Lithium battery charging state determination method and apparatus, and electronic device

Abstract

The invention discloses a lithium battery charging state determination method and apparatus, and an electronic device. The method comprises the following steps: exciting a target lithium battery by adopting a current with a target frequency; acquiring target electrochemical impedance spectroscopy data and target battery voltage data of the target lithium battery under the current of the target frequency; and determining a target charging state corresponding to the target lithium battery based on the target electrochemical impedance spectrum data, the target battery voltage data and a relationship among the electrochemical impedance spectrum data, the battery voltage data and the charging state of the target lithium battery under the current of the target frequency. The technical problem that the charging state of the battery cannot be accurately determined according to the battery voltage due to the fact that a lithium iron phosphate cell voltage-battery charging state curve has a flat area in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of batteries, and in particular to a method and device for determining the charging state of a lithium battery, and electronic equipment. Background Art

[0002] Lithium batteries are widely used in various fields, particularly electric vehicles, due to their high power density, reliable operating range, and long life. However, lithium batteries often face unpredictable usage patterns, necessitating a state assessment to facilitate control. State of Charge (SOC) is a key metric used to quantify the remaining available energy in a battery. SOC is defined as the ratio of the battery's remaining capacity to its maximum capacity. Accurately estimating SOC not only reflects the battery's current state but also improves operational reliability by preventing overcharging and overdischarging. Traditional methods, such as coulomb counting and battery voltage-based SOC estimation, have limitations in practical applications and cannot accurately estimate the SOC of lithium batteries.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, and electronic device for determining the charging state of a lithium battery, to at least solve the technical problem in the related art that the battery charging state cannot be accurately determined based on the battery voltage due to the presence of a flat area in the lithium iron phosphate battery cell voltage-battery charging state curve.

[0005] According to one aspect of an embodiment of the present invention, a method for determining a charging state of a lithium battery is provided, comprising: exciting a target lithium battery with a current of a target frequency; obtaining target electrochemical impedance spectroscopy data and target battery voltage data of the target lithium battery at the current of the target frequency; and determining a target charging state corresponding to the target lithium battery based on the target electrochemical impedance spectroscopy data and the target battery voltage data, as well as the relationship between the electrochemical impedance spectroscopy data, battery voltage data, and the charging state of the target lithium battery at the current of the target frequency.

[0006] Optionally, before stimulating the target lithium battery with a current of a target frequency, the method further includes: charging the target lithium battery in a fully discharged state, and obtaining electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery under currents of multiple frequencies when multiple charging states are reached respectively; determining the target frequency based on the electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery under currents of multiple frequencies when the target lithium battery reaches multiple charging states respectively; determining the relationship between the electrochemical impedance spectroscopy data, battery voltage data and charging state of the target lithium battery under the current of the target frequency based on the electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery under the current of the target frequency when the target lithium battery reaches multiple charging states respectively, as well as the multiple charging states.

[0007] Optionally, determining the target frequency based on the electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery under currents of multiple frequencies when the target lithium battery reaches multiple charging states respectively includes: obtaining the resistance data, reactance data, amplitude data and phase angle data of the target lithium battery under currents of the multiple frequencies when the target lithium battery reaches multiple charging states respectively based on the electrochemical impedance spectroscopy data of the target lithium battery under currents of the multiple frequencies when the target lithium battery reaches multiple charging states respectively; respectively determining the importance of the battery voltage data, resistance data, reactance data, amplitude data and phase angle data of the target lithium battery under currents of the multiple frequencies when the target lithium battery reaches multiple charging states respectively; and determining the target frequency from the multiple frequencies based on the importance of the battery voltage data, resistance data, reactance data, amplitude data and phase angle data of the target lithium battery under currents of the multiple frequencies when the target lithium battery reaches multiple charging states respectively.

[0008] Optionally, the target lithium battery in a fully discharged state is charged, and when multiple charging states are reached respectively, electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery under currents of multiple frequencies are obtained, including: selecting the multiple charging states at predetermined intervals between the fully discharged state and the fully charged state; charging the target lithium battery in a fully discharged state, and when multiple charging states are reached respectively, allowing the target lithium battery to stand for a predetermined time; exciting the target lithium battery after standing with currents of the multiple frequencies respectively; and obtaining electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery under currents of the multiple frequencies.

[0009] Optionally, when charging the target lithium battery in a fully discharged state and reaching multiple charging states respectively, before obtaining electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery under currents of multiple frequencies, it includes: performing a capacity test on the target lithium battery to determine the initial capacity of the target lithium battery, wherein the initial capacity is used to determine the multiple charging states.

[0010] Optionally, a target model is used to characterize the relationship between the electrochemical impedance spectroscopy data, battery voltage data and charge state of the target lithium battery under the current of the target frequency, wherein the target model is trained based on a convolutional neural network using an elastic network regularization method.

[0011] According to another aspect of the present invention, a device for determining a charging state of a lithium battery is provided, comprising: an excitation module for exciting a target lithium battery using a current of a target frequency; a first acquisition module for acquiring target electrochemical impedance spectroscopy data and target battery voltage data of the target lithium battery under the current of the target frequency; and a first determination module for determining a target charging state corresponding to the target lithium battery based on the target electrochemical impedance spectroscopy data and the target battery voltage data, as well as a relationship between the electrochemical impedance spectroscopy data, the battery voltage data, and the charging state of the target lithium battery under the current of the target frequency.

[0012] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for determining the charging status of a lithium battery.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes any one of the above-mentioned methods for determining the charging status of a lithium battery when running.

[0014] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any one of the methods for determining a charging state of a lithium battery are implemented.

[0015] In an embodiment of the present invention, a target lithium battery is stimulated by adopting a current of a target frequency; target electrochemical impedance spectrum data and target battery voltage data of the target lithium battery under the current of the target frequency are obtained; based on the target electrochemical impedance spectrum data and target battery voltage data, as well as the relationship between the electrochemical impedance spectrum data, battery voltage data and charging state of the target lithium battery under the current of the target frequency, the target charging state corresponding to the target lithium battery is determined, thereby achieving the purpose of determining the charging state of the lithium battery based on the electrochemical impedance spectrum data and battery voltage data of the lithium battery, thereby achieving the technical effect of more accurately determining the charging state of the lithium battery, and further solving the technical problem in the related art that the battery charging state cannot be accurately determined according to the battery voltage due to the presence of a flat area in the lithium iron phosphate battery cell voltage-battery charging state curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a method for determining a charging state of a lithium battery according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of an EIS-based SOC algorithm framework according to an optional embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of the decomposition of the internal resistance of a battery cell based on EIS according to an optional embodiment of the present invention;

[0020] Figure 4 is a schematic diagram of feature importance analysis based on a random forest algorithm according to an optional embodiment of the present invention;

[0021] Figure 5 4 is a structural block diagram of a device for determining a charging state of a lithium battery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following interpretations:

[0025] State of Charge (SOC), also known as state of charge, is a key parameter in battery technology that quantifies the remaining available energy in a battery. SOC is defined as the ratio of the remaining battery capacity to the battery's maximum capacity and can be expressed as a percentage (0% to 100%). Accurately estimating SOC not only reflects the battery's current state but also improves battery reliability by preventing overcharging and overdischarging, making it crucial for ensuring efficient and safe battery operation.

[0026] The Coulomb counting method, also known as the ampere-hour method, is a common method used in battery management technology to estimate the battery state of charge. Its basic principle is to measure the current flowing through the battery during charging and discharging and integrate these current values ​​to calculate the amount of charge already charged or discharged, thereby inferring the remaining battery capacity. The Coulomb counting method requires a known initial SOC value for real-time SOC calculation. Based on this known initial SOC value, the battery's charge and discharge process is continuously monitored to calculate the accumulated coulomb count, which is then used to update the SOC value. Therefore, its accuracy depends on determining the initial SOC and accurately measuring the charge and discharge currents. Its effectiveness is limited when it is difficult to determine the initial SOC. Furthermore, over long periods of use, the accumulated error of the current sensor may also affect the accuracy of the SOC estimation.

[0027] Due to the limitations of the coulomb counting method in practical applications, the related art estimates the state of charge of lithium batteries based on measured data from lithium batteries, such as battery voltage data obtained through various experiments. However, the battery voltage is significantly affected by the chemical process of charging and discharging the lithium battery. In order to obtain a relatively stable battery voltage of the current lithium battery, the lithium battery needs to be left at rest. During the resting process of the lithium battery, the lithium battery is in a state where it is not used or connected to an external circuit after the charging and discharging process is completed. During this period, the chemical reaction inside the lithium battery does not stop, but continues until a chemical equilibrium state is reached. As a result of this process, the battery voltage of the lithium battery gradually changes and tends to a stable value - the open circuit voltage (OCV), which is the voltage measured after the lithium battery is at rest and without external current. There is a nonlinear relationship between OCV and SOC, which is often used to construct an OCV-SOC curve to estimate the SOC of the lithium battery. However, this change in battery voltage during the resting process increases the complexity of estimating the SOC of the lithium battery based on battery voltage data.

[0028] Battery voltage is also affected by other factors. For example, different charge and discharge rates can lead to different battery voltage behavior. As the battery ages, the charge and discharge characteristics of lithium batteries change, affecting the battery voltage. Temperature fluctuations can also significantly affect battery voltage behavior. For example, in cold environments, lithium batteries reach the charge cutoff voltage faster than at normal temperatures. The impact of these other factors on battery voltage makes it difficult to accurately estimate SOC based solely on battery voltage data.

[0029] When estimating the SOC of a lithium battery using battery voltage data based on the OCV-SOC curve, the SOC can be estimated by obtaining the battery voltage data after standing and looking up the table. For lithium iron phosphate (LiFePO4, LFP) batteries, the chemical composition LiFePO4 is used as the positive electrode material of the LEP battery. Since the insertion and deinsertion of lithium ions in the LiFePO4 positive electrode are achieved through a two-phase reaction process, at a specific SOC level, this will form a two-phase equilibrium, allowing the LEP battery to maintain a stable OCV change over a wide SOC range, thereby forming a flat platform in the OCV-SOC curve of the LEP battery, which in turn limits the application of the method of estimating the SOC of lithium batteries based on battery voltage data.

[0030] In order to overcome the limitations of the OCV-SOC curve, an embodiment of the present invention proposes a method for determining the state of charge of a lithium battery by combining electrochemical impedance spectroscopy (EIS) technology. Among them, EIS is a test technology for analyzing the impedance changes of electrochemical systems such as batteries, electrodes, electrolytes, etc. with frequency. By applying a small AC signal of known frequency and amplitude and measuring the amplitude and phase of the system response, EIS can determine the impedance characteristics of the electrochemical system at different frequencies. Through EIS technology, rich information over a wide frequency range can be provided non-destructively, reflecting the electrochemical processes inside the battery in more detail, and providing a more comprehensive understanding of battery behavior and chemical reactions. Impedance here refers to the electrical impedance to the AC signal, including both resistance (the amount that hinders the flow of current) and reactance (impedance to the flow of current, caused by inductance and capacitance). Unlike measurement data such as voltage and current, the internal impedance of the battery is relatively consistent in a stable state and shows small changes, which enables EIS data to more reliably reflect changes in the battery state.

[0031] According to an embodiment of the present invention, an embodiment of a method for determining the charging state of a lithium battery is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] Figure 1 FIG. 1 is a flow chart of a method for determining a charging state of a lithium battery according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0033] Step S102: stimulating the target lithium battery with a current of a target frequency.

[0034] As an optional embodiment, the execution subject of the method of this embodiment can be a battery management system or an intelligent device for determining the charging state of a lithium battery. Among them, the battery management system is responsible for monitoring the health status, charging state, temperature and other key parameters of the battery pack, and can directly integrate the lithium battery charging state determination method to more accurately monitor the battery status (for example, charging state), and optimize the battery usage strategy and extend the battery life accordingly. The intelligent device can have a built-in processor and sensor, capable of performing electrochemical impedance spectroscopy measurements, and processing data locally or in the cloud to determine the charging state of the target lithium battery.

[0035] As an optional embodiment, the target lithium battery may be a lithium battery whose charging state is to be determined, and its type may be an LFP battery type. The current of the target frequency may be an AC signal of the target frequency, which may be an AC current or an AC voltage, and its purpose is to externally excite the target lithium battery. By using an electric current to excite the target lithium battery, that is, applying a small-amplitude AC signal to both ends of the target lithium battery, the response of the target lithium battery to the excitation can be obtained, that is, the electrochemical impedance spectrum data and battery voltage data of the target lithium battery under the current are obtained. Among them, the response is related to the frequency of the excitation, and currents of different frequencies can reveal the electrochemical behavior of different regions of the target lithium battery. When the current of the target frequency is used to excite the target lithium battery, the specific response of the target lithium battery to the excitation of the target frequency can be obtained, thereby determining the target charging state corresponding to the target lithium battery based on the specific response.

[0036] Step S104 , obtaining target electrochemical impedance spectroscopy data and target battery voltage data of the target lithium battery at a current of a target frequency.

[0037] As an optional embodiment, the target electrochemical impedance spectroscopy data may be electrochemical impedance spectroscopy data collected by applying a current of a target frequency across the target lithium battery, and the electrochemical impedance spectroscopy data may be in plural form. The target battery voltage data may be battery voltage data collected by applying a current of a target frequency across the target lithium battery. Specifically, when the target lithium battery is excited by a current of a target frequency, the target electrochemical impedance spectroscopy data of the target lithium battery at the current of the target frequency can be calculated by measuring the response generated by the target lithium battery (for example, the generated AC voltage). The target battery voltage data of the target lithium battery at the current of the target frequency can also be directly measured to obtain the target electrochemical impedance spectroscopy data and target battery voltage data of the target lithium battery at the current of the target frequency.

[0038] Step S106 , determining a target state of charge corresponding to the target lithium battery based on the target electrochemical impedance spectroscopy data and the target battery voltage data, as well as the relationship between the electrochemical impedance spectroscopy data, the battery voltage data, and the state of charge of the target lithium battery at the target frequency current.

[0039] As an optional embodiment, a variety of methods can be used to determine the target state of charge corresponding to the target lithium battery based on the target electrochemical impedance spectroscopy data and target battery voltage data. For example, the target state of charge corresponding to the target lithium battery can be determined based on the target electrochemical impedance spectroscopy data and target battery voltage data, as well as the relationship between the electrochemical impedance spectroscopy data, battery voltage data, and the state of charge of the target lithium battery at a current of a target frequency. The relationship between the electrochemical impedance spectroscopy data, battery voltage data, and the state of charge of the target lithium battery at a current of a target frequency can be determined in a variety of ways. For example, the relationship between the electrochemical impedance spectroscopy data, battery voltage data, and the state of charge of the target lithium battery at a current of a target frequency can be established using historical data or experimental data. The above method of determining the target state of charge corresponding to the target lithium battery can address the limitations of the OCV-SOC curve. In the flat region of the OCV-SOC curve, if the state of charge cannot be determined based solely on the battery voltage data, the target electrochemical impedance spectroscopy data and target battery voltage data can be used to determine the corresponding target state of charge based on the relationship between the electrochemical impedance spectroscopy data, battery voltage data, and the state of charge of the target lithium battery at a current of a target frequency. Since electrochemical impedance spectroscopy data can provide more detailed information about the electrochemical processes inside the battery and can show smaller fluctuations, the target charge state of the target lithium battery can be determined more accurately by combining electrochemical impedance spectroscopy data with battery voltage data.

[0040] By combining electrochemical impedance spectroscopy data and battery voltage data to determine the battery's state of charge, compared to determining the battery's state of charge based solely on battery open-circuit voltage data, the problem of being unable to accurately determine the battery's state of charge based on the battery voltage due to the presence of a flat area in the battery voltage-battery state of charge curve can be avoided. Because the electrochemical impedance spectroscopy data corresponds to different values ​​under different charge states, that is, the electrochemical impedance spectroscopy data-battery state of charge curve has a relatively obvious slope. Therefore, the above steps can achieve the purpose of determining the charge state of a lithium battery based on its electrochemical impedance spectroscopy data and battery voltage data, thereby achieving the technical effect of more accurately determining the charge state of a lithium battery.

[0041] As an optional embodiment, before the target lithium battery is stimulated by the current of the target frequency, the target frequency can be determined in a variety of ways, and the relationship between the electrochemical impedance spectroscopy data, the battery voltage data, and the charging state can be determined. For example, the target lithium battery in a fully discharged state can be charged, and when multiple charging states are reached, the electrochemical impedance spectroscopy data and the battery voltage data of the target lithium battery under currents of multiple frequencies are obtained; the target frequency is determined based on the electrochemical impedance spectroscopy data and the battery voltage data of the target lithium battery under currents of multiple frequencies when the target lithium battery reaches multiple charging states; the relationship between the electrochemical impedance spectroscopy data and the battery voltage data of the target lithium battery under currents of the target frequency and the charging state is determined based on the electrochemical impedance spectroscopy data and the battery voltage data of the target lithium battery under currents of the target frequency when the target lithium battery reaches multiple charging states, as well as multiple charging states.

[0042] By charging a fully discharged target lithium battery, each time the target lithium battery's charge state reaches one of multiple predetermined charge states, the target lithium battery is stimulated using currents of multiple frequencies. Electrochemical impedance spectroscopy data and battery voltage data are obtained for the target lithium battery at each of the multiple current frequencies when the target lithium battery reaches that charge state. This allows for full coverage of the electrochemical impedance spectroscopy and battery voltage data for different charge states and different excitation frequencies. This comprehensive coverage of electrochemical impedance spectroscopy and battery voltage data allows the determination of the excitation frequency that best describes the charge state, thereby determining the target frequency. Afterwards, based on the electrochemical impedance spectroscopy data and battery voltage data at the target frequency current when the target lithium battery reaches multiple charging states, as well as multiple charging states, the relationship between the electrochemical impedance spectroscopy data, battery voltage data and the charging state of the target lithium battery at the target frequency current is determined. Then, when determining the relationship and subsequently using the relationship to determine the charging state, it is only necessary to obtain the electrochemical impedance spectroscopy data and battery voltage data at the target frequency current. Compared with obtaining the electrochemical impedance spectroscopy data and battery voltage data at multiple frequencies current, the data acquisition time can be significantly reduced, and the relationship establishment time can be effectively reduced without affecting the accuracy of the charging state determination, thereby improving the speed of charging state determination.

[0043] As an optional embodiment, when determining the target frequency based on the electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at multiple frequencies of current when the target lithium battery reaches multiple charging states, a variety of methods can be used. For example, based on the electrochemical impedance spectroscopy data of the target lithium battery at multiple frequencies of current when the target lithium battery reaches multiple charging states, the resistance data, reactance data, amplitude data and phase angle data of the target lithium battery at multiple frequencies of current when the target lithium battery reaches multiple charging states can be obtained; the importance of the battery voltage data, resistance data, reactance data, amplitude data and phase angle data of the target lithium battery at multiple frequencies of current when the target lithium battery reaches multiple charging states can be determined respectively; based on the importance of the battery voltage data, resistance data, reactance data, amplitude data and phase angle data of the target lithium battery at multiple frequencies of current when the target lithium battery reaches multiple charging states, the target frequency can be determined from multiple frequencies. The real part of the electrochemical impedance spectroscopy data can represent resistance, and the imaginary part can represent reactance. The electrochemical impedance spectroscopy data itself also has amplitude and phase angle characteristics. Therefore, corresponding resistance data, reactance data, amplitude data, and phase angle data can be obtained, thereby representing more information based on multiple dimensions. The importance of the battery voltage data, resistance data, reactance data, amplitude data, and phase angle data at multiple current frequencies when the target lithium battery reaches multiple charge states can be the contribution of the battery voltage data, resistance data, reactance data, amplitude data, and phase angle data at multiple current frequencies to accurately determining the charge state when the target lithium battery reaches multiple charge states. For example, a random forest algorithm can be used to determine this importance. By analyzing the importance of data at multiple current frequencies, it is possible to determine which frequency(ies) of data at the current are most critical for determining the charge state, thereby ensuring that the accuracy of determining the charge state based on data at the determined target current frequency is similar to the accuracy of determining the charge state based on data at multiple current frequencies.

[0044] For example, taking 61 frequencies as the research object, at each frequency, representative characteristic parameters (such as resistance data, reactance data, amplitude data and phase angle data, etc.) are selected to analyze their changing trends under different charging states. The characteristic parameters and their corresponding frequencies that contribute most to the prediction of the charging state at different frequencies are identified through regression models and feature importance evaluation methods. Based on this result, the key frequency and characteristic parameter combinations are further selected as input variables to construct a coupling correlation model between electrochemical impedance spectroscopy data and the charging state, which helps to reveal the intrinsic connection between electrochemical impedance spectroscopy data and the charging state in the frequency domain dimension, and improve the accuracy and physical interpretability of the data-driven model in determining the charging state. For example, for 61 frequencies, 20 charging states, and the above-mentioned 5 characteristic data, the contribution of the 5 characteristic data to the 20 charging states under the 61 frequency excitation is determined respectively. Therefore, a 61*20*5 contribution matrix can be obtained. The elements in the contribution matrix are sorted, and the frequency corresponding to the element with the largest contribution is selected as the above-mentioned target frequency.

[0045] As an optional embodiment, when charging a fully discharged target lithium battery, various methods can be used to acquire electrochemical impedance spectroscopy (EIS) data and battery voltage data for the target lithium battery at multiple current frequencies while achieving multiple charge states. For example, multiple charge states can be selected at predetermined intervals between the fully discharged and fully charged states; the fully discharged target lithium battery can be charged and, after reaching multiple charge states, allowed to rest for predetermined periods of time; the rested target lithium battery can be stimulated with currents at multiple frequencies; and the EIS data and battery voltage data for the target lithium battery at the multiple current frequencies can be acquired. The fully discharged state can be 0%, the fully charged state can be 100%, the predetermined interval can be 5%, and the predetermined time can be 1 hour. By selecting multiple charge states at predetermined intervals between the fully discharged and fully charged states, the battery's charge state range can be fully covered, ensuring that the experiment captures the battery's behavior at different charge state levels. When charging the fully discharged target lithium battery, a constant current and constant voltage charging method can be used to charge to the selected multiple charge states. Once one of the selected multiple charging states is reached, the transient effects inside the battery can be reduced by allowing the battery to rest, allowing the chemical reactions inside the battery to stabilize, thereby obtaining electrochemical impedance spectroscopy data and battery voltage data that more accurately reflect the static electrochemical characteristics of the battery.

[0046] As an optional embodiment, when charging the target lithium battery in a fully discharged state, and when multiple charging states are reached respectively, before obtaining the electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at currents of multiple frequencies, a variety of methods can be used to determine the charging state. For example, the target lithium battery can be capacity tested to determine the initial capacity of the target lithium battery, wherein the initial capacity is used to determine multiple charging states. Since the charging state can be represented by the ratio of the remaining battery capacity to the maximum battery capacity, the remaining battery capacity can be obtained by charging the target lithium battery in a fully discharged state, and the maximum battery capacity can be determined by performing a capacity test on the target lithium battery to determine the initial capacity of the target lithium battery. By performing a capacity test on the target lithium battery and determining the initial capacity of the target lithium battery, a more accurate basis can be provided for the calculation of the charging state.

[0047] As an optional embodiment, the relationship between the electrochemical impedance spectroscopy data, battery voltage data and the state of charge of the target lithium battery at the current of the target frequency can be characterized in a variety of ways, for example, it can be characterized by a mathematical model or a neural network model. Due to the complexity of the above relationship, the use of a neural network model can fit the above relationship more accurately and conveniently. For example, a target model can be used to characterize the relationship between the electrochemical impedance spectroscopy data, battery voltage data and the state of charge of the target lithium battery at the current of the target frequency, wherein the target model is trained based on a convolutional neural network using elastic network regularization. The convolutional neural network can automatically learn and extract complex features from the input electrochemical impedance spectroscopy data and battery voltage data through the convolution layer. Elastic network regularization can combine the advantages of both L1 regularization and L2 regularization. L1 regularization achieves feature sparsity by compressing weights to zero, which helps to eliminate irrelevant or redundant features, while L2 regularization prevents a single weight from being too large by weight shrinkage, ensuring a smoother distribution of model parameters. Therefore, elastic network regularization can help the target model avoid overfitting during training, while performing feature selection to improve the predictive ability and generalization ability of the target model.

[0048] Through the above steps, it is possible to excite the target lithium battery with a current of a target frequency; obtain the target electrochemical impedance spectrum data and target battery voltage data of the target lithium battery under the current of the target frequency; and determine the target charging state corresponding to the target lithium battery based on the target electrochemical impedance spectrum data and target battery voltage data, as well as the relationship between the electrochemical impedance spectrum data, battery voltage data and charging state of the target lithium battery under the current of the target frequency. Since the electrochemical impedance spectrum data and battery voltage data of the lithium battery can more accurately reflect the charging state of the lithium battery relative to the open circuit voltage of the battery, there will be no situation where the battery charging state changes while the open circuit voltage remains flat. Therefore, the purpose of determining the charging state of the lithium battery based on the electrochemical impedance spectrum data and battery voltage data of the lithium battery is achieved, thereby achieving the technical effect of more accurately determining the charging state of the lithium battery, and further solving the technical problem in the related art that the battery charging state cannot be accurately determined according to the battery voltage due to the presence of a flat area in the battery voltage-battery charging state curve.

[0049] In combination with the above embodiments and optional embodiments, an optional implementation method is provided. In this optional implementation method, a solution to the OCV-SOC flat area of ​​lithium iron phosphate batteries based on EIS is proposed. This solution explores the dependency between SOC and the various components of EIS (for example, resistance, reactance, amplitude and phase angle) by conducting EIS-SOC experiments on LFP batteries. EIS measurement data is collected every 5% in the range of 0% to 100% SOC. Subsequently, the collected data is processed and analyzed to identify the correlation between EIS and battery SOC, aiming to solve the problem of the flat area of ​​the OCV-SOC curve of LFP batteries. This solution establishes the relationship between EIS characteristics and battery SOC through a convolutional neural network (CNN) combined with an elastic network (EN) regularization method (i.e., a CNN-EN model), without relying on explicit battery mathematical modeling. Using this solution, in this optional implementation method, an EIS experiment for SOC correlation analysis is designed and implemented, Figure 2 Schematic diagram of the SOC algorithm framework based on EIS according to an optional embodiment of the present invention, such as Figure 2 As shown, the experimental steps are as follows.

[0050] S1, select three large-capacity LFP batteries (i.e., the aforementioned target lithium batteries).

[0051] It should be noted that the embodiment of the present invention studies the relationship between the internal impedance of the battery and the state of charge, especially for large-capacity batteries with low internal impedance. Subsequently, the LFP battery will be pulse charged at a low C rate to analyze the OCV-SOC curve of the LFP battery, especially the flat area in the medium SOC range. The experimental data shows and visualizes the ability of EIS measurement data to reflect SOC changes in the medium SOC range. Among them, low C rate refers to C / 10 or lower. Low C rate charging is conducive to reaching steady-state conditions inside the battery, making the EIS measurement more accurate, and then more accurately capturing the details of the OCV changes, especially in the flat platform area where the SOC changes are more subtle. The capacity of the LFP battery can be 106 ampere hours.

[0052] S2, perform capacity detection test on three large-capacity LFP batteries.

[0053] First, each battery was placed in a thermostat set at 25 degrees Celsius and left to stand for 2 hours to reach a stable temperature state. Then, the battery was fully charged using the constant current-constant voltage (CC-CV) charging method and left to stand for 1 hour. Next, the battery was fully discharged to its lowest cutoff voltage. To activate the new battery, this charge and discharge cycle was repeated three times. The battery capacity was calculated by integrating the discharge current of the final cycle, which was defined as the initial capacity of the battery at 25 degrees Celsius. The initial capacity can be used for charge state calculations in subsequent experimental steps.

[0054] During the capacity test, after the battery is fully discharged, it is left to rest for one hour to reduce the impact of the subsequent rest period on the EIS measurement. Considering the practical application of EIS measurement, the choice of a one-hour rest time can balance the impact of the battery rest period on the EIS measurement with the flexibility of practical application.

[0055] S3, EIS measurements of three large-capacity LFP batteries.

[0056] The fully discharged battery is charged, and EIS measurement data is collected every 5% from 0% to 100% SOC. In order to characterize the battery impedance changes caused by the AC excitation current at different frequencies, the complete impedance spectrum corresponding to the AC excitation current in the frequency range of 0.01Hz to 10kHz can be measured for each SOC. The impedance changes of the battery at these frequencies are visualized through the Nyquist plot. Figure 3 Schematic diagram of the decomposition of the internal resistance of a battery cell based on EIS according to an optional embodiment of the present invention, as shown in FIG. Figure 3As shown. Impedance consists of two parts: resistance and reactance. In the Nyquist diagram, the frequency of the AC excitation current decreases from left to right from 10kHz to 0.01Hz. The high-frequency region represents the inductance of the battery and the measurement circuit. The ohmic resistance is represented by the intersection of the zero point and the real axis in the figure, reflecting the battery electrolyte resistance and the contact resistance of the measuring device. The medium-frequency region captures the electrochemical reactions of the battery, including the degradation of the solid electrolyte interface (SEI) and the charge transfer process. Since SEI degradation is less, the charge transfer resistance in this range is used to approximate the battery reaction. In the low-frequency region, the diffusion of lithium ions in the electrode is dominant, and the real axis part of the Nyquist diagram reflects the diffusion resistance of the battery. From the EIS-SOC experiment, not only the EIS measurement data but also the corresponding battery voltage data are collected and correlated with the OCV, which is measured after one hour of rest after pulse charging.

[0057] S4, feature analysis.

[0058] It should be noted that feature analysis includes the consecutive steps of feature extraction and selection in order to extract and reconstruct effective features from the original data.

[0059] Feature extraction expands the raw EIS data, which consists solely of real and imaginary components, into real (i.e., resistance), imaginary (i.e., reactance), amplitude, and phase angle features. This process enhances the representation of the raw data by providing more useful features. Feature extraction also extracts the OCV at different EIS measurement frequencies to characterize SOC changes. Ultimately, the raw EIS measurement data and voltage data are combined into a new dataset containing five sets of features, each consisting of 61 feature points, forming a 5x61 feature set. This dataset is organized in a matrix format.

[0060] In order to reduce the size of the original feature set (each SOC sample corresponds to a 5x61 feature set), feature selection is used to reduce the complexity of the features while retaining the necessary information to describe the relationship between the battery internal impedance, battery voltage and SOC. Feature selection is based on feature analysis using a random forest regression algorithm that can identify the features with the strongest correlation with the target variable. In random forest regression, the importance of a feature is measured by the contribution of each feature in reducing the variance of the dataset. The importance of a feature is determined by calculating the total reduction in the mean squared error for that feature across the entire forest. Features that have a greater impact on splitting the data and reducing the variance of the target variable are considered more important, while features with lower importance scores are considered relatively irrelevant.

[0061] Through feature selection, the feature size of each sample was reduced from 5x61 to 5x1, and irrelevant frequency points were removed. The importance of each feature was calculated and compared using the random forest algorithm. The most significant feature visualization results are shown below. Figure 4 As shown, Figure 4 : is a schematic diagram of the importance analysis of features based on the random forest algorithm according to an optional embodiment of the present invention, in which the EIS phase angle at 0.0125 Hz (i.e., the aforementioned target frequency) is identified as the most important feature. In addition, a graph of the relationship between the phase angle and the SOC of the same battery is plotted to illustrate the reliability of the results of the random forest. The phase angle shows a nearly linear relationship with the SOC in the low-frequency range, and similar patterns are observed in other batteries in the experiment. When the SOC increases from 0% to 100%, the phase angle increases by about 10 degrees, which is a relatively large change compared to the changes in resistance and reactance.

[0062] S5, model building.

[0063] The CNN-EN model, combining a convolutional neural network with elastic net regularization, is used to extract patterns from a feature set. The extracted patterns are then flattened and passed to a fully connected dense layer, transforming SOC estimation into a regression problem to generate an estimated SOC value. The CNN model, consisting of convolutional, pooling, flattening, and fully connected dense layers, excels at automatically learning hierarchical feature representations from raw data through convolutional layers. This makes it well-suited for extracting spatial or temporal patterns, but it is prone to overfitting when processing complex datasets or when training data is limited. To mitigate overfitting and enhance feature selection, elastic net regularization, combining L1 and L2 regularization, is introduced into the CNN model. Elastic net regularization implements feature selection within the CNN model by pruning unnecessary weights during training. L1 regularization introduces sparsity by driving certain weights to zero. When the corresponding weights are zero, some filters (or neuron connections) become inactive. This selective pruning acts as feature selection, allowing the model to focus on the most important filters and ignore irrelevant or noisy features. At the same time, L2 regularization prevents a single weight from being too large by shrinking the weight value, promoting a balanced weight distribution. Although L2 does not directly eliminate features, it complements L1 by ensuring a smooth and well-distributed feature representation.

[0064] The training data for the CNN-EN model accounts for 80% of the entire dataset, of which 10% is further allocated as validation data to monitor the performance of the model on unseen data during training.

[0065] The Adam optimizer is used for optimization through backpropagation and adaptive learning rate. The Adam optimizer starts with an initial learning rate of 0.001 and gradually adjusts the learning rate with a decay rate of 0.1 as the training rounds increase, gradually minimizing the difference between the predicted value and the target value.

[0066] After optimization, the hyperparameters of the CNN-EN model are shown in Table 1. Through this method, the CNN-EN model can not only extract the complex relationship between EIS features and SOC, but also effectively alleviate the overfitting phenomenon, while optimizing the model training efficiency and performance.

[0067] Table 1 Hyperparameter selection of CNN-EN model

[0068]

[0069] S6, Correlation analysis between EIS and SOC.

[0070] The correlation between EIS and SOC was analyzed based on data from a designed EIS-SOC experiment. As in the feature analysis step, features such as resistance, reactance, amplitude, and phase angle were extracted from the raw EIS data to characterize battery behavior at different SOC levels. The correlation between each EIS feature and SOC was analyzed to explore the potential relationship between EIS parameters and SOC.

[0071] The performance of the proposed CNN-EN model was evaluated using two different feature sets selected using random forests. The effectiveness and efficiency of each feature set were thoroughly investigated to demonstrate that EIS measurement data can address the flat regions of the OCV-SOC curve for LFP batteries, particularly in the mid-SOC range, enabling accurate SOC estimation in practical applications. Experimental results demonstrate that the EIS features selected at specific frequencies accurately reflect changes in battery SOC, improving the accuracy of SOC estimation while significantly reducing data acquisition time.

[0072] In order to study the change of the internal resistance of the battery with SOC at different frequencies, four frequencies from high to low were selected to represent the change of battery resistance at different SOC values. First, the results showed that in the relatively high-frequency region around 251Hz, the resistance can reflect the change of SOC. This frequency is mainly related to ohmic resistance. In the high-frequency region (also known as the inductive region), the resistance is mainly affected by the conductivity of the electrolyte, diaphragm and wire. At 251Hz, as the SOC increases, the resistance shows an upward trend. The total increase of the three batteries in the experiment is 0.02 megohms (mOhm).

[0073] In addition, in the frequency range below 1Hz (including the contributions of ohmic resistance, charge transfer resistance, and diffusion resistance), the resistance shows a downward trend as the SOC increases. At 0.3981Hz, the trend is almost linear, with a total resistance drop of 0.02mOhm as the SOC increases. At 0.0125Hz, the resistance shows an upward trend when the SOC is between approximately 25% and 60%, and a downward trend between 60% and 100%. This pattern is consistent among the three batteries tested.

[0074] On the other hand, at high frequencies (e.g., 2000 Hz), resistance fails to reflect changes in battery SOC, as the experimental data do not show a consistent pattern. At these frequencies, the capacitance of the battery cells and the measurement circuitry affects the observed resistance, rendering the pattern undetectable or unusable for SOC estimation.

[0075] These observations indicate that the resistance component of EIS can effectively reflect SOC changes, especially in the mid-SOC range (20% to 80%) where OCV is relatively constant with SOC changes.

[0076] S7, Feature Engineering and CNN-EN Model Research.

[0077] After a comprehensive analysis of the various features extracted from the EIS measurement data, the selected features were used to train the proposed CNN-EN regression model, aiming to establish a mapping relationship between EIS measurement data and SOC without explicit battery modeling. Among them, the features used for training include the normalized EIS data of the selected effective frequency points and the data collected in the frequency range of 10kHz to 0.01Hz. The performance of the model and the effectiveness of the features are evaluated by multiple regression indicators to verify the robustness and reliability of the EIS features and the CNN-EN model. Among them, the multiple regression indicators include mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE) and determination coefficient (R-squared, R 2 ).

[0078] First, a CNN-EN model was trained using a full-band EIS dataset (including resistance, reactance, amplitude, and phase angle) with a frequency range of 10kHz to 0.01Hz. By leveraging the detailed information in the EIS data, the CNN-EN model achieved SOC estimation, achieving an MSE of 0.0009, a MAE of 0.0227, an R-squared of 0.9870, and a MAPE of 0.0592. Although the model was able to accurately estimate SOC using full-band EIS data, the data collection process took 730 seconds because the impedance at each frequency needed to be measured by dividing the voltage response by the excitation AC current.

[0079] In order to optimize the data collection time and make the process more suitable for practical applications, the aforementioned feature selection method was adopted. Experimental results and random forest evaluation showed that the EIS measurements at 0.0125Hz were strongly correlated with the SOC in different batteries. Therefore, the EIS measurements at 0.0125Hz were extracted from a total of 61 frequency points to characterize the battery impedance behavior at different SOC levels. For comparison, the CNN-EN model was retrained using the same data partitioning (training, validation and test data). When modeled using the CNN-EN framework, the selected 0.0125Hz EIS features showed good results in SOC estimation. The model achieved estimation performance of MSE of 0.0012, MAE of 0.02753, R-squared of 0.9820 and MAPE of 0.0607.

[0080] The performance of data-driven SOC estimators is highly dependent on the quality of features used to capture the relationship between input and target values. Although single-frequency EIS data provides fewer features than full-band EIS data, estimation accuracy remains largely unchanged, with R-squared and MAPE remaining nearly constant, while MSE and MAE decrease only slightly. Table 2 shows a performance comparison of full-band EIS and single-frequency 0.0125Hz EIS in the CNN-EN model. The smaller number of features provided by single-frequency EIS data compared to full-band EIS data significantly reduces data collection and model training time. Due to the simplified structure of the EIS feature set, model training time is reduced by 6%. More importantly, the data collection burden is significantly reduced, requiring only single-frequency EIS data from the full frequency band, reducing data collection time by approximately 85%. Table 3 shows a comparison of the efficiency of full-band EIS and single-frequency 0.0125Hz EIS in the CNN-EN model.

[0081] This optimization of EIS measurement not only improves the efficiency of model training, but also improves the efficiency of EIS data collection, providing feasibility for the implementation of EIS measurement in practical applications, thereby enhancing the ability of SOC estimation in actual scenarios.

[0082] Table 2 Performance comparison of full-band EIS and single-frequency 0.0125Hz EIS in the CNN-EN model

[0083]

[0084] The experiments described above validate the effectiveness of an EIS-based solution for addressing the OCV-SOC plateau in lithium iron phosphate (LFP) cells, proposed in this optional embodiment. This method employs the non-destructive technique of electrochemical impedance spectroscopy (EIS) to characterize battery behavior at varying SOCs. Extensive EIS-SOC experiments were conducted on LFP batteries to explore the correlation between EIS and SOC. EIS measurement data were represented in various formats and feature selected using a random forest algorithm. A convolutional neural network model combined with an elastic network was proposed for SOC estimation based on EIS features. Feature analysis and a random forest regression model demonstrated that 0.0125Hz EIS measurement data can effectively characterize SOC variations from 0% to 100%. Furthermore, an evaluation of the SOC estimation performance of the CNN-EN model using EIS features revealed that by utilizing only 0.0125Hz EIS measurement data, data collection time can be reduced by approximately 85% compared to full-frequency EIS measurements from 10kHz to 0.01Hz, without significantly reducing SOC estimation accuracy. Furthermore, the results show that even using only a single frequency point, the CNN-EN model can still accurately estimate SOC, achieving high performance with an MSE of 0.0012, a MAE of 0.02753, an R-squared of 0.9820, and a MAPE of 0.0607. This demonstrates that this method can effectively address the following issues: Due to the chemical composition characteristics of LFP batteries, the battery voltage changes relatively steadily over a wide SOC range, which limits the effectiveness of OCV curves in SOC estimation, especially in the medium SOC range.

[0085] According to an embodiment of the present invention, a device for determining a charging state of a lithium battery is provided. Figure 5 is a structural block diagram of a device for determining a lithium battery charging state according to an embodiment of the present invention. Figure 5 As shown, the device includes: an excitation module 502, a first acquisition module 504 and a first determination module 506. The device is described below.

[0086] An excitation module 502 is configured to excite a target lithium battery using a current of a target frequency. A first acquisition module 504 is connected to the excitation module 502 and configured to obtain target electrochemical impedance spectroscopy data and target battery voltage data of the target lithium battery at a current of a target frequency. A first determination module 506 is connected to the first acquisition module 504 and configured to determine a target charging state corresponding to the target lithium battery based on the target electrochemical impedance spectroscopy data and target battery voltage data, as well as the relationship between the electrochemical impedance spectroscopy data, battery voltage data, and charging state of the target lithium battery at a current of a target frequency.

[0087] It should be noted here that the above-mentioned incentive module 502, the first acquisition module 504 and the first determination module 506 correspond to steps S102 to S106 in the embodiment, and the instances and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment.

[0088] As an optional embodiment, the device further includes: a second acquisition module, a second determination module, and a third determination module. The second acquisition module is configured to charge the target lithium battery in a fully discharged state, and acquire electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at currents of multiple frequencies when the target lithium battery reaches multiple charging states; the second determination module is connected to the second acquisition module, and is configured to determine the target frequency based on the electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at currents of multiple frequencies when the target lithium battery reaches multiple charging states; the third determination module is connected to the second determination module, and is configured to determine the relationship between the electrochemical impedance spectroscopy data, battery voltage data, and the charging state of the target lithium battery at the current of the target frequency based on the electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at the current of the target frequency when the target lithium battery reaches multiple charging states, as well as multiple charging states.

[0089] As an optional embodiment, the second determination module includes: a first acquisition unit, a first determination unit, and a second determination unit. The first acquisition unit is used to acquire resistance data, reactance data, amplitude data, and phase angle data of the target lithium battery under currents of multiple frequencies when the target lithium battery reaches multiple charging states respectively based on electrochemical impedance spectroscopy data of the target lithium battery under currents of multiple frequencies when the target lithium battery reaches multiple charging states respectively; the first determination unit is connected to the first acquisition unit and is used to respectively determine the importance of battery voltage data, resistance data, reactance data, amplitude data, and phase angle data of the target lithium battery under currents of multiple frequencies when the target lithium battery reaches multiple charging states respectively; the second determination unit is connected to the first determination unit and is used to determine the target frequency from multiple frequencies based on the importance of battery voltage data, resistance data, reactance data, amplitude data, and phase angle data of the target lithium battery under currents of multiple frequencies when the target lithium battery reaches multiple charging states respectively.

[0090] As an optional embodiment, the second acquisition module includes: a selection unit, a rest unit, an excitation unit, and a second acquisition unit. The selection unit is configured to select multiple charging states at predetermined intervals between a fully discharged state and a fully charged state; the rest unit is connected to the selection unit and configured to charge a target lithium battery in a fully discharged state and, when the multiple charging states are reached, to rest the target lithium battery for a predetermined period of time; the excitation unit is connected to the rest unit and configured to excite the rested target lithium battery using currents of multiple frequencies; and the second acquisition unit is connected to the excitation unit and configured to acquire electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery under the currents of the multiple frequencies.

[0091] As an optional embodiment, the second acquisition module further includes a detection unit, which is configured to perform a capacity detection on the target lithium battery to determine an initial capacity of the target lithium battery, wherein the initial capacity is used to determine multiple charging states.

[0092] As an optional embodiment, the third determination module includes a characterization unit. The characterization unit is configured to characterize the relationship between electrochemical impedance spectroscopy data, battery voltage data, and state of charge of a target lithium battery at a target frequency and current using a target model, wherein the target model is trained based on a convolutional neural network using elastic network regularization.

[0093] According to an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for determining the charging status of a lithium battery.

[0094] According to an embodiment of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes any one of the above-mentioned methods for determining the charging state of a lithium battery when running.

[0095] According to an embodiment of the present invention, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0096] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0097] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0099] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0100] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0102] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for determining the charging state of a lithium battery, characterized in that: include: Using current of target frequency to stimulate the target lithium battery; Obtaining target electrochemical impedance spectroscopy data and target battery voltage data of the target lithium battery at the current of the target frequency; The target state of charge corresponding to the target lithium battery is determined based on the target electrochemical impedance spectroscopy data and the target battery voltage data, as well as the relationship between the electrochemical impedance spectroscopy data, the battery voltage data and the state of charge of the target lithium battery at the current of the target frequency.

2. The method according to claim 1, characterized in that Before using the current of the target frequency to stimulate the target lithium battery, the method also includes: Charging the target lithium battery in a fully discharged state, and obtaining electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at currents of multiple frequencies when the target lithium battery reaches multiple charging states respectively; Determining the target frequency based on electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at currents of multiple frequencies when the target lithium battery reaches multiple states of charge respectively; Based on the electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at the current of the target frequency when the target lithium battery reaches multiple charging states respectively, and the multiple charging states, the relationship between the electrochemical impedance spectroscopy data, battery voltage data and charging state of the target lithium battery at the current of the target frequency is determined.

3. The method according to claim 2, characterized in that The determining the target frequency based on electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at currents of multiple frequencies when the target lithium battery reaches multiple charging states respectively includes: Obtaining resistance data, reactance data, amplitude data, and phase angle data of the target lithium battery under the currents of the multiple frequencies when the target lithium battery reaches the multiple states of charge, based on electrochemical impedance spectroscopy data of the target lithium battery under the currents of the multiple frequencies when the target lithium battery reaches the multiple states of charge; respectively determining the importance of battery voltage data, resistance data, reactance data, amplitude data, and phase angle data of the target lithium battery at the currents of the multiple frequencies when the target lithium battery reaches the multiple charging states; The target frequency is determined from the multiple frequencies based on the importance of battery voltage data, resistance data, reactance data, amplitude data and phase angle data of the target lithium battery under the currents of the multiple frequencies when the target lithium battery reaches multiple charging states respectively.

4. The method according to claim 2, characterized in that The step of charging the target lithium battery in a fully discharged state and obtaining electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at currents of multiple frequencies when the target lithium battery reaches multiple charging states respectively includes: selecting the plurality of charge states at predetermined intervals between a fully discharged state and a fully charged state; charging the target lithium battery in a fully discharged state, and allowing the target lithium battery to stand for a predetermined time when the target lithium battery reaches multiple charging states; Using the currents of the multiple frequencies to stimulate the target lithium battery after it has been left at rest; Obtain electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at currents of the multiple frequencies.

5. The method according to claim 2, characterized in that Before charging the target lithium battery in a fully discharged state and obtaining electrochemical impedance spectroscopy data and battery voltage data of the target lithium battery at currents of multiple frequencies when the target lithium battery reaches multiple charging states, the method includes: A capacity test is performed on the target lithium battery to determine an initial capacity of the target lithium battery, wherein the initial capacity is used to determine the multiple charging states.

6. The method according to claim 2, characterized in that A target model is used to characterize the relationship between the electrochemical impedance spectroscopy data, battery voltage data and the state of charge of the target lithium battery under the current of the target frequency, wherein the target model is trained based on a convolutional neural network using an elastic network regularization method.

7. A device for determining the charging state of a lithium battery, characterized in that: include: An excitation module, used to excite the target lithium battery using a current of a target frequency; A first acquisition module is used to obtain target electrochemical impedance spectroscopy data and target battery voltage data of the target lithium battery at the current of the target frequency; The first determination module is used to determine the target charge state corresponding to the target lithium battery based on the target electrochemical impedance spectroscopy data and the target battery voltage data, and the relationship between the electrochemical impedance spectroscopy data, the battery voltage data and the charge state of the target lithium battery at the current of the target frequency.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the lithium battery charging state determination method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: a memory storing an executable program; A processor is used to run the program, wherein the program executes the method for determining the charging state of a lithium battery according to any one of claims 1 to 6 when running.

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 6 are implemented.

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