A battery state monitoring method and device, electronic equipment and readable storage medium

Abstract

The application provides a battery state monitoring method and device, electronic equipment and a readable storage medium, wherein the method comprises: obtaining internal temperature data and internal total pressure data of a target battery under a plurality of preset temperature conditions in an initial state; the initial state is a state that has not undergone charging and discharging cycles; constructing a temperature-pressure correlation model according to the internal temperature data and the internal total pressure data; obtaining real-time internal temperature data and real-time internal total pressure data of the target battery during operation of the target battery; determining a temperature-pressure contribution value according to the real-time internal temperature data and the temperature-pressure correlation model; determining an aging characterization parameter according to the real-time internal total pressure data and the temperature-pressure contribution value; and outputting a health state evaluation result according to the aging characterization parameter. In this way, by correlating and decoupling the battery internal temperature and pressure, the temperature influence and the aging influence are quantitatively distinguished, and the accuracy of the battery state monitoring is improved.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a battery state monitoring method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] Cylindrical lithium-ion batteries are widely used in electric vehicles, energy storage power stations, and portable electronic devices due to their high energy density, long cycle life, and good structural stability. Accurate monitoring of battery health during use is crucial for ensuring safe equipment operation, extending battery life, and preventing safety incidents such as thermal runaway.

[0003] Existing battery state monitoring methods primarily rely on the acquisition and analysis of external battery parameters, such as voltage, current, and surface temperature, to indirectly infer the battery's internal state. Some solutions use sensors inside the battery to acquire internal pressure signals and correlate pressure changes with battery aging. However, during operation, the internal temperature of a battery fluctuates drastically and uncontrollably, while internal pressure is significantly affected by temperature. This results in highly volatile and irregular pressure signals, making it difficult to establish a stable correlation between pressure data collected under different operating conditions and the degree of aging, leading to low accuracy in battery state monitoring. Summary of the Invention

[0004] In view of this, embodiments of this application provide at least one battery state monitoring method, device, electronic device, and readable storage medium. By performing correlation analysis and decoupling processing on the internal temperature and air pressure of the battery, the effects of temperature and aging are quantitatively distinguished, thereby improving the accuracy of battery state monitoring.

[0005] This application mainly includes the following aspects: In a first aspect, embodiments of this application provide a battery state monitoring method, the method comprising: Acquire internal temperature data and internal total pressure data of the target battery under multiple preset temperature conditions in its initial state; the initial state is the state before it has undergone charge-discharge cycles. Based on the internal temperature data and the internal total air pressure data, a temperature-pressure correlation model for the target battery is constructed. During the operation of the target battery, real-time internal temperature data and real-time internal total air pressure data of the target battery are acquired; Based on the real-time internal temperature data and the temperature-pressure correlation model, the temperature and pressure contribution value of the target battery is determined. Based on the real-time internal total air pressure data and the temperature-pressure contribution value, the aging characterization parameters of the target battery are determined; Based on the aging characterization parameters, the health status assessment result of the target battery is output.

[0006] Secondly, embodiments of this application also provide a battery state monitoring device, the battery state monitoring device comprising: The data acquisition module is used to acquire internal temperature data and internal total air pressure data of the target battery under multiple preset temperature conditions in the initial state; the initial state is the state before charging and discharging cycles. The model building module is used to build a temperature-pressure correlation model of the target battery based on the internal temperature data and the internal total air pressure data. The real-time acquisition module is used to acquire real-time internal temperature data and real-time internal total air pressure data of the target battery during its operation. The contribution determination module is used to determine the temperature and pressure contribution value of the target battery based on the real-time internal temperature data and the temperature-pressure correlation model. An aging determination module is used to determine the aging characterization parameters of the target battery based on the real-time internal total air pressure data and the temperature-pressure contribution value. The result output module is used to output the health status assessment result of the target battery based on the aging characterization parameters.

[0007] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory through the bus, and the machine-readable instructions are executed by the processor to perform the steps of the battery state monitoring method as described above.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the battery state monitoring method as described above.

[0009] This application provides a battery state monitoring method, apparatus, electronic device, and readable storage medium. The method includes: acquiring internal temperature data and internal total pressure data of a target battery under multiple preset temperature conditions in an initial state; the initial state is a state that has not undergone charge-discharge cycles; constructing a temperature-pressure correlation model of the target battery based on the internal temperature data and internal total pressure data; acquiring real-time internal temperature data and real-time internal total pressure data of the target battery during operation; determining the temperature-pressure contribution value of the target battery based on the real-time internal temperature data and the temperature-pressure correlation model; determining aging characterization parameters of the target battery based on the real-time internal total pressure data and the temperature-pressure contribution value; and outputting a health status assessment result of the target battery based on the aging characterization parameters. In this way, by analyzing and decoupling the correlation between the battery's internal temperature and pressure, the influence of temperature and the influence of aging are quantitatively distinguished, improving the accuracy of battery state monitoring.

[0010] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart of a battery state monitoring method provided in an embodiment of this application is shown; Figure 2 This illustration shows one of the functional block diagrams of a battery state monitoring device provided in an embodiment of this application; Figure 3 This is a second functional block diagram of a battery state monitoring device provided in an embodiment of this application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of 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. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this 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.

[0014] To facilitate understanding of this application, the technical solutions provided in this application will be described in detail below with reference to specific embodiments.

[0015] Please see Figure 1 , Figure 1 This is a flowchart illustrating a battery state monitoring method provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the battery state monitoring method includes the following steps: S101, acquire the internal temperature data and internal total pressure data of the target battery under multiple preset temperature conditions in the initial state; the initial state is the state that has not undergone charge-discharge cycles.

[0016] Here, the internal temperature and total internal pressure data are physical parameters acquired by sensing units located inside the battery. In the initial state before the battery has undergone charge-discharge cycles, there are no aging products generated internally due to electrochemical reactions. The temperature and pressure data collected at this time reflect the inherent physical characteristics of the battery in its fresh state, including individual differences related to the manufacturing process, such as the amount of free gas inside the battery and the volume of the sealed cavity. The aging gas generated during subsequent cycles will be superimposed on this inherent pressure.

[0017] In this embodiment of the application, by performing data acquisition in a specific time window after the battery leaves the factory and before the first cycle, pure initial state data that is not affected by aging factors can be obtained. This data serves as a benchmark for all subsequent monitoring steps and is the basic premise for ensuring that the effects of temperature and aging can be quantitatively distinguished.

[0018] S102, Based on the internal temperature data and the internal total air pressure data, construct a temperature-pressure correlation model for the target battery.

[0019] Here, the temperature-pressure correlation model is a mathematical description of the inherent physical relationship between temperature and pressure inside the battery. Inside the sealed battery casing, gas pressure and temperature follow a positive correlation described by the ideal gas law. When the temperature rises, the thermal motion of molecules intensifies, and the gas pressure rises accordingly; when the temperature falls, the gas pressure falls accordingly. This gas pressure fluctuation caused by temperature changes is a dynamic physical phenomenon that occurs in real time during battery operation.

[0020] In this embodiment, a temperature-pressure correlation model is established by analyzing the correspondence between temperature data and air pressure data obtained under different temperature conditions in the initial state. This model characterizes the change law of internal air pressure of the target battery under pure temperature. Since this model is built based on initial state data and does not include the air pressure increment caused by gas production due to aging, it can serve as a temperature reference in subsequent cycles, separating the component of total air pressure caused by temperature changes at each moment.

[0021] S103, during the operation of the target battery, acquire the real-time internal temperature data and real-time internal total air pressure data of the target battery.

[0022] Here, after the target battery is put into actual use, its internal temperature continuously changes during operation as charge and discharge cycles proceed. At the same time, electrochemical reactions and side reactions lead to the gradual accumulation of aging products, and the total internal pressure also changes dynamically. Real-time internal temperature data and real-time internal total pressure data are collected synchronously under the actual operating conditions of the battery, and include comprehensive pressure information under the combined effects of temperature changes and aging gas production.

[0023] In this embodiment of the application, real-time data during operation is acquired using the same acquisition method as in the initial calibration stage, ensuring the consistency of data caliber between the initial model and real-time calculation, and providing a data foundation for subsequent quantitative separation temperature contribution value and aging contribution value.

[0024] S104, Based on the real-time internal temperature data and the temperature-pressure correlation model, determine the temperature-pressure contribution value of the target battery.

[0025] Here, the temperature-pressure contribution value refers to the pressure component in the total internal pressure of the battery at the current moment caused solely by the effect of temperature. Substituting the real-time internal temperature data into the temperature-pressure correlation model, the calculated value is the pressure value caused solely by the current temperature change if the battery has not aged, i.e., the pure temperature-pressure component after excluding the influence of aging-related gas generation.

[0026] In this embodiment, the temperature and pressure contribution values ​​at the current temperature are calculated in real time using a temperature-pressure correlation model, thus achieving a quantitative separation of the effects of temperature and aging. The real-time calculation of the temperature and pressure contribution values ​​allows for the dynamic tracking and quantitative separation of temperature-induced changes in total pressure.

[0027] S105, Based on the real-time internal total air pressure data and the temperature-pressure contribution value, determine the aging characterization parameters of the target battery.

[0028] Here, the aging characterization parameter refers to the remaining portion of the real-time internal total pressure after deducting the temperature and pressure contribution. The real-time internal total pressure includes the combined pressure value under the combined effects of temperature and aging factors. Subtracting the temperature and pressure contribution determined solely by the current temperature, the remaining pressure increment is the cumulative increase in pressure inside the battery caused by aging-related gas production.

[0029] In this embodiment, a quantitative parameter reflecting the degree of battery aging is obtained by calculating the difference between the real-time collected total gas pressure and the temperature-pressure contribution value. This aging characterization parameter is directly related to the amount of gas produced inside the battery; the more severe the aging, the more gas is produced by side reactions, and the larger the parameter value, thus achieving a quantitative characterization of the battery's aging state.

[0030] S106, based on the aging characterization parameters, output the health status assessment result of the target battery.

[0031] Here, the health status assessment result refers to the output information generated based on aging characterization parameters, used to characterize the current degree of battery aging. As a quantitative indicator reflecting the cumulative amount of gas produced inside the battery, the value of the aging characterization parameters directly corresponds to the battery's health status; the larger the value, the more severe the aging and the worse the health status.

[0032] In this embodiment of the application, by outputting aging characterization parameters as evaluation indicators of health status, external devices or battery management systems can know the current aging stage of the battery based on the quantitative parameters, thereby providing data support for subsequent battery maintenance, life prediction or safety warning.

[0033] Furthermore, acquiring the internal temperature data and internal total pressure data of the target battery under multiple preset temperature conditions in its initial state includes: Step a1: After confirming that the target battery is in the initial state, adjust the power of the target battery to a preset power value and place the target battery in a constant temperature environment.

[0034] Here, the initial state refers to the state of the battery after it leaves the factory and has not yet undergone any charge-discharge cycles. In this state, no aging products caused by electrochemical reactions have accumulated inside the battery, and it can reflect the inherent physical characteristics of the individual battery. The preset charge value corresponds to the state of the battery being completely discharged. In this charge state, the electrochemical activity inside the battery is the lowest, which helps to eliminate the interference of differences in state of charge on the internal gas pressure and facilitates the establishment of uniform benchmark conditions.

[0035] In this embodiment, by adjusting the battery power to a preset value and placing it in a constant temperature environment, different individual batteries are placed under uniform initial conditions before calibration, eliminating the baseline deviation introduced by the difference in remaining power and the difference in external ambient temperature between individual batteries, thereby making the temperature-pressure correlation models of different individual batteries comparable.

[0036] In one specific embodiment, three lithium iron phosphate batteries were selected, discharged to 0% SOC, and placed in a constant temperature chamber. Pure initial state data, unaffected by aging factors, were obtained by performing calibration after the batteries left the factory and before the first cycle.

[0037] Step a2: After the target battery reaches thermal equilibrium, the target battery is placed under each of the preset temperature conditions in sequence.

[0038] Here, thermal equilibrium refers to the state where the temperature of all regions inside the battery matches the temperature of the constant-temperature environment. In thermal equilibrium, the temperature field inside the battery is uniformly distributed, and there is no uneven pressure distribution caused by temperature gradients. The temperature and pressure data collected at this state can accurately reflect the inherent physical relationship between temperature and pressure. The preset temperature conditions include multiple different temperature points, and the battery sequentially undergoes a complete heating and isothermal process at each of these points.

[0039] In this embodiment of the application, by setting multiple preset temperature conditions, the temperature-pressure correspondence of the battery over a wide temperature range can be obtained, thereby establishing a more robust correlation model and enabling the model to have good predictive ability throughout the entire operating temperature range.

[0040] In one specific embodiment, the preset temperature conditions are three temperature points: 25°C, 35°C, and 45°C. The battery sequentially performs test steps at the three temperature points.

[0041] Step a3: Under each preset temperature condition, the target battery is heated to the preset temperature and maintained at a constant temperature for a preset duration.

[0042] Here, heating up refers to the process of raising the battery from its current temperature to a target preset temperature. The isothermal state refers to the battery's operating state where the temperature remains stable at the target preset temperature. The preset duration refers to the length of time the battery maintains this isothermal state. During the heating phase, the battery's internal temperature is dynamically changing, and the gas pressure fluctuates drastically. The rate of temperature rise and the temperature gradient are uncontrollable during this phase. However, once the isothermal state begins, the temperature remains constant, and the gas pressure tends to stabilize.

[0043] In this embodiment, maintaining a constant temperature for a preset duration provides sufficient time for the internal gas and temperature to reach adequate thermal equilibrium, ensuring that the collected pressure data reflects the true equilibrium pressure at that temperature, rather than transient fluctuations. This preset duration must be long enough to cover the complete relaxation process from the end of heating to complete pressure stabilization, while also considering testing efficiency.

[0044] In one specific embodiment, the following steps are performed: heating to 25°C for 1 hour, then holding at 25°C for 5 hours; heating to 35°C for 1 hour, then holding at 35°C for 5 hours; heating to 45°C for 1 hour, then holding at 45°C for 5 hours. By maintaining a constant temperature for 5 hours, sufficient time is provided for the internal gas and temperature to reach a adequate thermal equilibrium, ensuring that the collected gas pressure data reflects the true equilibrium gas pressure at that temperature.

[0045] Step a4: During the continuous constant temperature state, the internal temperature data and internal total air pressure data of the target battery are collected at a preset collection frequency.

[0046] Here, the duration of the isothermal state refers to the complete period of the isothermal phase. In the initial stage after the start of the isothermal state, the gas inside the battery may still be in the process of thermal relaxation, and the gas pressure data has not yet fully stabilized. The data in this stage is transitional and cannot truly reflect the temperature-gas pressure balance. The preset sampling frequency determines the temporal resolution of the data sampling. The higher the sampling frequency, the more data points are obtained, which is more beneficial for subsequent statistical analysis.

[0047] In this embodiment, after data acquisition, the raw data is filtered and transitional data during the heating phase is removed. Specifically, data acquisition is paused during the heating transition period after the start of the isothermal phase, and acquisition is resumed after the heating transition period ends, retaining only the stable data from the last period of each isothermal phase for model building. By removing the thermal relaxation transitional data at the beginning of the isothermal phase, the interference of the incompletely homogenized temperature distribution on the air pressure readings is effectively eliminated, ensuring that each data point used for modeling represents the true equilibrium air pressure at that temperature. Based on this, temperature and air pressure data are acquired synchronously, i.e., temperature and air pressure values ​​are recorded simultaneously at the same time point, ensuring a one-to-one correspondence between temperature and air pressure data over time, providing a reliable data foundation for establishing an accurate temperature-pressure correlation model. Filtering eliminates random errors introduced by sensor noise and environmental disturbances, further improving data quality.

[0048] In one specific embodiment, the data acquisition frequency is fixed at 2 seconds per acquisition, with temperature and air pressure acquired synchronously. After acquisition, the raw data undergoes filtering and the transitional data during the heating phase is removed, retaining only the stable data from the last hour of each isothermal phase for model building. By removing the thermal relaxation transitional data at the beginning of the isothermal phase, the interference of the incompletely homogenized temperature distribution on the air pressure readings is effectively eliminated, ensuring that each data point used for modeling represents the true equilibrium air pressure at that temperature. Filtering eliminates random errors introduced by sensor noise and environmental disturbances, further improving data quality.

[0049] Further, the step of collecting the internal temperature data and internal total air pressure data of the target battery at a preset collection frequency includes: Step b1: Continuously collect multiple sets of internal temperature data and multiple sets of internal total air pressure data.

[0050] Here, continuous data acquisition refers to the process of sequentially collecting multiple data points over a time series according to a preset acquisition frequency. During the stable period of the isothermal phase, temperature and pressure data are repeatedly collected at fixed time intervals to obtain multiple sets of data samples. Multiple sets of data refer to the collection of multiple data points acquired during the duration of the stable phase, with each set containing the temperature and pressure values ​​at the same moment. Because both sensors and the acquisition system have inherent noise, data acquired in a single instance may contain random errors. Therefore, it is necessary to continuously acquire multiple sets of data to obtain a sufficient sample size, providing a data foundation for subsequent statistical evaluation.

[0051] In this embodiment, the continuous sampling frequency is fixed at once every 2 seconds. By continuously sampling, multiple data points are obtained, making the subsequent dispersion calculation statistically significant. This sampling frequency ensures that subtle fluctuations in the air pressure signal can be captured without data redundancy due to overly dense sampling, thus achieving a balance between data quality and storage efficiency.

[0052] In one specific embodiment, during the last hour of the stable period of each isothermal phase, multiple sets of internal temperature data and internal total air pressure data are continuously collected at a frequency of 2 seconds per acquisition.

[0053] Step b2: Calculate the dispersion of the multiple sets of internal total air pressure data. If the dispersion does not exceed a preset threshold, then the collected multiple sets of data are determined to be valid internal temperature data and internal total air pressure data.

[0054] Here, dispersion refers to the fluctuation range between multiple sets of internal total air pressure data, used to evaluate the convergence and consistency of the data during the stable phase. Higher dispersion indicates greater fluctuations in the air pressure signal, suggesting the data has not yet reached a sufficiently stable state; lower dispersion indicates the air pressure signal has stabilized, and the collected data accurately reflects the equilibrium air pressure at that temperature. The preset threshold is the upper limit of dispersion used to determine whether the data is acceptable. When the dispersion of multiple sets of data is below this threshold, it indicates that the data quality meets the modeling requirements.

[0055] In this embodiment, the dispersion is quantitatively evaluated using standard deviation. Standard deviation is a statistical measure of the dispersion of each data point relative to the mean, objectively reflecting the fluctuation range of the data. When the standard deviation of a certain temperature range does not exceed 0.3 kPa, it indicates that the collected data in that temperature range is stable and reliable, meeting the modeling accuracy requirements. By quantitatively determining the dispersion, an objective assessment of data validity is achieved, eliminating the subjectivity introduced by manual judgment and improving the consistency and reliability of the modeling data.

[0056] Step b3: If the dispersion exceeds a preset threshold, then re-collect the internal temperature data and internal total air pressure data under the preset temperature conditions until the dispersion does not exceed the preset threshold.

[0057] Here, when the dispersion exceeds a preset threshold, it indicates that the collected data for the current temperature range fluctuates too much and cannot meet the modeling accuracy requirements, necessitating a re-execution of the data acquisition operation for that temperature point. Re-acquisition refers to re-executing the heating and isothermal step under the preset temperature condition to re-acquire multiple sets of temperature and air pressure data for that temperature point, and then recalculating the dispersion after re-acquisition for judgment, forming a cyclical process of acquisition, judgment, pass, or re-acquisition.

[0058] In this embodiment, a resampling mechanism is implemented to ensure that the collected data at each temperature point meets stability requirements before proceeding to subsequent modeling steps. Only when the data for a given temperature range is stable and meets the standards can the multiple sets of data collected at that temperature point be confirmed as valid data and used as input data for subsequent modeling. This mechanism effectively prevents data anomalies caused by occasional sensor fluctuations or environmental disturbances from being introduced into the model, thus ensuring the reliability of the baseline model from the outset.

[0059] Further, the step of constructing a temperature-pressure correlation model for the target battery based on the internal temperature data and the internal total pressure data includes: Step c1: The internal temperature data collected under each preset temperature condition is used as the independent variable and the internal total air pressure data is used as the dependent variable to form multiple data points.

[0060] Here, a data point refers to a data pair consisting of the internal temperature value and the corresponding internal total air pressure value collected at the same moment. Both temperature and air pressure are continuously changing physical quantities. The temperature value serves as the independent variable representing the external input condition, while the air pressure value serves as the dependent variable representing the system response. Multiple sets of data pairs are collected under multiple preset temperature conditions to obtain a set of data points distributed across multiple temperature points.

[0061] In this embodiment, the collected raw data is filtered to remove transitional data during the heating phase, retaining only the stable data from the last hour of each isothermal phase. The temperature and pressure data collected during the last hour of each isothermal phase are then combined into multiple data points. By removing non-equilibrium data from the heating phase, it is ensured that each data point used for modeling represents the true equilibrium pressure value at that temperature, effectively reducing the interference of data noise on the model fitting accuracy.

[0062] Step c2: Perform linear regression fitting on the multiple data points to obtain the linear regression equation.

[0063] Here, linear regression fitting is a statistical method that finds the best-fitting line by minimizing the sum of squared vertical distances between data points and the fitted line. In the temperature-pressure relationship, the goal of linear regression fitting is to find a line that best reflects the linear relationship between temperature and pressure, such that the sum of squared residuals from all data points to that line is minimized.

[0064] In this embodiment, the least squares method is used to perform linear regression on the temperature and air pressure during the isothermal phase. The least squares method determines the parameters of the regression line by minimizing the sum of squared residuals, so that the fitting result has the statistical characteristics of optimal unbiased estimation and can effectively suppress the influence of random errors on parameter estimation.

[0065] Step c3: Extract the slope parameter and intercept parameter of the linear regression equation, and use the slope parameter and intercept parameter as the model parameters of the temperature-pressure correlation model.

[0066] Here, the slope parameter is the regression coefficient of the temperature variable in the linear regression equation, representing the corresponding change in gas pressure for each unit change in temperature, reflecting the sensitivity of the gas inside the battery casing to temperature changes. The intercept parameter is the theoretical value of gas pressure when the temperature is zero in the linear regression equation, reflecting the inherent baseline level of the internal gas pressure in the initial state of the battery.

[0067] In this embodiment, slope and intercept parameters are extracted from the linear regression equation to form a temperature-pressure correlation model specific to this individual battery. The slope parameter represents the battery's temperature coefficient, measured in kPa / ℃, and the intercept parameter represents the battery's reference pressure, measured in kPa. These two parameters together determine the temperature-pressure contribution value of the battery at any temperature, and are key characteristic parameters that distinguish this battery from other individual batteries.

[0068] In a specific embodiment, taking a lithium iron phosphate battery as an example, the slope parameter k = 0.03 and the intercept parameter b = 100 are obtained. The slope parameter represents the battery's temperature coefficient, in kPa / ℃, and the intercept parameter represents the battery's reference pressure, in kPa. These two parameters together determine the temperature-pressure contribution value of the battery at any temperature, and are key characteristic parameters that distinguish this battery from other individual batteries.

[0069] Step c4: Calculate the goodness of fit of the linear regression equation. If the goodness of fit is less than the preset goodness of fit, re-execute the data collection under each preset temperature condition, and reconstruct the temperature-pressure correlation model based on the re-collected data until the goodness of fit is greater than or equal to the preset goodness of fit.

[0070] Here, goodness of fit is a quantitative indicator that evaluates how well the linear regression equation fits the data points, reflecting the model's ability to interpret the observed data. The closer the goodness of fit is to 1, the more closely the data points are distributed around the regression line, and the more significant the linear relationship between temperature and air pressure; the lower the goodness of fit, the more dispersed the data points are, and the weaker the linear relationship.

[0071] In this embodiment, the goodness of fit of the linear regression equation is used as the basis for evaluating the model quality. When the goodness of fit is less than the preset goodness of fit, it indicates that the linear relationship of the currently collected data points is not significant enough and cannot meet the modeling accuracy requirements. Data collection and model building under each preset temperature condition need to be repeated until the goodness of fit reaches the preset goodness of fit standard. A goodness of fit R² ≥ 0.99 is considered passing, indicating a very strong linear correlation between temperature and air pressure. The established regression model has an explanatory power of over 99% for air pressure data and can be reliably used for subsequent calculations of temperature and air pressure contribution values.

[0072] Further, determining the temperature and pressure contribution value of the target battery based on the real-time internal temperature data and the temperature-pressure correlation model includes: Step d1: Obtain the slope parameter and the intercept parameter of the temperature-pressure correlation model.

[0073] Here, the slope and intercept parameters are model parameters obtained through linear regression fitting during the initial calibration phase and are stored in the battery management system or the storage unit corresponding to the target battery. "Acquisition" refers to reading the values ​​of these two parameters from the storage unit for subsequent calculations. During cyclic operation, the battery management system needs to calculate the temperature and pressure contributions in real time; therefore, the model parameters stored during the calibration phase must be loaded into the computation unit before calculation.

[0074] In this embodiment, the temperature-pressure correlation model constructed during the initial calibration phase is stored as a parameter set specific to each battery, and this parameter set is called for calculation in every subsequent real-time monitoring. By using the model parameters established during the calibration phase as a fixed benchmark, it is ensured that the calculation of temperature and pressure contributions is always based on the same reference system, avoiding drift in calculation results caused by changes in model parameters.

[0075] In a specific embodiment, taking a lithium iron phosphate battery as an example, the stored slope parameter k=0.03 and intercept parameter b=100 are invoked.

[0076] Step d2: Using the real-time internal temperature data as the independent variable, calculate the corresponding dependent variable value according to the slope parameter and the intercept parameter.

[0077] Here, the real-time internal temperature data refers to the temperature values ​​collected in real time by sensors during battery operation, which are used as the independent variable input to the linear regression equation. Calculating the dependent variable value according to the slope and intercept parameters means substituting the real-time temperature into the temperature-pressure correlation model. The calculation is performed by using the battery's unique temperature coefficient and reference pressure obtained during the calibration phase to calculate the pressure value caused solely by temperature factors at the current temperature.

[0078] In this embodiment, temperature and pressure are calculated based on the relationship established during the calibration phase. Specifically, the internal temperature value at the current moment is acquired in real time, and the stored slope and intercept parameters are called. The temperature value is then substituted into the temperature-pressure correlation model for calculation to obtain the theoretical pressure value at the current temperature. This calculation process is continuously performed during the operation of the battery management system, and the calculation frequency is consistent with the data acquisition frequency to ensure that the corresponding temperature and pressure contribution value is obtained at each acquisition moment.

[0079] In one specific embodiment, at the 100th charge-discharge cycle of 0.5C, the real-time internal temperature T=30℃. Substituting T=30℃ into the model calculation... = 30 × 0.03 + 100 = 100.9 kPa. This calculation is performed continuously during the operation of the battery management system, implemented through a 16-bit MCU, with a single calculation taking ≤ 30 seconds, and the calculation frequency is consistent with the data acquisition frequency.

[0080] Step d3: The calculated dependent variable value is used as the temperature and pressure contribution value.

[0081] Here, the calculated dependent variable value is the pressure component generated solely by the temperature factor at the current temperature. This value is defined as the temperature-pressure contribution value, which is used in subsequent steps to separate the contribution of aging factors from the total pressure. The temperature-pressure contribution value represents the pressure value that would be caused by the current temperature change if the battery had not aged.

[0082] In this embodiment, the temperature and pressure contribution value is used as an intermediate calculation result and is output in real time, participating in the subsequent calculation of aging characterization parameters along with real-time internal temperature data and real-time internal total pressure data. The physical meaning of this value is the pure temperature and pressure component after excluding the influence of aging-related gas generation; it is a key intermediate quantity for subsequent quantitative separation of temperature and aging effects. By employing a 16-bit MCU to implement the above calculations, the calculation time for a single operation is controlled within 30 seconds, meeting the real-time requirements of battery status monitoring.

[0083] Further, determining the aging characterization parameters of the target battery based on the real-time internal total pressure data and the temperature-pressure contribution value includes: Step e1: Determine whether the real-time internal temperature data is within the coverage range of the multiple preset temperature conditions.

[0084] Here, the coverage range refers to the temperature interval between the lowest and highest temperatures among the multiple preset temperature conditions used in the initial calibration phase. Real-time internal temperature data continuously changes during battery operation, and may fall within or exceed the calibration temperature range. Determining whether the real-time temperature is within the coverage range is to determine whether the model established during the calibration phase can be directly used for calculations at the current temperature, or whether an extrapolation method is required.

[0085] In this embodiment, the preset temperature conditions during the calibration phase cover the main temperature range for normal battery operation. However, extreme temperature conditions exceeding this range may occur during actual battery operation. By setting a coverage range judgment step, the method can operate normally under all temperature conditions, avoiding calculation failures due to temperatures exceeding the calibration range.

[0086] Step e2, if yes, then calculate the difference between the real-time internal total air pressure data and the temperature-pressure contribution value, and use the difference as the aging characterization parameter.

[0087] Here, when the real-time internal temperature is within the coverage area, the temperature-pressure contribution value is directly calculated based on the temperature-pressure correlation model established during the calibration phase, representing the pressure component solely generated by the temperature factor at the current temperature. The real-time internal total pressure is the actual total pressure value collected at the current moment, encompassing the combined pressure of temperature and aging factors. The difference calculation involves subtracting the temperature-pressure contribution value from the real-time internal total pressure; the resulting pressure increment represents the cumulative increase in pressure caused by aging-related gas production.

[0088] In this embodiment, the difference is the aging characterization parameter, measured in kPa. When the battery is not aging, the real-time internal total pressure should equal the temperature-pressure contribution value, with a difference of zero. As aging progresses, the amount of gas produced by side reactions increases, the real-time internal total pressure gradually rises, and the difference gradually increases. By monitoring the trend of this difference, the battery aging process can be tracked in real time, and a capacity decay warning can be issued in advance when the difference reaches a preset threshold.

[0089] In one specific embodiment, the real-time internal total air pressure at the 100th cycle is... Step d2 calculates the temperature and pressure contribution values. Calculate the difference ,Will This difference serves as a parameter for characterizing aging. It represents the cumulative increase in internal pressure due to gas production during aging, expressed in kPa. When the battery is not aging, the real-time total internal pressure should equal the temperature-pressure contribution, resulting in a zero difference. As aging progresses, the amount of gas produced by side reactions increases, and the difference gradually increases. By monitoring the trend of this difference, the aging process of the battery can be tracked in real time, and the critical point of battery capacity decay can be predicted in advance.

[0090] Step e3: If not, the temperature and pressure contribution value is determined by extrapolation, and the difference between the real-time internal total pressure data and the temperature and pressure contribution value determined by extrapolation is calculated. The difference is used as the aging characterization parameter.

[0091] Here, when the real-time internal temperature exceeds the coverage range, the relationship established during the calibration phase remains applicable. The excess real-time temperature is simply substituted into the temperature-pressure correlation model to calculate the corresponding temperature and pressure contribution values. This calculation method is the same as in step d2, using the slope and intercept parameters obtained during the calibration phase to substitute the real-time temperature into the linear equation to calculate the corresponding dependent variable value. The only difference is that the independent variable value is outside the model's fitted data range. Since temperature and pressure maintain a linear relationship over a relatively wide temperature range, the extrapolation method can still provide reasonable temperature and pressure estimates within a limited interval exceeding the calibration temperature range.

[0092] In this embodiment, when the real-time internal temperature exceeds the calibrated temperature range, an extrapolation method is used to calculate the temperature-pressure contribution value. Then, the difference between the real-time total internal pressure and the extrapolated temperature-pressure contribution value is calculated as the aging characterization parameter. This extrapolation method ensures that aging characterization parameters can still be obtained even when the temperature exceeds the calibrated range, avoiding monitoring interruptions due to temperature boundary conditions. This allows the method to cover all temperature conditions during battery operation, improving the robustness and applicability of the monitoring method.

[0093] Furthermore, the output of the health status assessment result of the target battery includes: Step f1: Combine the real-time internal temperature data, the real-time internal total air pressure data, the temperature and pressure contribution values, and the aging characterization parameters into a synchronous output data set.

[0094] Here, the synchronous output data set refers to combining four parameters—real-time internal temperature data, real-time internal total pressure data, temperature-pressure contribution value, and aging characterization parameters—into a single dataset at the same time. Real-time internal temperature data reflects the current internal temperature state of the battery; real-time internal total pressure data reflects the combined pressure value under the combined effects of temperature and aging; the temperature-pressure contribution value represents the pressure component solely generated by temperature at the current temperature; and the aging characterization parameters represent the pressure increment generated by aging factors after deducting the influence of temperature. These four parameters together constitute a complete characterization of the battery's current state, providing data support for different application requirements.

[0095] In this embodiment of the application, the four parameters are combined into a synchronous output data group to ensure a one-to-one correspondence in time between the four data groups of temperature, total air pressure, temperature and air pressure contribution value and aging characterization parameters. This ensures that each item in the data group received by the battery management system comes from the same sampling time and will not cause misjudgment due to data asynchrony.

[0096] In a specific embodiment, taking the 100th cycle of a lithium iron phosphate battery as an example, =30℃ =102.3 kPa =100.9kPa =1.4kPa The four data points are combined into a synchronous output data set to ensure a one-to-one correspondence between the four data sets in time.

[0097] Step f2: Send the synchronous output data group to the battery management system of the target battery at a preset output frequency.

[0098] Here, the preset output frequency refers to the time interval at which the synchronous output data set is sent to the battery management system. The synchronous output data set is processed in the data processing unit of the battery management system. After receiving the synchronous output data set, the battery management system can obtain four data points: the current temperature of the battery, the total air pressure, the temperature-pressure contribution value, and the aging characterization parameters.

[0099] In this embodiment, by synchronously outputting and sending four data points—temperature, total air pressure, temperature-pressure contribution value, and aging characterization parameters—to the battery management system (BMS), the BMS can assess the battery's aging status in real time based on the aging characterization parameters, assisting in subsequent lifespan prediction and safety warning decision-making. Through a preset output frequency, data is continuously reported to the BMS at fixed time intervals, forming a continuous data stream that provides continuous and complete data input for the BMS's real-time decision-making. The aging characterization parameters, as a quantitative assessment indicator of health status, directly reflect the battery's current relative health level; a higher value indicates more severe aging and a worse health status. The BMS can determine the battery's capacity decay rate based on the trend of this parameter over time, predicting the battery's capacity decay critical point in advance, thereby achieving continuous tracking and assessment of the battery's health status.

[0100] Furthermore, before acquiring the internal temperature data and internal total pressure data of the target battery under multiple preset temperature conditions in its initial state, the method further includes: Step g1: Obtain the historical operation records of the target battery.

[0101] Here, historical operation records refer to the recorded data of all charge and discharge operations of the target battery since it left the factory, including the time, number of operations, capacity, and other relevant information. This record can be stored in the battery management system or the battery's storage unit, and contains the battery's complete usage history from manufacturing to the present moment.

[0102] In this embodiment of the application, by obtaining the historical operation records of the target battery, it is possible to objectively know whether the battery has undergone charge-discharge cycles, providing objective data basis for judging whether the battery has an initial state.

[0103] Step g2: Based on the historical operation records, determine whether the target battery is in the initial state.

[0104] Here, the initial state is the state in which the battery has not undergone any charge-discharge cycles, meaning that the battery has not undergone any charge-discharge operations since it left the factory. The battery's initial state is determined based on the cumulative number of charge-discharge cycles recorded in the historical operation log. If the cumulative number of charge-discharge cycles is zero, the battery is determined to be in the initial state; if the cumulative number of charge-discharge cycles is not zero, the battery is determined not to be in the initial state.

[0105] In this embodiment of the application, the status is determined by historical operation records, which avoids the uncertainty of inferring the initial status based solely on indirect information such as appearance or manufacturing time, and achieves accurate identification of the initial status.

[0106] Step g3: If yes, adjust the power of the target battery to a preset power value and place the target battery in a constant temperature environment.

[0107] Here, the preset charge value refers to the preset state of charge that the battery needs to reach before the initial calibration, and the constant temperature environment refers to the external environmental conditions that can maintain a constant temperature.

[0108] In this embodiment, after determining that the battery is in the initial state, the battery charge is adjusted to a preset charge value to eliminate the influence of the difference in state of charge on the internal air pressure, so as to facilitate the establishment of a unified reference condition. Then, the battery is placed in a constant temperature environment so that the battery has a unified and controlled starting ambient temperature in subsequent temperature point tests, avoiding the interference of external ambient temperature fluctuations on the calibration results.

[0109] Step g4: If not, then terminate the execution of subsequent steps.

[0110] Here, when it is determined that the battery is not in its initial state, the battery has already undergone charge-discharge cycles, and aging products may have been generated inside it. It is impossible to obtain pure initial state data that is not affected by aging factors. Under this condition, the temperature-pressure correlation model established will not be able to serve as a benchmark for removing the influence of temperature in subsequent cycles.

[0111] In this embodiment of the application, by terminating the execution of subsequent steps, the model failure caused by forcibly performing calibration on batteries that do not meet the initial state conditions is avoided. This ensures that the battery state monitoring method is only executed on batteries that have an initial state at the source, thus ensuring the validity and reliability of the monitoring data.

[0112] This application provides a battery state monitoring method, comprising: acquiring internal temperature data and internal total pressure data of a target battery under multiple preset temperature conditions in an initial state; the initial state being a state that has not undergone charge-discharge cycles; constructing a temperature-pressure correlation model of the target battery based on the internal temperature data and internal total pressure data; acquiring real-time internal temperature data and real-time internal total pressure data of the target battery during operation; determining the temperature-pressure contribution value of the target battery based on the real-time internal temperature data and the temperature-pressure correlation model; determining aging characterization parameters of the target battery based on the real-time internal total pressure data and the temperature-pressure contribution value; and outputting a health status assessment result of the target battery based on the aging characterization parameters. In this way, by analyzing and decoupling the correlation between the battery's internal temperature and pressure, the influence of temperature and the influence of aging are quantitatively distinguished, improving the accuracy of battery state monitoring.

[0113] Based on the same application concept, this application also provides a battery state monitoring device corresponding to the battery state monitoring method provided in the above embodiments. Since the principle of the device in this application is similar to the battery state monitoring method in the above embodiments of this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0114] Please see Figure 2 , Figure 2 This is one of the functional block diagrams of a battery state monitoring device provided in an embodiment of this application. Figure 2 As shown, the battery state monitoring device 200 provided in this application embodiment includes: The data acquisition module 210 is used to acquire internal temperature data and internal total air pressure data of the target battery under multiple preset temperature conditions in the initial state; the initial state is the state that has not undergone charge-discharge cycles.

[0115] The model building module 220 is used to build a temperature-pressure correlation model of the target battery based on the internal temperature data and the internal total air pressure data.

[0116] The real-time acquisition module 230 is used to acquire real-time internal temperature data and real-time internal total air pressure data of the target battery during the operation of the target battery.

[0117] The contribution determination module 240 is used to determine the temperature and pressure contribution value of the target battery based on the real-time internal temperature data and the temperature-pressure correlation model.

[0118] The aging determination module 250 is used to determine the aging characterization parameters of the target battery based on the real-time internal total air pressure data and the temperature-pressure contribution value.

[0119] The result output module 260 is used to output the health status assessment result of the target battery based on the aging characterization parameters.

[0120] Furthermore, when the data acquisition module 210 acquires internal temperature data and internal total pressure data of the target battery under multiple preset temperature conditions in its initial state, the data acquisition module 210 is specifically used for: After confirming that the target battery is in the initial state, the power of the target battery is adjusted to a preset power value, and the target battery is placed in a constant temperature environment; After the target battery reaches thermal equilibrium, the target battery is placed under each of the preset temperature conditions in sequence; Under each preset temperature condition, the target battery is heated to the preset temperature and maintained at a constant temperature for a preset duration; During the period of constant temperature, the internal temperature data and internal total air pressure data of the target battery are collected at a preset collection frequency.

[0121] Furthermore, when the data acquisition module 210 is used to acquire the internal temperature data and internal total air pressure data of the target battery according to a preset acquisition frequency, the data acquisition module 210 is specifically used for: Continuously collect multiple sets of internal temperature data and multiple sets of internal total air pressure data; Calculate the dispersion of the multiple sets of internal total air pressure data. If the dispersion does not exceed a preset threshold, then the collected multiple sets of data are determined to be valid internal temperature data and internal total air pressure data. If the dispersion exceeds a preset threshold, the internal temperature data and internal total air pressure data under the preset temperature conditions are re-collected until the dispersion does not exceed the preset threshold.

[0122] Furthermore, when the model building module 220 constructs a temperature-pressure correlation model of the target battery based on the internal temperature data and the internal total air pressure data, the model building module 220 is specifically used for: The internal temperature data collected under each preset temperature condition is used as the independent variable and the internal total air pressure data is used as the dependent variable to form multiple data points; A linear regression fit is performed on the multiple data points to obtain the linear regression equation; Extract the slope parameter and intercept parameter of the linear regression equation, and use the slope parameter and intercept parameter as the model parameters of the temperature-pressure correlation model; Calculate the goodness of fit of the linear regression equation. If the goodness of fit is less than the preset goodness of fit, re-execute the data collection under each preset temperature condition, and reconstruct the temperature-pressure correlation model based on the re-collected data until the goodness of fit is greater than or equal to the preset goodness of fit.

[0123] Furthermore, when determining the temperature and pressure contribution value of the target battery based on the real-time internal temperature data and the temperature-pressure correlation model, the contribution determination module 240 is specifically used for: Obtain the slope parameter and the intercept parameter of the temperature-pressure correlation model; Using the real-time internal temperature data as the independent variable, the corresponding dependent variable value is calculated according to the slope parameter and the intercept parameter; The calculated dependent variable value is used as the temperature and pressure contribution value.

[0124] Furthermore, when the aging determination module 250 determines the aging characterization parameters of the target battery based on the real-time internal total pressure data and the temperature-pressure contribution value, the aging determination module 250 is specifically used for: Determine whether the real-time internal temperature data falls within the coverage range of the multiple preset temperature conditions; If so, the difference between the real-time internal total air pressure data and the temperature-pressure contribution value is calculated, and the difference is used as the aging characterization parameter. If not, the temperature and pressure contribution value is determined by extrapolation, and the difference between the real-time internal total pressure data and the temperature and pressure contribution value determined by extrapolation is calculated. The difference is used as the aging characterization parameter.

[0125] Furthermore, when outputting the health status assessment result of the target battery, the result output module 260 is specifically used for: The real-time internal temperature data, the real-time internal total air pressure data, the temperature and air pressure contribution values, and the aging characterization parameters are combined into a synchronous output data set. The synchronous output data group is sent to the battery management system of the target battery at a preset output frequency.

[0126] Further, please refer to Figure 3 , Figure 3 This is a second functional block diagram of a battery state monitoring device provided in an embodiment of this application. Figure 3 As shown, the battery status monitoring device 200 further includes a condition determination module 270, which is used for: Obtain the historical operation records of the target battery; Based on the historical operation records, determine whether the target battery is in the initial state; If so, adjust the power of the target battery to the preset power value and place the target battery in a constant temperature environment; If not, then the subsequent steps will be terminated.

[0127] This application provides a battery state monitoring device, comprising: a data acquisition module for acquiring internal temperature data and internal total pressure data of a target battery under multiple preset temperature conditions in its initial state (the initial state being a state that has not undergone charge-discharge cycles); a model building module for constructing a temperature-pressure correlation model of the target battery based on the internal temperature data and internal total pressure data; a real-time acquisition module for acquiring real-time internal temperature data and real-time internal total pressure data of the target battery during operation; a contribution determination module for determining the temperature and pressure contribution value of the target battery based on the real-time internal temperature data and the temperature-pressure correlation model; an aging determination module for determining aging characterization parameters of the target battery based on the real-time internal total pressure data and the temperature and pressure contribution value; and a result output module for outputting a health status assessment result of the target battery based on the aging characterization parameters. Thus, by analyzing and decoupling the correlation between the battery's internal temperature and pressure, the effects of temperature and aging are quantitatively distinguished, improving the accuracy of battery state monitoring.

[0128] Based on the same application concept, please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.

[0129] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 and the memory 420 communicate through the bus 430. When the machine-readable instructions are executed by the processor 410, they perform the steps of the battery status monitoring method provided in the above embodiment. For specific implementation details, please refer to the method embodiment, which will not be repeated here.

[0130] Based on the same concept, this application also provides a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it executes the steps of the battery state monitoring method provided in the above embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0131] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0132] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0135] If the aforementioned functions are implemented as software functional units 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 portion 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 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. The aforementioned storage medium includes 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.

[0136] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0137] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A battery state monitoring method, characterized in that, The method includes: Acquire internal temperature data and internal total pressure data of the target battery under multiple preset temperature conditions in its initial state; the initial state is the state before it has undergone charge-discharge cycles. Based on the internal temperature data and the internal total air pressure data, a temperature-pressure correlation model for the target battery is constructed. During the operation of the target battery, real-time internal temperature data and real-time internal total air pressure data of the target battery are acquired; Based on the real-time internal temperature data and the temperature-pressure correlation model, the temperature and pressure contribution value of the target battery is determined. Based on the real-time internal total air pressure data and the temperature-pressure contribution value, the aging characterization parameters of the target battery are determined; Based on the aging characterization parameters, the health status assessment result of the target battery is output.

2. The battery state monitoring method according to claim 1, characterized in that, The acquisition of internal temperature data and internal total pressure data of the target battery under multiple preset temperature conditions in its initial state includes: After confirming that the target battery is in the initial state, the power of the target battery is adjusted to a preset power value, and the target battery is placed in a constant temperature environment; After the target battery reaches thermal equilibrium, the target battery is placed under each of the preset temperature conditions in sequence; Under each preset temperature condition, the target battery is heated to the preset temperature and maintained at a constant temperature for a preset duration; During the period of constant temperature, the internal temperature data and internal total air pressure data of the target battery are collected at a preset collection frequency.

3. The battery state monitoring method according to claim 2, characterized in that, The process of collecting the internal temperature data and internal total air pressure data of the target battery at a preset collection frequency includes: Continuously collect multiple sets of internal temperature data and multiple sets of internal total air pressure data; Calculate the dispersion of the multiple sets of internal total air pressure data. If the dispersion does not exceed a preset threshold, then the collected multiple sets of data are determined to be valid internal temperature data and internal total air pressure data. If the dispersion exceeds a preset threshold, the internal temperature data and internal total air pressure data under the preset temperature conditions are re-collected until the dispersion does not exceed the preset threshold.

4. The battery state monitoring method according to claim 1, characterized in that, The step of constructing a temperature-pressure correlation model for the target battery based on the internal temperature data and the internal total air pressure data includes: The internal temperature data collected under each preset temperature condition is used as the independent variable and the internal total air pressure data is used as the dependent variable to form multiple data points; A linear regression fit is performed on the multiple data points to obtain the linear regression equation; Extract the slope parameter and intercept parameter of the linear regression equation, and use the slope parameter and intercept parameter as the model parameters of the temperature-pressure correlation model; Calculate the goodness of fit of the linear regression equation. If the goodness of fit is less than the preset goodness of fit, re-execute the data collection under each preset temperature condition, and reconstruct the temperature-pressure correlation model based on the re-collected data until the goodness of fit is greater than or equal to the preset goodness of fit.

5. The battery state monitoring method according to claim 1, characterized in that, The step of determining the temperature and pressure contribution value of the target battery based on the real-time internal temperature data and the temperature-pressure correlation model includes: Obtain the slope parameter and the intercept parameter of the temperature-pressure correlation model; Using the real-time internal temperature data as the independent variable, the corresponding dependent variable value is calculated according to the slope parameter and the intercept parameter; The calculated dependent variable value is used as the temperature and pressure contribution value.

6. The battery state monitoring method according to claim 1, characterized in that, The step of determining the aging characterization parameters of the target battery based on the real-time internal total pressure data and the temperature-pressure contribution value includes: Determine whether the real-time internal temperature data falls within the coverage range of the multiple preset temperature conditions; If so, the difference between the real-time internal total air pressure data and the temperature-pressure contribution value is calculated, and the difference is used as the aging characterization parameter. If not, the temperature and pressure contribution value is determined by extrapolation, and the difference between the real-time internal total pressure data and the temperature and pressure contribution value determined by extrapolation is calculated. The difference is used as the aging characterization parameter.

7. The battery state monitoring method according to claim 1, characterized in that, The output of the health status assessment result of the target battery includes: The real-time internal temperature data, the real-time internal total air pressure data, the temperature and air pressure contribution values, and the aging characterization parameters are combined into a synchronous output data set. The synchronous output data group is sent to the battery management system of the target battery at a preset output frequency.

8. A battery state monitoring device, characterized in that, The battery status monitoring device includes: The data acquisition module is used to acquire internal temperature data and internal total air pressure data of the target battery under multiple preset temperature conditions in the initial state; the initial state is the state before charging and discharging cycles. The model building module is used to build a temperature-pressure correlation model of the target battery based on the internal temperature data and the internal total air pressure data. The real-time acquisition module is used to acquire real-time internal temperature data and real-time internal total air pressure data of the target battery during its operation. The contribution determination module is used to determine the temperature and pressure contribution value of the target battery based on the real-time internal temperature data and the temperature-pressure correlation model. An aging determination module is used to determine the aging characterization parameters of the target battery based on the real-time internal total air pressure data and the temperature-pressure contribution value. The result output module is used to output the health status assessment result of the target battery based on the aging characterization parameters.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the machine-readable instructions are executed by the processor to perform the steps of the battery state monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the battery state monitoring method as described in any one of claims 1 to 7.

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