liquid phase diffusion polarization of the positive and negative electrodes.

[0013] In an optional implementation, the liquid phase diffusion time constant The calculation expression is: ; Where i represents the positive or negative electrode; The electrode thickness represents the i-electrode region; The effective diffusion coefficient of the electrolyte representing the i-electrode region; and / or, The liquid phase diffusion polarization The calculation expression is: ; in, The liquid-phase diffusion polarization represents the i-electrode region; R represents the gas constant; T represents the instantaneous temperature; F represents the Faraday constant; Represents the lithium-ion mobility coefficient; This represents the change in the concentration of lithium ions in the liquid phase; This represents the initial concentration of the electrolyte.

[0014] In an optional implementation, the method for calculating ohmic polarization includes: The liquid phase resistance of the positive and negative electrodes is calculated based on the effective ionic conductivity of the electrolyte, and the ohmic polarization of the positive and negative electrode regions is determined based on the liquid phase resistance. The ohmic polarization of the diaphragm region is calculated based on the effective ionic conductivity of the electrolyte in the diaphragm region.

[0015] In an optional implementation, the ohmic polarization calculation expression for the positive and negative electrode regions is: ; Where i represents the positive or negative electrode; The i-th electrode region represents ohmic polarization; I represents telecommunications workflow. The thickness of the i-electrode region; The solid-state conductivity of the i-electrode region; A represents the area of ​​the band-edge electrode; The effective ionic conductivity of the electrolyte in the i-electrode region; and / or, The expression for calculating the ohmic polarization of the diaphragm region is: ; in, The ohmic polarization represents the diaphragm region; Represents diaphragm thickness; The effective ionic conductivity of the electrolyte in the diaphragm region.

[0016] In an optional embodiment, determining the discharge voltage of the lithium-ion battery based on the liquid-phase diffusion polarization, the ohmic polarization, the electrode equilibrium potential, the electrochemical polarization, and the average solid-phase diffusion polarization includes: Calculate the positive and negative electrode potentials respectively; The discharge voltage of the lithium-ion battery is calculated based on the positive electrode potential, the negative electrode potential, and the separator region polarization electrode, which is part of ohmic polarization.

[0017] In an optional implementation, the calculation expressions for the positive electrode potential and the negative electrode potential are as follows: ; Where i represents the positive electrode p or the negative electrode n; The potential representing the i-electrode region; The electrode equilibrium potential representing the i-electrode region; The electrochemical polarization representing the i-electrode region; The average solid-phase diffusion polarization representing the i-electrode region; The liquid-phase diffusion polarization representing the i-electrode region; The ohmic polarization representing the i-electrode region; and / or, The formula for calculating the discharge voltage of the lithium-ion battery is as follows: ; in, This represents the discharge voltage of the lithium-ion battery. Represents electrode potential In this context, i represents the positive electrode potential at the positive terminal. Represents electrode potential In this context, 'i' represents the negative electrode potential when the electrode is negative. The polarization voltage representing the diaphragm region is configured as the ohmic polarization of the diaphragm region.

[0018] In an optional implementation, the average solid-state diffusion polarization of the i-electrode region The calculation expression is: ; Where i represents the positive or negative electrode; The average solid-state diffusion polarization of electrode region i is represented by R; the gas constant is represented by T; the instantaneous temperature is represented by F; and the Faraday constant is represented by F. The maximum lithium intercalation concentration of the active material in the i-electrode region; The average surface lithium intercalation concentration of particles in the i-electrode region; The average lithium intercalation concentration of particles in the i-electrode region.

[0019] In an optional implementation, the electrochemical polarization of the i-electrode region is calculated as follows: ; Where i represents the positive or negative electrode; Represents the electrochemical polarization of electrode region i; R represents the gas constant; T represents the instantaneous temperature; F represents the Faraday constant; α represents the electromechanical reaction transfer coefficient. This represents the average current density in the i-electrode region. This represents the average current density in the i-electrode region.

[0020] In a second aspect, the present invention provides a low-temperature discharge performance prediction device, comprising: The parameter acquisition module is used to acquire the instantaneous temperature of the lithium-ion battery. The parameter determination module determines the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient based on the instantaneous temperature. The parameter calculation module is used to calculate the effective conductivity and effective diffusion coefficient of the electrolyte based on the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient. The polarization calculation module is used to calculate the liquid-phase diffusion polarization and ohmic polarization of the lithium-ion battery during the discharge process using the effective conductivity and the effective diffusion coefficient. The voltage determination module is used to determine the discharge voltage of the lithium-ion battery based on the liquid-phase diffusion polarization, the ohmic polarization, the electrode equilibrium potential, the electrochemical polarization, and the average solid-phase diffusion polarization.

[0021] Thirdly, the present invention provides a computer device including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the low-temperature discharge performance prediction method as described in the foregoing embodiments.

[0022] Fourthly, the present invention provides a computer storage medium storing a computer program, which, when executed on a processor, implements the low-temperature discharge performance prediction method as described in the foregoing embodiments.

[0023] This application relates to a method, apparatus, device, and storage medium for predicting low-temperature discharge performance. The core of the prediction method lies in distinguishing between the "theoretical equilibrium" parameters and the "effective" parameters of the electrolyte. Common knowledge and background materials show that existing electrochemical models (such as the P2D model) typically use the Arrhenius formula to directly correlate the electrolyte's conductivity and diffusion coefficient with instantaneous temperature. However, this approach has a major drawback: it ignores the kinetic hysteresis characteristics of the electrolyte. Especially in low-temperature environments (such as -30°C to 0°C), when the battery temperature rises rapidly due to self-heating, the electrolyte's microstructure adjustment requires time, and the recovery of its conductivity and diffusion coefficient (similar to a "thawing" process) significantly lags behind the temperature change.

[0024] This method addresses the lack of modeling for electrolyte kinetic hysteresis effects by introducing a "based on theory...calculation...effective" step. This step essentially establishes a dynamic evolution process, making the electrolyte's transport characteristics no longer an instantaneous response to temperature, but rather exhibiting a time-lag process.

[0025] Secondly, this method explicitly uses the calculated "effective conductivity" and "effective diffusion coefficient" for subsequent calculations of "liquid-phase diffusion polarization" and "ohmic polarization." This is the key to solving the problem of inaccurate low-temperature predictions. The main reason for the huge deviation (up to 15-20%) in voltage predictions during low-temperature startup is that traditional models overestimate the ionic conductivity and diffusion capacity of the electrolyte. By using "effective" parameters that are more physically accurate, this method can significantly improve the accuracy of predicting the discharge voltage of lithium-ion batteries (especially lithium iron phosphate batteries) under low-temperature conditions.

[0026] Finally, this method, while considering the aforementioned hysteresis effect, also combines electrode equilibrium potential, electrochemical polarization, and average solid-phase diffusion polarization to jointly determine the final discharge voltage. This simplified electrochemical model, which integrates multiple polarizations (and corrects for key liquid-phase and ohmic polarizations), can accurately capture the changing characteristics of the voltage curve (such as the disappearance of the voltage plateau and the trend of continuous decline) under conditions where traditional models fail, such as low-temperature high-current discharge. As shown in the examples, the average error of the predicted voltage can be significantly reduced from over 50mV in traditional models to below 15mV, thus providing a more reliable theoretical tool for the low-temperature design and optimization of lithium-ion batteries. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and therefore should not be considered as a limitation on the scope of protection of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic diagram of the hardware operating environment involved in an embodiment of the low-temperature discharge performance prediction method of the present invention; Figure 2 This is a flowchart illustrating Embodiment 1 of the low-temperature discharge performance prediction method of the present invention; Figure 3 This is a detailed flowchart of step S500 in Example 3 of the low-temperature discharge performance prediction method of the present invention; Figure 4 This is a schematic diagram of the 1C discharge results after standing at -30℃ in Example 6 of the low-temperature discharge performance prediction method of the present invention; Figure 5 This is a schematic diagram of the module connection of the low-temperature discharge performance prediction device of the present invention. Detailed Implementation

[0029] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0030] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0031] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0032] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0033] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0034] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0035] like Figure 1 The diagram shown is a structural schematic of the hardware operating environment of the terminal involved in an embodiment of the present invention.

[0036] The AAV variant tissue-specific screening system of this invention can be a PC, or a mobile terminal device such as a smartphone, tablet, or portable computer. This AAV variant tissue-specific screening system may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen, an input unit such as a keyboard, or a remote control; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a stable memory, such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001. Optionally, the AAV variant tissue-specific screening system may also include RF (Radio Frequency) circuitry, audio circuitry, a Wi-Fi module, etc. In addition, this AAV variant tissue-specific screening system can also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, which will not be described in detail here.

[0037] Those skilled in the art will understand that Figure 1 The AAV variant tissue-specific screening system shown is not intended to limit it and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. Figure 1 As shown, the memory 1005, which is a computer-readable storage medium, may include an operating system, a data interface control program, a network connection program, and an AAV variant tissue-specific screening program.

[0038] In summary, the method provided by this invention corrects the shortcomings of existing models that neglect electrolyte kinetic hysteresis ("thawing" effect) at low temperatures by calculating "effective conductivity" and "effective diffusion coefficient". By applying these more physically accurate "effective" parameters to the calculation of key liquid-phase diffusion polarization and ohmic polarization, and combining them with average solid-phase diffusion polarization, electrochemical polarization, and other terms, this method can accurately predict low-temperature discharge voltage curves, significantly reducing the average prediction error from over 50mV to below 15mV.

[0039] Example 1 Reference Figure 2 This embodiment provides a method for predicting low-temperature discharge performance, including: Step S100: Obtain the instantaneous temperature of the lithium-ion battery.

[0040] This step is the input step of the method. It means that at any point during the method's execution, the exact current temperature of the battery needs to be known. This process can be a real-time physical measurement, or it can read data from a data log or another thermal model to obtain a specific temperature value (which can be represented as T).

[0041] This step ensures that all subsequent calculations are based on the actual thermal state of the battery, which is fundamental for accurate electrochemical calculations, as almost all parameters inside the battery (such as conductivity and diffusion coefficient) are strongly dependent on temperature.

[0042] In terms of specific implementation, the physical implementation can be achieved by collecting temperature data in real time through temperature sensors (such as thermocouples or thermistors) deployed on the surface of the battery pack or cell.

[0043] In terms of simulation implementation, the instantaneous temperature T can be used as a preset input variable in software (such as MATLAB or Python scripts) or as the output result of another battery thermal model at the current moment.

[0044] Step S200: Based on the instantaneous temperature, determine the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient.

[0045] This step calculates the conductivity and diffusion coefficient values ​​that the electrolyte "should" have under ideal, complete equilibrium conditions at the instantaneous temperature T obtained in step S100.

[0046] This is a theoretical calculation that does not consider any delay effects, thus yielding two theoretical parameter values: (1) Theoretical equilibrium conductivity; (2) Theoretical equilibrium diffusion coefficient.

[0047] It establishes a clear, temperature-dependent "target value" or "steady-state value" for subsequent steps.

[0048] This step is typically achieved through a known mathematical relationship that describes the steady-state properties of the material. For example, the Arrhenius formula can be used to describe this relationship.

[0049] Step S300: Calculate the effective conductivity and effective diffusion coefficient of the electrolyte based on the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient.

[0050] This step is the core step in realizing the "hysteresis effect". It calculates the actual conductivity and diffusion coefficient values ​​of the electrolyte at the current moment.

[0051] This "effective" value will not immediately equal the "theoretical" value calculated in step S200, but will dynamically "catch up" with that theoretical value at a certain rate over time, thus obtaining two dynamically updated parameter values: Effective electrical conductivity and effective diffusion coefficient.

[0052] This is the main advantage of this method. It simulates the physical reality that the adjustment of the electrolyte's microstructure takes time (i.e., the "thawing" process). This step prevents the model from overestimating the electrolyte's performance during rapid temperature changes (such as low-temperature startup), thus making predictions more accurate.

[0053] Step S400: Calculate the liquid-phase diffusion polarization and ohmic polarization of the lithium-ion battery during the discharge process using the effective conductivity and the effective diffusion coefficient.

[0054] This step applies the "effective" parameters obtained in step S300 to the polarization calculation of the electrochemical model. Polarization is a general term for various losses that cause the battery voltage to deviate from its ideal voltage.

[0055] The above-mentioned ohmic polarization refers to the voltage drop when current flows through the resistor. The key here is the resistance of the electrolyte, which depends on the "effective conductivity".

[0056] The aforementioned liquid-phase diffusion polarization results in voltage loss due to the uneven lithium-ion concentration in the electrolyte. The degree of this unevenness depends on the diffusion capacity of the ions, i.e., the "effective diffusion coefficient".

[0057] This step yields two specific voltage drop values: liquid-phase diffusion polarization and ohmic polarization.

[0058] Because this step uses more physically accurate "effective" parameters (instead of ideal "theoretical" parameters), the voltage drop calculated is more accurate, avoiding prediction bias caused by traditional models underestimating these losses at low temperatures.

[0059] Step S500: Determine the discharge voltage of the lithium-ion battery based on the liquid-phase diffusion polarization, the ohmic polarization, the electrode equilibrium potential, the electrochemical polarization, and the average solid-phase diffusion polarization.

[0060] This is the final step of the method: summing all voltage components to derive the battery's total output voltage. This involves subtracting all major voltage drop terms from the battery's ideal open-circuit voltage (electrode equilibrium potential). The final predicted value is then obtained: the battery terminal voltage.

[0061] This step provides a final voltage prediction that integrates multiple physicochemical processes, including key, modified liquid phase and ohmic polarization. This value accurately reflects the battery's true performance under complex conditions such as low temperatures, significantly improving prediction accuracy (e.g., reducing the error from >50mV to <15mV).

[0062] Example 2 This embodiment provides a method for predicting low-temperature discharge performance. Based on the aforementioned embodiment, step S200, based on the instantaneous temperature, determines the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient, including: Step S210: Determine the theoretical equilibrium conductivity using the Arrhenius formula. and the theoretical equilibrium diffusion coefficient : [Formula 1]: ; [Formula 2]: ; Wherein, T represents the instantaneous temperature; Represents reference temperature; The theoretical equilibrium conductivity represents the instantaneous temperature T. The theoretical equilibrium diffusion coefficient represents the instantaneous temperature T. Represents reference temperature Electrolyte conductivity at the specified levels; Represents reference temperature Electrolyte diffusion coefficient; and It is the activation energy.

[0063] This embodiment clarifies an implementation algorithm with clear physical meaning. The Arrhenius equation is the standard model describing the effect of temperature on chemical reaction rates (or material transport properties). By adopting this approach, the method provides a clear, calculable "target value" or "steady-state value" that conforms to the fundamental principles of electrochemistry for subsequent steps, such as calculating hysteresis effects.

[0064] Specifically, this step can be implemented on a computer device (such as a processor) through direct mathematical calculation. Input: Instantaneous temperature T. Constants: Pre-stored or calibrated constants in memory, including: reference temperature T. ref The parameters include reference conductivity, reference diffusion coefficient, activation energy of conductivity, and activation energy of diffusion coefficient. Specific algorithms may include: (1) The processor reads T and all the above constants from memory.

[0065] (2) Processor execution The calculation.

[0066] (3) The processor multiplies the result of the previous step by... and .

[0067] (4) The processor performs exponential operations (exp[...]) on the two results above respectively.

[0068] (5) The processor finally multiplies the result of the exponentiation operation by... and ,get and .

[0069] Output: and The calculation result is passed to the next step.

[0070] In some embodiments, the effective conductivity The calculation expression [Formula 3] is: / =( ( )- ) / ; in, ( ) represents the theoretical equilibrium conductivity; This represents the conductivity hysteresis time constant.

[0071] In some embodiments, the effective diffusion coefficient The calculation expression [Formula 4] is: / =( - ) / ; in, This represents the theoretical equilibrium diffusion coefficient; This represents the diffusion coefficient lag time constant.

[0072] The above steps (calculation expressions) introduce two first-order hysteresis differential equations to simulate the "hysteresis effect" or "thawing" process of electrolyte parameters.

[0073] Regarding the effective conductivity, Formula 3 means that the rate of change of the effective conductivity depends on the difference between the theoretical equilibrium conductivity and the current effective conductivity. This catch-up rate is regulated by the conductivity lag time constant.

[0074] Similarly, for the effective diffusion coefficient, Equation 4 means that the rate of change of the effective diffusion coefficient depends on the difference between the theoretical equilibrium diffusion coefficient and the current effective diffusion coefficient, and its rate is adjusted by the diffusion coefficient lag time constant.

[0075] This step dynamically calculates and updates the values ​​of "effective conductivity" and "effective diffusion coefficient" by solving the two differential equations at each time step dt (e.g., by numerical integration in a computer).

[0076] This is the step that realizes the core advantage of this method. It accurately simulates the kinetic hysteresis effect of electrolytes at low temperatures (e.g., -30°C to 0°C) due to rapid temperature changes using a specific mathematical model. This avoids the problem of traditional models (such as those using only the Arrhenius equation) overestimating electrolyte performance, thus making subsequent polarization and voltage predictions more accurate.

[0077] In some embodiments, the calibration method for the conductivity hysteresis time constant includes: Receive conductivity calibration data; the conductivity calibration data includes: electrolyte temperature from low temperature Move to room temperature Data on conductivity changes over time t obtained in the environment and the aforementioned and steady-state conductivity and The conductivity calibration data is fitted to [Formula 5] by the processor: ; The conductivity hysteresis time constant is obtained by solving the problem. .

[0078] The above refers to the conductivity hysteresis time constant. The processor first "receives" a set of conductivity calibration data. This data may come from a physical experiment: rapidly transferring the electrolyte from a low temperature to room temperature.

[0079] The data includes: measured conductivity as a function of time t. and initial and final steady-state conductivity and Then the processor will process this set of measured data. The data is fitted to a specific exponential recovery model using algorithms (such as least squares), as shown in Equation 5.

[0080] During this fitting process As the only unknown parameter, the processor obtains its value by "solving" through this fitting algorithm.

[0081] In some embodiments, the method for calibrating the diffusion coefficient hysteresis time constant includes: Receive diffusion coefficient calibration data; the diffusion coefficient calibration data includes: electrolyte from low temperature Move to room temperature Data on the diffusion coefficient as a function of time t obtained in the environment and the aforementioned and steady-state diffusion coefficient of the following and The diffusion coefficient calibration data is fitted to [Equation 6] by the processor: ; The diffusion coefficient lag time constant is obtained by solving the problem. .

[0082] Regarding the diffusion coefficient hysteresis time constant The processor "receives" a similar set of diffusion coefficient calibration data, including measured values. , and Then the processor will The data is fitted to the corresponding exponential recovery model (as shown in Equation 6). Finally, the processor "solves" to obtain... .

[0083] The final result of the above steps is to obtain two key physical constants: the conductivity hysteresis time constant and the diffusion coefficient hysteresis time constant.

[0084] The advantage of this step is that it provides an automated and repeatable calibration method for obtaining the input parameters necessary for the core algorithm. It quantifies complex physicochemical phenomena (hysteresis effects) into two specific time constants that can be determined experimentally and computationally, enabling the practical application of dynamic models (differential equations).

[0085] Example 3 This embodiment provides a method for predicting low-temperature discharge performance. Based on the aforementioned embodiment, step S400, using the effective conductivity and the effective diffusion coefficient, calculates the liquid-phase diffusion polarization and ohmic polarization of the lithium-ion battery during the discharge process, including: Step S410, calculate according to the expression [Formula 7]: Calculate the effective ionic conductivity of the electrolyte in the positive electrode, negative electrode, and membrane regions. .

[0086] Step S420, calculate according to the expression [Formula 8]: Calculate the effective diffusion coefficient of the electrolyte in the positive and negative electrode regions. Furthermore, based on the aforementioned Calculate the liquid phase diffusion polarization; Where i represents the positive electrode, negative electrode, or membrane region; This represents the porosity of the corresponding region; This represents the Bruggeman constant for the corresponding region.

[0087] The purpose of this step is to convert the "global" effective parameters into "regional" effective parameters that take into account the porous media structure (porosity and tortuosity). These "regional" parameters are the accurate input values ​​that are actually used to calculate the polarization of that region.

[0088] The above steps involve two core mathematical processes, both of which utilize Bruggeman correction: Formula 7 defines the "global" effective conductivity. Multiply by the "porosity" of region (i) ) raised to the power of "Bruggeman constant".

[0089] Porosity This refers to the volume proportion (less than 1) of the electrolyte in this region. The "Bruggeman constant" is an empirical index (usually 1.5 or greater) used to correct for the increase in transport resistance caused by the tortuous path (torsional rigidity) of the pores. This calculation is performed separately for the positive electrode, negative electrode, and membrane regions.

[0090] Formula 8 is entirely similar to the calculation of conductivity. It uses the "global" effective diffusion coefficient. Multiply by the same Bruggeman correction factor. This calculation is performed separately for the positive and negative electrode regions. (Note: Ohmic polarization mainly occurs in the membrane region, while liquid-phase diffusion polarization mainly occurs in the electrode regions where the reaction takes place.)

[0091] The direct result of the above steps is the acquisition of two "regionalized" effective parameters: the effective ionic conductivity of the electrolyte, which is the actual ionic conductivity inside the positive electrode, negative electrode and membrane regions, taking into account the influence of pore structure; and the effective diffusion coefficient of the electrolyte, which is the actual diffusion coefficient inside the positive electrode and negative electrode regions, taking into account the influence of pore structure.

[0092] The advantage of this step is that it greatly improves the physical realism of the model. Instead of simply using the “global” parameters of the electrolyte directly to calculate polarization, it considers the actual hindering effect of the battery’s internal microstructure (porosity and tortuosity) on ion transport.

[0093] Without this correction, the model will overestimate the electrolyte's transport capacity within the porous electrode, thereby underestimating ohmic polarization and liquid-phase diffusion polarization. This would result in an overestimation of the voltage predicted at low temperatures and high currents, which would be inconsistent with reality.

[0094] By introducing Bruggeman correction, this method can more accurately quantify the true transport resistance inside the electrodes and diaphragm.

[0095] In some embodiments, the method for calculating the liquid-phase diffusion polarization includes: (1) Calculate the liquid phase diffusion time constants of the positive and negative electrodes based on the effective diffusion coefficient of the electrolyte; (2) Based on the liquid phase diffusion time constant, determine the change in liquid phase lithium ion concentration, and then determine the liquid phase diffusion polarization of the positive and negative electrodes.

[0096] In the above steps, the "effective diffusion coefficient of the electrolyte" is first used to calculate an intermediate parameter: the "liquid phase diffusion time constant". .

[0097] Then, the method uses the "liquid phase diffusion time constant" obtained in the previous step to determine another key intermediate state quantity, namely the "liquid phase lithium ion concentration change". Finally, based on this concentration change, the final "liquid phase diffusion polarization" is calculated.

[0098] The direct result of this step is the step-by-step calculation of the value of "liquid phase diffusion polarization". The logical chain is: effective diffusion coefficient of electrolyte → liquid phase diffusion time constant → change in liquid phase lithium ion concentration → liquid phase diffusion polarization.

[0099] The advantage of this method is that it provides a clear, hierarchical algorithmic logic. Instead of treating the calculation of liquid-phase diffusion polarization as a black box, it decomposes it into physically meaningful intermediate steps (time constant and concentration change), which makes the entire calculation process more structured and easier to implement.

[0100] Furthermore, the liquid phase diffusion time constant The calculation expression [Formula 9] is: ; Where i represents the positive or negative electrode; The electrode thickness represents the i-electrode region; The effective diffusion coefficient of the electrolyte representing the i-electrode region.

[0101] Formula 9 provides the "liquid phase diffusion time constant". The specific calculation method is as follows. It shows that this time constant is related to the "electrode thickness". It is proportional to the square of the value and is related to the "effective diffusion coefficient of the electrolyte". It is inversely proportional. That is, the thicker the electrode and the slower the diffusion, the longer it takes for ions to reach a steady state in that region (i.e., the time constant).

[0102] Furthermore, the liquid phase diffusion polarization The calculation expression [Formula 10] is: ; in, The liquid-phase diffusion polarization represents the i-electrode region; R represents the gas constant; T represents the instantaneous temperature; F represents the Faraday constant; Represents the lithium-ion mobility coefficient; This represents the change in the concentration of lithium ions in the liquid phase; This represents the initial concentration of the electrolyte.

[0103] Formula 10 provides for "liquid-phase diffusion polarization". The final calculation method is essentially a Nernst-type equation based on concentration polarization. It shows that the magnitude of the polarization voltage depends on several factors: (1) Physical constants such as R (gas constant), T (instantaneous temperature), and F (Faraday constant).

[0104] (2) (Lithium-ion mobility coefficient) is a parameter that describes the intrinsic properties of an electrolyte.

[0105] (3) This is determined by "changes in the concentration of lithium ions in the liquid phase". And "initial electrolyte concentration" The concentration gradient term is determined.

[0106] In some embodiments, the method for calculating ohmic polarization includes: (1) Calculate the liquid phase resistance of the positive and negative electrodes based on the effective ionic conductivity of the electrolyte, and determine the ohmic polarization of the positive and negative electrode regions based on the liquid phase resistance; The above steps clearly indicate that the ohmic polarization of the electrode is determined based on the "liquid phase resistance." This "liquid phase resistance" is calculated using the "effective ionic conductivity of the electrolyte."

[0107] (2) Calculate the ohmic polarization of the diaphragm region based on the effective ionic conductivity of the electrolyte in the diaphragm region.

[0108] This step attributes the remaining ohmic polarization to the membrane, noting that this is also calculated based on the "effective ionic conductivity of the electrolyte in the membrane region".

[0109] The logical result of this step is to decompose the total loss of ohmic polarization into three components: positive ohmic polarization, negative ohmic polarization, and diaphragm ohmic polarization, thus preparing for the final voltage summation.

[0110] The advantage of this step is that it provides a clearer computational framework that better reflects physical reality. It correctly identifies that ohmic polarization primarily occurs in the porous electrodes and porous membranes through which the electrolyte flows, and calculates these contributions separately, thereby improving the accuracy of the model.

[0111] Furthermore, the calculation expression for the ohmic polarization of the positive and negative electrode regions [Formula 11] is as follows: ; Where i represents the positive or negative electrode; The i-th electrode region represents ohmic polarization; I represents telecommunications workflow. The thickness of the i-electrode region; The solid-state conductivity of the i-electrode region; A represents the area of ​​the band-edge electrode; The effective ionic conductivity of the electrolyte in the i-electrode region represents the conductivity of the electrolyte. Furthermore, the calculation expression for the ohmic polarization of the diaphragm region [Formula 12] is as follows: ; in, The ohmic polarization represents the diaphragm region; Represents diaphragm thickness; The effective ionic conductivity of the electrolyte in the diaphragm region.

[0112] Formulas 11 and 12 were used to calculate three specific values ​​of ohmic polarization: the ohmic polarization of the positive electrode, the negative electrode, and the separator. The above calculation method provides a specific algorithm for implementing the logic. These formulas correctly incorporate the effects of low-temperature hysteresis into the calculation of ohmic polarization.

[0113] Example 4 refer to Figure 3 This embodiment provides a method for predicting low-temperature discharge performance. Based on the aforementioned embodiment, step S500, based on the liquid-phase diffusion polarization, the ohmic polarization, the electrode equilibrium potential, the electrochemical polarization, and the average solid-phase diffusion polarization, determines the discharge voltage of the lithium-ion battery, including: Step S510: Calculate the positive electrode potential and the negative electrode potential respectively.

[0114] This step is the first step in determining the total voltage. It does not directly calculate the battery's terminal voltage, but first calculates the potential of each of the two main components that make up the battery: the positive and negative electrodes.

[0115] Specifically, this involves classifying and algebraically summing all the aforementioned polarization terms and equilibrium potentials according to their respective electrodes (positive or negative).

[0116] For example, further, the calculation expressions for the positive electrode potential and the negative electrode potential [Formula 13] are as follows: ; Where i represents the positive electrode p or the negative electrode n; The potential representing the i-electrode region; The electrode equilibrium potential representing the i-electrode region; The electrochemical polarization representing the i-electrode region; The average solid-phase diffusion polarization representing the i-electrode region; The liquid-phase diffusion polarization representing the i-electrode region; The ohmic polarization representing the i-electrode region.

[0117] Step S520: Calculate the discharge voltage of the lithium-ion battery based on the positive electrode potential, the negative electrode potential, and the separator region polarization electrode, which is part of ohmic polarization.

[0118] This is the second step in determining the total voltage, and also the final calculation step in the entire method. It combines the two electrode potentials obtained in the previous step to derive the battery's overall output voltage.

[0119] Specifically, the "positive electrode potential" obtained in the previous step can be subtracted from the "negative electrode potential". Then, an independent ohmic polarization term outside the electrode is added, namely "the diaphragm region polarized electrode as part of the ohmic polarization".

[0120] Through the above steps, the final output value of the entire prediction method is obtained: "the discharge voltage of the lithium-ion battery".

[0121] Furthermore, the calculation expression for the discharge voltage of the lithium-ion battery [Formula 14] is as follows: ; in, This represents the discharge voltage of the lithium-ion battery. Represents electrode potential In this context, i represents the positive electrode potential at the positive terminal. Represents electrode potential In this context, 'i' represents the negative electrode potential when the electrode is negative. The polarization voltage representing the diaphragm region is configured as the ohmic polarization of the diaphragm region.

[0122] Furthermore, the average solid-state diffusion polarization in the i-electrode region The calculation expression [Formula 15] is: ; Where i represents the positive or negative electrode; The average solid-state diffusion polarization of electrode region i is represented by R; the gas constant is represented by T; the instantaneous temperature is represented by F; and the Faraday constant is represented by F. The maximum lithium intercalation concentration of the active material in the i-electrode region; The average surface lithium intercalation concentration of particles in the i-electrode region; The average lithium intercalation concentration of particles in the i-electrode region.

[0123] Furthermore, the calculation expression for the electrochemical polarization in the i-electrode region [Formula 16] is as follows: ; Where i represents the positive or negative electrode; Represents the electrochemical polarization of electrode region i; R represents the gas constant; T represents the instantaneous temperature; F represents the Faraday constant; α represents the electromechanical reaction transfer coefficient. This represents the average current density in the i-electrode region. This represents the average current density in the i-electrode region.

[0124] Example 5 To better illustrate the prediction method provided in the foregoing embodiments, this embodiment implements the method as follows. The detailed steps and related explanations are as follows: Step 1, Calibration of electrolyte hysteresis characteristics: Measurement A certain amount of electrolyte (preferably the same as the amount injected into the test battery) is placed in the electrolyte cell. Both ends of the electrolyte are made of stainless steel. The electrolytic cell is then heated at a low temperature. To ensure uniform internal temperature, the battery should be kept at a constant temperature of -30℃ for at least 12 hours. Then, it should be moved from the low-temperature environment to a room temperature of 25℃. Then, impedance tests at a specific frequency (100kHz~10Hz) were performed every 30 seconds to obtain the electrolyte conductivity. The fitting formula is shown in Formula 5, which yields the conductivity hysteresis time constant at different temperatures. .in, for static conductivity, for The static conductivity at that point.

[0125] Measurement A certain amount of electrolyte (preferably the same as the amount injected into the test battery) is placed in the electrolyte, with both ends of the electrolyte being metallic lithium. The electrolytic cell is then heated at a low temperature. (e.g., -30℃) keep the battery at a constant temperature for at least 12 hours to ensure uniform internal temperature. Then move the battery from the low-temperature environment to room temperature (25℃). The following steps were performed: A Git test was then conducted every 30 seconds to obtain the apparent diffusion coefficient of the electrolyte from the voltage changes observed during the Git test. The fitting formula is shown in Equation 6, yielding the diffusion coefficient hysteresis time constant at different temperatures. .in, for static conductivity, for The static conductivity at that point.

[0126] The above results were obtained at different temperatures. and The effective conductivity and effective diffusion coefficient of the electrolyte can be calculated by interpolating and substituting them into the differential equation in step 2 below.

[0127] Step 2, establish the electrolyte kinetic hysteresis model: Introducing the effective conductivity of the electrolyte and effective diffusion coefficient As time-dependent state variables, a system of first-order lag differential equations is established, as shown in Equations 3 and 4. The solution is then obtained. and Substitute these values ​​into the calculations of liquid-phase diffusion polarization and ohmic polarization in subsequent steps.

[0128] Step 3: Calculate the polarization potential of the positive electrode region.

[0129] (1) Solid-phase diffusion polarization The average solid-state diffusion polarization in the positive electrode region is shown in Equation 15 (where i=p), and the terms on the right-hand side of the above equation are recursively derived.

[0130] The formula for calculating the average lithium intercalation concentration per particle is: , in, This represents the maximum lithium intercalation concentration for the positive electrode active material. For electrode volume, This represents the volume fraction of the active material. For electrode area, The thickness of the positive electrode region; Average surface lithium intercalation concentration of particles ,in, , , ,in , which is the total surface area of ​​the positive electrode. Let be the particle radius. At the initial moment, , All It can be determined by the battery's initial SOC. is the solid-phase diffusion coefficient.

[0131] (2) Liquid-phase diffusion polarization Liquid-phase diffusion polarization originates from the presence of liquid Li in the thickness direction of the electrode region. + Concentration difference, taking the positive electrode region as an example, the liquid-phase diffusion polarization from the current collector to the membrane is referenced by formula 10, where R is the gas constant and T is the temperature. It is Li + migration coefficient This represents the initial concentration of the electrolyte. ,and The calculation uses formula 9, where The calculation is obtained using Formula 8. To account for the temperature hysteresis effect in the electrolyte diffusion coefficient, Porosity of the positive electrode region is the Bruggeman constant for the positive pole region. Initially, the electrolyte concentration in the electrode region was... .

[0132] (3) Electrochemical polarization Electrochemical polarization is the overpotential caused by charge transfer reactions and is described by the Butler-Volmer equation. Taking the positive electrode region as an example, the electrochemical polarization in this region is calculated using Equation 16, where... , The electrode specific surface area. ,in = , = , =0.5 is the electrode reaction transfer coefficient.

[0133] (4) Ohmic polarization Ohmic polarization includes liquid-phase ohmic polarization and solid-phase ohmic polarization. Taking the positive electrode region as an example, the ohmic polarization of this region is expressed by Equation 11, where the solid-phase resistance... Liquid phase resistance ), For solid-state conductivity, The effective ionic conductivity of the electrolyte is calculated using Formula 7, and its expression can be written as follows: , To account for the temperature hysteresis of electrolyte electronic conductivity, This represents the liquid phase volume in the positive electrode region, i.e., the porosity.

[0134] The voltage in the positive region is referenced in Formula 13.

[0135] Step 4: Calculate the polarization potential of the negative electrode region: The polarization voltage composition and calculation method in the negative electrode region are the same as those in the positive electrode region, except that the subscripts in the above formula are changed. Replace with .

[0136] The voltage in the negative region is referenced in Formula 13.

[0137] Step 5: Calculate the polarization potential of the diaphragm region: Because the liquid concentration gradient in the diaphragm region is small and its thickness is smaller than that of the electrode, the concentration polarization in this region is ignored, and only the ohmic polarization in this region is considered. Therefore, Formula 12 is used to calculate the ohmic polarization of the diaphragm. ,in, Formula 7 can be used for calculation, and its specific expression is as follows: , For the diaphragm thickness, For membrane porosity, is the Bruggeman constant for the diaphragm region.

[0138] Finally, the battery voltage is calculated using formula 14, which can be summarized as follows: .

[0139] It should be noted that in steps 1 and 2... ( ) and ( The steady-state conductivity and diffusion coefficient of the electrolyte at different temperatures can be obtained from the material supplier or measured by EIS and gitt.

[0140] In step 3, during the solid-phase diffusion polarization calculation, The maximum lithium intercalation concentration of the positive electrode active material can be calculated from the material's molecular formula and true density. The electrode area; Electrode thickness; This refers to the volume fraction of the active material. is the particle radius, and all are process design parameters; The initial lithium intercalation concentration of the positive electrode active material can be obtained from the initial state of charge (SOC) of the battery. The solid-phase diffusion coefficient of the cathode material is temperature-dependent and can be described by the Arrhenius equation. , ,in, The solid-phase diffusion coefficient is given at a reference temperature (25℃). In response to the activation energy, these two data points can be obtained through EIS testing or parameter identification. During the calculation of liquid-phase diffusion polarization, It is Li + The migration coefficient is generally taken as 0.3 to 0.5 (refer to the literature for values). The porosity of the positive electrode region can be obtained from the cell design parameters; is the Bruggeman constant for the positive pole region, which is taken as 1.5.

[0141] Electrochemical polarization calculation process This represents the positive electrode exchange current density, whose value is temperature-dependent and can be described by the Arrhenius equation. , ,in, The solid-phase diffusion coefficient is given at a reference temperature (25℃). To correspond to the activation energy, these two data points can be obtained through EIS testing or parameter identification.

[0142] During the calculation of Ohm polarization, The electronic conductivity of the positive electrode active material is provided by the supplier or obtained by testing with a powder resistance meter. During the diaphragm region polarization voltmeter process... The membrane porosity can be provided by the supplier; Let be the Bruggeman constant for the diaphragm region, taken as 1.5. The equilibrium potentials of the positive and negative electrodes. It can be obtained by assembling positive and negative electrode materials into a coin cell and measuring the 0.04C charge-discharge curves at different temperatures, which are temperature-dependent.

[0143] ; in, It is 25℃. This is the equilibrium potential at 25℃.

[0144] Example 6 This embodiment aims to verify the practical application effect of the aforementioned low-temperature discharge performance prediction method (hereinafter referred to as the "improved model"). The verification object is a pouch lithium iron phosphate battery with a capacity of 70Ah and a rated voltage of 3.2V.

[0145] 1. Model Establishment and Implementation Process: The specific implementation process of this method is as follows: (1) Model building: Using simulation software (such as MATLAB or Python scripts) to build an electrochemical model based on the formulas in the aforementioned embodiments.

[0146] (2) Input / output definition: In the established model, battery temperature and external current are the input parameters of the model, and battery terminal voltage is the output parameter of the model.

[0147] (3) Parameter identification: Before the model is used for prediction, key electrochemical parameters need to be identified (e.g., optimization algorithms such as genetic algorithms can be used).

[0148] (4) Prediction Execution: After parameter identification is completed, the model can predict the dynamic voltage of the battery by inputting the current and battery temperature. The input temperature data can be obtained from the measured temperature or generated by the battery thermal model.

[0149] 2. Comparison of experimental conditions and results: To verify the accuracy of the model at low temperatures, this embodiment sets specific experimental conditions: the research object (70Ah soft-pack lithium iron phosphate battery) is left to stand at -30℃ for a long time, and then discharged at a 1C rate.

[0150] refer to Figure 4 The figure shows a comparison of the "measured voltage curve", the "traditional P2D model" simulation curve and the simulation curve of the "improved model" of this invention (which has taken into account the electrolyte kinetic hysteresis effect) under this operating condition.

[0151] 3. Conclusion Analysis: pass Figure 4 The comparison clearly shows: (1) In terms of prediction accuracy: the average voltage error of the traditional P2D model is greater than 50mV. However, the "improved model" in this embodiment has a voltage prediction error of less than 15mV, which is a significant reduction in error.

[0152] (2) In terms of feature capture: the "improved model" can accurately capture the real feature changes of the low-temperature discharge curve, including the disappearance of the voltage plateau and the trend of continuous voltage decline. Its simulation results are significantly more consistent with the measured data.

[0153] refer to Figure 5 In this embodiment of the application, a low-temperature discharge performance prediction device is also provided, comprising: The parameter acquisition module 10 is used to acquire the instantaneous temperature of the lithium-ion battery; The parameter determination module 20 determines the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient based on the instantaneous temperature. The parameter calculation module 30 is used to calculate the effective conductivity and effective diffusion coefficient of the electrolyte based on the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient. The polarization calculation module 40 is used to calculate the liquid-phase diffusion polarization and ohmic polarization of the lithium-ion battery during the discharge process using the effective conductivity and the effective diffusion coefficient. The voltage determination module 50 is used to determine the discharge voltage of the lithium-ion battery based on the liquid-phase diffusion polarization, the ohmic polarization, the electrode equilibrium potential, the electrochemical polarization, and the average solid-phase diffusion polarization.

[0154] It is understood that the device in this embodiment corresponds to the low-temperature discharge performance prediction method in the above embodiments, and the options in the above embodiments are also applicable to this embodiment, so they will not be described again here.

[0155] This application also provides a computer device, which includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the low-temperature discharge performance prediction method as described in the foregoing embodiments.

[0156] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0157] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0158] This application also provides a computer storage medium storing a computer program, which, when executed on a processor, implements the low-temperature discharge performance prediction method as described in the foregoing embodiments.

[0159] The computer storage medium can be a readable storage medium, a non-volatile storage medium, or a volatile storage medium. For example, the computer storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0161] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0162] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0163] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for predicting low-temperature discharge performance, characterized in that, include: Obtain the instantaneous temperature of the lithium-ion battery; Based on the instantaneous temperature, the theoretical equilibrium conductivity and theoretical equilibrium diffusion coefficient are determined; Calculate the effective conductivity and effective diffusion coefficient of the electrolyte based on the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient. Using the effective conductivity and the effective diffusion coefficient, the liquid-phase diffusion polarization and ohmic polarization of the lithium-ion battery during the discharge process are calculated. The discharge voltage of the lithium-ion battery is determined based on the liquid-phase diffusion polarization, the ohmic polarization, the electrode equilibrium potential, the electrochemical polarization, and the average solid-phase diffusion polarization.

2. The method for predicting low-temperature discharge performance as described in claim 1, characterized in that, The determination of the theoretical equilibrium conductivity and theoretical equilibrium diffusion coefficient based on the instantaneous temperature includes: The theoretical equilibrium conductivity was determined using the Arrhenius formula. and the theoretical equilibrium diffusion coefficient : ； ； Wherein, T represents the instantaneous temperature; Represents reference temperature; The theoretical equilibrium conductivity represents the instantaneous temperature T. The theoretical equilibrium diffusion coefficient represents the instantaneous temperature T. Represents reference temperature Electrolyte conductivity at the specified levels; Represents reference temperature Electrolyte diffusion coefficient; and For activation energy; and / or, The effective conductivity The calculation expression is: / =( ( )- ) / ； in, ( ) represents the theoretical equilibrium conductivity; Represents the conductivity hysteresis time constant; and / or, The effective diffusion coefficient The calculation expression is: / =( - ) / ； in, This represents the theoretical equilibrium diffusion coefficient; This represents the diffusion coefficient lag time constant.

3. The method for predicting low-temperature discharge performance as described in claim 2, characterized in that, The calibration method for the conductivity hysteresis time constant includes: Receive conductivity calibration data; the conductivity calibration data includes: electrolyte temperature from low temperature Move to room temperature Data on conductivity changes over time t obtained in the environment and the aforementioned and steady-state conductivity and The conductivity calibration data is fitted to the following by the processor: The conductivity hysteresis time constant is obtained by solving the problem. ; and / or, The calibration method for the diffusion coefficient hysteresis time constant includes: Receive diffusion coefficient calibration data; the diffusion coefficient calibration data includes: electrolyte from low temperature Move to room temperature Data on the diffusion coefficient as a function of time t obtained in the environment and the aforementioned and steady-state diffusion coefficient of the following and The processor fits the diffusion coefficient calibration data to: The diffusion coefficient lag time constant is obtained by solving the problem. .

4. The method for predicting low-temperature discharge performance as described in claim 1, characterized in that, The calculation of liquid-phase diffusion polarization and ohmic polarization of the lithium-ion battery during discharge using the effective conductivity and the effective diffusion coefficient includes: Based on the calculation expression respectively Calculate the effective ionic conductivity of the electrolyte in the positive electrode, negative electrode, and membrane regions. ;as well as, Based on the calculation expression respectively Calculate the effective diffusion coefficient of the electrolyte in the positive and negative electrode regions. Furthermore, based on the aforementioned Calculate the liquid phase diffusion polarization; Where i represents the positive electrode, negative electrode, or membrane region; This represents the porosity of the corresponding region; Represents the Bruggeman constant for the corresponding region; and / or, The method for calculating liquid-phase diffusion polarization includes: The liquid phase diffusion time constants of the positive and negative electrodes are calculated based on the effective diffusion coefficient of the electrolyte. The change in liquid phase lithium ion concentration is determined based on the liquid phase diffusion time constant, thereby determining the liquid phase diffusion polarization of the positive and negative electrodes.

5. The method for predicting low-temperature discharge performance as described in claim 4, characterized in that, The liquid phase diffusion time constant The calculation expression is: ； Where i represents the positive or negative electrode; The electrode thickness represents the i-electrode region; The effective diffusion coefficient of the electrolyte representing the i-electrode region; and / or, The liquid phase diffusion polarization The calculation expression is: ； in, The liquid-phase diffusion polarization represents the i-electrode region; R represents the gas constant; T represents the instantaneous temperature; F represents the Faraday constant; Represents the lithium-ion mobility coefficient; This represents the change in the concentration of lithium ions in the liquid phase; This represents the initial concentration of the electrolyte.

6. The method for predicting low-temperature discharge performance as described in claim 1, characterized in that, The method for calculating ohmic polarization includes: The liquid phase resistance of the positive and negative electrodes is calculated based on the effective ionic conductivity of the electrolyte, and the ohmic polarization of the positive and negative electrode regions is determined based on the liquid phase resistance. The ohmic polarization of the diaphragm region is calculated based on the effective ionic conductivity of the electrolyte in the diaphragm region.

7. The method for predicting low-temperature discharge performance as described in claim 6, characterized in that, The calculation expressions for the Ohmic polarization of the positive and negative electrode regions are as follows: ； Where i represents the positive or negative electrode; The i-th electrode region represents ohmic polarization; I represents telecommunications workflow. The thickness of the i-electrode region; The solid-state conductivity of the i-electrode region; A represents the area of ​​the band-edge electrode; The effective ionic conductivity of the electrolyte in the i-electrode region; and / or, The expression for calculating the ohmic polarization of the diaphragm region is: ； in, The ohmic polarization represents the diaphragm region; Represents diaphragm thickness; The effective ionic conductivity of the electrolyte in the diaphragm region.

8. The method for predicting low-temperature discharge performance as described in claim 1, characterized in that, The determination of the discharge voltage of the lithium-ion battery based on the liquid-phase diffusion polarization, the ohmic polarization, the electrode equilibrium potential, the electrochemical polarization, and the average solid-phase diffusion polarization includes: Calculate the positive and negative electrode potentials respectively; The discharge voltage of the lithium-ion battery is calculated based on the positive electrode potential, the negative electrode potential, and the separator region polarization electrode, which is part of ohmic polarization.

9. The method for predicting low-temperature discharge performance as described in claim 8, characterized in that, The calculation expressions for the positive and negative electrode potentials are as follows: ； Where i represents the positive electrode p or the negative electrode n; The potential representing the i-electrode region; The electrode equilibrium potential representing the i-electrode region; The electrochemical polarization representing the i-electrode region; The average solid-phase diffusion polarization representing the i-electrode region; The liquid-phase diffusion polarization representing the i-electrode region; The ohmic polarization representing the i-electrode region; and / or, The formula for calculating the discharge voltage of the lithium-ion battery is as follows: ； in, This represents the discharge voltage of the lithium-ion battery. Represents electrode potential In this context, i represents the positive electrode potential at the positive terminal. Represents electrode potential In this context, 'i' represents the negative electrode potential when the electrode is negative. The polarization voltage representing the diaphragm region is configured as the ohmic polarization of the diaphragm region.

10. The method for predicting low-temperature discharge performance as described in claim 9, characterized in that, The average solid-state diffusion polarization in the i-electrode region The calculation expression is: ； Where i represents the positive or negative electrode; The average solid-state diffusion polarization represents the i-electrode region; R represents the gas constant; T represents the instantaneous temperature; and F represents the Faraday constant. The maximum lithium intercalation concentration of the active material in the i-electrode region; The average surface lithium intercalation concentration of particles in the i-electrode region; The average lithium intercalation concentration of particles in the i-electrode region; and / or, The calculation expression for the electrochemical polarization in the i-electrode region is: ； Where i represents the positive or negative electrode; Represents the electrochemical polarization of electrode region i; R represents the gas constant; T represents the instantaneous temperature; F represents the Faraday constant; α represents the electromechanical reaction transfer coefficient. This represents the average current density in the i-electrode region. This represents the average current density in the i-electrode region.

11. A low-temperature discharge performance prediction device, characterized in that, include: The parameter acquisition module is used to acquire the instantaneous temperature of the lithium-ion battery. The parameter determination module determines the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient based on the instantaneous temperature. The parameter calculation module is used to calculate the effective conductivity and effective diffusion coefficient of the electrolyte based on the theoretical equilibrium conductivity and the theoretical equilibrium diffusion coefficient. The polarization calculation module is used to calculate the liquid-phase diffusion polarization and ohmic polarization of the lithium-ion battery during the discharge process using the effective conductivity and the effective diffusion coefficient. The voltage determination module is used to determine the discharge voltage of the lithium-ion battery based on the liquid-phase diffusion polarization, the ohmic polarization, the electrode equilibrium potential, the electrochemical polarization, and the average solid-phase diffusion polarization.

12. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the low-temperature discharge performance prediction method according to any one of claims 1-10.

13. A computer storage medium, characterized in that, It stores a computer program, which, when executed on a processor, implements the low-temperature discharge performance prediction method according to any one of claims 1-10.

Images