Battery health state determination method and device, equipment, medium and program product

Abstract

The invention relates to a battery health state determination method and device, equipment, a medium and a program product. The method comprises the steps that a response voltage signal of a target lithium ion battery under a preset input excitation signal is acquired, the preset input excitation signal excites the target lithium ion battery through multi-harmonic injection current, and the preset input excitation signal comprises multiple frequency components; determining impedance spectrum data of the target lithium ion battery according to the response voltage signal; and determining the health state of the target lithium ion battery according to the impedance spectrum data. By adopting the method, the accuracy of determining the health state of the battery can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of battery impedance spectrum early warning calculation, and in particular to a method, device, equipment, medium and program product for determining a battery health status. Background Art

[0002] In the field of lithium-ion batteries, electrochemical impedance spectroscopy (EIS) is an important tool for evaluating the health and performance of batteries. EIS obtains detailed information about the internal processes of the battery by measuring the impedance of the battery at different frequencies. Especially in the low-frequency band, EIS can reveal complex chemical and physical phenomena such as the diffusion process and electrode reaction kinetics inside the battery, which are closely related to the health of the battery.

[0003] However, traditional EIS analysis faces challenges in processing low-frequency data. Low-frequency signals are often interfered by environmental noise, resulting in reduced data resolution, making it difficult to accurately extract useful information, and resulting in large errors when determining the battery health status. Summary of the invention

[0004] Based on this, it is necessary to provide a battery health status determination method, device, equipment, medium and program product that can accurately determine the battery health status in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for determining a battery health status, comprising:

[0006] Acquire a response voltage signal of a target lithium-ion battery under a preset input excitation signal, wherein the preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components;

[0007] Determine impedance spectrum data of the target lithium-ion battery according to the response voltage signal;

[0008] Determine the health status of the target lithium-ion battery based on impedance spectroscopy data.

[0009] In one embodiment, the response voltage signal is a time domain signal, and determining the impedance spectrum data of the target lithium-ion battery according to the response voltage signal includes:

[0010] Determine the frequency domain data corresponding to the response voltage signal; determine the complex impedance of the target lithium-ion battery at each frequency according to the ratio of the frequency domain data corresponding to the response voltage signal to the frequency domain data corresponding to the preset input excitation signal; determine the impedance spectrum data of the target lithium-ion battery according to the complex impedance of the target lithium-ion battery at different frequencies.

[0011] In one embodiment, determining a health state of a target lithium-ion battery based on impedance spectrum data includes:

[0012] The impedance spectrum data is preprocessed, and the preprocessed impedance spectrum data is input into the classification model to determine the state probability of the target lithium-ion battery at time t, where the state of the target lithium-ion battery may include a normal state, a slightly abnormal state, a seriously abnormal state, and a fault state, and time t is any time in the response voltage signal; the preset risk level of each state of the target lithium-ion battery is obtained; the safety function of the target lithium-ion battery at time t is determined according to the sum of the products of the probabilities of each state at time t and the preset risk levels corresponding to each state; and the health state of the target lithium-ion battery is determined according to the safety function at time t.

[0013] In one embodiment, determining the health state of the target lithium-ion battery according to the safety function at time t includes:

[0014] If the safety function at time t is greater than a first preset threshold, and / or an increment between the safety function at time t and the safety function at the previous moment is greater than a second preset threshold, it is determined that the target lithium-ion battery is in an abnormal state; and a safety warning signal is output, the safety warning signal being used to describe that there is an abnormality in the health state of the target lithium-ion battery.

[0015] In one embodiment, the frequency range of each frequency component in the preset input excitation signal is 0.05 Hz to 0.5 Hz, and the preset input excitation signal is a combination of at least two sinusoidal wave signals.

[0016] In a second aspect, the present application also provides a battery health status determination system, including: a collection device, a signal generation device and a control device;

[0017] A signal generating device, used to generate a preset input excitation signal, the preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components;

[0018] The acquisition device is connected to both the target lithium-ion battery and the control device, and is used to acquire a response voltage signal of the target lithium-ion battery under a preset input excitation signal, and transmit the response voltage signal to the control device;

[0019] The control device is used to obtain a response voltage signal of a target lithium-ion battery under a preset input excitation signal; determine impedance spectrum data of the target lithium-ion battery according to the response voltage signal; and determine a health state of the target lithium-ion battery according to the impedance spectrum data.

[0020] In one of the embodiments, the control device is also used to implement any method of the first aspect mentioned above.

[0021] In a third aspect, the present application also provides a battery health status determination device, comprising:

[0022] An acquisition module, used to acquire a response voltage signal of a target lithium-ion battery under a preset input excitation signal, wherein the preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components;

[0023] A first determination module, used to determine impedance spectrum data of a target lithium-ion battery according to a response voltage signal;

[0024] The second determination module is used to determine the health state of the target lithium-ion battery according to the impedance spectrum data.

[0025] In one of the embodiments, the response voltage signal is a time domain signal, and the first determination module is specifically used to determine the frequency domain data corresponding to the response voltage signal; determine the complex impedance of the target lithium-ion battery at each frequency according to the ratio of the frequency domain data corresponding to the response voltage signal to the frequency domain data corresponding to the preset input excitation signal; and determine the impedance spectrum data of the target lithium-ion battery according to the complex impedance of the target lithium-ion battery at different frequencies.

[0026] In one embodiment, the second determination module is specifically used to preprocess the impedance spectrum data, and input the preprocessed impedance spectrum data into the classification model to determine the state probability of the target lithium-ion battery at time t, the state of the target lithium-ion battery may include a normal state, a slightly abnormal state, a seriously abnormal state and a fault state, and time t is any time in the response voltage signal; obtain the preset risk level of each state of the target lithium-ion battery; determine the safety function of the target lithium-ion battery at time t according to the sum of the products of the probabilities of each state at time t and the preset risk levels corresponding to each state; and determine the health state of the target lithium-ion battery according to the safety function at time t.

[0027] In one embodiment, the second determination module is specifically used to determine that the target lithium-ion battery is in an abnormal state if the safety function at time t is greater than a first preset threshold, and / or the increment between the safety function at time t and the safety function at the previous time is greater than a second preset threshold; and output a safety warning signal, the safety warning signal being used to describe that there is an abnormality in the health state of the target lithium-ion battery.

[0028] In one embodiment, the frequency range of each frequency component in the preset input excitation signal is 0.05 Hz to 0.5 Hz, and the preset input excitation signal is a combination of at least two sinusoidal wave signals.

[0029] In a fourth aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any of the methods described in the first aspect when executing the computer program.

[0030] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any of the methods described in the first aspect above.

[0031] In a sixth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0032] The above-mentioned battery health status determination method, device, equipment, medium and program product obtain the response voltage signal of the target lithium-ion battery under the preset input excitation signal, the preset input excitation signal excites the target lithium-ion battery through multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components; determines the impedance spectrum data of the target lithium-ion battery according to the response voltage signal; determines the health status of the target lithium-ion battery according to the impedance spectrum data, so that the lithium-ion battery is excited by the multi-harmonic injection current, which ensures that the battery impedance characteristics at different frequencies can be obtained at the same time, making the impedance spectrum data more complete and reliable, and thus when determining the battery health status based on the impedance spectrum data, it can improve the accuracy of determining the battery health status. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0034] FIG1 is a diagram showing an application environment of a method for determining a battery health status in one embodiment;

[0035] FIG2 is a schematic flow chart of a method for determining a battery health status in one embodiment;

[0036] FIG3 is a simulation waveform diagram of each frequency in a multi-harmonic injection current in one embodiment;

[0037] FIG4 is a simulation waveform diagram of a time-domain input excitation signal and a time-domain response voltage signal corresponding to a multi-harmonic injection current in another embodiment;

[0038] FIG5 is a schematic flow chart of the steps of determining impedance spectrum data of a target lithium-ion battery in one embodiment;

[0039] FIG6 is a schematic flow chart of a step of determining a health status of a target lithium-ion battery in one embodiment;

[0040] FIG7 is a schematic flow chart of a step of determining a health status of a target lithium-ion battery in another embodiment;

[0041] FIG8 is a flow chart of a method for determining a battery health status in another embodiment;

[0042] FIG9 is a structural block diagram of a device for determining a battery health status in one embodiment;

[0043] FIG. 10 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] The embodiment of the present application provides a method for determining the health status of a battery, which can be applied to a battery health status determination system 100 as shown in FIG1. ​​The system includes: a collection device 110, a signal generating device 111 and a control device 112; the signal generating device 111 is used to generate a preset input excitation signal, the preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components; the collection device 110 is connected to both the target lithium-ion battery and the control device 112, and is used to collect the response voltage signal of the target lithium-ion battery under the preset input excitation signal, and transmit the response voltage signal to the control device; the control device 112 is used to obtain the response voltage signal of the target lithium-ion battery under the preset input excitation signal, determine the impedance spectrum data of the target lithium-ion battery according to the response voltage signal, and determine the health status of the target lithium-ion battery according to the impedance spectrum data, and the control device 112 can be a computer device.

[0046] In an exemplary embodiment, as shown in FIG2 , a method for determining a battery health state is provided, which is described by taking the method applied to the control device in FIG1 as an example, and includes the following steps 201 to 203. Among them:

[0047] S201, obtaining a response voltage signal of a target lithium-ion battery under a preset input excitation signal.

[0048] The preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components.

[0049] Optionally, the multi-harmonic injection current can be obtained by injecting harmonic signals of multiple frequencies at one time to stimulate the target lithium-ion battery, so that the response voltage signal obtained can obtain information at multiple frequency points. That is to say, the preset input excitation signal excites the target lithium-ion battery based on the multi-harmonic injection current, so that the impedance data of multiple frequency points can be measured simultaneously. Compared with the traditional frequency-by-frequency testing method, the time required for the battery measurement process is reduced, and the test efficiency can be effectively improved. At the same time, this multi-harmonic injection method can simultaneously obtain the impedance characteristics at different frequencies, which can make the impedance spectrum data more complete and reliable.

[0050] Optionally, a preset input excitation signal can be generated by a signal generator, and a response voltage signal corresponding to the target lithium-ion battery can be obtained by an acquisition device. The acquisition device transmits the acquired response voltage signal to the control device, wherein the signal generator can flexibly change the frequency range and frequency of the harmonics, and can correspond to the impedance characteristic detection of different types of batteries. By dynamically adjusting the harmonic frequency, the detection sensitivity of the target lithium-ion battery can be optimized to ensure that a reliable response voltage signal can still be obtained in a complex or changeable use environment, thereby obtaining reliable impedance spectrum data.

[0051] Optionally, since the growth of the solid electrolyte interface and the positive electrode electrolyte interface in the target lithium-ion battery will cause a significant change in the impedance value of the EIS low-frequency band, the impedance of a special frequency in the low-frequency band can be used to determine the health status of the target lithium-ion battery, and the preset input excitation signal includes multiple frequency components to cover the required low-frequency band range.

[0052] Optionally, the preset input excitation signal may be a sine wave signal, a square wave signal, a combination of multiple sine wave signals, or a combination of multiple square wave signals, which is not limited in the embodiments of the present application.

[0053] Optionally, the frequency range of each frequency component in the preset input excitation signal is 0.05 Hz to 0.5 Hz, and the preset input excitation signal is a combination of at least two sinusoidal wave signals.

[0054] Exemplarily, as shown in Figure 3, it is a simulation waveform diagram of each frequency in the preset input excitation signal. The preset input excitation signal may include 4 sinusoidal wave signals, including a frequency component of 0.05Hz for sinusoidal wave signal 301, a frequency component of 0.1Hz for sinusoidal wave signal 302, a frequency component of 0.2Hz for sinusoidal wave signal 303, and a frequency component of 0.5Hz for sinusoidal wave signal 304.

[0055] Optionally, the sine wave signal has good mathematical and physical properties and can accurately reflect the electrochemical response of the lithium-ion battery.

[0056] Optionally, depending on the input excitation signal, the EIS measurement may include a frequency domain measurement method and a time domain measurement method, which is not limited in the embodiments of the present application.

[0057] S202, determining impedance spectrum data of the target lithium-ion battery according to the response voltage signal.

[0058] Optionally, the impedance spectrum data is a series of data obtained by analyzing the response of the target lithium-ion battery to the response voltage signal at different frequencies. The impedance spectrum data of the target lithium-ion battery has the advantages of being able to characterize the aging mechanism of the target lithium-ion battery and being independent of the working conditions of the target lithium-ion battery.

[0059] Optionally, when the EIS measurement is a frequency domain measurement method, input excitation signals of different frequencies can be applied to the target lithium-ion battery, and the response voltage signal generated by the target lithium-ion battery can be obtained at the same time, that is, the preset input excitation signal and the response voltage signal are both frequency domain signals, and the complex impedance of the target lithium-ion battery at the current frequency is obtained according to the ratio of voltage to current, thereby obtaining impedance spectrum data.

[0060] Optionally, when the EIS measurement is a time domain measurement method, input excitation signals of different frequencies can be applied to the target lithium-ion battery, and the simulation waveform of the input excitation signal can be shown as 401 in Figure 4. At the same time, the response voltage signal generated by the target lithium-ion battery is obtained, and the simulation waveform of the response voltage signal can be shown as 402 in Figure 4, that is, the preset input excitation signal and the response voltage signal are both time domain signals, and the time domain signal is converted into the frequency domain using a mathematical analysis method, and the converted data is calculated to obtain impedance spectrum data.

[0061] S203, determining a health status of the target lithium-ion battery according to the impedance spectrum data.

[0062] In a possible implementation, when determining the health state of a target lithium-ion battery based on impedance spectrum data, the health state of the target lithium-ion battery may be determined by fitting the impedance spectrum data to obtain equivalent circuit parameters.

[0063] In another possible implementation, when determining the health state of the target lithium-ion battery based on the impedance spectrum data, an internal resistance growth model and a capacity degradation model can be established based on the impedance spectrum data, and the two models can be fused to obtain a fusion model, and the fusion model can be used to determine the health state of the target lithium-ion battery.

[0064] In another possible implementation, the health state of the target lithium-ion battery is determined based on the complex impedance value at time t in the impedance spectrum data and a preset impedance value, or the health state of the target lithium-ion battery is determined based on the difference between the complex impedance value at time t in the impedance spectrum data and the complex impedance value at a preset interval time.

[0065] For example, take the lithium-ion battery model LG Chem INR18650-MJ1 as an example, the battery capacity is 3500mAh and the internal resistance is 18 , using sinusoidal current excitation, the frequency range is 0.05 Hz to 0.5 Hz, the current amplitude is set to 5 A, and the impedance spectrum data corresponds to a complex impedance of 20 at a frequency of 0.1 Hz. , in line with the internal impedance characteristics, meet the preset impedance value, the target lithium-ion battery is in good health; if at a frequency of 0.3Hz, the impedance of the target lithium-ion battery changes from 18 Increase to 30 , indicating that the target lithium-ion battery may have safety hazards.

[0066] The above-mentioned method for determining the health state of the battery obtains the response voltage signal of the target lithium-ion battery under the preset input excitation signal, the preset input excitation signal excites the target lithium-ion battery through multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components; determines the impedance spectrum data of the target lithium-ion battery according to the response voltage signal; determines the health state of the target lithium-ion battery according to the impedance spectrum data, so that the lithium-ion battery is excited by the multi-harmonic injection current, which ensures that the battery impedance characteristics under different frequencies can be obtained at the same time, making the impedance spectrum data more complete and reliable, and thus when the battery health state is determined based on the impedance spectrum data, the accuracy of determining the battery health state can be improved.

[0067] In an exemplary embodiment, optionally, the response voltage signal is a time domain signal, as shown in FIG5 , determining the impedance spectrum data of the target lithium-ion battery according to the response voltage signal includes the following steps 501 to 503. Wherein:

[0068] S501, determining frequency domain data corresponding to a response voltage signal.

[0069] Optionally, the frequency domain data corresponding to the response voltage signal may be determined by Fourier transform, Laplace transform or wavelet decomposition, wherein the frequency domain data corresponding to the response voltage signal may include amplitude and phase information of each frequency.

[0070] S502, determining the complex impedance of the target lithium-ion battery at each frequency according to the ratio of the frequency domain data corresponding to the response voltage signal to the frequency domain data corresponding to the preset input excitation signal.

[0071] Optionally, the frequency domain data corresponding to the preset input excitation signal may be determined by Fourier transform, Laplace transform or wavelet decomposition.

[0072] Optionally, the complex impedance of the target lithium-ion battery at each frequency can be determined according to the following formula:

[0073]

[0074] in, is the complex impedance of the target lithium-ion battery at each frequency, is the frequency domain data corresponding to the response voltage signal, It is the frequency domain signal corresponding to the preset input excitation signal.

[0075] S503, determining impedance spectrum data of the target lithium-ion battery according to the complex impedance of the target lithium-ion battery at different frequencies.

[0076] In one possible implementation, a Nyquist curve can be plotted according to the complex impedance of the target lithium-ion battery at different frequencies to obtain impedance spectrum data of the target lithium-ion battery, wherein the x-axis of the curve is the real part of the impedance and the y-axis is the imaginary part of the impedance.

[0077] In another possible implementation, a Bode plot may be drawn based on the complex impedance of the target lithium-ion battery at different frequencies to obtain impedance spectrum data of the target lithium-ion battery, wherein the Bode plot may respectively display the logarithmic changes of impedance magnitude and phase angle with frequency.

[0078] The frequency domain data corresponding to the response voltage signal is determined above, and the complex impedance of the target lithium-ion battery at each frequency is determined according to the ratio of the frequency domain data corresponding to the response voltage signal to the frequency domain data corresponding to the preset input excitation signal. The impedance spectrum data of the target lithium-ion battery is determined according to the complex impedance of the target lithium-ion battery at different frequencies. Since multiple frequency sweep measurements of the battery are not required during time domain measurement, the test time is short, and the battery impedance characteristics at different frequencies can be obtained at the same time, making the impedance spectrum data more complete and reliable.

[0079] In an exemplary embodiment, optionally, as shown in FIG6 , determining the health status of the target lithium-ion battery according to the impedance spectrum data includes the following steps 601 to 604 . Among them:

[0080] S601, preprocessing the impedance spectrum data, and inputting the preprocessed impedance spectrum data into a classification model to determine the state probability of the target lithium-ion battery at time t.

[0081] The state of the target lithium-ion battery may include a normal state, a slightly abnormal state, a seriously abnormal state, and a fault state, and time t is any time in the response voltage signal.

[0082] Optionally, the impedance spectrum data can be preprocessed, including data cleaning, deletion of abnormal data, data completion, etc.

[0083] In one possible implementation, the classification model can be implemented based on the logistic regression algorithm, which is very fast to train and has very good interpretability.

[0084] In one possible implementation, the classification model can be implemented based on a decision tree algorithm. The decision tree is a non-parametric supervised learning method that can summarize decision rules from a series of data consisting of attributes and labels, and present these rules in a tree diagram structure to solve classification problems. The model is not very sensitive to data, does not require data preprocessing, and has a fast training speed.

[0085] In one possible implementation, the classification model can be implemented based on the support vector algorithm, which is supported by rigorous mathematical theory, has strong interpretability, simplifies the classification problem, and the final decision function is determined by only a few support vectors. The complexity of the calculation depends on the number of support vectors rather than the dimension of the sample space, thus avoiding the "curse of dimensionality" to a certain extent.

[0086] Optionally, in an embodiment of the present application, the classification model refers to a model obtained in advance through machine learning training that can predict the state of the target lithium-ion battery at each moment based on the preprocessed impedance spectrum data. The model input is the preprocessed impedance spectrum data, and the output is the probability of the state of the target lithium-ion battery at each moment.

[0087] Optionally, the probability of each state can be determined by the softmax function in the classification model, which can be expressed by the following formula:

[0088]

[0089] in, is the score of the i-th state at time t, which is determined by the classification model based on the input. is the probability of the i-th state at time t, i can be 0, 1, 2, 3, corresponding to the four possible states of the target lithium-ion battery.

[0090] S602, obtaining a preset risk level of each state of the target lithium-ion battery.

[0091] Optionally, corresponding risk levels may be set according to various states of the target lithium-ion battery and stored in a storage unit.

[0092] Exemplarily, the preset risk levels for each state may include the following: Under normal conditions, the risk level 0, in a mildly abnormal state, the risk level 1, in a serious abnormal state, the risk level 3, in the fault state, the risk level is 5.

[0093] S603, determining the safety function of the target lithium-ion battery at time t according to the sum of the products of the probabilities of each state at time t and the preset risk levels corresponding to each state.

[0094] Optionally, the safety function of the target lithium-ion battery at time t can be determined by the following formula:

[0095]

[0096] in, is the safety function at time t, is the preset risk level corresponding to the i-th state.

[0097] S604, determining the health state of the target lithium-ion battery according to the safety function at time t.

[0098] Optionally, as shown in FIG7 , determining the health state of the target lithium-ion battery according to the safety function at time t includes the following steps 701 to 702. Among them:

[0099] S701, if the safety function at time t is greater than a first preset threshold, and / or the increment between the safety function at time t and the safety function at the previous time is greater than a second preset threshold, it is determined that the target lithium-ion battery is in an abnormal state.

[0100] Optionally, when the safety function at time t is greater than a first preset threshold, it can be determined that the target lithium-ion battery is in an abnormal state; or when the increment between the safety function at time t and the safety function at the previous moment is greater than a second preset threshold, it can be determined that the target lithium-ion battery is in an abnormal state. It can be understood that when the increment between the safety function at time t and the safety function at a preset time interval is greater than the second preset threshold, it can also be determined that the target lithium-ion battery is in an abnormal state; or, when the safety function at time t is greater than the first preset threshold, and the increment between the safety function at time t and the safety function at the previous moment is greater than the second preset threshold, it is determined that the target lithium-ion battery is in an abnormal state. The embodiments of the present application are not limited to this.

[0101] S702, output a safety warning signal.

[0102] Among them, the safety warning signal is used to describe the abnormality of the health status of the target lithium-ion battery.

[0103] Optionally, when it is determined that the target lithium-ion battery is in an abnormal state, a safety warning signal can be generated. The safety warning signal can include the unique identification of the target lithium-ion battery and the abnormal time (i.e., time t), and the safety warning signal is output to the user, which can remind the user that the target lithium-ion battery may have safety hazards, so that the user can check or replace it in time, reduce battery safety risks, and improve the safety of battery use.

[0104] Optionally, when the safety warning signal is output to the user, the safety warning signal can be output to the user's terminal device by means of a communication alarm, such as email, text message or message push, and / or the safety warning signal can be displayed to the user through a monitoring display screen, and the user can be reminded by a sound alarm or a visual alarm, such as a buzzer or flashing light.

[0105] As an optional implementation, as shown in FIG8 , the method for determining the battery health status provided in the embodiment of the present application may include the following specific steps:

[0106] S801, obtaining a response voltage signal of a target lithium-ion battery under a preset input excitation signal.

[0107] The preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components.

[0108] The frequency range of each frequency component in the preset input excitation signal is 0.05 Hz to 0.5 Hz, and the preset input excitation signal is at least 2 combinations of sinusoidal wave signals.

[0109] S802, determining frequency domain data corresponding to the response voltage signal.

[0110] S803, determining the complex impedance of the target lithium-ion battery at each frequency according to the ratio of the frequency domain data corresponding to the response voltage signal to the frequency domain data corresponding to the preset input excitation signal.

[0111] S804, determining impedance spectrum data of the target lithium-ion battery according to the complex impedance of the target lithium-ion battery at different frequencies.

[0112] S805, preprocessing the impedance spectrum data, and inputting the preprocessed impedance spectrum data into a classification model to determine the state probability of the target lithium-ion battery at time t.

[0113] The state of the target lithium-ion battery may include a normal state, a slightly abnormal state, a seriously abnormal state, and a fault state, and time t is any time in the response voltage signal.

[0114] S806, obtaining a preset risk level of each state of the target lithium-ion battery.

[0115] S807, determining the safety function of the target lithium-ion battery at time t according to the sum of the products of the probabilities of each state at time t and the preset risk levels corresponding to each state.

[0116] S808: If the safety function at time t is greater than a first preset threshold, and / or the increment between the safety function at time t and the safety function at a previous time is greater than a second preset threshold, it is determined that the target lithium-ion battery is in an abnormal state.

[0117] S809, output a safety warning signal.

[0118] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0119] Based on the same inventive concept, the embodiment of the present application also provides a battery health status determination system for implementing the battery health status determination method described above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method. Therefore, the specific limitations in one or more system embodiments provided below can refer to the limitations of the battery health status determination method above, and will not be repeated here. The structure of the battery health status determination system can refer to Figure 1, including: a collection device, a signal generating device and a control device;

[0120] A signal generating device, used to generate a preset input excitation signal, the preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components;

[0121] The acquisition device is connected to both the target lithium-ion battery and the control device, and is used to acquire a response voltage signal of the target lithium-ion battery under a preset input excitation signal, and transmit the response voltage signal to the control device;

[0122] The control device is used to obtain a response voltage signal of a target lithium-ion battery under a preset input excitation signal; determine impedance spectrum data of the target lithium-ion battery according to the response voltage signal; and determine a health state of the target lithium-ion battery according to the impedance spectrum data.

[0123] Based on the same inventive concept, the embodiment of the present application also provides a battery health status determination device for implementing the battery health status determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the battery health status determination device provided below can refer to the limitations of the battery health status determination method above, and will not be repeated here.

[0124] In an exemplary embodiment, as shown in FIG9 , a battery health status determination device 900 is provided, comprising: an acquisition module 901 , a first determination module 902 , and a second determination module 903 . Among them:

[0125] An acquisition module 901 is used to acquire a response voltage signal of a target lithium-ion battery under a preset input excitation signal, wherein the preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components;

[0126] A first determination module 902, configured to determine impedance spectrum data of a target lithium-ion battery according to a response voltage signal;

[0127] The second determination module 903 is used to determine the health state of the target lithium-ion battery according to the impedance spectrum data.

[0128] In one embodiment, the response voltage signal is a time domain signal, and the first determination module 902 is specifically used to determine the frequency domain data corresponding to the response voltage signal; determine the complex impedance of the target lithium-ion battery at each frequency according to the ratio of the frequency domain data corresponding to the response voltage signal to the frequency domain data corresponding to the preset input excitation signal; and determine the impedance spectrum data of the target lithium-ion battery according to the complex impedance of the target lithium-ion battery at different frequencies.

[0129] In one embodiment, the second determination module 903 is specifically used to preprocess the impedance spectrum data, and input the preprocessed impedance spectrum data into the classification model to determine the state probability of the target lithium-ion battery at time t, where the state of the target lithium-ion battery may include a normal state, a slightly abnormal state, a seriously abnormal state, and a fault state, and time t is any time in the response voltage signal; obtain the preset risk level of each state of the target lithium-ion battery; determine the safety function of the target lithium-ion battery at time t according to the sum of the products of the probabilities of each state at time t and the preset risk levels corresponding to each state; and determine the health state of the target lithium-ion battery according to the safety function at time t.

[0130] In one embodiment, the second determination module 903 is specifically used to determine that the target lithium-ion battery is in an abnormal state if the safety function at time t is greater than a first preset threshold, and / or the increment between the safety function at time t and the safety function at the previous time is greater than a second preset threshold; and output a safety warning signal, the safety warning signal being used to describe that there is an abnormality in the health state of the target lithium-ion battery.

[0131] In one embodiment, the frequency range of each frequency component in the preset input excitation signal is 0.05 Hz to 0.5 Hz, and the preset input excitation signal is a combination of at least two sinusoidal wave signals.

[0132] Each module in the above-mentioned battery health status determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0133] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG10. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store XX data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining a battery health state is implemented.

[0134] Those skilled in the art will understand that the structure shown in FIG. 10 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0135] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps described in any of the above method embodiments when executing the computer program.

[0136] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps described in any of the above method embodiments are implemented.

[0137] In one embodiment, a computer program product is provided, including a computer program, which implements the steps described in any of the above method embodiments when executed by a processor.

[0138] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0139] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0140] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a battery health state, characterized in that: The method comprises: Acquire a response voltage signal of a target lithium-ion battery under a preset input excitation signal, wherein the preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components; Determining impedance spectrum data of the target lithium-ion battery according to the response voltage signal; The health state of the target lithium-ion battery is determined according to the impedance spectrum data.

2. The method according to claim 1, characterized in that The response voltage signal is a time domain signal, and determining the impedance spectrum data of the target lithium-ion battery according to the response voltage signal includes: Determining frequency domain data corresponding to the response voltage signal; Determining the complex impedance of the target lithium-ion battery at each frequency according to a ratio of the frequency domain data corresponding to the response voltage signal to the frequency domain data corresponding to the preset input excitation signal; According to the complex impedance of the target lithium-ion battery at different frequencies, impedance spectrum data of the target lithium-ion battery is determined.

3. The method according to claim 2, characterized in that Determining the health status of the target lithium-ion battery according to the impedance spectrum data includes: Preprocessing the impedance spectrum data, and inputting the preprocessed impedance spectrum data into a classification model to determine the state probability of the target lithium-ion battery at time t, where the state of the target lithium-ion battery may include a normal state, a slightly abnormal state, a seriously abnormal state, and a fault state, and time t is any time in the response voltage signal; Obtaining a preset risk level for each state of the target lithium-ion battery; Determine the safety function of the target lithium-ion battery at time t according to the sum of the products of the probabilities of each state at time t and the preset risk levels corresponding to each state; The health state of the target lithium-ion battery is determined according to the safety function at the time t.

4. The method according to claim 3, characterized in that The determining the health state of the target lithium-ion battery according to the safety function at the time t includes: If the safety function at time t is greater than a first preset threshold, and / or an increment between the safety function at time t and the safety function at a previous time is greater than a second preset threshold, it is determined that the target lithium-ion battery is in an abnormal state; Output a safety warning signal, wherein the safety warning signal is used to describe that the health status of the target lithium-ion battery is abnormal.

5. The method according to claim 1, characterized in that The frequency range of each frequency component in the preset input excitation signal is 0.05 Hz to 0.5 Hz, and the preset input excitation signal is a combination of at least two sinusoidal wave signals.

6. A battery health status determination system, characterized in that: The system comprises: a collection device, a signal generating device and a control device; The signal generating device is used to generate a preset input excitation signal, wherein the preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components; The acquisition device is connected to both the target lithium-ion battery and the control device, and is used to acquire a response voltage signal of the target lithium-ion battery under a preset input excitation signal, and transmit the response voltage signal to the control device; The control device is used to obtain a response voltage signal of a target lithium-ion battery under a preset input excitation signal; determine impedance spectrum data of the target lithium-ion battery according to the response voltage signal; and determine a health state of the target lithium-ion battery according to the impedance spectrum data.

7. A battery health status determination device, characterized in that: The device comprises: An acquisition module, used to acquire a response voltage signal of a target lithium-ion battery under a preset input excitation signal, wherein the preset input excitation signal excites the target lithium-ion battery through a multi-harmonic injection current, and the preset input excitation signal includes multiple frequency components; A first determination module, configured to determine impedance spectrum data of the target lithium-ion battery according to the response voltage signal; The second determination module is used to determine the health state of the target lithium-ion battery according to the impedance spectrum data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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