A battery monitoring method, electronic device, storage medium, and program product

Abstract

The application discloses a battery monitoring method, an electronic device, a storage medium and a program product. It relates to the technical field of battery safety protection, and the battery monitoring method comprises the following steps: when a target battery is subjected to charging and discharging, acquiring dynamic electrochemical impedance spectrum (EIS) data of the target battery at the current time, wherein the dynamic EIS data comprises dynamic impedance and a dynamic phase angle; respectively correcting the dynamic impedance and the dynamic phase angle to obtain equivalent impedance and an equivalent phase angle; predicting the internal temperature of the target battery according to the equivalent phase angle; and monitoring the target battery according to the internal temperature and the equivalent impedance. The scheme provided by the application can realize accurate and timely early warning and valve opening diagnosis of battery thermal runaway based on dynamic EIS data of the battery, so that the system safety is improved.

Description

Technical Field

[0001] This invention relates to the field of battery safety protection technology, and in particular to a battery monitoring method, electronic device, storage medium, and program product. Background Technology

[0002] Batteries, as energy storage devices that convert chemical energy into electrical energy, have long been deeply integrated into modern society's production and daily life, becoming a key hub connecting energy production and consumption. During operation, batteries typically generate a large amount of heat. The accumulation of this heat causes the battery temperature to rise rapidly, and higher temperatures accelerate internal electrochemical reactions, leading to excessively high battery temperatures or large temperature differences, and even triggering safety accidents such as thermal runaway and fires. Therefore, early warning systems for battery thermal runaway are of great significance for improving battery safety.

[0003] Currently, most methods for early warning of thermal runaway in batteries rely on external temperature / static electrochemical impedance spectroscopy (EIS) data, which has a certain time lag. Moreover, the external temperature / static EIS data of the battery cannot reflect the actual operating conditions of the battery, resulting in low prediction accuracy. Summary of the Invention

[0004] This invention provides a battery monitoring method, electronic device, storage medium, and program product that can achieve accurate and timely early warning and valve opening diagnosis of battery thermal runaway based on the battery's dynamic EIS data, thereby improving system safety.

[0005] According to one aspect of the present invention, a battery monitoring method is provided, comprising: acquiring dynamic electrochemical impedance spectroscopy (EIS) data of the target battery at the current time when the target battery is being charged and discharged, wherein the dynamic EIS data includes dynamic impedance and dynamic phase angle; correcting the dynamic impedance and dynamic phase angle respectively to obtain equivalent impedance and equivalent phase angle; predicting the internal temperature of the target battery based on the equivalent phase angle; and monitoring the target battery based on the internal temperature and equivalent impedance.

[0006] Optionally, the dynamic impedance and dynamic phase angle are corrected separately to obtain the equivalent impedance and equivalent phase angle, including: obtaining the EIS mapping relationship corresponding to the cell type of the target battery, wherein the EIS mapping relationship includes a first sub-relationship and a second sub-relationship, and the EIS mapping relationship is obtained by fitting the calibrated static EIS data and calibrated dynamic EIS data of the battery corresponding to the cell type; performing modulus mapping on the dynamic impedance based on the first sub-relationship to obtain the equivalent impedance; and performing phase relaxation correction on the dynamic phase angle based on the second sub-relationship to obtain the equivalent phase angle.

[0007] Optionally, the first subrelation is represented as The second sub-relation is represented as ;in, For equivalent impedance, For dynamic impedance, The charging and discharging current of the target battery. This is the actual measured surface temperature of the battery cell at the current moment. For the equivalent phase angle, For dynamic phase angle, , , , , , All are constants.

[0008] Optionally, based on the equivalent phase angle, the internal temperature of the target battery is predicted, including: obtaining a temperature prediction model corresponding to the cell type of the target battery, wherein the temperature prediction model is expressed as... , This indicates the predicted internal temperature of the battery. Indicates the phase angle. , , , All are constants; the internal temperature is obtained by substituting the equivalent phase angle into the temperature prediction model.

[0009] Optionally, for any cell type, the method for constructing a temperature prediction model corresponding to the cell type includes: obtaining test EIS data of the cell type in a fully charged state and at different temperatures; determining the phase angle and temperature data corresponding to the shuttle frequency from the test EIS data; and fitting the phase angle and temperature data corresponding to the shuttle frequency to obtain a temperature prediction model corresponding to the cell type.

[0010] Optionally, the target battery is monitored based on its internal temperature and equivalent impedance, including: determining the real part of the impedance at the current moment based on the equivalent impedance; determining whether the real part of the impedance at the current moment has changed relative to the real part of the impedance at the previous moment, and determining that the target battery has experienced a valve opening event when a change occurs; and determining the current state of the target battery based on its internal temperature.

[0011] Optionally, the current state of the target battery is determined based on the internal temperature, including: determining the relationship between the internal temperature and a first preset threshold and a second preset threshold, wherein the first preset threshold is less than the second preset threshold; if the internal temperature is less than the first preset threshold, the current state of the target battery is determined to be a normal operating state; if the internal temperature is greater than or equal to the first preset threshold, the current state of the target battery is determined to be a state about to enter thermal runaway, and a first alarm signal is output; if the internal temperature is greater than or equal to the second preset threshold and the external temperature of the target battery is greater than or equal to a third preset threshold, the current state of the target battery is determined to be a thermal runaway state, and a second alarm signal is output.

[0012] According to another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the battery monitoring method of any embodiment of the present invention.

[0013] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the battery monitoring method of any embodiment of the present invention.

[0014] According to another aspect of the present invention, a computer program product is provided, the computer program product including a computer program that, when executed by a processor, implements the battery monitoring method of any embodiment of the present invention.

[0015] The technical solution of this invention involves acquiring dynamic EIS data of the target battery during charging and discharging, and correcting the dynamic impedance and dynamic phase angle included in the dynamic EIS data to obtain equivalent impedance and equivalent phase angle. Then, based on the equivalent phase angle, the internal temperature of the target battery is predicted. Finally, the target battery is monitored based on the internal temperature and equivalent impedance. Compared with existing battery thermal runaway early warning methods, firstly, since this invention is based on dynamic EIS data during the charging and discharging process of the target battery, the dynamic EIS data can reflect the actual operating conditions of the battery. This invention further corrects the dynamic impedance and dynamic phase angle to eliminate errors, thereby providing an accurate data basis for subsequent monitoring. Secondly, predicting the internal temperature of the target battery based on the equivalent phase angle achieves non-invasive measurement of the internal temperature, avoiding the lag in subsequent early warnings, and reducing monitoring costs, thus having wider applicability. Thirdly, monitoring the target battery based on internal temperature and equivalent impedance not only enables early warning of battery thermal runaway but also enables valve opening diagnosis of the battery, providing more comprehensive protection for the battery and improving system safety.

[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a battery monitoring method provided in Embodiment 1 of the present invention;

[0019] Figure 2 This is a schematic flowchart of a battery monitoring method provided in Embodiment 2 of the present invention;

[0020] Figure 3 This is a static EIS test comparison chart provided in Embodiment 2 of the present invention;

[0021] Figure 4 This is a dynamic EIS test comparison chart provided in Embodiment 2 of the present invention;

[0022] Figure 5 This is a schematic diagram of the real and imaginary parts of an equivalent impedance provided in Embodiment 2 of the present invention;

[0023] Figure 6 This is a schematic diagram of the phase angle corresponding to the shuttle frequency provided in Embodiment 2 of the present invention;

[0024] Figure 7 This is a graph showing the relationship between the phase angle corresponding to the shuttle frequency and the internal temperature of the battery, provided in Embodiment 2 of the present invention.

[0025] Figure 8 This is a schematic diagram of a thermal runaway early warning signal, a thermal runaway signal, and a valve opening diagnostic signal provided in Embodiment 2 of the present invention;

[0026] Figure 9 This is a schematic diagram of the structure of a battery monitoring device provided in Embodiment 3 of the present invention;

[0027] Figure 10 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart illustrating a battery monitoring method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where a target battery is being monitored. The method can be executed by a battery monitoring device, which can be implemented in hardware and / or software. This battery monitoring device can be configured in electronic devices (such as computers, servers, or battery management devices such as battery management systems (BMS)). Figure 1 As shown, the method includes:

[0032] S110. When the target battery is being charged and discharged, acquire the dynamic electrochemical impedance spectroscopy (EIS) data of the target battery at the current moment, wherein the dynamic EIS data includes dynamic impedance and dynamic phase angle.

[0033] The battery monitoring method provided by this invention can be executed in real time or periodically. The target battery is any battery that needs to be monitored, for example, a lithium-ion battery. The current time is the time corresponding to the current execution of the battery monitoring method.

[0034] In this invention, dynamic EIS data refers to the EIS data measured during the charging and discharging process of the target battery, while static EIS data refers to the EIS data measured when the target battery is not being charged or discharged. Specifically, the measurement of EIS data needs to be tailored to the differences in the battery's operating state, with targeted design of test procedures, control parameters, and environmental conditions to ensure that the data accurately reflects the electrochemical characteristics of the battery under the corresponding state.

[0035] In one embodiment, a method for acquiring dynamic EIS data of a target battery at a current moment may include: superimposing a small-amplitude sinusoidal AC voltage or current signal (i.e., an excitation signal) onto the target battery, the frequency of which is in the low-frequency (0.1-10Hz) and high-frequency (1k-10kHz) range; measuring the AC current or voltage response through the target battery; and for each frequency point, calculating the ratio of voltage to current to obtain the dynamic impedance, and calculating the phase difference to obtain the dynamic phase angle.

[0036] S120. Correct the dynamic impedance and dynamic phase angle respectively to obtain the equivalent impedance and equivalent phase angle.

[0037] Since dynamic EIS data is measured when the target battery is charging and discharging, although it can reflect the actual operating conditions of the battery, it fluctuates more than static EIS data. Therefore, this invention also needs to correct the dynamic EIS data to eliminate errors and obtain more accurate data.

[0038] Specifically, dynamic impedance and dynamic phase angle can be corrected separately to obtain equivalent impedance and equivalent phase angle. This is because impedance and phase angle are different types of parameters, and using different correction methods will better suit the parameter characteristics. It is understandable that there is no sequential relationship between the process of correcting dynamic impedance and the process of correcting dynamic phase angle.

[0039] To correct dynamic impedance to obtain equivalent impedance: In one implementation, an impedance correction value is first determined, and then the dynamic impedance is corrected based on this value to obtain the equivalent impedance. In another implementation, the dynamic impedance is corrected based on an impedance correction relationship to obtain the equivalent impedance. This impedance correction relationship can be predetermined or obtained by fitting calibrated static and dynamic EIS data. Yet another implementation utilizes a correction model to correct the dynamic impedance to obtain the equivalent impedance.

[0040] To correct the dynamic phase angle and obtain the equivalent phase angle: In one implementation, a phase correction value can be determined first, and then the dynamic phase angle can be corrected based on this value to obtain the equivalent phase angle. In another implementation, the dynamic phase angle can be corrected based on a phase correction relationship to obtain the equivalent phase angle. This phase correction relationship can be predetermined or obtained by fitting calibrated static EIS data and calibrated dynamic EIS data. In yet another implementation, a correction model can be used to correct the dynamic phase angle to obtain the equivalent phase angle.

[0041] It should be noted that the aforementioned correction model for dynamic impedance and correction model for dynamic phase angle can be one model or two different models.

[0042] In this way, we obtained accurate equivalent impedance and equivalent phase angle that reflect the actual operating conditions of the battery, providing an accurate data basis for subsequent monitoring.

[0043] S130. Predict the internal temperature of the target battery based on the equivalent phase angle.

[0044] In EIS spectra, the phase angle is extremely sensitive to temperature changes: an increase in temperature will significantly accelerate the electrochemical reaction kinetics, resulting in a shift in the peak frequency and amplitude of the phase angle. This relationship is often more monotonic and unique than the relationship between impedance amplitude and temperature, and therefore can be used as a basis for predicting the internal temperature of the battery.

[0045] Specifically, a mapping relationship / temperature prediction model between phase angle and temperature can be established in advance through experiments. After obtaining the equivalent phase angle, the internal temperature of the target battery can be predicted through this mapping relationship / temperature prediction model.

[0046] Internal temperature, distinct from external temperature (i.e., the surface temperature on the battery casing), reflects the temperature of the core region where electrochemical reactions occur. Therefore, it avoids the warning lag that can result from subsequent monitoring using external temperature. Furthermore, the method of predicting the internal temperature of a target battery using equivalent phase angles allows for non-invasive measurement of internal temperature without damaging the battery structure, reducing monitoring costs and broadening its applicability.

[0047] S140. Monitor the target battery based on its internal temperature and equivalent impedance.

[0048] After obtaining the internal temperature of the target battery, the target battery can be monitored by combining the equivalent impedance.

[0049] Specifically, the detection of the target battery may include, but is not limited to, at least one of the following: thermal runaway early warning, valve opening diagnosis, battery state of health (SOH) assessment, power analysis, and fault analysis.

[0050] The technical solution of this invention involves acquiring dynamic EIS data of the target battery during charging and discharging, and correcting the dynamic impedance and dynamic phase angle included in the dynamic EIS data to obtain equivalent impedance and equivalent phase angle. Then, based on the equivalent phase angle, the internal temperature of the target battery is predicted. Finally, the target battery is monitored based on the internal temperature and equivalent impedance. Compared with existing battery thermal runaway early warning methods, firstly, since this invention is based on dynamic EIS data during the charging and discharging process of the target battery, the dynamic EIS data can reflect the actual operating conditions of the battery. This invention further corrects the dynamic impedance and dynamic phase angle to eliminate errors, thereby providing an accurate data basis for subsequent monitoring. Secondly, predicting the internal temperature of the target battery based on the equivalent phase angle achieves non-invasive measurement of the internal temperature, avoiding the lag in subsequent early warnings, and reducing monitoring costs, thus having wider applicability. Thirdly, monitoring the target battery based on internal temperature and equivalent impedance not only enables early warning of battery thermal runaway but also enables valve opening diagnosis of the battery, providing more comprehensive protection for the battery and improving system safety.

[0051] Example 2

[0052] Figure 2 This is a flowchart illustrating a battery monitoring method provided in Embodiment 2 of the present invention. Based on Embodiment 1, this embodiment provides a specific monitoring method. Figure 2 As shown, the method includes:

[0053] S210. When the target battery is charging and discharging, acquire the dynamic EIS data of the target battery at the current moment, wherein the dynamic EIS data includes dynamic impedance and dynamic phase angle.

[0054] The target battery is any battery that needs to be monitored. The current time is the moment the battery monitoring method is currently executing, while the previous time is the time immediately preceding the current time, i.e., the moment the battery monitoring method last executed. Therefore, the current time and the previous time are relative concepts in time.

[0055] In this invention, dynamic EIS data refers to the EIS data measured by the target battery during charging and discharging, while static EIS data refers to the EIS data measured by the target battery when it is not being charged or discharged.

[0056] In one embodiment, a method for acquiring dynamic EIS data of a target battery at a current moment may include: superimposing a small-amplitude sinusoidal AC voltage or current signal (i.e., an excitation signal) onto the target battery, the frequency of which is in the low-frequency (0.1-10Hz) and high-frequency (1k-10kHz) range; measuring the AC current or voltage response through the target battery; and for each frequency point, calculating the ratio of voltage to current to obtain the dynamic impedance, and calculating the phase difference to obtain the dynamic phase angle.

[0057] S220. Obtain the EIS mapping relationship corresponding to the cell type of the target battery. The EIS mapping relationship includes a first sub-relationship and a second sub-relationship. The EIS mapping relationship is obtained by fitting the calibration static EIS data and calibration dynamic EIS data of the battery with the corresponding cell type.

[0058] Since dynamic EIS data is measured when the target battery is charging and discharging, although it can reflect the actual operating conditions of the battery, it fluctuates more than static EIS data. Therefore, this invention also needs to correct the dynamic EIS data to eliminate errors and obtain more accurate data.

[0059] Specifically, dynamic EIS data can be corrected based on the EIS mapping relationship corresponding to the cell type of the target battery.

[0060] In one embodiment, the EIS mapping relationship is associated with the cell type of the battery; that is, one cell type corresponds to one set of EIS mapping relationships. Each set of EIS mapping relationships includes a first sub-relationship and a second sub-relationship. The first sub-relationship characterizes the magnitude mapping relationship between dynamic and static impedances, and the second sub-relationship characterizes the correction relationship between static phase angles. The EIS mapping relationship is obtained by fitting calibrated static EIS data and calibrated dynamic EIS data of the battery corresponding to the cell type.

[0061] To improve monitoring efficiency, corresponding EIS mapping relationships can be pre-determined for various cell types. Then, in actual monitoring applications, the corresponding EIS mapping relationship can be directly selected based on the cell type of the target battery.

[0062] For example, taking any type of battery cell as an example, the method for determining the EIS mapping relationship corresponding to that type of battery cell is described in detail. Figure 3 This is a static EIS test comparison chart provided in Embodiment 2 of the present invention. Figure 4 This is a dynamic EIS test comparison chart provided in Embodiment 2 of the present invention. Figure 3 and Figure 4As shown, at a preset temperature (e.g., 25℃), the static EIS data obtained from different testing platforms are quite similar, with an error of only ±5μΩ at the low and mid frequencies. In contrast, the dynamic EIS data exhibits relatively larger fluctuations, with an error of ±10μΩ at the low and mid frequencies. Therefore, it is evident that the error of the dynamic EIS data is greater than that of the static EIS data. Based on this, the tested static and dynamic EIS data can be used as calibration data for static and dynamic EIS, respectively. Nonlinear optimization fitting yields the first and second sub-relationships, with a fitting coefficient greater than 0.99.

[0063] In one embodiment, the first sub-relation is represented as The second sub-relation is represented as .in, For equivalent impedance, For dynamic impedance, The charging and discharging current of the target battery. This is the actual measured surface temperature of the battery cell at the current moment. For the equivalent phase angle, For dynamic phase angle, , , , , , All are constants (corresponding to different cell types) , , , , , The value may vary.

[0064] S230. Based on the first sub-relation, the dynamic impedance is mapped to the modulus to obtain the equivalent impedance. Based on the second sub-relation, the dynamic phase angle is relaxed and corrected to obtain the equivalent phase angle.

[0065] Figure 5 This is a schematic diagram of the real and imaginary parts of an equivalent impedance provided in Embodiment 2 of the present invention. Figure 5 As shown, the corrected equivalent impedance approximates the static EIS impedance with smaller fluctuations. Therefore, the equivalent impedance and equivalent phase angle not only reflect the actual operating conditions of the battery, but are also more accurate, providing an accurate data basis for subsequent monitoring.

[0066] S240. Obtain the temperature prediction model corresponding to the cell type of the target battery.

[0067] In EIS spectra, the phase angle is extremely sensitive to temperature changes: an increase in temperature will significantly accelerate the electrochemical reaction kinetics, resulting in a shift in the peak frequency and amplitude of the phase angle. This relationship is often more monotonic and unique than the relationship between impedance amplitude and temperature, and therefore can be used as a basis for predicting the internal temperature of the battery.

[0068] To improve monitoring efficiency, corresponding temperature prediction models can be pre-determined for various cell types. Then, in actual monitoring applications, the corresponding temperature prediction model can be directly selected based on the cell type of the target battery.

[0069] For example, taking any type of battery cell as an example, the method for constructing a temperature prediction model corresponding to the type of battery cell is described in detail: Step 1. Obtain test EIS data of the battery cell type in a fully charged state and at different temperatures; Step 2. Determine the phase angle and temperature data corresponding to the shuttle frequency from the test EIS data; Step 3. Fit the phase angle and temperature data corresponding to the shuttle frequency to obtain the temperature prediction model corresponding to the type of battery cell. Figure 6 This is a schematic diagram of the phase angle corresponding to the shuttle frequency provided in Embodiment 2 of the present invention. Figure 7 This is a graph showing the relationship between the phase angle corresponding to the shuttle frequency and the internal temperature of the battery, provided in Embodiment 2 of the present invention. The relationship between the phase angle corresponding to the shuttle frequency and the temperature is the closest. Therefore, by fitting the phase angle and temperature data corresponding to the shuttle frequency, a temperature prediction model corresponding to the cell type is obtained.

[0070] In one embodiment, the temperature prediction model is represented as follows: , This indicates the predicted internal temperature of the battery. Indicates the phase angle. , , , All are constants (corresponding to different cell types) , , , The value may vary.

[0071] Optionally, during the fitting process, for , , , Different upper and lower limits (which can be understood as constraints) can be set. The ultimate goal of fitting is to achieve an estimation accuracy of root mean square error (RMSE) greater than 0.99.

[0072] S250. Substitute the equivalent phase angle into the temperature prediction model to obtain the internal temperature.

[0073] Internal temperature, distinct from external temperature (i.e., the surface temperature of the battery casing), reflects the temperature of the core region where electrochemical reactions occur. Therefore, it avoids the warning lag caused by subsequent monitoring using external temperature. For example, in a 300W thermal runaway scenario, triggering an alarm 70 minutes earlier detects abnormal internal temperature rise earlier than a surface temperature alarm (71 minutes earlier). This demonstrates that thermal runaway warnings based on estimations of the cell's internal temperature can provide early warnings much further in advance. Furthermore, using the equivalent phase angle to predict the target battery's internal temperature allows for non-invasive measurement of internal temperature without damaging the battery structure, reducing monitoring costs and broadening its applicability.

[0074] S260. Determine the real part of the impedance at the current moment based on the equivalent impedance.

[0075] S270. Determine whether the real part of the impedance at the current moment has changed relative to the real part of the impedance at the previous moment, and determine that the target battery has experienced a valve opening event when a change occurs.

[0076] In this embodiment, the detection of the target battery includes at least: thermal runaway early warning and valve opening diagnosis.

[0077] For valve opening diagnosis, the real part of the impedance at the current moment can be determined based on the equivalent impedance, and it can be determined whether the real part of the impedance at the current moment has changed relative to the real part of the impedance at the previous moment. If a change occurs, it can be determined that the target battery has experienced a valve opening event.

[0078] Optionally, whether a jump has occurred in the real part of the impedance at the current moment relative to the real part of the impedance at the previous moment can be determined by whether the absolute value of the difference between the real part of the impedance at the current moment and the real part of the impedance at the previous moment is greater than a preset difference. If the absolute value of the difference between the real part of the impedance at the current moment and the real part of the impedance at the previous moment is greater than the preset difference, a jump is determined to have occurred; otherwise, no jump is determined to have occurred. The preset difference value can be set according to actual needs, for example, 10μΩ, 15μΩ, 20μΩ, 25μΩ, 30μΩ, etc.

[0079] S280. Determine the current state of the target battery based on its internal temperature.

[0080] For thermal runaway warning, the current state of the target battery can be determined based on the internal temperature to determine whether it is in a thermal runaway state, and then to determine whether an alarm signal needs to be issued.

[0081] Specifically, the method for determining the current state of the target battery based on its internal temperature includes: determining the relationship between the internal temperature and a first preset threshold and a second preset threshold, wherein the first preset threshold is less than the second preset threshold; if the internal temperature is less than the first preset threshold, the current state of the target battery is determined to be a normal operating state; if the internal temperature is greater than or equal to the first preset threshold, the current state of the target battery is determined to be an impending thermal runaway state, and a first alarm signal is output; if the internal temperature is greater than or equal to the second preset threshold and the external temperature of the target battery is greater than or equal to a third preset threshold, the current state of the target battery is determined to be a thermal runaway state, and a second alarm signal is output.

[0082] The external temperature of the target battery can be read by a temperature sensor. The values ​​of the first, second, and third preset thresholds can be set according to actual needs. For example, the first preset threshold is 70°C, the second preset threshold is 80°C, and the third preset threshold is 45°C.

[0083] For example, assuming the first preset threshold is 70℃, the second preset threshold is 80℃, and the third preset threshold is 45℃, if the internal temperature is 75℃, the target battery is about to enter a thermal runaway state and outputs a first alarm signal (i.e., a thermal runaway warning signal); if the internal temperature is 60℃, the target battery is in normal operation; if the internal temperature is 85℃ and the external temperature is greater than 50℃, the target battery is in a thermal runaway state and outputs a second alarm signal (i.e., a thermal runaway signal).

[0084] Figure 8 This is a schematic diagram of a thermal runaway early warning signal, a thermal runaway signal, and a valve opening diagnostic signal provided in Embodiment 2 of the present invention. Figure 8 As shown, the battery monitoring method of the present invention can trigger thermal runaway warning 20-70 minutes earlier than the valve opening diagnostic signal, thus providing more comprehensive protection for the battery and improving the safety of the system.

[0085] Optionally, the testing of the target battery may also include at least one of the following: SOH assessment, power analysis, and fault analysis.

[0086] State of Harshness (SOH) Assessment: Battery aging typically leads to an increase in internal resistance. SOH can be assessed by monitoring changes in equivalent impedance (especially at specific frequencies, such as 1 kHz). Power Analysis: A battery's peak power capability is strongly dependent on its internal resistance and internal temperature. Knowing the accurate internal temperature and equivalent impedance allows for a more precise prediction of the battery's charge / discharge power limits at the next moment, thereby optimizing energy distribution and preventing battery damage. Fault Analysis: Abnormal temperature and impedance changes may indicate faults such as internal short circuits and lithium plating. By analyzing abnormal change patterns in equivalent impedance and equivalent phase angle in real time, early warnings can be issued before the onset of faults.

[0087] The technical solution of this invention involves acquiring dynamic EIS data of the target battery during charging and discharging, and correcting the dynamic impedance and dynamic phase angle included in the dynamic EIS data to obtain equivalent impedance and equivalent phase angle. Then, based on the equivalent phase angle, the internal temperature of the target battery is predicted. Finally, the target battery is monitored based on the internal temperature and equivalent impedance. Compared with existing battery thermal runaway early warning methods, firstly, since this invention is based on dynamic EIS data during the charging and discharging process of the target battery, the dynamic EIS data can reflect the actual operating conditions of the battery. This invention further corrects the dynamic impedance and dynamic phase angle to eliminate errors, thereby providing an accurate data basis for subsequent monitoring. Secondly, predicting the internal temperature of the target battery based on the equivalent phase angle achieves non-invasive measurement of the internal temperature, avoiding the lag in subsequent early warnings, and reducing monitoring costs, thus having wider applicability. Thirdly, monitoring the target battery based on internal temperature and equivalent impedance not only enables early warning of battery thermal runaway but also enables valve opening diagnosis of the battery, providing more comprehensive protection for the battery and improving system safety.

[0088] Example 3

[0089] Figure 9 This is a schematic diagram of the structure of a battery monitoring device provided in Embodiment 3 of the present invention. Figure 9 As shown, the device includes: a data acquisition module 901, a correction module 902, a prediction module 903, and a monitoring module 904.

[0090] The data acquisition module 901 is used to acquire the dynamic electrochemical impedance spectroscopy (EIS) data of the target battery at the current moment when the target battery is being charged and discharged. The dynamic EIS data includes dynamic impedance and dynamic phase angle.

[0091] The correction module 902 is used to correct the dynamic impedance and dynamic phase angle respectively to obtain the equivalent impedance and equivalent phase angle;

[0092] Prediction module 903 is used to predict the internal temperature of the target battery based on the equivalent phase angle;

[0093] The monitoring module 904 is used to monitor the target battery based on its internal temperature and equivalent impedance.

[0094] Optionally, the correction module 902 is specifically used to obtain the EIS mapping relationship corresponding to the cell type of the target battery. The EIS mapping relationship includes a first sub-relationship and a second sub-relationship. The EIS mapping relationship is obtained by fitting the calibration static EIS data and calibration dynamic EIS data of the battery with the corresponding cell type. Based on the first sub-relationship, the dynamic impedance is mapped to the modulus to obtain the equivalent impedance. Based on the second sub-relationship, the dynamic phase angle is corrected by phase relaxation to obtain the equivalent phase angle.

[0095] Optionally, the first sub-relation is represented as The second sub-relation is represented as ;in, For equivalent impedance, For dynamic impedance, The charging and discharging current of the target battery. This is the actual measured surface temperature of the battery cell at the current moment. For the equivalent phase angle, For dynamic phase angle, , , , , , All are constants.

[0096] Optionally, the prediction module 903 is specifically used to obtain a temperature prediction model corresponding to the cell type of the target battery, wherein the temperature prediction model is represented as... , This indicates the predicted internal temperature of the battery. Indicates the phase angle. , , , All are constants; the internal temperature is obtained by substituting the equivalent phase angle into the temperature prediction model.

[0097] Optionally, the prediction module 903 is also used to construct a temperature prediction model corresponding to the cell type. The specific method includes: acquiring test EIS data of the cell type battery in a fully charged state and at different temperatures; determining the phase angle and temperature data corresponding to the shuttle frequency from the test EIS data; fitting the phase angle and temperature data corresponding to the shuttle frequency to obtain the temperature prediction model corresponding to the cell type.

[0098] Optionally, the monitoring module 904 is specifically used to determine the real part of the impedance at the current moment based on the equivalent impedance; determine whether the real part of the impedance at the current moment has changed relative to the real part of the impedance at the previous moment, and determine that the target battery has experienced a valve opening event when a change occurs; and determine the current state of the target battery based on the internal temperature.

[0099] Optionally, the monitoring module 904 is specifically used to determine the relationship between the internal temperature and a first preset threshold and a second preset threshold, wherein the first preset threshold is less than the second preset threshold; if the internal temperature is less than the first preset threshold, the current state of the target battery is determined to be a normal operating state; if the internal temperature is greater than or equal to the first preset threshold, the current state of the target battery is determined to be about to enter a thermal runaway state, and a first alarm signal is output; if the internal temperature is greater than or equal to the second preset threshold and the external temperature of the target battery is greater than or equal to a third preset threshold, the current state of the target battery is determined to be a thermal runaway state, and a second alarm signal is output.

[0100] The battery monitoring device provided in this embodiment of the invention can execute the battery monitoring method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0101] Example 4

[0102] Figure 10 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0103] like Figure 10 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0104] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0105] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as battery monitoring methods.

[0106] In some embodiments, the battery monitoring method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the battery monitoring method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the battery monitoring method by any other suitable means (e.g., by means of firmware).

[0107] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0108] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0109] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0110] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0111] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0112] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0113] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the battery monitoring method provided in any embodiment of this invention.

[0114] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0115] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0116] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A battery monitoring method, characterized in that, include: When the target battery is being charged and discharged, the dynamic electrochemical impedance spectroscopy (EIS) data of the target battery at the current time is acquired, wherein the dynamic EIS data includes dynamic impedance and dynamic phase angle; The dynamic impedance and the dynamic phase angle are corrected respectively to obtain the equivalent impedance and the equivalent phase angle; Predict the internal temperature of the target battery based on the equivalent phase angle; Based on the equivalent impedance, determine the real part of the impedance at the current moment; Determine whether the real part of the impedance at the current moment has changed relative to the real part of the impedance at the previous moment, and determine that the target battery has experienced a valve opening event when a change occurs; The current state of the target battery is determined based on the internal temperature.

2. The battery monitoring method according to claim 1, characterized in that, The step of correcting the dynamic impedance and the dynamic phase angle to obtain the equivalent impedance and the equivalent phase angle includes: Obtain the EIS mapping relationship corresponding to the cell type of the target battery, wherein the EIS mapping relationship includes a first sub-relationship and a second sub-relationship, and the EIS mapping relationship is obtained by fitting the calibration static EIS data and calibration dynamic EIS data of the battery corresponding to the cell type; Based on the first sub-relationship, the dynamic impedance is mapped to a modulus to obtain the equivalent impedance, and based on the second sub-relationship, the dynamic phase angle is relaxed and corrected to obtain the equivalent phase angle.

3. The battery monitoring method according to claim 2, characterized in that, The first sub-relation is represented as The second sub-relation is represented as ; in, The equivalent impedance is... The dynamic impedance is... The charging and discharging current of the target battery. This is the actual measured surface temperature of the battery cell at the current moment. The equivalent phase angle is... The dynamic phase angle, , , , , , All are constants.

4. The battery monitoring method according to claim 1, characterized in that, The step of predicting the internal temperature of the target battery based on the equivalent phase angle includes: Obtain the temperature prediction model corresponding to the cell type of the target battery, wherein the temperature prediction model is represented as follows: , This indicates the predicted internal temperature of the battery. Indicates the phase angle. , , , All are constants; The internal temperature is obtained by substituting the equivalent phase angle into the temperature prediction model.

5. The battery monitoring method according to claim 4, characterized in that, For any cell type, a method for constructing a temperature prediction model corresponding to that cell type includes: Obtain the test EIS data of the battery of the cell type under full charge and different temperatures; Determine the phase angle and temperature data corresponding to the shuttle frequency from the tested EIS data; By fitting the phase angle and temperature data corresponding to the shuttle frequency, a temperature prediction model corresponding to the cell type is obtained.

6. The battery monitoring method according to claim 1, characterized in that, Determining the current state of the target battery based on the internal temperature includes: Determine the relationship between the internal temperature and a first preset threshold and a second preset threshold, wherein the first preset threshold is less than the second preset threshold; If the internal temperature is less than the first preset threshold, then the current state of the target battery is determined to be a normal operating state. If the internal temperature is greater than or equal to the first preset threshold, the current state of the target battery is determined to be about to enter a thermal runaway state, and a first alarm signal is output. If the internal temperature is greater than or equal to the second preset threshold and the external temperature of the target battery is greater than or equal to the third preset threshold, then the current state of the target battery is determined to be thermal runaway, and a second alarm signal is output.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the battery monitoring method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the battery monitoring method according to any one of claims 1-6.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the battery monitoring method according to any one of claims 1-6.

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